{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-03T23:08:35.661Z","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a","content":"Monday 2026-01-05. Note from Daniel Achterberg (VP Customer Experience):\n\nSubject: Where I'd like to start — support & shopper experience\n\nHi Mateo,\n\nGood to have you on this with me. I'm still building my map of the business, so I want to keep this week broad — I'd rather get a solid first read on a few big questions than a deep dive on one. Here's what's on my mind, roughly in priority order:\n\n1. **How are we doing on response and resolution times, and is it getting better or worse?** I keep hearing \"we're slow\" anecdotally but have no sense of the trend, or whether it varies a lot by merchant. I need a baseline I can put in front of the leadership team.\n\n2. **Where is the support load actually coming from?** What kinds of problems are shoppers contacting merchants about most, and has that mix been shifting lately? I want to know if there's a theme we could fix at the platform level instead of merchant-by-merchant.\n\n3. **What does the returns and refunds experience look like from the shopper's side?** How often shoppers end up wanting their money back, how long that takes to resolve, and whether that's a smooth process or a source of frustration. This feels close to our reputation.\n\n4. **How satisfied are shoppers overall, and where is satisfaction weakest?** I'd like a sense of satisfaction levels and whether certain merchants or situations stand out as problem spots.\n\n5. **(If there's room this week) What are reviews and ratings telling us?** Even a first look at how shoppers rate their purchases, and whether that lines up with what we see in support, would help me understand if there are blind spots in the ticket data.\n\nWhat I need these for: I'm meeting merchant success and the exec team soon and want a credible, numbers-backed view of shopper experience — not just war stories. I also want to start spotting the two or three things that, if we fixed them, would move the needle most broadly across merchants, rather than chasing individual complaints.\n\nGiven the slot constraints, let's prioritize #1 through #3 this week if we can't fit all five — those feel most urgent to me. Happy to talk through any of this before you lock in requests.\n\nThanks,\nDaniel\n\nNight 1 of 4: you have 3 report slots tonight."}
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{"parentUuid":null,"isSidechain":false,"promptId":"ebb65904-7956-4b71-8c78-0975972921f1","type":"user","message":{"role":"user","content":"Monday 2026-01-05. Note from Daniel Achterberg (VP Customer Experience):\n\nSubject: Where I'd like to start — support & shopper experience\n\nHi Mateo,\n\nGood to have you on this with me. I'm still building my map of the business, so I want to keep this week broad — I'd rather get a solid first read on a few big questions than a deep dive on one. Here's what's on my mind, roughly in priority order:\n\n1. **How are we doing on response and resolution times, and is it getting better or worse?** I keep hearing \"we're slow\" anecdotally but have no sense of the trend, or whether it varies a lot by merchant. I need a baseline I can put in front of the leadership team.\n\n2. **Where is the support load actually coming from?** What kinds of problems are shoppers contacting merchants about most, and has that mix been shifting lately? I want to know if there's a theme we could fix at the platform level instead of merchant-by-merchant.\n\n3. **What does the returns and refunds experience look like from the shopper's side?** How often shoppers end up wanting their money back, how long that takes to resolve, and whether that's a smooth process or a source of frustration. This feels close to our reputation.\n\n4. **How satisfied are shoppers overall, and where is satisfaction weakest?** I'd like a sense of satisfaction levels and whether certain merchants or situations stand out as problem spots.\n\n5. **(If there's room this week) What are reviews and ratings telling us?** Even a first look at how shoppers rate their purchases, and whether that lines up with what we see in support, would help me understand if there are blind spots in the ticket data.\n\nWhat I need these for: I'm meeting merchant success and the exec team soon and want a credible, numbers-backed view of shopper experience — not just war stories. I also want to start spotting the two or three things that, if we fixed them, would move the needle most broadly across merchants, rather than chasing individual complaints.\n\nGiven the slot constraints, let's prioritize #1 through #3 this week if we can't fit all five — those feel most urgent to me. Happy to talk through any of this before you lock in requests.\n\nThanks,\nDaniel\n\nNight 1 of 4: you have 3 report slots tonight."},"uuid":"c6c6fabb-9f92-4e25-96bd-40afab9e8ddd","timestamp":"2026-10-03T23:08:35.671Z","permissionMode":"default","promptSource":"sdk","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a","version":"2.1.275","gitBranch":"HEAD"}
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{"parentUuid":"3fcfdc0d-1eda-4838-8c22-ecdb1cfcbab3","isSidechain":false,"attachment":{"type":"prompt_snapshot","systemPrompt":["# Tidewick Commerce: company briefing\n\n**Tidewick Commerce** is an all-in-one commerce platform for mid-size merchants. It provides:\n\n- storefront and checkout;\n- payments;\n- inventory and fulfillment;\n- subscriptions;\n- a light B2B CRM;\n- a helpdesk;\n- marketing tools.\n\nTidewick was founded in 2018 in Amsterdam. It has offices in London and Austin and about 400 employees.\n\n## Merchants\n\nAbout 120 merchants run their business on Tidewick, and each merchant is a tenant of the platform. A handful of large merchants generate most of the volume, with a long tail of small ones behind them.\n\nThe merchants work in these verticals:\n\n- apparel and footwear;\n- consumer electronics;\n- beauty;\n- home and living;\n- sports and outdoor;\n- food and coffee subscriptions;\n- pet supplies;\n- a few B2B wholesalers (industrial supplies, office equipment).\n\nMerchants are mostly based in the UK, the Netherlands, Germany, France, the Nordics and the US. Their shoppers come from about 40 countries.\n\n## How Tidewick makes money\n\nMerchants pay a monthly platform plan. Tidewick also keeps a processing fee on every payment it settles for them; payouts show this as gross, fee and net.\n\n## The data\n\n- Production runs on one multi-tenant database: every business table carries the merchant's `tenant_id`.\n- The production database is replicated nightly into the analytics warehouse.\n- Tidewick moved to this database at the start of 2023, so history starts on 2023-01-01.\n- Timestamps are stored in UTC.\n- Amounts are in the merchant's currency. `exchange_rates` has daily rates from EUR.\n- The full table catalog is in `schema.md`.\n\n## Today\n\nToday is **Monday, 2026-01-05**. The warehouse holds complete data through 2025-12-31.\n\nTidewick has just hired several new leaders. Each of them wants to understand their part of the business quickly and relies on the analytics team to do so.\n\nThe analytics team works through the warehouse, as described in `handbook.md`.\n\n\n# Analytics handbook\n\nThis handbook explains how the analytics team at Tidewick turns questions into numbers.\n\n## The people\n\n- **Leaders** (CEO, CFO, department heads) bring broad questions about their area.\n- **Analysts** each work for one leader. An analyst:\n  - turns the leader's questions into concrete report requests;\n  - reads the printed reports and follows up;\n  - writes a short memo for the leader at the end of the week.\n- **Data engineers** turn each report request into one SQL query. They can check the query against the schema as often as they like before submitting it.\n\n## The warehouse is expensive\n\nThe warehouse is shared and metered, and every executed query is billed to the department. We therefore run reports as an overnight batch:\n\n- An analyst has **3 report slots per night** and the week has **4 nights**.\n- The memo is due on Friday morning.\n- A slot is spent whenever a request is submitted, whether it runs, fails or is bounced by review.\n- Unused slots do not carry over.\n- Checking a query against the schema (a \"dry run\") is free. It runs the query against an empty copy of the warehouse schema, so it catches errors and shows the output columns, but it never reads or shows any data.\n\nThink before you request. Make one request answer one question well.\n\n## One request, one report\n\nEach request produces one printed report:\n\n- **Title and question.** Every report has a title and the analyst's question it answers.\n- **One grain.** A report is one table at a single grain: every row is the same kind of thing (a country, a week, a merchant, a carrier and month). The columns are measures or attributes of that thing.\n- **The page has a width.** A report has at most 12 columns, and about 10 is comfortable.\n- **The page has a length.** At most 50 rows are printed. The report notes how many rows the query returned in total.\n- **Readable columns.** Order the rows so the important ones come first, and give columns readable names.\n\nDense reports are good. For example, one row per country and month can carry these columns:\n\n- orders;\n- revenue;\n- refund rate;\n- share of late deliveries;\n- tickets per 100 orders;\n- the same month last year.\n\nData engineering reviews every request before it runs and **bounces bundled ones**, where several unrelated questions are packed into one report. Reports wider than 12 columns are bounced too. A bounced request still uses its slot.\n\nIf you have two questions, request two reports.\n\n## House SQL standards (for data engineers)\n\n- Write **one SELECT statement**. Do not end it with a semicolon.\n- Use **standard SQL** wherever possible.\n- **Dates:** never use `CURRENT_DATE` or `now()`. The reporting date is 2026-01-05 and the data ends on 2025-12-31.\n- **Deterministic ordering:** always finish with an ORDER BY whose tie-breaks make the order unique.\n- **Tenants:** filter or group by `tenant_id` when a question is about particular merchants. Join `tenants` for merchant names.\n- **Money:** amounts are in the currency given next to them (usually a `currency_code` column; a merchant's main currency is `tenants.default_currency_code`).\n  - Convert amounts before adding them up across merchants that use different currencies.\n  - `exchange_rates` holds a rate from EUR (`base_code`) for every day; weekends carry the previous business day's rate.\n- **Rates:** round rates and averages for the page.\n- **Catalog:** the table catalog (`schema.md`) lists every table, column, foreign key and the allowed codes of status columns.\n\n\n# Table catalog\n\n# Tidewick Commerce data warehouse: table catalog\n\nThe production database, replicated nightly. Every business table carries the merchant's\n`tenant_id`. Timestamps are UTC. Money columns are in the row's `currency_code` (merchants sell\nin their own currency); `exchange_rates` has daily EUR reference rates.\nStatus columns reference small lookup tables; their codes are listed with each table.\nAll tables live in one schema: refer to them by name only (`shipments`, not `sales.shipments`).\nThe sections below only group the tables by business area.\n\n## Area: analytics\n\n### web_sessions\nVisitor analytics sessions, distinct from sessions (auth). Anonymous traffic still produces a session with a null user_id.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint -> users\n- `anonymous_id` uuid (Client-generated visitor identifier used to stitch sessions before the user authenticates.)\n- `started_at` timestamp not null\n- `ended_at` timestamp\n- `landing_page` varchar(2048)\n- `referrer` varchar(2048)\n- `utm_source` varchar(255)\n- `utm_medium` varchar(255)\n- `utm_campaign` varchar(255)\n- `ip_address` varchar(45)\n- `user_agent` varchar(2048)\n- `is_bounce` boolean not null\n- `page_view_count` integer not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### events\nThe primary product analytics fact table. Each row is one tracked event with a free-form event_name and schemaless JSON properties.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint -> users\n- `web_session_id` bigint -> web_sessions (Nullable link to the analytics web session this event occurred in; server-side events may have none.)\n- `event_name` varchar(64) not null\n- `properties` json not null\n- `occurred_at` timestamp not null\n- `created_at` timestamp not null\n\n### page_views\nIndividual page hits within a web session, used for path and dwell-time analysis.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `web_session_id` bigint not null -> web_sessions\n- `url` varchar(2048) not null\n- `path` varchar(2048)\n- `title` varchar(255)\n- `referrer` varchar(2048)\n- `duration_seconds` integer\n- `viewed_at` timestamp not null\n- `created_at` timestamp not null\n\n### device_profiles\nBrowser/device fingerprints observed per tenant, optionally linked to a user once authenticated.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint -> users\n- `fingerprint` varchar(255) not null\n- `device_type` varchar(64)\n- `os` varchar(64)\n- `os_version` varchar(64)\n- `browser` varchar(64)\n- `browser_version` varchar(64)\n- `screen_width` integer\n- `screen_height` integer\n- `first_seen_at` timestamp not null\n- `last_seen_at` timestamp not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, fingerprint)\n\n### feature_usage\nPer-user, per-day product feature adoption counters feeding engagement and stickiness metrics.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint -> users\n- `feature_key` varchar(64) not null (Stable identifier of the product feature being measured (e.g. bulk_export, dashboard_share).)\n- `usage_count` integer not null\n- `usage_day` date not null\n- `last_used_at` timestamp\n- `occurred_at` timestamp not null\n- `created_at` timestamp not null\n- unique: (tenant_id, user_id, feature_key, usage_day)\n\n### experiments\nServer-side product experiments (feature experiments), distinct from ab_tests which are message-level tests.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `key` varchar(64) not null\n- `name` varchar(255) not null\n- `description` varchar(4000)\n- `hypothesis` varchar(4000)\n- `status` varchar(32) not null one of: draft, running, paused, completed, archived\n- `primary_metric` varchar(255)\n- `starts_at` timestamp\n- `ends_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (tenant_id, key)\n\n### experiment_variants\nVariants (arms) of an experiment; exactly one may be flagged is_control, with allocation_pct summing across arms.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `experiment_id` bigint not null -> experiments\n- `key` varchar(64) not null\n- `name` varchar(255) not null\n- `is_control` boolean not null\n- `allocation_pct` decimal(5,2) not null\n- `config` json not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (experiment_id, key)\n\n### experiment_assignments\nRecords which variant a user (or anonymous visitor) was bucketed into for an experiment.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `experiment_id` bigint not null -> experiments\n- `variant_id` bigint not null -> experiment_variants\n- `user_id` bigint -> users\n- `anonymous_id` uuid\n- `assigned_at` timestamp not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (experiment_id, user_id)\n\n### funnels\nOrdered conversion-funnel definitions evaluated over the events stream.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `description` varchar(4000)\n- `is_active` boolean not null\n- `window_days` integer\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (tenant_id, name)\n\n### funnel_steps\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `funnel_id` bigint not null -> funnels\n- `step_order` integer not null\n- `name` varchar(255) not null\n- `event_name` varchar(64) not null\n- `filters` json not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (funnel_id, step_order)\n\n### cohorts\nNamed user cohorts, dynamic (rule-based) or static, used for retention and segmentation analysis.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `description` varchar(4000)\n- `definition` json not null\n- `is_dynamic` boolean not null\n- `member_count` integer not null\n- `computed_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (tenant_id, name)\n\n### cohort_members\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `cohort_id` bigint not null -> cohorts\n- `user_id` bigint not null -> users\n- `added_at` timestamp not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (cohort_id, user_id)\n\n### metrics_daily\nPre-aggregated daily metric values per tenant; one row per (tenant, metric_key, day) for fast dashboard reads.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `metric_key` varchar(64) not null\n- `metric_date` date not null\n- `metric_value` decimal(20,6) not null\n- `dimensions` json not null (Optional breakdown labels (e.g. {\"channel\":\"organic\"}) when a metric is sliced; empty object for the headline total.)\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, metric_key, metric_date)\n\n### kpi_snapshots\nPeriodic captures of headline KPIs (MRR, active users, conversion rate) with the prior-period value for trend deltas.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `kpi_key` varchar(64) not null\n- `period_grain` varchar(64) not null\n- `period_start` date not null\n- `period_end` date not null\n- `kpi_value` decimal(20,6) not null\n- `previous_value` decimal(20,6)\n- `metadata` json not null\n- `captured_at` timestamp not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, kpi_key, period_grain, period_start)\n\n## Area: audit\n\n### audit_log\nCentral append-only audit trail of meaningful mutations and security actions. The audited row is referenced polymorphically via entity_schema/entity_table/entity_id (no FK).\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `action` varchar(32) not null one of: create, update, delete, login, logout, export, access_denied\n- `actor_user_id` bigint -> users\n- `actor_type` varchar(64) not null (Principal kind that performed the action: user, api_key, service_account, or system.)\n- `entity_schema` varchar(64)\n- `entity_table` varchar(64)\n- `entity_id` bigint (Primary-key id of the audited row in entity_schema.entity_table; intentionally not a foreign key.)\n- `summary` varchar(4000)\n- `metadata` json not null\n- `ip_address` varchar(45)\n- `user_agent` varchar(2048)\n- `request_id` varchar(64)\n- `occurred_at` timestamp not null\n- `created_at` timestamp not null\n\n### change_history\nColumn-level before/after JSON diffs for an audited row, usually linked to an audit_log entry.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `audit_log_id` bigint -> audit_log\n- `actor_user_id` bigint -> users\n- `entity_schema` varchar(64) not null\n- `entity_table` varchar(64) not null\n- `entity_id` bigint not null\n- `changed_columns` varchar(255)[] not null\n- `before_data` json not null\n- `after_data` json not null\n- `occurred_at` timestamp not null\n- `created_at` timestamp not null\n\n### access_log\nAppend-only record of reads of sensitive/PII data, for compliance and breach forensics.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `actor_user_id` bigint -> users\n- `entity_schema` varchar(64) not null\n- `entity_table` varchar(64) not null\n- `entity_id` bigint\n- `access_kind` varchar(64) not null\n- `field_names` varchar(255)[] not null\n- `purpose` varchar(64)\n- `ip_address` varchar(45)\n- `user_agent` varchar(2048)\n- `occurred_at` timestamp not null\n- `created_at` timestamp not null\n\n### data_exports\ndata_exports — GDPR / data-portability export jobs. Tracks request through completion; the produced artifact lives in object storage (file_url).\n- `id` bigint not null\n- `public_id` uuid not null\n- `tenant_id` bigint not null -> tenants\n- `requested_by_user_id` bigint -> users\n- `subject_user_id` bigint -> users\n- `status` varchar(64) not null\n- `format` varchar(64) not null\n- `file_url` varchar(2048)\n- `row_count` bigint\n- `requested_at` timestamp not null\n- `completed_at` timestamp\n- `expires_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (public_id)\n\n### consent_records\nAppend-only per-user consent grants and withdrawals by purpose; the latest row per (user, purpose) is the effective consent state.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint not null -> users\n- `purpose` varchar(64) not null\n- `status` varchar(32) not null one of: granted, withdrawn, expired\n- `source` varchar(64)\n- `policy_version` varchar(64) (Version of the privacy policy / terms the user consented to at the time.)\n- `ip_address` varchar(45)\n- `granted_at` timestamp\n- `withdrawn_at` timestamp\n- `expires_at` timestamp\n- `occurred_at` timestamp not null\n- `created_at` timestamp not null\n\n### compliance_events\ncompliance_events — flagged compliance incidents / policy violations needing review (suspicious access, retention overrun, failed DSAR SLA, ...).\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `event_type` varchar(64) not null\n- `severity` varchar(64) not null\n- `detected_by_user_id` bigint -> users\n- `entity_schema` varchar(64)\n- `entity_table` varchar(64)\n- `entity_id` bigint\n- `details` json not null\n- `resolved` boolean not null\n- `resolved_at` timestamp\n- `occurred_at` timestamp not null\n- `created_at` timestamp not null\n\n### data_subject_requests\nGDPR/CCPA data-subject requests (access, erasure, rectification, portability) tracked through their processing lifecycle.\n- `id` bigint not null\n- `public_id` uuid not null\n- `tenant_id` bigint not null -> tenants\n- `subject_user_id` bigint -> users\n- `subject_email` varchar(254)\n- `request_type` varchar(64) not null (Kind of DSAR: access, erasure, rectification, portability, or restriction.)\n- `status` varchar(64) not null\n- `assigned_to_user_id` bigint -> users\n- `data_export_id` bigint -> data_exports\n- `notes` varchar(4000)\n- `received_at` timestamp not null\n- `due_at` timestamp\n- `completed_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (public_id)\n\n### retention_policies\nPer-entity data-retention rules (retention_days + action_on_expiry) driving automated purges.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `entity_schema` varchar(64) not null\n- `entity_table` varchar(64) not null\n- `retention_days` integer not null\n- `action_on_expiry` varchar(64) not null\n- `is_active` boolean not null\n- `last_run_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, entity_schema, entity_table)\n\n### legal_holds\nActive legal/investigation holds that suspend retention-driven deletion for a subject or entity scope.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `reason` varchar(4000)\n- `retention_policy_id` bigint -> retention_policies\n- `subject_user_id` bigint -> users\n- `entity_schema` varchar(64)\n- `entity_table` varchar(64)\n- `entity_id` bigint\n- `placed_by_user_id` bigint -> users\n- `is_active` boolean not null\n- `placed_at` timestamp not null\n- `released_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### audit_log_archive\nCold archive of aged-out audit_log rows; same shape as audit_log, written once by the archival job. source_audit_log_id preserves the original id.\n- `id` bigint not null\n- `source_audit_log_id` bigint\n- `tenant_id` bigint not null -> tenants\n- `action` varchar(32) not null one of: create, update, delete, login, logout, export, access_denied\n- `actor_user_id` bigint\n- `actor_type` varchar(64) not null\n- `entity_schema` varchar(64)\n- `entity_table` varchar(64)\n- `entity_id` bigint\n- `summary` varchar(4000)\n- `metadata` json not null\n- `ip_address` varchar(45)\n- `user_agent` varchar(2048)\n- `request_id` varchar(64)\n- `occurred_at` timestamp not null\n- `archived_at` timestamp not null\n- `created_at` timestamp not null\n\n## Area: billing\n\n### billing_profiles\nCustomer billing entity per tenant: legal name, tax id, billing email, net payment terms. One marked is_default per tenant.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint -> users\n- `legal_name` varchar(255) not null\n- `billing_email` varchar(254)\n- `tax_id` varchar(64)\n- `net_terms_days` integer not null (Payment terms in days (Net-0/Net-30/Net-60) before an invoice is past due.)\n- `default_currency_code` varchar(3) not null -> currencies\n- `is_default` boolean not null\n- `metadata` json not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n\n### payment_methods\nStored payment instruments. Only a gateway token plus brand/last4 are kept; raw card numbers are never stored.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint -> users\n- `billing_profile_id` bigint -> billing_profiles\n- `kind` varchar(32) not null one of: card, bank_transfer, paypal, wallet, wire, credit\n- `gateway` varchar(64) not null\n- `gateway_token` varchar(255) not null\n- `brand` varchar(255)\n- `last4` varchar(4)\n- `exp_month` smallint\n- `exp_year` smallint\n- `is_default` boolean not null\n- `billing_address_id` bigint -> addresses\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (gateway, gateway_token)\n\n### payments\nMoney-in captures against an order and/or invoice. amount_refunded tracks cumulative refunds for partial-refund accounting.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `order_id` bigint -> orders\n- `invoice_id` bigint -> invoices\n- `payment_method_id` bigint -> payment_methods\n- `status` varchar(32) not null one of: pending, authorized, captured, failed, voided, refunded, partially_refunded\n- `amount` decimal(14,4) not null\n- `amount_refunded` decimal(14,4) not null (Running total of amounts refunded against this payment; must never exceed amount.)\n- `currency_code` varchar(3) not null -> currencies\n- `gateway` varchar(64) not null\n- `gateway_charge_id` varchar(255)\n- `authorized_at` timestamp\n- `captured_at` timestamp\n- `failure_code` varchar(64)\n- `metadata` json not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### payment_attempts\nEach gateway round-trip for a payment; a payment may need several attempts before it captures.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `payment_id` bigint not null -> payments\n- `attempt_number` integer not null\n- `status` varchar(32) not null one of: pending, authorized, captured, failed, voided, refunded, partially_refunded\n- `amount` decimal(14,4) not null\n- `currency_code` varchar(3) not null -> currencies\n- `gateway_response` json not null\n- `error_code` varchar(64)\n- `error_message` varchar(4000)\n- `attempted_at` timestamp not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (payment_id, attempt_number)\n\n### payment_refunds\nRefunds reversing all or part of a captured payment, tracked through the refund_status lifecycle.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `payment_id` bigint not null -> payments\n- `status` varchar(32) not null one of: pending, succeeded, failed, cancelled\n- `amount` decimal(14,4) not null\n- `currency_code` varchar(3) not null -> currencies\n- `reason` varchar(4000)\n- `gateway_refund_id` varchar(255)\n- `processed_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### invoices\nBilling documents (one-off or subscription). amount_due drives dunning; public_id is the opaque external identifier.\n- `id` bigint not null\n- `public_id` uuid not null\n- `tenant_id` bigint not null -> tenants\n- `billing_profile_id` bigint -> billing_profiles\n- `user_id` bigint -> users\n- `order_id` bigint -> orders\n- `subscription_id` bigint -> subscriptions\n- `invoice_number` varchar(64) not null\n- `status` varchar(32) not null one of: draft, open, paid, void, uncollectible, past_due\n- `currency_code` varchar(3) not null -> currencies\n- `subtotal` decimal(14,4) not null\n- `tax_total` decimal(14,4) not null\n- `discount_total` decimal(14,4) not null\n- `total` decimal(14,4) not null\n- `amount_paid` decimal(14,4) not null\n- `amount_due` decimal(14,4) not null (Outstanding balance still owed; reaches 0 when the invoice is fully paid.)\n- `issued_at` timestamp\n- `due_at` timestamp\n- `paid_at` timestamp\n- `voided_at` timestamp\n- `notes` varchar(4000)\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (public_id); (tenant_id, invoice_number)\n\n### invoice_lines\nLine items on an invoice; optionally tied to a subscription_item, with the service period it covers.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `invoice_id` bigint not null -> invoices\n- `subscription_item_id` bigint -> subscription_items\n- `description` varchar(4000) not null\n- `quantity` decimal(14,4) not null\n- `unit_price` decimal(14,4) not null\n- `line_total` decimal(14,4) not null\n- `tax_amount` decimal(14,4) not null\n- `currency_code` varchar(3) not null -> currencies\n- `period_start` timestamp\n- `period_end` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### credit_notes\nCredit memos issued against invoices for returns, goodwill, or corrections.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `invoice_id` bigint -> invoices\n- `billing_profile_id` bigint -> billing_profiles\n- `credit_note_number` varchar(64) not null\n- `currency_code` varchar(3) not null -> currencies\n- `subtotal` decimal(14,4) not null\n- `tax_total` decimal(14,4) not null\n- `total` decimal(14,4) not null\n- `reason` varchar(4000)\n- `issued_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, credit_note_number)\n\n### credit_note_lines\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `credit_note_id` bigint not null -> credit_notes\n- `invoice_line_id` bigint -> invoice_lines\n- `description` varchar(4000) not null\n- `quantity` decimal(14,4) not null\n- `unit_price` decimal(14,4) not null\n- `line_total` decimal(14,4) not null\n- `tax_amount` decimal(14,4) not null\n- `currency_code` varchar(3) not null -> currencies\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### payouts\nSettlements of collected funds out to the tenant; net = gross minus fees.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `currency_code` varchar(3) not null -> currencies\n- `gross_amount` decimal(14,4) not null\n- `fee_amount` decimal(14,4) not null\n- `net_amount` decimal(14,4) not null\n- `status` varchar(64) not null\n- `gateway_payout_id` varchar(255)\n- `arrival_date` date\n- `paid_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### payout_items\nThe individual payments settled within a payout; each payment appears in at most one payout.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `payout_id` bigint not null -> payouts\n- `payment_id` bigint not null -> payments\n- `amount` decimal(14,4) not null\n- `fee_amount` decimal(14,4) not null\n- `currency_code` varchar(3) not null -> currencies\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (payment_id)\n\n### ledger_accounts\nledger_accounts — the tenant's chart of accounts for the double-entry ledger. type is the accounting class (asset/liability/revenue/...).\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `code` varchar(64) not null\n- `name` varchar(255) not null\n- `account_type` varchar(64) not null\n- `currency_code` varchar(3) not null -> currencies\n- `is_active` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, code)\n\n### ledger_entries\nImmutable append-only double-entry ledger lines. Postings sharing an entry_group form one balanced journal entry.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `ledger_account_id` bigint not null -> ledger_accounts\n- `entry_group` uuid not null (Groups the debit/credit rows of a single balanced journal entry (their amounts must net to zero).)\n- `direction` varchar(32) not null one of: debit, credit\n- `amount` decimal(14,4) not null\n- `currency_code` varchar(3) not null -> currencies\n- `payment_id` bigint -> payments\n- `invoice_id` bigint -> invoices\n- `description` varchar(4000)\n- `occurred_at` timestamp not null\n- `created_at` timestamp not null\n\n### plans\nSubscription products. code is the stable per-tenant key; default_interval sets the billing cadence.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `code` varchar(64) not null\n- `name` varchar(255) not null\n- `description` varchar(4000)\n- `default_interval` varchar(32) not null one of: day, week, month, quarter, year\n- `interval_count` integer not null\n- `trial_period_days` integer not null\n- `is_active` boolean not null\n- `is_public` boolean not null\n- `metadata` json not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (tenant_id, code)\n\n### plan_features\nFeature entitlements/limits per plan; limit_value NULL means unlimited.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `plan_id` bigint not null -> plans\n- `feature_key` varchar(64) not null\n- `limit_value` bigint (Quota for the feature (e.g. seats, api_calls); NULL means unlimited.)\n- `is_enabled` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (plan_id, feature_key)\n\n### plan_prices\nPrice points for a plan, one per currency and billing interval.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `plan_id` bigint not null -> plans\n- `currency_code` varchar(3) not null -> currencies\n- `billing_interval` varchar(32) not null one of: day, week, month, quarter, year\n- `interval_count` integer not null\n- `amount` decimal(14,4) not null\n- `is_active` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (plan_id, currency_code, billing_interval, interval_count)\n\n### subscriptions\nA user's recurring commitment to a plan. current_period_* bound the next invoice; cancel_at_period_end defers cancellation.\n- `id` bigint not null\n- `public_id` uuid not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint not null -> users\n- `plan_id` bigint not null -> plans\n- `billing_profile_id` bigint -> billing_profiles\n- `payment_method_id` bigint -> payment_methods\n- `status` varchar(32) not null one of: trialing, active, past_due, canceled, paused, incomplete\n- `currency_code` varchar(3) not null -> currencies\n- `quantity` integer not null\n- `trial_ends_at` timestamp\n- `current_period_start` timestamp\n- `current_period_end` timestamp\n- `cancel_at_period_end` boolean not null (When true the subscription cancels at current_period_end rather than immediately.)\n- `canceled_at` timestamp\n- `started_at` timestamp not null\n- `ended_at` timestamp\n- `metadata` json not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (public_id)\n\n### subscription_items\nLine items / add-ons of a subscription; unit_amount snapshots the price at the time of subscription.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `subscription_id` bigint not null -> subscriptions\n- `plan_price_id` bigint -> plan_prices\n- `description` varchar(4000)\n- `quantity` integer not null\n- `unit_amount` decimal(14,4) not null\n- `currency_code` varchar(3) not null -> currencies\n- `is_metered` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### usage_records\nMetered usage reported for a subscription_item, aggregated into consumption-based invoice lines.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `subscription_item_id` bigint not null -> subscription_items\n- `quantity` decimal(14,4) not null\n- `unit` varchar(64)\n- `action` varchar(64) not null\n- `occurred_at` timestamp not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### dunning_attempts\nRetry sequence chasing payment on a past-due invoice; next_attempt_at schedules the following retry.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `invoice_id` bigint not null -> invoices\n- `subscription_id` bigint -> subscriptions\n- `payment_id` bigint -> payments\n- `attempt_number` integer not null\n- `outcome` varchar(64) not null\n- `next_attempt_at` timestamp\n- `attempted_at` timestamp not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (invoice_id, attempt_number)\n\n### tax_registrations\nTenant tax IDs per jurisdiction (VAT/GST/sales tax) governing how tax is charged in a region.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `billing_profile_id` bigint -> billing_profiles\n- `country_code` varchar(2) not null\n- `jurisdiction` varchar(64)\n- `tax_type` varchar(64) not null\n- `registration_number` varchar(64) not null (The official tax/VAT registration number issued by the jurisdiction.)\n- `is_active` boolean not null\n- `valid_from` date\n- `valid_until` date\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, country_code, tax_type)\n\n### wallets\nStored-credit balances per user and currency (prepaid credit, refunds-to-credit, promo balance).\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint not null -> users\n- `currency_code` varchar(3) not null -> currencies\n- `balance` decimal(14,4) not null\n- `is_active` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (user_id, currency_code)\n\n## Area: catalog\n\n### brands\nManufacturer/brand records that products are grouped under.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `slug` varchar(255) not null\n- `description` varchar(4000)\n- `logo_url` varchar(2048)\n- `website_url` varchar(2048)\n- `is_active` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (tenant_id, slug)\n\n### categories\nHierarchical product taxonomy; parent_id is a self-reference to the parent category.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `parent_id` bigint -> categories\n- `name` varchar(255) not null\n- `slug` varchar(255) not null\n- `description` varchar(4000)\n- `sort_position` integer not null\n- `is_active` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (tenant_id, slug)\n\n### category_closure\nTransitive closure of the category tree (ancestor, descendant, depth) for fast subtree queries.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `ancestor_id` bigint not null -> categories\n- `descendant_id` bigint not null -> categories\n- `depth` integer not null (Number of edges between ancestor and descendant; 0 is the self-row.)\n- `created_at` timestamp not null\n- unique: (ancestor_id, descendant_id)\n\n### products\nProduct master records; public_id is the opaque external identifier exposed via API/URL.\n- `id` bigint not null\n- `public_id` uuid not null\n- `tenant_id` bigint not null -> tenants\n- `brand_id` bigint -> brands\n- `primary_category_id` bigint -> categories\n- `name` varchar(255) not null\n- `slug` varchar(255) not null\n- `description` varchar(4000)\n- `status` varchar(32) not null one of: draft, active, archived\n- `is_visible` boolean not null\n- `published_at` timestamp\n- `seo_title` varchar(255)\n- `seo_description` varchar(4000)\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (public_id); (tenant_id, slug)\n\n### product_variants\nSellable SKUs of a product; sku is unique per tenant. Reference price only — authoritative pricing lives in the pricing schema.\n- `id` bigint not null\n- `public_id` uuid not null\n- `tenant_id` bigint not null -> tenants\n- `product_id` bigint not null -> products\n- `sku` varchar(64) not null\n- `barcode` varchar(64)\n- `name` varchar(255)\n- `sort_position` integer not null\n- `price_amount` decimal(14,4)\n- `currency_code` varchar(3) -> currencies\n- `weight_grams` decimal(14,4)\n- `is_default` boolean not null (Exactly one variant per product may be the default (enforced by a partial unique index).)\n- `is_active` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (public_id); (tenant_id, sku)\n\n### product_categories\nproduct_categories — junction products×categories (a product can sit in many categories beyond its primary one).\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `product_id` bigint not null -> products\n- `category_id` bigint not null -> categories\n- `sort_position` integer not null\n- `created_at` timestamp not null\n- unique: (product_id, category_id)\n\n### attributes\nDefinitions of product/variant attributes (e.g. Color, Size); kind drives value interpretation.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `code` varchar(64) not null\n- `name` varchar(255) not null\n- `kind` varchar(32) not null one of: text, number, boolean, enum, color, dimension\n- `is_variant_defining` boolean not null (True when this attribute differentiates variants (Color, Size) rather than being descriptive.)\n- `is_filterable` boolean not null\n- `unit` varchar(64)\n- `sort_position` integer not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, code)\n\n### attribute_values\nAllowed values for enum-kind attributes.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `attribute_id` bigint not null -> attributes\n- `value_code` varchar(255) not null\n- `label` varchar(255)\n- `sort_position` integer not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (attribute_id, value_code)\n\n### variant_attribute_values\nJunction binding a variant to its attribute/value pairs (the combination that makes it unique).\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `variant_id` bigint not null -> product_variants\n- `attribute_id` bigint not null -> attributes\n- `attribute_value_id` bigint -> attribute_values\n- `value_text` varchar(4000)\n- `created_at` timestamp not null\n- unique: (variant_id, attribute_id)\n\n### product_media\nImages, video, and documents attached to a product and optionally a specific variant.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `product_id` bigint not null -> products\n- `variant_id` bigint -> product_variants\n- `kind` varchar(32) not null one of: image, video, document, model_3d\n- `url` varchar(2048) not null\n- `alt_text` varchar(4000)\n- `sort_position` integer not null\n- `is_primary` boolean not null\n- `width_px` integer\n- `height_px` integer\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### collections\nMerchandising groupings of products; may be manual or rule-driven (is_automated).\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `slug` varchar(255) not null\n- `description` varchar(4000)\n- `is_automated` boolean not null\n- `rules` json\n- `starts_at` timestamp\n- `ends_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (tenant_id, slug)\n\n### collection_products\ncollection_products — junction collections×products.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `collection_id` bigint not null -> collections\n- `product_id` bigint not null -> products\n- `sort_position` integer not null\n- `created_at` timestamp not null\n- unique: (collection_id, product_id)\n\n### tags\ntags — free-form labels applied to products.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `slug` varchar(255) not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, slug)\n\n### product_tags\nproduct_tags — junction products×tags.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `product_id` bigint not null -> products\n- `tag_id` bigint not null -> tags\n- `created_at` timestamp not null\n- unique: (product_id, tag_id)\n\n### product_reviews\nCustomer reviews of products, rated 1-5 stars; one review per user per product.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `product_id` bigint not null -> products\n- `user_id` bigint -> users\n- `rating` integer not null\n- `title` varchar(255)\n- `body` varchar(65535)\n- `is_verified_purchase` boolean not null (True when the reviewer is known to have purchased the product.)\n- `is_approved` boolean not null\n- `helpful_count` integer not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (product_id, user_id)\n\n### review_votes\nreview_votes — helpfulness votes cast by users on a review.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `review_id` bigint not null -> product_reviews\n- `user_id` bigint not null -> users\n- `is_helpful` boolean not null\n- `created_at` timestamp not null\n- unique: (review_id, user_id)\n\n### product_relations\nDirectional cross-sell/upsell/related links between two products.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `product_id` bigint not null -> products\n- `related_product_id` bigint not null -> products\n- `relation_kind` varchar(64) not null\n- `sort_position` integer not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (product_id, related_product_id, relation_kind)\n\n### product_bundles\nHeader for a bundle/kit sold as a single offering, composed of variant components.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `product_id` bigint -> products\n- `name` varchar(255) not null\n- `slug` varchar(255) not null\n- `description` varchar(4000)\n- `is_active` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, slug)\n\n### bundle_items\nComponent variants and quantities that make up a product bundle.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `bundle_id` bigint not null -> product_bundles\n- `variant_id` bigint not null -> product_variants\n- `quantity` integer not null\n- `sort_position` integer not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (bundle_id, variant_id)\n\n## Area: comms\n\n### notification_templates\nReusable, per-tenant notification bodies keyed by code and channel; rendered into notifications and email/sms logs.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `code` varchar(64) not null\n- `name` varchar(255) not null\n- `channel` varchar(64) not null\n- `subject` varchar(255)\n- `body` varchar(65535) not null\n- `locale` varchar(5)\n- `is_active` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, code)\n\n### notifications\nPer-user notifications with a queued→sent→delivered→read lifecycle. template_id is null when composed ad hoc.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint not null -> users\n- `template_id` bigint -> notification_templates\n- `channel` varchar(64) not null\n- `status` varchar(32) not null one of: queued, sent, delivered, read, failed\n- `title` varchar(255) not null\n- `body` varchar(65535)\n- `data` json not null\n- `sent_at` timestamp\n- `delivered_at` timestamp\n- `read_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### notification_preferences\nPer-user opt-in matrix across channels and categories; an absent row means the tenant default applies.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint not null -> users\n- `channel` varchar(64) not null\n- `category` varchar(255) not null\n- `is_enabled` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (user_id, channel, category)\n\n### message_threads\nIn-app conversation threads between a tenant and a user. last_message_at is denormalized for inbox sorting.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint -> users\n- `subject` varchar(255)\n- `channel` varchar(64) not null\n- `is_closed` boolean not null\n- `last_message_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### thread_messages\nthread_messages — individual messages within a thread. direction marks whether the message is inbound (from the user) or outbound (from the tenant).\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `thread_id` bigint not null -> message_threads\n- `sender_user_id` bigint -> users\n- `direction` varchar(32) not null one of: inbound, outbound (inbound = sent by the user toward the tenant; outbound = sent by the tenant toward the user.)\n- `body` varchar(65535) not null\n- `attachments` json not null\n- `read_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### webhooks\nSubscriber HTTP endpoints that receive signed event callbacks. Only the hash of the signing secret is stored.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `created_by_user_id` bigint -> users\n- `name` varchar(255) not null\n- `target_url` varchar(2048) not null\n- `secret_hash` varchar(255) not null (Hash of the HMAC signing secret used to sign delivery payloads; the plaintext secret is shown once at creation.)\n- `event_types` varchar(64)[] not null\n- `is_active` boolean not null\n- `last_delivered_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### webhook_deliveries\nAppend-only log of each webhook delivery attempt, including HTTP response_code and retry attempt number.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `webhook_id` bigint not null -> webhooks\n- `event_type` varchar(64) not null\n- `status` varchar(32) not null one of: pending, success, failed, retrying\n- `attempt` integer not null (1-based retry counter; multiple rows per logical event when a delivery is retried.)\n- `response_code` integer\n- `request_body` json\n- `response_body` varchar(65535)\n- `duration_ms` integer\n- `delivered_at` timestamp\n- `created_at` timestamp not null\n\n### email_log\nImmutable record of every outbound email; recipient_email is denormalized since the user or address may be off-platform.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint -> users\n- `template_id` bigint -> notification_templates\n- `recipient_email` varchar(254) not null\n- `from_email` varchar(254)\n- `subject` varchar(255)\n- `provider` varchar(64)\n- `provider_message_id` varchar(255)\n- `status` varchar(32) not null one of: pending, success, failed, retrying\n- `error_message` varchar(4000)\n- `sent_at` timestamp\n- `created_at` timestamp not null\n\n### sms_log\nsms_log — IMMUTABLE record of each outbound SMS. (Intentionally uncommented at table level — see CONVENTIONS.md on deliberate comment gaps.)\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint -> users\n- `template_id` bigint -> notification_templates\n- `recipient_phone` varchar(32) not null\n- `from_number` varchar(32)\n- `body` varchar(65535) not null\n- `provider` varchar(64)\n- `provider_message_id` varchar(255)\n- `status` varchar(32) not null one of: pending, success, failed, retrying\n- `error_message` varchar(4000)\n- `segments` integer not null\n- `sent_at` timestamp\n- `created_at` timestamp not null\n\n### push_tokens\nDevice push-notification tokens per user; deactivated rather than deleted when a device unregisters.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint not null -> users\n- `platform` varchar(64) not null\n- `token` varchar(255) not null\n- `device_name` varchar(255)\n- `is_active` boolean not null\n- `last_used_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, token)\n\n### unsubscribes\nSuppression list of addresses/channels that must be excluded from further sends; user_id may be null for off-platform addresses.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint -> users\n- `channel` varchar(64) not null\n- `address` varchar(255) not null\n- `category` varchar(255) not null\n- `reason` varchar(4000)\n- `unsubscribed_at` timestamp not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, channel, address, category)\n\n### contact_channels\nVerified contact endpoints (email/phone) per user with a verification lifecycle; one primary per kind via a partial unique index.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint not null -> users\n- `kind` varchar(64) not null\n- `channel_address` varchar(255) not null\n- `is_primary` boolean not null\n- `is_verified` boolean not null\n- `verified_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (user_id, kind, channel_address)\n\n## Area: crm\n\n### accounts\nThe tenant's own B2B customer companies. Tenant-scoped; distinct from tenants (the platform's customers).\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `legal_name` varchar(255)\n- `domain` varchar(255)\n- `industry` varchar(255)\n- `employee_count` integer\n- `annual_revenue` decimal(14,4)\n- `currency_code` varchar(3) -> currencies\n- `owner_user_id` bigint -> users (Internal sales rep (users) who owns the account relationship.)\n- `phone` varchar(32)\n- `website` varchar(2048)\n- `is_active` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (tenant_id, name)\n\n### contacts\nPeople who work at a CRM account. primary_email is denormalized; the full set lives in contact_emails.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `account_id` bigint -> accounts\n- `owner_user_id` bigint -> users\n- `first_name` varchar(255)\n- `last_name` varchar(255)\n- `primary_email` varchar(254)\n- `phone` varchar(32)\n- `job_title` varchar(255)\n- `department` varchar(255)\n- `is_primary` boolean not null\n- `do_not_contact` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n\n### contact_emails\nAll email addresses for a contact; at most one flagged is_primary per contact.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `contact_id` bigint not null -> contacts\n- `email` varchar(254) not null\n- `label` varchar(255) not null\n- `is_primary` boolean not null\n- `verified_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (contact_id, email)\n\n### leads\nUnqualified inbound interest before it becomes an account/contact; converts forward once qualified.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `owner_user_id` bigint -> users\n- `first_name` varchar(255)\n- `last_name` varchar(255)\n- `email` varchar(254)\n- `phone` varchar(32)\n- `company` varchar(255)\n- `job_title` varchar(255)\n- `status` varchar(32) not null one of: new, contacted, qualified, unqualified, converted\n- `source` varchar(64)\n- `score` integer not null (Lead score (0+); higher means more sales-ready.)\n- `converted_account_id` bigint -> accounts\n- `converted_contact_id` bigint -> contacts\n- `converted_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### pipelines\nNamed sales pipelines per tenant; at most one is the default. Stages live in opportunity_stages.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `description` varchar(4000)\n- `is_default` boolean not null\n- `is_active` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, name)\n\n### opportunity_stages\nOrdered lookup of stages within a pipeline. position sets funnel order; win_probability drives weighted forecasts.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `pipeline_id` bigint not null -> pipelines\n- `name` varchar(255) not null\n- `sort_position` integer not null\n- `win_probability` decimal(5,4) not null (Default close probability for opportunities in this stage, 0..1.)\n- `is_won` boolean not null\n- `is_lost` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (pipeline_id, sort_position); (pipeline_id, name)\n\n### opportunities\nDeals in flight: an account being worked through a pipeline stage with an expected amount.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `account_id` bigint not null -> accounts\n- `pipeline_id` bigint not null -> pipelines\n- `stage_id` bigint not null -> opportunity_stages\n- `primary_contact_id` bigint -> contacts\n- `owner_user_id` bigint -> users\n- `name` varchar(255) not null\n- `amount` decimal(14,4) not null\n- `currency_code` varchar(3) not null -> currencies\n- `probability` decimal(5,4)\n- `source` varchar(64)\n- `is_closed` boolean not null\n- `is_won` boolean not null\n- `expected_close_date` date\n- `closed_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### deals\nClosed-won contracts realized from an opportunity, holding the booked contract value and term.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `account_id` bigint not null -> accounts\n- `opportunity_id` bigint -> opportunities\n- `owner_user_id` bigint -> users\n- `name` varchar(255) not null\n- `contract_value` decimal(14,4) not null\n- `currency_code` varchar(3) not null -> currencies\n- `signed_at` date\n- `starts_at` date\n- `ends_at` date\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### deal_line_items\nProducts sold on a deal, priced per unit in the deal currency.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `deal_id` bigint not null -> deals\n- `product_id` bigint -> products\n- `description` varchar(4000)\n- `quantity` integer not null\n- `unit_price` decimal(14,4) not null\n- `discount_pct` decimal(5,4) not null\n- `currency_code` varchar(3) not null -> currencies\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### activities\nLogged interactions (calls, emails, meetings) against an account, contact, or opportunity.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `kind` varchar(32) not null one of: call, email, meeting, note, task\n- `account_id` bigint -> accounts\n- `contact_id` bigint -> contacts\n- `opportunity_id` bigint -> opportunities\n- `owner_user_id` bigint -> users\n- `subject` varchar(255)\n- `body` varchar(65535)\n- `occurred_at` timestamp not null\n- `duration_minutes` integer\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### crm_notes\ncrm_notes — free-text notes attached to a CRM entity (polymorphic target).\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `account_id` bigint -> accounts\n- `contact_id` bigint -> contacts\n- `opportunity_id` bigint -> opportunities\n- `author_user_id` bigint -> users\n- `body` varchar(65535) not null\n- `is_pinned` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### tasks\nFollow-up to-dos assigned to reps, optionally linked to a CRM entity.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `assignee_user_id` bigint -> users\n- `account_id` bigint -> accounts\n- `contact_id` bigint -> contacts\n- `opportunity_id` bigint -> opportunities\n- `title` varchar(255) not null\n- `description` varchar(4000)\n- `priority` varchar(64) not null\n- `is_done` boolean not null\n- `due_at` timestamp\n- `completed_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### account_relationships\nParent/subsidiary and other directed links between accounts within a tenant.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `parent_account_id` bigint not null -> accounts\n- `child_account_id` bigint not null -> accounts\n- `relationship_type` varchar(64) not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (parent_account_id, child_account_id, relationship_type)\n\n### contact_lists\ncontact_lists — static, manually-curated lists of contacts.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `owner_user_id` bigint -> users\n- `name` varchar(255) not null\n- `description` varchar(4000)\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, name)\n\n### contact_list_members\ncontact_list_members — junction of contacts into static lists.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `contact_list_id` bigint not null -> contact_lists\n- `contact_id` bigint not null -> contacts\n- `added_by_user_id` bigint -> users\n- `created_at` timestamp not null\n- unique: (contact_list_id, contact_id)\n\n## Area: geo\n\n### countries\nISO-3166 country reference. Global, shared by all tenants.\n- `id` bigint not null\n- `iso2` varchar(2) not null\n- `iso3` varchar(3) not null\n- `name` varchar(255) not null\n- `phone_code` varchar(32)\n- `continent` varchar(64)\n- unique: (iso2); (iso3)\n\n### regions\n- `id` bigint not null\n- `country_id` bigint not null -> countries\n- `code` varchar(64)\n- `name` varchar(255) not null\n\n### cities\n- `id` bigint not null\n- `region_id` bigint -> regions\n- `country_id` bigint not null -> countries\n- `name` varchar(255) not null\n- `latitude` decimal(9,6)\n- `longitude` decimal(9,6)\n\n### currencies\nISO-4217 currencies. The code column (e.g. USD) is the FK target used by all monetary tables.\n- `id` bigint not null\n- `code` varchar(3) not null\n- `name` varchar(255) not null\n- `symbol` varchar(64)\n- `minor_unit` smallint not null (Number of decimal places (2 for USD, 0 for JPY).)\n- unique: (code)\n\n### exchange_rates\nDaily FX rates between currency pairs, used to normalize multi-currency revenue.\n- `id` bigint not null\n- `base_code` varchar(3) not null -> currencies\n- `quote_code` varchar(3) not null -> currencies\n- `rate` decimal(12,6) not null\n- `as_of` date not null\n- unique: (base_code, quote_code, as_of)\n\n### calendar_days\nCalendar dimension: one row per day from 2023-01-01 to 2026-12-31 with week, month and quarter attributes. Join to it to build complete time series (days, weeks or months without activity still appear).\n- `calendar_date` date not null (The day.)\n- `year_num` integer not null (Calendar year.)\n- `quarter_num` smallint not null (Calendar quarter, 1-4.)\n- `month_num` smallint not null (Calendar month, 1-12.)\n- `month_name` varchar(9) not null (English month name, e.g. 'November'.)\n- `month_label` varchar(7) not null (Year and month as 'YYYY-MM'.)\n- `day_of_month` smallint not null (Day of the month, 1-31.)\n- `day_of_year` smallint not null (Day of the year, 1-366.)\n- `iso_year` integer not null (ISO 8601 week-numbering year (differs from year_num around New Year).)\n- `iso_week` smallint not null (ISO 8601 week number, 1-53.)\n- `iso_day_of_week` smallint not null (ISO day of the week: 1 = Monday ... 7 = Sunday.)\n- `day_name` varchar(9) not null (English day name, e.g. 'Monday'.)\n- `is_weekend` boolean not null (Saturday or Sunday.)\n- `week_start` date not null (Monday of the ISO week.)\n- `month_start` date not null (First day of the month.)\n- `quarter_start` date not null (First day of the quarter.)\n\n### locales\n- `id` bigint not null\n- `code` varchar(5) not null\n- `name` varchar(255) not null\n- `native_name` varchar(255)\n- unique: (code)\n\n### timezones\n- `id` bigint not null\n- `name` varchar(255) not null\n- `utc_offset_minutes` integer not null\n- unique: (name)\n\n### addresses\nPostal addresses owned by a tenant and optionally a user. At most one default address per user.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint -> users\n- `label` varchar(255)\n- `line1` varchar(255) not null\n- `line2` varchar(255)\n- `city` varchar(255)\n- `region_id` bigint -> regions\n- `country_id` bigint not null -> countries\n- `postal_code` varchar(64)\n- `latitude` decimal(9,6)\n- `longitude` decimal(9,6)\n- `is_default` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### address_validations\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `address_id` bigint not null -> addresses\n- `provider` varchar(64) not null\n- `is_valid` boolean not null\n- `response` json\n- `validated_at` timestamp not null\n\n### postal_zones\n- `id` bigint not null\n- `country_id` bigint not null -> countries\n- `code` varchar(64) not null\n- `name` varchar(255) not null\n- unique: (country_id, code)\n\n## Area: identity\n\n### tenants\nCustomer organizations on the platform; the root of multi-tenancy. Every tenant-scoped row hangs off a tenant_id.\n- `id` bigint not null\n- `public_id` uuid not null\n- `name` varchar(255) not null\n- `slug` varchar(255) not null\n- `status` varchar(32) not null one of: trial, active, past_due, suspended, churned\n- `default_currency_code` varchar(3) not null -> currencies (ISO-4217 default currency.)\n- `default_locale` varchar(5) not null -> locales\n- `industry` varchar(255)\n- `employee_count` integer\n- `trial_ends_at` timestamp\n- `signup_source` varchar(64)\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (public_id); (slug)\n\n### tenant_settings\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `setting_key` varchar(64) not null\n- `setting_value` json not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, setting_key)\n\n### users\nEnd users and staff belonging to a tenant. Email is unique per tenant, not globally.\n- `id` bigint not null\n- `public_id` uuid not null\n- `tenant_id` bigint not null -> tenants\n- `email` varchar(254) not null\n- `full_name` varchar(255)\n- `status` varchar(32) not null one of: active, invited, suspended, deactivated\n- `is_staff` boolean not null (True for internal/admin operators of the tenant, as opposed to customer end-users.)\n- `locale` varchar(5) -> locales\n- `timezone` varchar(64)\n- `password_hash` varchar(255)\n- `last_login_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (public_id); (tenant_id, email)\n\n### user_profiles\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint not null -> users\n- `avatar_url` varchar(2048)\n- `bio` varchar(4000)\n- `phone` varchar(32)\n- `job_title` varchar(255)\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (user_id)\n\n### user_preferences\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint not null -> users\n- `theme` varchar(64) not null\n- `email_opt_in` boolean not null\n- `marketing_opt_in` boolean not null\n- `locale_override` varchar(5)\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (user_id)\n\n### roles\nPer-tenant RBAC roles. is_system marks built-in roles that cannot be deleted.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `description` varchar(4000)\n- `is_system` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, name)\n\n### permissions\nGlobal catalog of permission codes (e.g. orders.refund). Shared across all tenants.\n- `id` bigint not null\n- `code` varchar(64) not null\n- `description` varchar(4000)\n- `category` varchar(255)\n- unique: (code)\n\n### role_permissions\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `role_id` bigint not null -> roles\n- `permission_id` bigint not null -> permissions\n- `created_at` timestamp not null\n- unique: (role_id, permission_id)\n\n### user_roles\nAssignment of roles to users. granted_by_user_id records who made the grant.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint not null -> users\n- `role_id` bigint not null -> roles\n- `granted_by_user_id` bigint -> users\n- `created_at` timestamp not null\n- unique: (user_id, role_id)\n\n### teams\nteams — hierarchical sub-org groupings.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `parent_team_id` bigint -> teams\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, name)\n\n### team_members\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `team_id` bigint not null -> teams\n- `user_id` bigint not null -> users\n- `team_role` varchar(64) not null\n- `created_at` timestamp not null\n- unique: (team_id, user_id)\n\n### api_keys\nProgrammatic API credentials for a tenant. Only the hash and a short prefix are stored.\n- `id` bigint not null\n- `public_id` uuid not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `prefix` varchar(64) not null\n- `hashed_key` varchar(64) not null\n- `status` varchar(32) not null one of: active, revoked, expired\n- `created_by_user_id` bigint -> users\n- `last_used_at` timestamp\n- `expires_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (public_id); (prefix)\n\n### personal_access_tokens\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint not null -> users\n- `name` varchar(255) not null\n- `hashed_token` varchar(255) not null\n- `scopes` varchar(64)[] not null\n- `last_used_at` timestamp\n- `expires_at` timestamp\n- `created_at` timestamp not null\n- unique: (hashed_token)\n\n### sessions\nAuthenticated browser/app sessions. revoked_at non-null means force-logged-out.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint not null -> users\n- `token_hash` varchar(255) not null\n- `ip_address` varchar(45)\n- `user_agent` varchar(2048)\n- `started_at` timestamp not null\n- `last_seen_at` timestamp not null\n- `expires_at` timestamp not null\n- `revoked_at` timestamp\n- unique: (token_hash)\n\n### oauth_accounts\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint not null -> users\n- `provider` varchar(64) not null\n- `provider_account_id` varchar(255) not null\n- `access_token_hash` varchar(255)\n- `created_at` timestamp not null\n- unique: (provider, provider_account_id)\n\n### mfa_devices\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint not null -> users\n- `kind` varchar(64) not null\n- `secret_hash` varchar(255) not null\n- `confirmed_at` timestamp\n- `created_at` timestamp not null\n\n### invitations\nPending invitations to join a tenant; consumed when status becomes accepted.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `email` varchar(254) not null\n- `role_id` bigint -> roles\n- `invited_by_user_id` bigint -> users\n- `status` varchar(32) not null one of: pending, accepted, expired, revoked\n- `token_hash` varchar(255) not null\n- `expires_at` timestamp not null\n- `accepted_at` timestamp\n- `created_at` timestamp not null\n- unique: (token_hash)\n\n### password_resets\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint not null -> users\n- `token_hash` varchar(255) not null\n- `expires_at` timestamp not null\n- `used_at` timestamp\n- `created_at` timestamp not null\n- unique: (token_hash)\n\n### service_accounts\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `description` varchar(4000)\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, name)\n\n### login_attempts\nAudit of authentication attempts, successful and failed, for security analytics.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint -> users\n- `email` varchar(254)\n- `succeeded` boolean not null\n- `ip_address` varchar(45)\n- `user_agent` varchar(2048)\n- `occurred_at` timestamp not null\n\n## Area: inventory\n\n### warehouses\nPhysical fulfillment sites for a tenant, each anchored to a postal address; one is flagged as the default.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `address_id` bigint -> addresses\n- `code` varchar(64) not null\n- `name` varchar(255) not null\n- `is_default` boolean not null\n- `is_active` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (tenant_id, code)\n\n### stock_locations\nBins, shelves and zones inside a warehouse. Stock is counted at this granularity, not at the warehouse level.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `warehouse_id` bigint not null -> warehouses\n- `code` varchar(64) not null\n- `name` varchar(255)\n- `kind` varchar(64) not null\n- `is_pickable` boolean not null\n- `is_active` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (warehouse_id, code)\n\n### suppliers\nVendors that goods are purchased from on purchase orders.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `code` varchar(64) not null\n- `name` varchar(255) not null\n- `contact_email` varchar(254)\n- `contact_phone` varchar(32)\n- `lead_time_days` integer\n- `is_active` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (tenant_id, code)\n\n### supplier_products\nPer-supplier sourcing of a variant: their SKU and unit cost; is_preferred marks the default source.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `supplier_id` bigint not null -> suppliers\n- `variant_id` bigint not null -> product_variants\n- `supplier_sku` varchar(64)\n- `unit_cost` decimal(14,4)\n- `currency_code` varchar(3) -> currencies\n- `min_order_qty` integer\n- `lead_time_days` integer\n- `is_preferred` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (supplier_id, variant_id)\n\n### stock_items\nLive on-hand quantity of a variant in a single location; one row per (variant, location). The movements ledger is its history.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `variant_id` bigint not null -> product_variants\n- `stock_location_id` bigint not null -> stock_locations\n- `quantity` integer not null\n- `reserved_quantity` integer not null (Portion of quantity earmarked by open reservations and not available to sell.)\n- `reorder_point` integer (Replenishment trigger: at or below this on-hand level the variant is flagged for reorder.)\n- `reorder_quantity` integer\n- `last_counted_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (variant_id, stock_location_id)\n\n### stock_movements\nImmutable append-only ledger of inventory quantity changes; quantity is signed (in positive, out negative) and the running sum reproduces stock_items.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `variant_id` bigint not null -> product_variants\n- `stock_location_id` bigint not null -> stock_locations\n- `lot_id` bigint -> lots\n- `kind` varchar(32) not null one of: receipt, shipment, adjustment, transfer, return, cycle_count\n- `quantity` integer not null\n- `reference_type` varchar(64)\n- `reference_id` bigint (Loose pointer to the source document (PO line, order, adjustment) identified by reference_type; not a foreign key.)\n- `note` varchar(4000)\n- `created_by_user_id` bigint -> users\n- `occurred_at` timestamp not null\n- `created_at` timestamp not null\n\n### stock_reservations\nSoft holds that earmark stock for an order so it is not double-sold; order_id references orders by id but has no FK (forward module).\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `variant_id` bigint not null -> product_variants\n- `stock_location_id` bigint not null -> stock_locations\n- `order_id` bigint -> orders\n- `quantity` integer not null\n- `expires_at` timestamp\n- `released_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### purchase_orders\nReplenishment orders sent to a supplier and delivered to a warehouse; public_id is the opaque external identifier.\n- `id` bigint not null\n- `public_id` uuid not null\n- `tenant_id` bigint not null -> tenants\n- `supplier_id` bigint not null -> suppliers\n- `warehouse_id` bigint -> warehouses\n- `po_number` varchar(64) not null\n- `status` varchar(32) not null one of: draft, submitted, partially_received, received, cancelled\n- `currency_code` varchar(3) not null -> currencies\n- `expected_total` decimal(14,4) not null (Denormalized order total; the authoritative figure is the sum over purchase_order_lines.)\n- `notes` varchar(4000)\n- `ordered_at` timestamp\n- `expected_at` timestamp\n- `received_at` timestamp\n- `created_by_user_id` bigint -> users\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (public_id); (tenant_id, po_number)\n\n### purchase_order_lines\nLine items on a purchase order: variant, quantity and agreed unit cost, with received quantity accruing as goods arrive.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `purchase_order_id` bigint not null -> purchase_orders\n- `variant_id` bigint not null -> product_variants\n- `quantity_ordered` integer not null\n- `quantity_received` integer not null\n- `unit_cost` decimal(14,4) not null\n- `currency_code` varchar(3) not null -> currencies\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (purchase_order_id, variant_id)\n\n### lots\nBatch/lot tracking for a variant, carrying manufacture and expiry dates for FEFO picking and recalls.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `variant_id` bigint not null -> product_variants\n- `supplier_id` bigint -> suppliers\n- `lot_code` varchar(64) not null\n- `manufactured_at` date\n- `expires_at` date\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, variant_id, lot_code)\n\n### serial_numbers\nserial_numbers — individually tracked units (one row per physical unit), optionally grouped under a lot and located in a stock location.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `variant_id` bigint not null -> product_variants\n- `lot_id` bigint -> lots\n- `stock_location_id` bigint -> stock_locations\n- `serial` varchar(64) not null\n- `status` varchar(64) not null\n- `received_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, serial)\n\n### inbound_receipts\nGoods-receipt headers recording a physical delivery into a warehouse, usually against a purchase order.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `purchase_order_id` bigint -> purchase_orders\n- `warehouse_id` bigint not null -> warehouses\n- `receipt_number` varchar(64) not null\n- `carrier` varchar(64)\n- `tracking_number` varchar(64)\n- `received_by_user_id` bigint -> users\n- `received_at` timestamp not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, receipt_number)\n\n### receipt_lines\nreceipt_lines — the quantities of each variant actually received on a receipt, put away into a specific location (and optionally a lot).\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `inbound_receipt_id` bigint not null -> inbound_receipts\n- `purchase_order_line_id` bigint -> purchase_order_lines\n- `variant_id` bigint not null -> product_variants\n- `stock_location_id` bigint -> stock_locations\n- `lot_id` bigint -> lots\n- `quantity_received` integer not null\n- `quantity_rejected` integer not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### stock_adjustments\nManual corrections to on-hand stock (cycle counts, shrinkage, damage); quantity_delta is signed and a reason is required.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `variant_id` bigint not null -> product_variants\n- `stock_location_id` bigint not null -> stock_locations\n- `lot_id` bigint -> lots\n- `quantity_delta` integer not null\n- `reason` varchar(4000) not null\n- `note` varchar(4000)\n- `adjusted_by_user_id` bigint -> users\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n## Area: marketing\n\n### email_templates\nReusable email subject/body templates used by campaigns and per-recipient sends.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `subject` varchar(255) not null\n- `body_html` varchar(65535)\n- `body_text` varchar(65535)\n- `from_name` varchar(255)\n- `from_email` varchar(254)\n- `is_active` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (tenant_id, name)\n\n### segments\nAudience definitions; the selection logic lives in the rules jsonb and is materialized into segment_members.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `description` varchar(4000)\n- `rules` json not null\n- `is_dynamic` boolean not null (True if membership is recomputed from rules on a schedule; false for static lists.)\n- `member_count` integer not null\n- `last_refreshed_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (tenant_id, name)\n\n### segment_members\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `segment_id` bigint not null -> segments\n- `user_id` bigint not null -> users\n- `added_at` timestamp not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (segment_id, user_id)\n\n### landing_pages\nlanding_pages — campaign destination pages. Referenced by campaigns and utm_links, so defined ahead of them.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `slug` varchar(255) not null\n- `title` varchar(255) not null\n- `url` varchar(2048)\n- `content_html` varchar(65535)\n- `is_published` boolean not null\n- `published_at` timestamp\n- `view_count` bigint not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (tenant_id, slug)\n\n### campaigns\nMarketing campaigns; may target a segment, render via a template, drive to a landing page, and link a pricing promotion.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `description` varchar(4000)\n- `channel` varchar(64) not null\n- `status` varchar(32) not null one of: draft, scheduled, sending, sent, paused, archived\n- `segment_id` bigint -> segments\n- `email_template_id` bigint -> email_templates\n- `landing_page_id` bigint -> landing_pages\n- `promotion_id` bigint -> promotions\n- `budget` decimal(14,4)\n- `currency_code` varchar(3) -> currencies\n- `scheduled_at` timestamp\n- `started_at` timestamp\n- `ended_at` timestamp\n- `created_by_user_id` bigint -> users\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (tenant_id, name)\n\n### campaign_messages\nOne row per recipient send; send_status tracks the delivery and engagement lifecycle.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `campaign_id` bigint not null -> campaigns\n- `user_id` bigint not null -> users\n- `email_template_id` bigint -> email_templates\n- `status` varchar(32) not null one of: queued, sent, delivered, opened, clicked, bounced, complained, failed, unsubscribed\n- `recipient_email` varchar(254)\n- `sent_at` timestamp\n- `delivered_at` timestamp\n- `opened_at` timestamp\n- `clicked_at` timestamp\n- `bounced_at` timestamp\n- `unsubscribed_at` timestamp\n- `error_message` varchar(4000)\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### ab_tests\nA/B test definitions; winner_variant_id records the chosen variant once the experiment completes.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `campaign_id` bigint -> campaigns\n- `name` varchar(255) not null\n- `hypothesis` varchar(4000)\n- `status` varchar(32) not null one of: draft, running, paused, completed, archived\n- `goal_metric` varchar(255)\n- `started_at` timestamp\n- `ended_at` timestamp\n- `winner_variant_id` bigint\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (tenant_id, name)\n\n### ab_variants\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `ab_test_id` bigint not null -> ab_tests\n- `name` varchar(255) not null\n- `is_control` boolean not null\n- `traffic_weight` decimal(5,4) not null (Fraction of eligible traffic routed to this variant; weights across a test should sum to ~1.)\n- `config` json not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (ab_test_id, name)\n\n### ab_assignments\nSticky assignment of a user to one variant within an A/B test; converted_at records goal completion.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `ab_test_id` bigint not null -> ab_tests\n- `ab_variant_id` bigint not null -> ab_variants\n- `user_id` bigint not null -> users\n- `assigned_at` timestamp not null\n- `converted_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (ab_test_id, user_id)\n\n### attributions\nTouch attributions crediting a campaign with a conversion; order_id is set for purchase conversions.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint -> users\n- `campaign_id` bigint -> campaigns\n- `order_id` bigint -> orders\n- `model` varchar(64) not null\n- `touch_point` varchar(64)\n- `weight` decimal(5,4) not null (Fractional credit for this touch under the attribution model (e.g. linear, last_touch).)\n- `attributed_revenue` decimal(14,4)\n- `currency_code` varchar(3) -> currencies\n- `occurred_at` timestamp not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### referrals\nReferral codes owned by a referring user, with optional reward and redemption caps.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `referrer_user_id` bigint not null -> users\n- `code` varchar(64) not null\n- `reward_amount` decimal(14,4)\n- `currency_code` varchar(3) -> currencies\n- `max_redemptions` integer\n- `redemption_count` integer not null\n- `is_active` boolean not null\n- `expires_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (tenant_id, code)\n\n### referral_redemptions\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `referral_id` bigint not null -> referrals\n- `referred_user_id` bigint not null -> users\n- `order_id` bigint -> orders\n- `reward_amount` decimal(14,4)\n- `currency_code` varchar(3) -> currencies\n- `redeemed_at` timestamp not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (referral_id, referred_user_id)\n\n### loyalty_programs\nLoyalty: program defs -> per-user accounts (balance) -> immutable earn/burn transaction ledger.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `description` varchar(4000)\n- `points_per_currency` decimal(14,4) not null\n- `currency_code` varchar(3) not null -> currencies\n- `is_active` boolean not null\n- `starts_at` timestamp\n- `ends_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (tenant_id, name)\n\n### loyalty_accounts\nPer-user point balance within a loyalty program; points_balance is the current redeemable total.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `loyalty_program_id` bigint not null -> loyalty_programs\n- `user_id` bigint not null -> users\n- `points_balance` bigint not null\n- `lifetime_points` bigint not null\n- `tier` varchar(64)\n- `enrolled_at` timestamp not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (loyalty_program_id, user_id)\n\n### loyalty_transactions\nImmutable earn/burn ledger of loyalty point movements; append-only, never updated.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `loyalty_account_id` bigint not null -> loyalty_accounts\n- `user_id` bigint not null -> users\n- `direction` varchar(64) not null\n- `points` bigint not null\n- `reason` varchar(4000)\n- `order_id` bigint -> orders\n- `balance_after` bigint (Account points_balance immediately after this entry was applied.)\n- `occurred_at` timestamp not null\n- `created_at` timestamp not null\n\n### utm_links\nTracked links carrying UTM parameters, optionally tied to a campaign and landing page.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `campaign_id` bigint -> campaigns\n- `landing_page_id` bigint -> landing_pages\n- `slug` varchar(255) not null\n- `target_url` varchar(2048) not null\n- `utm_source` varchar(255)\n- `utm_medium` varchar(255)\n- `utm_campaign` varchar(255)\n- `utm_term` varchar(255)\n- `utm_content` varchar(255)\n- `click_count` bigint not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (tenant_id, slug)\n\n## Area: ops\n\n### feature_flags\nFeature toggle and rollout definitions, addressed by a tenant-unique key. kind distinguishes boolean toggles from multivariate and percentage rollouts.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `key` varchar(64) not null\n- `name` varchar(255) not null\n- `description` varchar(4000)\n- `kind` varchar(32) not null one of: boolean, multivariate, percentage_rollout\n- `is_enabled` boolean not null\n- `default_value` json not null (Fallback value served when no rule or override matches; jsonb to support multivariate payloads, not just booleans.)\n- `rollout_percentage` integer\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, key)\n\n### feature_flag_rules\nOrdered targeting rules for a flag; the first rule whose jsonb conditions match decides the served value.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `feature_flag_id` bigint not null -> feature_flags\n- `sort_position` integer not null\n- `conditions` json not null\n- `serve_value` json not null\n- `is_active` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (feature_flag_id, sort_position)\n\n### feature_flag_overrides\nExplicit per-user or tenant-wide overrides that bypass rule evaluation for a flag.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `feature_flag_id` bigint not null -> feature_flags\n- `user_id` bigint -> users\n- `override_value` json not null\n- `reason` varchar(4000)\n- `expires_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### settings\nTyped key/value configuration per tenant. value_type tags how the jsonb value should be interpreted by the application.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `setting_key` varchar(64) not null\n- `setting_value` json not null\n- `value_type` varchar(64) not null (Logical type hint for value (string/number/boolean/json/date); the column itself is always jsonb.)\n- `is_secret` boolean not null\n- `description` varchar(4000)\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, setting_key)\n\n### jobs\nBackground job definitions registered per tenant; job_runs records each execution.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `queue` varchar(64) not null\n- `handler` varchar(64) not null\n- `payload` json not null\n- `max_attempts` integer not null\n- `is_enabled` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, name)\n\n### job_runs\nImmutable record of a single background job execution, including status, attempt number and timing.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `job_id` bigint not null -> jobs\n- `status` varchar(32) not null one of: queued, running, succeeded, failed, cancelled, retrying\n- `attempt` integer not null\n- `queued_at` timestamp not null\n- `started_at` timestamp\n- `finished_at` timestamp\n- `duration_ms` integer\n- `error_message` varchar(4000)\n- `result_payload` json\n- `created_at` timestamp not null\n\n### integrations\nThird-party service connections for a tenant (e.g. ERP, shipping, accounting), with current status and config.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `provider` varchar(64) not null\n- `name` varchar(255) not null\n- `status` varchar(32) not null one of: connected, disconnected, error, syncing\n- `config` json not null\n- `connected_by_user_id` bigint -> users\n- `connected_at` timestamp\n- `last_synced_at` timestamp\n- `last_error` varchar(4000)\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, provider, name)\n\n### integration_syncs\nImmutable record of a single data-sync run against an integration.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `integration_id` bigint not null -> integrations\n- `status` varchar(32) not null one of: queued, running, succeeded, failed, cancelled, retrying\n- `direction` varchar(64) not null\n- `records_processed` integer not null\n- `records_failed` integer not null\n- `started_at` timestamp\n- `finished_at` timestamp\n- `error_message` varchar(4000)\n- `created_at` timestamp not null\n\n### files\nMetadata for files uploaded to object storage; the actual blob lives in storage_bucket/storage_key, not in the database.\n- `id` bigint not null\n- `public_id` uuid not null\n- `tenant_id` bigint not null -> tenants\n- `filename` varchar(255) not null\n- `content_type` varchar(64) not null\n- `byte_size` bigint not null\n- `storage_bucket` varchar(255) not null\n- `storage_key` varchar(2048) not null\n- `checksum` varchar(255)\n- `uploaded_by_user_id` bigint -> users\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (public_id); (storage_bucket, storage_key)\n\n### file_versions\nVersion chain for a file; is_current flags the active version (one per file, enforced by partial unique index).\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `file_id` bigint not null -> files\n- `version` integer not null\n- `byte_size` bigint not null\n- `storage_key` varchar(2048) not null\n- `checksum` varchar(255)\n- `is_current` boolean not null\n- `created_by_user_id` bigint -> users\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (file_id, version)\n\n### import_batches\nBulk data-import runs with row-level progress counters and a terminal status.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `entity_type` varchar(64) not null\n- `source_filename` varchar(255)\n- `file_id` bigint -> files\n- `status` varchar(32) not null one of: pending, processing, completed, failed, partially_failed\n- `total_rows` integer not null\n- `processed_rows` integer not null\n- `succeeded_rows` integer not null\n- `failed_rows` integer not null\n- `started_by_user_id` bigint -> users\n- `started_at` timestamp\n- `finished_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### import_errors\nImmutable per-row failures captured during an import batch, with the offending raw row preserved as jsonb.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `import_batch_id` bigint not null -> import_batches\n- `source_row_number` integer not null\n- `column_name` varchar(64)\n- `error_code` varchar(64) not null\n- `error_message` varchar(4000) not null\n- `raw_row` json\n- `occurred_at` timestamp not null\n- `created_at` timestamp not null\n\n### scheduled_tasks\nCron-expression driven recurring tasks; next_run_at is the precomputed next fire time used by the scheduler.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `cron_expression` varchar(2048) not null\n- `timezone` varchar(64) not null\n- `job_id` bigint -> jobs\n- `is_enabled` boolean not null\n- `last_run_at` timestamp\n- `next_run_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, name)\n\n### system_health_checks\nsystem_health_checks — immutable periodic health probe results.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `component` varchar(255) not null\n- `is_healthy` boolean not null\n- `latency_ms` integer\n- `details` json\n- `checked_at` timestamp not null\n- `created_at` timestamp not null\n\n### rate_limits\nRequest quota windows scoped per tenant or per API key; an api_key-null row is the tenant-wide default for a scope.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `api_key_id` bigint -> api_keys\n- `limit_scope` varchar(64) not null\n- `max_requests` integer not null\n- `window_seconds` integer not null\n- `is_enabled` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### secrets_vault\nReferences and metadata for secrets held in an external vault; never stores plaintext, only a provider pointer (external_ref).\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `key` varchar(64) not null\n- `provider` varchar(64) not null\n- `external_ref` varchar(255) not null (Opaque pointer into the external secret store (e.g. a Vault path or KMS key id); resolved at runtime, never the secret value itself.)\n- `version` integer not null\n- `last_rotated_at` timestamp\n- `expires_at` timestamp\n- `created_by_user_id` bigint -> users\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, key)\n\n## Area: pricing\n\n### price_lists\nNamed price books, each scoped to one currency; a tenant keeps a default list per currency plus contract/regional books.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `code` varchar(64) not null\n- `currency_code` varchar(3) not null -> currencies\n- `is_default` boolean not null\n- `is_active` boolean not null\n- `starts_at` timestamp\n- `ends_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (tenant_id, code)\n\n### prices\nA variant's price within a price list over a validity window; an exclusion constraint forbids overlapping windows for the same list/variant/min_quantity.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `price_list_id` bigint not null -> price_lists\n- `variant_id` bigint not null -> product_variants\n- `currency_code` varchar(3) not null -> currencies\n- `amount` decimal(14,4) not null\n- `compare_at_amount` decimal(14,4) (Optional strike-through / \"was\" price shown next to the effective amount.)\n- `min_quantity` integer not null\n- `valid_from` timestamp not null\n- `valid_to` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### discounts\nReusable discount definitions; coupons, price_rules and promotions point at these for the actual reduction.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `kind` varchar(32) not null one of: percentage, fixed_amount, free_shipping, bogo\n- `discount_value` decimal(14,4) not null (Percentage (0-100) when kind=percentage, else a fixed monetary amount in currency_code.)\n- `currency_code` varchar(3) -> currencies\n- `max_amount` decimal(14,4)\n- `min_subtotal` decimal(14,4)\n- `applies_to_shipping` boolean not null\n- `is_active` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n\n### coupons\nRedeemable codes granting a discount; code is unique per tenant and usage is capped by usage_limit / per_customer_limit.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `discount_id` bigint not null -> discounts\n- `code` varchar(64) not null\n- `status` varchar(32) not null one of: active, scheduled, expired, disabled\n- `usage_limit` integer\n- `per_customer_limit` integer\n- `times_redeemed` integer not null (Denormalized running count of redemptions, capped by usage_limit.)\n- `starts_at` timestamp\n- `expires_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (tenant_id, code)\n\n### coupon_redemptions\nImmutable record of each coupon use; order_id references orders.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `coupon_id` bigint not null -> coupons\n- `user_id` bigint -> users\n- `order_id` bigint -> orders\n- `discount_amount` decimal(14,4) not null\n- `currency_code` varchar(3) not null -> currencies\n- `redeemed_at` timestamp not null\n- `created_at` timestamp not null\n\n### tax_categories\ntax_categories — product tax classes (e.g. standard goods, digital, food, exempt). Variants/products are mapped to one of these to find their rate.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `code` varchar(64) not null\n- `description` varchar(4000)\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, code)\n\n### tax_zones\nGeographic tax regions defined at country, region or postal-zone granularity.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `country_id` bigint -> countries\n- `region_id` bigint -> regions\n- `postal_zone_id` bigint -> postal_zones\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### tax_rates\nEffective tax rate for a (tax_category, tax_zone) pair, with an optional validity window so rate changes stay auditable.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `tax_category_id` bigint not null -> tax_categories\n- `tax_zone_id` bigint not null -> tax_zones\n- `name` varchar(255) not null\n- `rate` decimal(8,5) not null (Fractional rate in [0,1]; e.g. 0.20000 for 20% VAT.)\n- `is_compound` boolean not null\n- `is_inclusive` boolean not null (True when displayed prices already include this tax (tax-inclusive pricing).)\n- `valid_from` timestamp not null\n- `valid_to` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### price_rules\nprice_rules — conditional pricing rule headers (e.g. \"10% off when cart has 3+ items from brand X\"). Conditions live in price_rule_conditions. The reward references a discount definition. priority orders evaluation; lower = first.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `discount_id` bigint -> discounts\n- `name` varchar(255) not null\n- `priority` integer not null\n- `is_active` boolean not null\n- `stackable` boolean not null\n- `starts_at` timestamp\n- `ends_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### price_rule_conditions\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `price_rule_id` bigint not null -> price_rules\n- `attribute` varchar(255) not null\n- `operator` varchar(64) not null\n- `condition_value` json not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### promotions\nCampaign-linked promotions; standalone by design so campaigns can reference promotions, never the reverse.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `discount_id` bigint -> discounts\n- `coupon_id` bigint -> coupons\n- `name` varchar(255) not null\n- `slug` varchar(255) not null\n- `campaign_code` varchar(64) (Free-text correlation handle to a marketing campaign; not an FK since marketing is a higher-numbered module.)\n- `is_active` boolean not null\n- `starts_at` timestamp\n- `ends_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (tenant_id, slug)\n\n### promotion_products\nJunction restricting a promotion to specific products.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `promotion_id` bigint not null -> promotions\n- `product_id` bigint not null -> products\n- `created_at` timestamp not null\n- unique: (promotion_id, product_id)\n\n## Area: sales\n\n### carts\nShopping carts, anonymous (user_id null) or owned. Converts to an order; abandoned carts feed recovery campaigns.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint -> users\n- `session_token` varchar(255)\n- `status` varchar(32) not null one of: active, converted, abandoned, merged\n- `currency_code` varchar(3) not null -> currencies\n- `item_count` integer not null\n- `subtotal` decimal(14,4) not null\n- `abandoned_at` timestamp\n- `converted_at` timestamp\n- `expires_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### cart_items\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `cart_id` bigint not null -> carts\n- `product_variant_id` bigint not null -> product_variants\n- `quantity` integer not null\n- `unit_price` decimal(14,4) not null\n- `currency_code` varchar(3) not null -> currencies\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (cart_id, product_variant_id)\n\n### orders\nCentral order fact table. public_id is the opaque id shown to customers; totals are denormalized money on the row.\n- `id` bigint not null\n- `public_id` uuid not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint not null -> users\n- `cart_id` bigint -> carts\n- `order_number` varchar(64) not null (Human-facing sequential order number, unique per tenant (distinct from the opaque public_id).)\n- `status` varchar(32) not null one of: pending, confirmed, paid, partially_fulfilled, fulfilled, cancelled, refunded\n- `billing_address_id` bigint -> addresses\n- `shipping_address_id` bigint -> addresses\n- `currency_code` varchar(3) not null -> currencies\n- `subtotal` decimal(14,4) not null\n- `discount_total` decimal(14,4) not null\n- `tax_total` decimal(14,4) not null\n- `shipping_total` decimal(14,4) not null\n- `grand_total` decimal(14,4) not null (Final amount charged: subtotal - discount_total + tax_total + shipping_total.)\n- `customer_email` varchar(254)\n- `customer_note` varchar(4000)\n- `placed_at` timestamp\n- `cancelled_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (public_id); (tenant_id, order_number)\n\n### order_items\nLine items of an order; sku and name are snapshotted at purchase time so historical orders survive catalog changes.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `order_id` bigint not null -> orders\n- `product_variant_id` bigint not null -> product_variants\n- `sku` varchar(64) not null\n- `name` varchar(255) not null\n- `quantity` integer not null\n- `quantity_fulfilled` integer not null (Running count of units already dispatched across fulfillments; never exceeds quantity.)\n- `unit_price` decimal(14,4) not null\n- `discount_total` decimal(14,4) not null\n- `tax_total` decimal(14,4) not null\n- `line_total` decimal(14,4) not null\n- `currency_code` varchar(3) not null -> currencies\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### order_status_history\nAppend-only audit of order status transitions; immutable (no updated_at).\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `order_id` bigint not null -> orders\n- `from_status` varchar(32) one of: pending, confirmed, paid, partially_fulfilled, fulfilled, cancelled, refunded\n- `to_status` varchar(32) not null one of: pending, confirmed, paid, partially_fulfilled, fulfilled, cancelled, refunded\n- `changed_by_user_id` bigint -> users\n- `note` varchar(4000)\n- `created_at` timestamp not null\n\n### order_discounts\nDiscounts and coupon redemptions applied to an order; discount_id/coupon_id reference the pricing module.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `order_id` bigint not null -> orders\n- `discount_id` bigint -> discounts\n- `coupon_id` bigint -> coupons\n- `code` varchar(64)\n- `description` varchar(4000)\n- `amount` decimal(14,4) not null\n- `currency_code` varchar(3) not null -> currencies\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### fulfillments\nA batch of order items dispatched together from one warehouse; tracks pick/pack/ship lifecycle.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `order_id` bigint not null -> orders\n- `warehouse_id` bigint -> warehouses\n- `status` varchar(32) not null one of: pending, picked, packed, shipped, delivered, returned\n- `reference` varchar(255)\n- `packed_at` timestamp\n- `shipped_at` timestamp\n- `delivered_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### fulfillment_items\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `fulfillment_id` bigint not null -> fulfillments\n- `order_item_id` bigint not null -> order_items\n- `quantity` integer not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (fulfillment_id, order_item_id)\n\n### shipments\nCarrier shipment for a fulfillment, with tracking number and delivery timestamps.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `fulfillment_id` bigint not null -> fulfillments\n- `carrier` varchar(64) not null\n- `service_level` varchar(64)\n- `tracking_number` varchar(64)\n- `tracking_url` varchar(2048)\n- `shipping_cost` decimal(14,4)\n- `currency_code` varchar(3) -> currencies\n- `weight_grams` integer\n- `shipped_at` timestamp\n- `delivered_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### shipment_tracking\nshipment_tracking — append-only stream of carrier tracking events. IMMUTABLE: occurred_at only, no updated_at.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `shipment_id` bigint not null -> shipments\n- `status` varchar(64) not null\n- `description` varchar(4000)\n- `location` varchar(255)\n- `occurred_at` timestamp not null\n- `created_at` timestamp not null\n\n### order_returns\nReturn merchandise authorization (RMA) header; status walks requested -> approved -> received -> refunded.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `order_id` bigint not null -> orders\n- `rma_number` varchar(64) not null\n- `status` varchar(32) not null one of: requested, approved, received, refunded, rejected\n- `reason` varchar(4000)\n- `refund_total` decimal(14,4) not null\n- `currency_code` varchar(3) not null -> currencies\n- `requested_at` timestamp not null\n- `approved_at` timestamp\n- `received_at` timestamp\n- `refunded_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, rma_number)\n\n### return_items\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `return_id` bigint not null -> order_returns\n- `order_item_id` bigint not null -> order_items\n- `quantity` integer not null\n- `reason` varchar(4000)\n- `restock` boolean not null (Whether the returned units should be added back to sellable inventory.)\n- `refund_amount` decimal(14,4) not null\n- `currency_code` varchar(3) not null -> currencies\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (return_id, order_item_id)\n\n### gift_cards\nStored-value gift cards; code is unique per tenant and balance decreases as it is redeemed against orders.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `code` varchar(64) not null\n- `status` varchar(64) not null\n- `initial_balance` decimal(14,4) not null\n- `balance` decimal(14,4) not null\n- `currency_code` varchar(3) not null -> currencies\n- `issued_to_user_id` bigint -> users\n- `issued_order_id` bigint -> orders\n- `expires_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, code)\n\n### gift_card_transactions\nImmutable ledger of gift-card balance changes; positive amount credits, negative debits.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `gift_card_id` bigint not null -> gift_cards\n- `order_id` bigint -> orders\n- `amount` decimal(14,4) not null\n- `balance_after` decimal(14,4) not null\n- `currency_code` varchar(3) not null -> currencies\n- `note` varchar(4000)\n- `created_at` timestamp not null\n\n### order_notes\norder_notes — internal staff notes attached to an order (not the customer note that lives on the order row itself).\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `order_id` bigint not null -> orders\n- `author_user_id` bigint -> users\n- `body` varchar(65535) not null\n- `is_pinned` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### draft_orders\nMerchant-created quotes/draft orders, optionally converted into a real order.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint -> users\n- `created_by_user_id` bigint -> users\n- `converted_order_id` bigint -> orders\n- `name` varchar(255)\n- `status` varchar(64) not null\n- `currency_code` varchar(3) not null -> currencies\n- `subtotal` decimal(14,4) not null\n- `grand_total` decimal(14,4) not null\n- `payload` json not null\n- `expires_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n## Area: support\n\n### support_agents\nSupport staff who handle tickets; a support-specific extension of an users record carrying availability and load limits.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint not null -> users\n- `display_name` varchar(255)\n- `signature` varchar(255)\n- `is_available` boolean not null\n- `max_open_tickets` integer not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, user_id)\n\n### agent_groups\nNamed groups of agents used for ticket routing and assignment.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `description` varchar(4000)\n- `is_active` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, name)\n\n### agent_group_members\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `agent_group_id` bigint not null -> agent_groups\n- `support_agent_id` bigint not null -> support_agents\n- `is_lead` boolean not null\n- `created_at` timestamp not null\n- unique: (agent_group_id, support_agent_id)\n\n### sla_policies\nLookup of service-level targets (first-response and resolution minutes), usually keyed by ticket priority.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `priority` varchar(32) one of: low, normal, high, urgent\n- `first_response_target_minutes` integer not null\n- `resolution_target_minutes` integer not null\n- `business_hours_only` boolean not null (When true, SLA timers pause outside the tenant business calendar rather than running 24/7.)\n- `is_active` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, name)\n\n### support_tags\nsupport_tags — tag catalog for categorizing tickets.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `color` varchar(64)\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, name)\n\n### knowledge_categories\nKnowledge base: hierarchical categories + articles + per-article feedback.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `slug` varchar(255) not null\n- `parent_category_id` bigint -> knowledge_categories\n- `sort_position` integer not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, slug)\n\n### knowledge_articles\nHelp-center articles published to customers; soft-deletable via deleted_at.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `knowledge_category_id` bigint -> knowledge_categories\n- `author_user_id` bigint -> users\n- `title` varchar(255) not null\n- `slug` varchar(255) not null\n- `body` varchar(65535)\n- `is_published` boolean not null\n- `view_count` bigint not null\n- `helpful_count` integer not null (Denormalized count of positive article_feedback rows, kept for fast sorting.)\n- `not_helpful_count` integer not null\n- `published_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (tenant_id, slug)\n\n### article_feedback\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `knowledge_article_id` bigint not null -> knowledge_articles\n- `user_id` bigint -> users\n- `was_helpful` boolean not null\n- `comment` varchar(4000)\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### macros\nCanned reply templates agents insert into ticket messages. Soft-deletable.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `created_by_agent_id` bigint -> support_agents\n- `title` varchar(255) not null\n- `body` varchar(65535) not null\n- `is_shared` boolean not null\n- `usage_count` bigint not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (tenant_id, title)\n\n### tickets\nCore customer support request. requester_user_id is the customer; assignee_agent_id is the owning agent (null = unassigned queue).\n- `id` bigint not null\n- `public_id` uuid not null\n- `tenant_id` bigint not null -> tenants\n- `requester_user_id` bigint not null -> users\n- `assignee_agent_id` bigint -> support_agents\n- `agent_group_id` bigint -> agent_groups\n- `sla_policy_id` bigint -> sla_policies\n- `subject` varchar(255) not null\n- `status` varchar(32) not null one of: new, open, pending, on_hold, solved, closed\n- `priority` varchar(32) not null one of: low, normal, high, urgent\n- `channel` varchar(32) not null one of: email, chat, phone, web, social, api\n- `first_response_at` timestamp (Timestamp of the first public agent reply, used to measure SLA first-response compliance.)\n- `resolved_at` timestamp\n- `closed_at` timestamp\n- `last_message_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (public_id)\n\n### ticket_messages\nReplies on a ticket: is_public=true are customer-visible, is_public=false are internal agent notes.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `ticket_id` bigint not null -> tickets\n- `author_user_id` bigint -> users\n- `author_agent_id` bigint -> support_agents\n- `body` varchar(65535) not null\n- `is_public` boolean not null\n- `channel` varchar(32) one of: email, chat, phone, web, social, api\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### ticket_events\nImmutable append-only audit of ticket changes such as status transitions, reassignment, and priority changes.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `ticket_id` bigint not null -> tickets\n- `actor_user_id` bigint -> users\n- `actor_agent_id` bigint -> support_agents\n- `event_type` varchar(64) not null\n- `old_value` varchar(4000)\n- `new_value` varchar(4000)\n- `metadata` json not null\n- `occurred_at` timestamp not null\n- `created_at` timestamp not null\n\n### ticket_tags_map\nticket_tags_map — junction tickets × support_tags.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `ticket_id` bigint not null -> tickets\n- `support_tag_id` bigint not null -> support_tags\n- `created_at` timestamp not null\n- unique: (ticket_id, support_tag_id)\n\n### sla_breaches\nRecorded SLA-target breaches on a ticket, with how many minutes the target was missed by.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `ticket_id` bigint not null -> tickets\n- `sla_policy_id` bigint -> sla_policies\n- `breach_kind` varchar(64) not null\n- `target_at` timestamp not null\n- `breached_at` timestamp not null\n- `minutes_over` integer\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### csat_responses\nCustomer satisfaction rating (1-5) collected after a ticket is resolved; one response per ticket.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `ticket_id` bigint not null -> tickets\n- `respondent_user_id` bigint -> users\n- `rated_agent_id` bigint -> support_agents (The agent the customer was rating, captured at submission in case the ticket is later reassigned.)\n- `score` integer not null\n- `comment` varchar(4000)\n- `submitted_at` timestamp not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (ticket_id)\n\n\n# Your role\n\nYou are Mateo Ruiz, a data analyst at Tidewick Commerce. You work for Daniel Achterberg, VP Customer Experience. Your working style is comparative: likes year-over-year and peer comparisons across merchants and countries.\n\nThis week you answer your leader's questions with warehouse reports, following the analytics handbook:\n\n- Each night you may request up to 3 reports. The week has 4 nights, and the memo is due on Friday morning.\n- Every submitted request uses a slot, also when it is bounced or fails.\n- A data engineer writes the SQL for each request. You describe precisely what you need:\n  - the question;\n  - the grain (what one row is);\n  - the columns and their definitions;\n  - filters and time windows;\n  - the tables you believe hold the answer.\n- One request answers one question at one grain. Two questions need two requests.\n\nEach night, read the reports critically before you plan the next requests:\n\n- Do the totals make sense?\n- Is something unusual, such as a spike, a drop, an outlier, or a number that disagrees with another?\n- What would explain it?\n\nFollow up: drill down into what looks off, test your explanations, and compare against a baseline. Spend your slots where they teach you the most; you do not have to use all of them.\n\nHand in tonight's requests in the structured form you are given. Put your brief reasoning in its `notes` field. An empty list of requests skips the night.\n"]},"type":"attachment","uuid":"4db2cdd6-76fb-4953-b2ae-c0949787b139","timestamp":"2026-10-03T23:08:35.677Z","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a","version":"2.1.275","gitBranch":"HEAD"}
{"type":"last-prompt","lastPrompt":"Monday 2026-01-05. Note from Daniel Achterberg (VP Customer Experience):  Subject: Where I'd like to start — support & shopper experience  Hi Mateo,  Good to have you on this with me. I'm still buildi…","leafUuid":"4db2cdd6-76fb-4953-b2ae-c0949787b139","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a"}
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{"type":"ai-title","aiTitle":"Shopper experience baseline review","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a"}
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{"parentUuid":"900b7f0b-3a39-4703-bb24-edd00cc150f0","isSidechain":false,"message":{"model":"claude-sonnet-5","id":"msg_011Cfg9PZatJnYiNjbpJ6Mqw","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01MQRZTFohKpyopfC8Hccf6a","name":"StructuredOutput","input":{"notes":"Daniel's top priority is #1 (response/resolution trend, and variance by merchant), so I'm spending 2 of tonight's 3 slots there: a platform-wide monthly trend (to see direction of travel) and a per-merchant snapshot for the latest full year (to see spread across merchants, since he likes peer comparisons). The third slot starts #2 (where load comes from) with a tag-based mix comparing the last two half-years to catch any shift. I'm deliberately holding off on YoY columns inside the trend report itself since the 36-month series already shows year-over-year pattern, and saving width for the core time metrics. Returns/refunds (#3), satisfaction (#4) and reviews (#5) are next on my list for later nights.","requests":[{"title":"Support response & resolution time trend, monthly","question":"Is first-response and resolution speed on support tickets getting better or worse over time?","grain":"one calendar month, platform-wide across all tenants","columns":["month (YYYY-MM)","tickets created","tickets with a first response (count)","avg first response hours","median first response hours","tickets resolved (count)","avg resolution hours","median resolution hours","SLA breach rate (%)"],"details":"Source: tickets, joined to sla_breaches for breach rate. Grain the month on tickets.created_at (date_trunc to month / use calendar_days.month_label for the label). Window: 2023-01-01 through 2025-12-31 (full history, 36 rows). First response hours = extract(epoch from first_response_at - created_at)/3600, only for tickets where first_response_at is not null (report both the count with a response and the avg/median so the denominator is clear). Resolution hours = extract(epoch from resolved_at - created_at)/3600 for tickets where resolved_at is not null. SLA breach rate = distinct tickets in that month's cohort with at least one row in sla_breaches, divided by tickets created that month, as a percentage rounded to 1 decimal. Round all hour figures to 1 decimal. Note in the report how many of the 36 months are returned, and flag that the most recent 1-2 months will understate resolution/response times for tickets still open (right-censoring) so we read the tail with caution. Order by month ascending.","builds_on":[]},{"title":"Response & resolution times by merchant, 2025","question":"How much does support speed vary across merchants, and who is fastest/slowest?","grain":"one row per tenant (merchant), for tickets created in 2025","columns":["merchant name","tickets created","avg first response hours","median first response hours","avg resolution hours","median resolution hours","SLA breach rate (%)"],"details":"Source: tickets joined to tenants (for name) and sla_breaches (for breach rate). Filter tickets.created_at in 2025-01-01 through 2025-12-31. Same hour definitions as the monthly trend report (first_response_at - created_at, resolved_at - created_at, both in hours, only over tickets where that timestamp is not null). SLA breach rate = tenant's distinct tickets with >=1 sla_breaches row / tenant's tickets created in 2025, as a percentage. Only include tenants with at least 20 tickets created in 2025 to avoid noisy small-sample rows. Order by tickets created descending, tie-break by merchant name ascending; report will show at most 50 rows so note total tenant count meeting the threshold and how many are printed.","builds_on":["Support response & resolution time trend, monthly"]},{"title":"Ticket topic mix and recent shift","question":"What kinds of problems are shoppers contacting merchants about most, and has that mix shifted in the last 6 months versus the prior 6?","grain":"one row per support tag (platform-wide, tag name)","columns":["tag name","tickets, last 6 months (Jul-Dec 2025)","tickets, prior 6 months (Jan-Jun 2025)","share of tickets, last 6 months (%)","share of tickets, prior 6 months (%)","share change (percentage points)","avg resolution hours, last 6 months"],"details":"Source: tickets joined to ticket_tags_map and support_tags (group by support_tags.name across all tenants, since we want the platform-level theme). A ticket can carry multiple tags; count each ticket once per tag it carries (so a ticket with 2 tags contributes to both tag rows - note this in the report so totals don't have to sum to ticket volume). Period A = tickets.created_at between 2025-07-01 and 2025-12-31 inclusive; period B = 2025-01-01 to 2025-06-30 inclusive. Share of tickets = tag's ticket count in that period / total tagged-ticket-tag-rows in that period, as a percentage rounded to 1 decimal. Share change = share in period A minus share in period B, in percentage points, rounded to 1 decimal. avg resolution hours (period A only) = extract(epoch from resolved_at - created_at)/3600 averaged over that tag's resolved tickets in period A, rounded to 1 decimal. Order by tickets in the last 6 months descending, tie-break by tag name ascending. Report total distinct tag count returned (expect well under 50 rows).","builds_on":[]}]},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":49318,"cache_read_input_tokens":0,"output_tokens":4387,"output_tokens_details":{"thinking_tokens":2439},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":49318,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":4387,"cache_read_input_tokens":0,"cache_creation_input_tokens":49318,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":49318},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"wireToolInputs":{"toolu_01MQRZTFohKpyopfC8Hccf6a":{"notes":"Daniel's top priority is #1 (response/resolution trend, and variance by merchant), so I'm spending 2 of tonight's 3 slots there: a platform-wide monthly trend (to see direction of travel) and a per-merchant snapshot for the latest full year (to see spread across merchants, since he likes peer comparisons). The third slot starts #2 (where load comes from) with a tag-based mix comparing the last two half-years to catch any shift. I'm deliberately holding off on YoY columns inside the trend report itself since the 36-month series already shows year-over-year pattern, and saving width for the core time metrics. Returns/refunds (#3), satisfaction (#4) and reviews (#5) are next on my list for later nights.","requests":[{"title":"Support response & resolution time trend, monthly","question":"Is first-response and resolution speed on support tickets getting better or worse over time?","grain":"one calendar month, platform-wide across all tenants","columns":["month (YYYY-MM)","tickets created","tickets with a first response (count)","avg first response hours","median first response hours","tickets resolved (count)","avg resolution hours","median resolution hours","SLA breach rate (%)"],"details":"Source: tickets, joined to sla_breaches for breach rate. Grain the month on tickets.created_at (date_trunc to month / use calendar_days.month_label for the label). Window: 2023-01-01 through 2025-12-31 (full history, 36 rows). First response hours = extract(epoch from first_response_at - created_at)/3600, only for tickets where first_response_at is not null (report both the count with a response and the avg/median so the denominator is clear). Resolution hours = extract(epoch from resolved_at - created_at)/3600 for tickets where resolved_at is not null. SLA breach rate = distinct tickets in that month's cohort with at least one row in sla_breaches, divided by tickets created that month, as a percentage rounded to 1 decimal. Round all hour figures to 1 decimal. Note in the report how many of the 36 months are returned, and flag that the most recent 1-2 months will understate resolution/response times for tickets still open (right-censoring) so we read the tail with caution. Order by month ascending.","builds_on":[]},{"title":"Response & resolution times by merchant, 2025","question":"How much does support speed vary across merchants, and who is fastest/slowest?","grain":"one row per tenant (merchant), for tickets created in 2025","columns":["merchant name","tickets created","avg first response hours","median first response hours","avg resolution hours","median resolution hours","SLA breach rate (%)"],"details":"Source: tickets joined to tenants (for name) and sla_breaches (for breach rate). Filter tickets.created_at in 2025-01-01 through 2025-12-31. Same hour definitions as the monthly trend report (first_response_at - created_at, resolved_at - created_at, both in hours, only over tickets where that timestamp is not null). SLA breach rate = tenant's distinct tickets with >=1 sla_breaches row / tenant's tickets created in 2025, as a percentage. Only include tenants with at least 20 tickets created in 2025 to avoid noisy small-sample rows. Order by tickets created descending, tie-break by merchant name ascending; report will show at most 50 rows so note total tenant count meeting the threshold and how many are printed.","builds_on":["Support response & resolution time trend, monthly"]},{"title":"Ticket topic mix and recent shift","question":"What kinds of problems are shoppers contacting merchants about most, and has that mix shifted in the last 6 months versus the prior 6?","grain":"one row per support tag (platform-wide, tag name)","columns":["tag name","tickets, last 6 months (Jul-Dec 2025)","tickets, prior 6 months (Jan-Jun 2025)","share of tickets, last 6 months (%)","share of tickets, prior 6 months (%)","share change (percentage points)","avg resolution hours, last 6 months"],"details":"Source: tickets joined to ticket_tags_map and support_tags (group by support_tags.name across all tenants, since we want the platform-level theme). A ticket can carry multiple tags; count each ticket once per tag it carries (so a ticket with 2 tags contributes to both tag rows - note this in the report so totals don't have to sum to ticket volume). Period A = tickets.created_at between 2025-07-01 and 2025-12-31 inclusive; period B = 2025-01-01 to 2025-06-30 inclusive. Share of tickets = tag's ticket count in that period / total tagged-ticket-tag-rows in that period, as a percentage rounded to 1 decimal. Share change = share in period A minus share in period B, in percentage points, rounded to 1 decimal. avg resolution hours (period A only) = extract(epoch from resolved_at - created_at)/3600 averaged over that tag's resolved tickets in period A, rounded to 1 decimal. Order by tickets in the last 6 months descending, tie-break by tag name ascending. Report total distinct tag count returned (expect well under 50 rows).","builds_on":[]}]}},"apiBlockIndex":1,"requestId":"req_011Cfg9PZ8boj86zYWAZZLUp","type":"assistant","uuid":"b6aa93b6-5514-4099-a9dc-eaa832e22f1b","timestamp":"2026-10-03T23:09:19.310Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"b6aa93b6-5514-4099-a9dc-eaa832e22f1b","isSidechain":false,"attachment":{"type":"structured_output","data":{"notes":"Daniel's top priority is #1 (response/resolution trend, and variance by merchant), so I'm spending 2 of tonight's 3 slots there: a platform-wide monthly trend (to see direction of travel) and a per-merchant snapshot for the latest full year (to see spread across merchants, since he likes peer comparisons). The third slot starts #2 (where load comes from) with a tag-based mix comparing the last two half-years to catch any shift. I'm deliberately holding off on YoY columns inside the trend report itself since the 36-month series already shows year-over-year pattern, and saving width for the core time metrics. Returns/refunds (#3), satisfaction (#4) and reviews (#5) are next on my list for later nights.","requests":[{"title":"Support response & resolution time trend, monthly","question":"Is first-response and resolution speed on support tickets getting better or worse over time?","grain":"one calendar month, platform-wide across all tenants","columns":["month (YYYY-MM)","tickets created","tickets with a first response (count)","avg first response hours","median first response hours","tickets resolved (count)","avg resolution hours","median resolution hours","SLA breach rate (%)"],"details":"Source: tickets, joined to sla_breaches for breach rate. Grain the month on tickets.created_at (date_trunc to month / use calendar_days.month_label for the label). Window: 2023-01-01 through 2025-12-31 (full history, 36 rows). First response hours = extract(epoch from first_response_at - created_at)/3600, only for tickets where first_response_at is not null (report both the count with a response and the avg/median so the denominator is clear). Resolution hours = extract(epoch from resolved_at - created_at)/3600 for tickets where resolved_at is not null. SLA breach rate = distinct tickets in that month's cohort with at least one row in sla_breaches, divided by tickets created that month, as a percentage rounded to 1 decimal. Round all hour figures to 1 decimal. Note in the report how many of the 36 months are returned, and flag that the most recent 1-2 months will understate resolution/response times for tickets still open (right-censoring) so we read the tail with caution. Order by month ascending.","builds_on":[]},{"title":"Response & resolution times by merchant, 2025","question":"How much does support speed vary across merchants, and who is fastest/slowest?","grain":"one row per tenant (merchant), for tickets created in 2025","columns":["merchant name","tickets created","avg first response hours","median first response hours","avg resolution hours","median resolution hours","SLA breach rate (%)"],"details":"Source: tickets joined to tenants (for name) and sla_breaches (for breach rate). Filter tickets.created_at in 2025-01-01 through 2025-12-31. Same hour definitions as the monthly trend report (first_response_at - created_at, resolved_at - created_at, both in hours, only over tickets where that timestamp is not null). SLA breach rate = tenant's distinct tickets with >=1 sla_breaches row / tenant's tickets created in 2025, as a percentage. Only include tenants with at least 20 tickets created in 2025 to avoid noisy small-sample rows. Order by tickets created descending, tie-break by merchant name ascending; report will show at most 50 rows so note total tenant count meeting the threshold and how many are printed.","builds_on":["Support response & resolution time trend, monthly"]},{"title":"Ticket topic mix and recent shift","question":"What kinds of problems are shoppers contacting merchants about most, and has that mix shifted in the last 6 months versus the prior 6?","grain":"one row per support tag (platform-wide, tag name)","columns":["tag name","tickets, last 6 months (Jul-Dec 2025)","tickets, prior 6 months (Jan-Jun 2025)","share of tickets, last 6 months (%)","share of tickets, prior 6 months (%)","share change (percentage points)","avg resolution hours, last 6 months"],"details":"Source: tickets joined to ticket_tags_map and support_tags (group by support_tags.name across all tenants, since we want the platform-level theme). A ticket can carry multiple tags; count each ticket once per tag it carries (so a ticket with 2 tags contributes to both tag rows - note this in the report so totals don't have to sum to ticket volume). Period A = tickets.created_at between 2025-07-01 and 2025-12-31 inclusive; period B = 2025-01-01 to 2025-06-30 inclusive. Share of tickets = tag's ticket count in that period / total tagged-ticket-tag-rows in that period, as a percentage rounded to 1 decimal. Share change = share in period A minus share in period B, in percentage points, rounded to 1 decimal. avg resolution hours (period A only) = extract(epoch from resolved_at - created_at)/3600 averaged over that tag's resolved tickets in period A, rounded to 1 decimal. Order by tickets in the last 6 months descending, tie-break by tag name ascending. Report total distinct tag count returned (expect well under 50 rows).","builds_on":[]}]},"toolUseID":"toolu_01MQRZTFohKpyopfC8Hccf6a"},"type":"attachment","uuid":"82edd5ec-3bef-405f-9d6d-765565662647","timestamp":"2026-10-03T23:09:19.314Z","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a","version":"2.1.275","gitBranch":"HEAD"}
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{"parentUuid":"b053f217-7e45-445f-9b7c-0a850a9946d7","isSidechain":false,"attachment":{"type":"prompt_snapshot","systemPrompt":["# Tidewick Commerce: company briefing\n\n**Tidewick Commerce** is an all-in-one commerce platform for mid-size merchants. It provides:\n\n- storefront and checkout;\n- payments;\n- inventory and fulfillment;\n- subscriptions;\n- a light B2B CRM;\n- a helpdesk;\n- marketing tools.\n\nTidewick was founded in 2018 in Amsterdam. It has offices in London and Austin and about 400 employees.\n\n## Merchants\n\nAbout 120 merchants run their business on Tidewick, and each merchant is a tenant of the platform. A handful of large merchants generate most of the volume, with a long tail of small ones behind them.\n\nThe merchants work in these verticals:\n\n- apparel and footwear;\n- consumer electronics;\n- beauty;\n- home and living;\n- sports and outdoor;\n- food and coffee subscriptions;\n- pet supplies;\n- a few B2B wholesalers (industrial supplies, office equipment).\n\nMerchants are mostly based in the UK, the Netherlands, Germany, France, the Nordics and the US. Their shoppers come from about 40 countries.\n\n## How Tidewick makes money\n\nMerchants pay a monthly platform plan. Tidewick also keeps a processing fee on every payment it settles for them; payouts show this as gross, fee and net.\n\n## The data\n\n- Production runs on one multi-tenant database: every business table carries the merchant's `tenant_id`.\n- The production database is replicated nightly into the analytics warehouse.\n- Tidewick moved to this database at the start of 2023, so history starts on 2023-01-01.\n- Timestamps are stored in UTC.\n- Amounts are in the merchant's currency. `exchange_rates` has daily rates from EUR.\n- The full table catalog is in `schema.md`.\n\n## Today\n\nToday is **Monday, 2026-01-05**. The warehouse holds complete data through 2025-12-31.\n\nTidewick has just hired several new leaders. Each of them wants to understand their part of the business quickly and relies on the analytics team to do so.\n\nThe analytics team works through the warehouse, as described in `handbook.md`.\n\n\n# Analytics handbook\n\nThis handbook explains how the analytics team at Tidewick turns questions into numbers.\n\n## The people\n\n- **Leaders** (CEO, CFO, department heads) bring broad questions about their area.\n- **Analysts** each work for one leader. An analyst:\n  - turns the leader's questions into concrete report requests;\n  - reads the printed reports and follows up;\n  - writes a short memo for the leader at the end of the week.\n- **Data engineers** turn each report request into one SQL query. They can check the query against the schema as often as they like before submitting it.\n\n## The warehouse is expensive\n\nThe warehouse is shared and metered, and every executed query is billed to the department. We therefore run reports as an overnight batch:\n\n- An analyst has **3 report slots per night** and the week has **4 nights**.\n- The memo is due on Friday morning.\n- A slot is spent whenever a request is submitted, whether it runs, fails or is bounced by review.\n- Unused slots do not carry over.\n- Checking a query against the schema (a \"dry run\") is free. It runs the query against an empty copy of the warehouse schema, so it catches errors and shows the output columns, but it never reads or shows any data.\n\nThink before you request. Make one request answer one question well.\n\n## One request, one report\n\nEach request produces one printed report:\n\n- **Title and question.** Every report has a title and the analyst's question it answers.\n- **One grain.** A report is one table at a single grain: every row is the same kind of thing (a country, a week, a merchant, a carrier and month). The columns are measures or attributes of that thing.\n- **The page has a width.** A report has at most 12 columns, and about 10 is comfortable.\n- **The page has a length.** At most 50 rows are printed. The report notes how many rows the query returned in total.\n- **Readable columns.** Order the rows so the important ones come first, and give columns readable names.\n\nDense reports are good. For example, one row per country and month can carry these columns:\n\n- orders;\n- revenue;\n- refund rate;\n- share of late deliveries;\n- tickets per 100 orders;\n- the same month last year.\n\nData engineering reviews every request before it runs and **bounces bundled ones**, where several unrelated questions are packed into one report. Reports wider than 12 columns are bounced too. A bounced request still uses its slot.\n\nIf you have two questions, request two reports.\n\n## House SQL standards (for data engineers)\n\n- Write **one SELECT statement**. Do not end it with a semicolon.\n- Use **standard SQL** wherever possible.\n- **Dates:** never use `CURRENT_DATE` or `now()`. The reporting date is 2026-01-05 and the data ends on 2025-12-31.\n- **Deterministic ordering:** always finish with an ORDER BY whose tie-breaks make the order unique.\n- **Tenants:** filter or group by `tenant_id` when a question is about particular merchants. Join `tenants` for merchant names.\n- **Money:** amounts are in the currency given next to them (usually a `currency_code` column; a merchant's main currency is `tenants.default_currency_code`).\n  - Convert amounts before adding them up across merchants that use different currencies.\n  - `exchange_rates` holds a rate from EUR (`base_code`) for every day; weekends carry the previous business day's rate.\n- **Rates:** round rates and averages for the page.\n- **Catalog:** the table catalog (`schema.md`) lists every table, column, foreign key and the allowed codes of status columns.\n\n\n# Table catalog\n\n# Tidewick Commerce data warehouse: table catalog\n\nThe production database, replicated nightly. Every business table carries the merchant's\n`tenant_id`. Timestamps are UTC. Money columns are in the row's `currency_code` (merchants sell\nin their own currency); `exchange_rates` has daily EUR reference rates.\nStatus columns reference small lookup tables; their codes are listed with each table.\nAll tables live in one schema: refer to them by name only (`shipments`, not `sales.shipments`).\nThe sections below only group the tables by business area.\n\n## Area: analytics\n\n### web_sessions\nVisitor analytics sessions, distinct from sessions (auth). Anonymous traffic still produces a session with a null user_id.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint -> users\n- `anonymous_id` uuid (Client-generated visitor identifier used to stitch sessions before the user authenticates.)\n- `started_at` timestamp not null\n- `ended_at` timestamp\n- `landing_page` varchar(2048)\n- `referrer` varchar(2048)\n- `utm_source` varchar(255)\n- `utm_medium` varchar(255)\n- `utm_campaign` varchar(255)\n- `ip_address` varchar(45)\n- `user_agent` varchar(2048)\n- `is_bounce` boolean not null\n- `page_view_count` integer not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### events\nThe primary product analytics fact table. Each row is one tracked event with a free-form event_name and schemaless JSON properties.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint -> users\n- `web_session_id` bigint -> web_sessions (Nullable link to the analytics web session this event occurred in; server-side events may have none.)\n- `event_name` varchar(64) not null\n- `properties` json not null\n- `occurred_at` timestamp not null\n- `created_at` timestamp not null\n\n### page_views\nIndividual page hits within a web session, used for path and dwell-time analysis.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `web_session_id` bigint not null -> web_sessions\n- `url` varchar(2048) not null\n- `path` varchar(2048)\n- `title` varchar(255)\n- `referrer` varchar(2048)\n- `duration_seconds` integer\n- `viewed_at` timestamp not null\n- `created_at` timestamp not null\n\n### device_profiles\nBrowser/device fingerprints observed per tenant, optionally linked to a user once authenticated.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint -> users\n- `fingerprint` varchar(255) not null\n- `device_type` varchar(64)\n- `os` varchar(64)\n- `os_version` varchar(64)\n- `browser` varchar(64)\n- `browser_version` varchar(64)\n- `screen_width` integer\n- `screen_height` integer\n- `first_seen_at` timestamp not null\n- `last_seen_at` timestamp not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, fingerprint)\n\n### feature_usage\nPer-user, per-day product feature adoption counters feeding engagement and stickiness metrics.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint -> users\n- `feature_key` varchar(64) not null (Stable identifier of the product feature being measured (e.g. bulk_export, dashboard_share).)\n- `usage_count` integer not null\n- `usage_day` date not null\n- `last_used_at` timestamp\n- `occurred_at` timestamp not null\n- `created_at` timestamp not null\n- unique: (tenant_id, user_id, feature_key, usage_day)\n\n### experiments\nServer-side product experiments (feature experiments), distinct from ab_tests which are message-level tests.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `key` varchar(64) not null\n- `name` varchar(255) not null\n- `description` varchar(4000)\n- `hypothesis` varchar(4000)\n- `status` varchar(32) not null one of: draft, running, paused, completed, archived\n- `primary_metric` varchar(255)\n- `starts_at` timestamp\n- `ends_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (tenant_id, key)\n\n### experiment_variants\nVariants (arms) of an experiment; exactly one may be flagged is_control, with allocation_pct summing across arms.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `experiment_id` bigint not null -> experiments\n- `key` varchar(64) not null\n- `name` varchar(255) not null\n- `is_control` boolean not null\n- `allocation_pct` decimal(5,2) not null\n- `config` json not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (experiment_id, key)\n\n### experiment_assignments\nRecords which variant a user (or anonymous visitor) was bucketed into for an experiment.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `experiment_id` bigint not null -> experiments\n- `variant_id` bigint not null -> experiment_variants\n- `user_id` bigint -> users\n- `anonymous_id` uuid\n- `assigned_at` timestamp not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (experiment_id, user_id)\n\n### funnels\nOrdered conversion-funnel definitions evaluated over the events stream.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `description` varchar(4000)\n- `is_active` boolean not null\n- `window_days` integer\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (tenant_id, name)\n\n### funnel_steps\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `funnel_id` bigint not null -> funnels\n- `step_order` integer not null\n- `name` varchar(255) not null\n- `event_name` varchar(64) not null\n- `filters` json not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (funnel_id, step_order)\n\n### cohorts\nNamed user cohorts, dynamic (rule-based) or static, used for retention and segmentation analysis.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `description` varchar(4000)\n- `definition` json not null\n- `is_dynamic` boolean not null\n- `member_count` integer not null\n- `computed_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (tenant_id, name)\n\n### cohort_members\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `cohort_id` bigint not null -> cohorts\n- `user_id` bigint not null -> users\n- `added_at` timestamp not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (cohort_id, user_id)\n\n### metrics_daily\nPre-aggregated daily metric values per tenant; one row per (tenant, metric_key, day) for fast dashboard reads.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `metric_key` varchar(64) not null\n- `metric_date` date not null\n- `metric_value` decimal(20,6) not null\n- `dimensions` json not null (Optional breakdown labels (e.g. {\"channel\":\"organic\"}) when a metric is sliced; empty object for the headline total.)\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, metric_key, metric_date)\n\n### kpi_snapshots\nPeriodic captures of headline KPIs (MRR, active users, conversion rate) with the prior-period value for trend deltas.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `kpi_key` varchar(64) not null\n- `period_grain` varchar(64) not null\n- `period_start` date not null\n- `period_end` date not null\n- `kpi_value` decimal(20,6) not null\n- `previous_value` decimal(20,6)\n- `metadata` json not null\n- `captured_at` timestamp not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, kpi_key, period_grain, period_start)\n\n## Area: audit\n\n### audit_log\nCentral append-only audit trail of meaningful mutations and security actions. The audited row is referenced polymorphically via entity_schema/entity_table/entity_id (no FK).\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `action` varchar(32) not null one of: create, update, delete, login, logout, export, access_denied\n- `actor_user_id` bigint -> users\n- `actor_type` varchar(64) not null (Principal kind that performed the action: user, api_key, service_account, or system.)\n- `entity_schema` varchar(64)\n- `entity_table` varchar(64)\n- `entity_id` bigint (Primary-key id of the audited row in entity_schema.entity_table; intentionally not a foreign key.)\n- `summary` varchar(4000)\n- `metadata` json not null\n- `ip_address` varchar(45)\n- `user_agent` varchar(2048)\n- `request_id` varchar(64)\n- `occurred_at` timestamp not null\n- `created_at` timestamp not null\n\n### change_history\nColumn-level before/after JSON diffs for an audited row, usually linked to an audit_log entry.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `audit_log_id` bigint -> audit_log\n- `actor_user_id` bigint -> users\n- `entity_schema` varchar(64) not null\n- `entity_table` varchar(64) not null\n- `entity_id` bigint not null\n- `changed_columns` varchar(255)[] not null\n- `before_data` json not null\n- `after_data` json not null\n- `occurred_at` timestamp not null\n- `created_at` timestamp not null\n\n### access_log\nAppend-only record of reads of sensitive/PII data, for compliance and breach forensics.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `actor_user_id` bigint -> users\n- `entity_schema` varchar(64) not null\n- `entity_table` varchar(64) not null\n- `entity_id` bigint\n- `access_kind` varchar(64) not null\n- `field_names` varchar(255)[] not null\n- `purpose` varchar(64)\n- `ip_address` varchar(45)\n- `user_agent` varchar(2048)\n- `occurred_at` timestamp not null\n- `created_at` timestamp not null\n\n### data_exports\ndata_exports — GDPR / data-portability export jobs. Tracks request through completion; the produced artifact lives in object storage (file_url).\n- `id` bigint not null\n- `public_id` uuid not null\n- `tenant_id` bigint not null -> tenants\n- `requested_by_user_id` bigint -> users\n- `subject_user_id` bigint -> users\n- `status` varchar(64) not null\n- `format` varchar(64) not null\n- `file_url` varchar(2048)\n- `row_count` bigint\n- `requested_at` timestamp not null\n- `completed_at` timestamp\n- `expires_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (public_id)\n\n### consent_records\nAppend-only per-user consent grants and withdrawals by purpose; the latest row per (user, purpose) is the effective consent state.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint not null -> users\n- `purpose` varchar(64) not null\n- `status` varchar(32) not null one of: granted, withdrawn, expired\n- `source` varchar(64)\n- `policy_version` varchar(64) (Version of the privacy policy / terms the user consented to at the time.)\n- `ip_address` varchar(45)\n- `granted_at` timestamp\n- `withdrawn_at` timestamp\n- `expires_at` timestamp\n- `occurred_at` timestamp not null\n- `created_at` timestamp not null\n\n### compliance_events\ncompliance_events — flagged compliance incidents / policy violations needing review (suspicious access, retention overrun, failed DSAR SLA, ...).\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `event_type` varchar(64) not null\n- `severity` varchar(64) not null\n- `detected_by_user_id` bigint -> users\n- `entity_schema` varchar(64)\n- `entity_table` varchar(64)\n- `entity_id` bigint\n- `details` json not null\n- `resolved` boolean not null\n- `resolved_at` timestamp\n- `occurred_at` timestamp not null\n- `created_at` timestamp not null\n\n### data_subject_requests\nGDPR/CCPA data-subject requests (access, erasure, rectification, portability) tracked through their processing lifecycle.\n- `id` bigint not null\n- `public_id` uuid not null\n- `tenant_id` bigint not null -> tenants\n- `subject_user_id` bigint -> users\n- `subject_email` varchar(254)\n- `request_type` varchar(64) not null (Kind of DSAR: access, erasure, rectification, portability, or restriction.)\n- `status` varchar(64) not null\n- `assigned_to_user_id` bigint -> users\n- `data_export_id` bigint -> data_exports\n- `notes` varchar(4000)\n- `received_at` timestamp not null\n- `due_at` timestamp\n- `completed_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (public_id)\n\n### retention_policies\nPer-entity data-retention rules (retention_days + action_on_expiry) driving automated purges.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `entity_schema` varchar(64) not null\n- `entity_table` varchar(64) not null\n- `retention_days` integer not null\n- `action_on_expiry` varchar(64) not null\n- `is_active` boolean not null\n- `last_run_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, entity_schema, entity_table)\n\n### legal_holds\nActive legal/investigation holds that suspend retention-driven deletion for a subject or entity scope.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `reason` varchar(4000)\n- `retention_policy_id` bigint -> retention_policies\n- `subject_user_id` bigint -> users\n- `entity_schema` varchar(64)\n- `entity_table` varchar(64)\n- `entity_id` bigint\n- `placed_by_user_id` bigint -> users\n- `is_active` boolean not null\n- `placed_at` timestamp not null\n- `released_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### audit_log_archive\nCold archive of aged-out audit_log rows; same shape as audit_log, written once by the archival job. source_audit_log_id preserves the original id.\n- `id` bigint not null\n- `source_audit_log_id` bigint\n- `tenant_id` bigint not null -> tenants\n- `action` varchar(32) not null one of: create, update, delete, login, logout, export, access_denied\n- `actor_user_id` bigint\n- `actor_type` varchar(64) not null\n- `entity_schema` varchar(64)\n- `entity_table` varchar(64)\n- `entity_id` bigint\n- `summary` varchar(4000)\n- `metadata` json not null\n- `ip_address` varchar(45)\n- `user_agent` varchar(2048)\n- `request_id` varchar(64)\n- `occurred_at` timestamp not null\n- `archived_at` timestamp not null\n- `created_at` timestamp not null\n\n## Area: billing\n\n### billing_profiles\nCustomer billing entity per tenant: legal name, tax id, billing email, net payment terms. One marked is_default per tenant.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint -> users\n- `legal_name` varchar(255) not null\n- `billing_email` varchar(254)\n- `tax_id` varchar(64)\n- `net_terms_days` integer not null (Payment terms in days (Net-0/Net-30/Net-60) before an invoice is past due.)\n- `default_currency_code` varchar(3) not null -> currencies\n- `is_default` boolean not null\n- `metadata` json not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n\n### payment_methods\nStored payment instruments. Only a gateway token plus brand/last4 are kept; raw card numbers are never stored.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint -> users\n- `billing_profile_id` bigint -> billing_profiles\n- `kind` varchar(32) not null one of: card, bank_transfer, paypal, wallet, wire, credit\n- `gateway` varchar(64) not null\n- `gateway_token` varchar(255) not null\n- `brand` varchar(255)\n- `last4` varchar(4)\n- `exp_month` smallint\n- `exp_year` smallint\n- `is_default` boolean not null\n- `billing_address_id` bigint -> addresses\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (gateway, gateway_token)\n\n### payments\nMoney-in captures against an order and/or invoice. amount_refunded tracks cumulative refunds for partial-refund accounting.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `order_id` bigint -> orders\n- `invoice_id` bigint -> invoices\n- `payment_method_id` bigint -> payment_methods\n- `status` varchar(32) not null one of: pending, authorized, captured, failed, voided, refunded, partially_refunded\n- `amount` decimal(14,4) not null\n- `amount_refunded` decimal(14,4) not null (Running total of amounts refunded against this payment; must never exceed amount.)\n- `currency_code` varchar(3) not null -> currencies\n- `gateway` varchar(64) not null\n- `gateway_charge_id` varchar(255)\n- `authorized_at` timestamp\n- `captured_at` timestamp\n- `failure_code` varchar(64)\n- `metadata` json not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### payment_attempts\nEach gateway round-trip for a payment; a payment may need several attempts before it captures.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `payment_id` bigint not null -> payments\n- `attempt_number` integer not null\n- `status` varchar(32) not null one of: pending, authorized, captured, failed, voided, refunded, partially_refunded\n- `amount` decimal(14,4) not null\n- `currency_code` varchar(3) not null -> currencies\n- `gateway_response` json not null\n- `error_code` varchar(64)\n- `error_message` varchar(4000)\n- `attempted_at` timestamp not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (payment_id, attempt_number)\n\n### payment_refunds\nRefunds reversing all or part of a captured payment, tracked through the refund_status lifecycle.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `payment_id` bigint not null -> payments\n- `status` varchar(32) not null one of: pending, succeeded, failed, cancelled\n- `amount` decimal(14,4) not null\n- `currency_code` varchar(3) not null -> currencies\n- `reason` varchar(4000)\n- `gateway_refund_id` varchar(255)\n- `processed_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### invoices\nBilling documents (one-off or subscription). amount_due drives dunning; public_id is the opaque external identifier.\n- `id` bigint not null\n- `public_id` uuid not null\n- `tenant_id` bigint not null -> tenants\n- `billing_profile_id` bigint -> billing_profiles\n- `user_id` bigint -> users\n- `order_id` bigint -> orders\n- `subscription_id` bigint -> subscriptions\n- `invoice_number` varchar(64) not null\n- `status` varchar(32) not null one of: draft, open, paid, void, uncollectible, past_due\n- `currency_code` varchar(3) not null -> currencies\n- `subtotal` decimal(14,4) not null\n- `tax_total` decimal(14,4) not null\n- `discount_total` decimal(14,4) not null\n- `total` decimal(14,4) not null\n- `amount_paid` decimal(14,4) not null\n- `amount_due` decimal(14,4) not null (Outstanding balance still owed; reaches 0 when the invoice is fully paid.)\n- `issued_at` timestamp\n- `due_at` timestamp\n- `paid_at` timestamp\n- `voided_at` timestamp\n- `notes` varchar(4000)\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (public_id); (tenant_id, invoice_number)\n\n### invoice_lines\nLine items on an invoice; optionally tied to a subscription_item, with the service period it covers.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `invoice_id` bigint not null -> invoices\n- `subscription_item_id` bigint -> subscription_items\n- `description` varchar(4000) not null\n- `quantity` decimal(14,4) not null\n- `unit_price` decimal(14,4) not null\n- `line_total` decimal(14,4) not null\n- `tax_amount` decimal(14,4) not null\n- `currency_code` varchar(3) not null -> currencies\n- `period_start` timestamp\n- `period_end` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### credit_notes\nCredit memos issued against invoices for returns, goodwill, or corrections.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `invoice_id` bigint -> invoices\n- `billing_profile_id` bigint -> billing_profiles\n- `credit_note_number` varchar(64) not null\n- `currency_code` varchar(3) not null -> currencies\n- `subtotal` decimal(14,4) not null\n- `tax_total` decimal(14,4) not null\n- `total` decimal(14,4) not null\n- `reason` varchar(4000)\n- `issued_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, credit_note_number)\n\n### credit_note_lines\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `credit_note_id` bigint not null -> credit_notes\n- `invoice_line_id` bigint -> invoice_lines\n- `description` varchar(4000) not null\n- `quantity` decimal(14,4) not null\n- `unit_price` decimal(14,4) not null\n- `line_total` decimal(14,4) not null\n- `tax_amount` decimal(14,4) not null\n- `currency_code` varchar(3) not null -> currencies\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### payouts\nSettlements of collected funds out to the tenant; net = gross minus fees.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `currency_code` varchar(3) not null -> currencies\n- `gross_amount` decimal(14,4) not null\n- `fee_amount` decimal(14,4) not null\n- `net_amount` decimal(14,4) not null\n- `status` varchar(64) not null\n- `gateway_payout_id` varchar(255)\n- `arrival_date` date\n- `paid_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### payout_items\nThe individual payments settled within a payout; each payment appears in at most one payout.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `payout_id` bigint not null -> payouts\n- `payment_id` bigint not null -> payments\n- `amount` decimal(14,4) not null\n- `fee_amount` decimal(14,4) not null\n- `currency_code` varchar(3) not null -> currencies\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (payment_id)\n\n### ledger_accounts\nledger_accounts — the tenant's chart of accounts for the double-entry ledger. type is the accounting class (asset/liability/revenue/...).\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `code` varchar(64) not null\n- `name` varchar(255) not null\n- `account_type` varchar(64) not null\n- `currency_code` varchar(3) not null -> currencies\n- `is_active` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, code)\n\n### ledger_entries\nImmutable append-only double-entry ledger lines. Postings sharing an entry_group form one balanced journal entry.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `ledger_account_id` bigint not null -> ledger_accounts\n- `entry_group` uuid not null (Groups the debit/credit rows of a single balanced journal entry (their amounts must net to zero).)\n- `direction` varchar(32) not null one of: debit, credit\n- `amount` decimal(14,4) not null\n- `currency_code` varchar(3) not null -> currencies\n- `payment_id` bigint -> payments\n- `invoice_id` bigint -> invoices\n- `description` varchar(4000)\n- `occurred_at` timestamp not null\n- `created_at` timestamp not null\n\n### plans\nSubscription products. code is the stable per-tenant key; default_interval sets the billing cadence.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `code` varchar(64) not null\n- `name` varchar(255) not null\n- `description` varchar(4000)\n- `default_interval` varchar(32) not null one of: day, week, month, quarter, year\n- `interval_count` integer not null\n- `trial_period_days` integer not null\n- `is_active` boolean not null\n- `is_public` boolean not null\n- `metadata` json not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (tenant_id, code)\n\n### plan_features\nFeature entitlements/limits per plan; limit_value NULL means unlimited.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `plan_id` bigint not null -> plans\n- `feature_key` varchar(64) not null\n- `limit_value` bigint (Quota for the feature (e.g. seats, api_calls); NULL means unlimited.)\n- `is_enabled` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (plan_id, feature_key)\n\n### plan_prices\nPrice points for a plan, one per currency and billing interval.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `plan_id` bigint not null -> plans\n- `currency_code` varchar(3) not null -> currencies\n- `billing_interval` varchar(32) not null one of: day, week, month, quarter, year\n- `interval_count` integer not null\n- `amount` decimal(14,4) not null\n- `is_active` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (plan_id, currency_code, billing_interval, interval_count)\n\n### subscriptions\nA user's recurring commitment to a plan. current_period_* bound the next invoice; cancel_at_period_end defers cancellation.\n- `id` bigint not null\n- `public_id` uuid not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint not null -> users\n- `plan_id` bigint not null -> plans\n- `billing_profile_id` bigint -> billing_profiles\n- `payment_method_id` bigint -> payment_methods\n- `status` varchar(32) not null one of: trialing, active, past_due, canceled, paused, incomplete\n- `currency_code` varchar(3) not null -> currencies\n- `quantity` integer not null\n- `trial_ends_at` timestamp\n- `current_period_start` timestamp\n- `current_period_end` timestamp\n- `cancel_at_period_end` boolean not null (When true the subscription cancels at current_period_end rather than immediately.)\n- `canceled_at` timestamp\n- `started_at` timestamp not null\n- `ended_at` timestamp\n- `metadata` json not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (public_id)\n\n### subscription_items\nLine items / add-ons of a subscription; unit_amount snapshots the price at the time of subscription.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `subscription_id` bigint not null -> subscriptions\n- `plan_price_id` bigint -> plan_prices\n- `description` varchar(4000)\n- `quantity` integer not null\n- `unit_amount` decimal(14,4) not null\n- `currency_code` varchar(3) not null -> currencies\n- `is_metered` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### usage_records\nMetered usage reported for a subscription_item, aggregated into consumption-based invoice lines.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `subscription_item_id` bigint not null -> subscription_items\n- `quantity` decimal(14,4) not null\n- `unit` varchar(64)\n- `action` varchar(64) not null\n- `occurred_at` timestamp not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### dunning_attempts\nRetry sequence chasing payment on a past-due invoice; next_attempt_at schedules the following retry.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `invoice_id` bigint not null -> invoices\n- `subscription_id` bigint -> subscriptions\n- `payment_id` bigint -> payments\n- `attempt_number` integer not null\n- `outcome` varchar(64) not null\n- `next_attempt_at` timestamp\n- `attempted_at` timestamp not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (invoice_id, attempt_number)\n\n### tax_registrations\nTenant tax IDs per jurisdiction (VAT/GST/sales tax) governing how tax is charged in a region.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `billing_profile_id` bigint -> billing_profiles\n- `country_code` varchar(2) not null\n- `jurisdiction` varchar(64)\n- `tax_type` varchar(64) not null\n- `registration_number` varchar(64) not null (The official tax/VAT registration number issued by the jurisdiction.)\n- `is_active` boolean not null\n- `valid_from` date\n- `valid_until` date\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, country_code, tax_type)\n\n### wallets\nStored-credit balances per user and currency (prepaid credit, refunds-to-credit, promo balance).\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint not null -> users\n- `currency_code` varchar(3) not null -> currencies\n- `balance` decimal(14,4) not null\n- `is_active` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (user_id, currency_code)\n\n## Area: catalog\n\n### brands\nManufacturer/brand records that products are grouped under.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `slug` varchar(255) not null\n- `description` varchar(4000)\n- `logo_url` varchar(2048)\n- `website_url` varchar(2048)\n- `is_active` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (tenant_id, slug)\n\n### categories\nHierarchical product taxonomy; parent_id is a self-reference to the parent category.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `parent_id` bigint -> categories\n- `name` varchar(255) not null\n- `slug` varchar(255) not null\n- `description` varchar(4000)\n- `sort_position` integer not null\n- `is_active` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (tenant_id, slug)\n\n### category_closure\nTransitive closure of the category tree (ancestor, descendant, depth) for fast subtree queries.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `ancestor_id` bigint not null -> categories\n- `descendant_id` bigint not null -> categories\n- `depth` integer not null (Number of edges between ancestor and descendant; 0 is the self-row.)\n- `created_at` timestamp not null\n- unique: (ancestor_id, descendant_id)\n\n### products\nProduct master records; public_id is the opaque external identifier exposed via API/URL.\n- `id` bigint not null\n- `public_id` uuid not null\n- `tenant_id` bigint not null -> tenants\n- `brand_id` bigint -> brands\n- `primary_category_id` bigint -> categories\n- `name` varchar(255) not null\n- `slug` varchar(255) not null\n- `description` varchar(4000)\n- `status` varchar(32) not null one of: draft, active, archived\n- `is_visible` boolean not null\n- `published_at` timestamp\n- `seo_title` varchar(255)\n- `seo_description` varchar(4000)\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (public_id); (tenant_id, slug)\n\n### product_variants\nSellable SKUs of a product; sku is unique per tenant. Reference price only — authoritative pricing lives in the pricing schema.\n- `id` bigint not null\n- `public_id` uuid not null\n- `tenant_id` bigint not null -> tenants\n- `product_id` bigint not null -> products\n- `sku` varchar(64) not null\n- `barcode` varchar(64)\n- `name` varchar(255)\n- `sort_position` integer not null\n- `price_amount` decimal(14,4)\n- `currency_code` varchar(3) -> currencies\n- `weight_grams` decimal(14,4)\n- `is_default` boolean not null (Exactly one variant per product may be the default (enforced by a partial unique index).)\n- `is_active` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (public_id); (tenant_id, sku)\n\n### product_categories\nproduct_categories — junction products×categories (a product can sit in many categories beyond its primary one).\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `product_id` bigint not null -> products\n- `category_id` bigint not null -> categories\n- `sort_position` integer not null\n- `created_at` timestamp not null\n- unique: (product_id, category_id)\n\n### attributes\nDefinitions of product/variant attributes (e.g. Color, Size); kind drives value interpretation.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `code` varchar(64) not null\n- `name` varchar(255) not null\n- `kind` varchar(32) not null one of: text, number, boolean, enum, color, dimension\n- `is_variant_defining` boolean not null (True when this attribute differentiates variants (Color, Size) rather than being descriptive.)\n- `is_filterable` boolean not null\n- `unit` varchar(64)\n- `sort_position` integer not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, code)\n\n### attribute_values\nAllowed values for enum-kind attributes.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `attribute_id` bigint not null -> attributes\n- `value_code` varchar(255) not null\n- `label` varchar(255)\n- `sort_position` integer not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (attribute_id, value_code)\n\n### variant_attribute_values\nJunction binding a variant to its attribute/value pairs (the combination that makes it unique).\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `variant_id` bigint not null -> product_variants\n- `attribute_id` bigint not null -> attributes\n- `attribute_value_id` bigint -> attribute_values\n- `value_text` varchar(4000)\n- `created_at` timestamp not null\n- unique: (variant_id, attribute_id)\n\n### product_media\nImages, video, and documents attached to a product and optionally a specific variant.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `product_id` bigint not null -> products\n- `variant_id` bigint -> product_variants\n- `kind` varchar(32) not null one of: image, video, document, model_3d\n- `url` varchar(2048) not null\n- `alt_text` varchar(4000)\n- `sort_position` integer not null\n- `is_primary` boolean not null\n- `width_px` integer\n- `height_px` integer\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### collections\nMerchandising groupings of products; may be manual or rule-driven (is_automated).\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `slug` varchar(255) not null\n- `description` varchar(4000)\n- `is_automated` boolean not null\n- `rules` json\n- `starts_at` timestamp\n- `ends_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (tenant_id, slug)\n\n### collection_products\ncollection_products — junction collections×products.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `collection_id` bigint not null -> collections\n- `product_id` bigint not null -> products\n- `sort_position` integer not null\n- `created_at` timestamp not null\n- unique: (collection_id, product_id)\n\n### tags\ntags — free-form labels applied to products.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `slug` varchar(255) not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, slug)\n\n### product_tags\nproduct_tags — junction products×tags.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `product_id` bigint not null -> products\n- `tag_id` bigint not null -> tags\n- `created_at` timestamp not null\n- unique: (product_id, tag_id)\n\n### product_reviews\nCustomer reviews of products, rated 1-5 stars; one review per user per product.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `product_id` bigint not null -> products\n- `user_id` bigint -> users\n- `rating` integer not null\n- `title` varchar(255)\n- `body` varchar(65535)\n- `is_verified_purchase` boolean not null (True when the reviewer is known to have purchased the product.)\n- `is_approved` boolean not null\n- `helpful_count` integer not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (product_id, user_id)\n\n### review_votes\nreview_votes — helpfulness votes cast by users on a review.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `review_id` bigint not null -> product_reviews\n- `user_id` bigint not null -> users\n- `is_helpful` boolean not null\n- `created_at` timestamp not null\n- unique: (review_id, user_id)\n\n### product_relations\nDirectional cross-sell/upsell/related links between two products.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `product_id` bigint not null -> products\n- `related_product_id` bigint not null -> products\n- `relation_kind` varchar(64) not null\n- `sort_position` integer not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (product_id, related_product_id, relation_kind)\n\n### product_bundles\nHeader for a bundle/kit sold as a single offering, composed of variant components.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `product_id` bigint -> products\n- `name` varchar(255) not null\n- `slug` varchar(255) not null\n- `description` varchar(4000)\n- `is_active` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, slug)\n\n### bundle_items\nComponent variants and quantities that make up a product bundle.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `bundle_id` bigint not null -> product_bundles\n- `variant_id` bigint not null -> product_variants\n- `quantity` integer not null\n- `sort_position` integer not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (bundle_id, variant_id)\n\n## Area: comms\n\n### notification_templates\nReusable, per-tenant notification bodies keyed by code and channel; rendered into notifications and email/sms logs.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `code` varchar(64) not null\n- `name` varchar(255) not null\n- `channel` varchar(64) not null\n- `subject` varchar(255)\n- `body` varchar(65535) not null\n- `locale` varchar(5)\n- `is_active` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, code)\n\n### notifications\nPer-user notifications with a queued→sent→delivered→read lifecycle. template_id is null when composed ad hoc.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint not null -> users\n- `template_id` bigint -> notification_templates\n- `channel` varchar(64) not null\n- `status` varchar(32) not null one of: queued, sent, delivered, read, failed\n- `title` varchar(255) not null\n- `body` varchar(65535)\n- `data` json not null\n- `sent_at` timestamp\n- `delivered_at` timestamp\n- `read_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### notification_preferences\nPer-user opt-in matrix across channels and categories; an absent row means the tenant default applies.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint not null -> users\n- `channel` varchar(64) not null\n- `category` varchar(255) not null\n- `is_enabled` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (user_id, channel, category)\n\n### message_threads\nIn-app conversation threads between a tenant and a user. last_message_at is denormalized for inbox sorting.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint -> users\n- `subject` varchar(255)\n- `channel` varchar(64) not null\n- `is_closed` boolean not null\n- `last_message_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### thread_messages\nthread_messages — individual messages within a thread. direction marks whether the message is inbound (from the user) or outbound (from the tenant).\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `thread_id` bigint not null -> message_threads\n- `sender_user_id` bigint -> users\n- `direction` varchar(32) not null one of: inbound, outbound (inbound = sent by the user toward the tenant; outbound = sent by the tenant toward the user.)\n- `body` varchar(65535) not null\n- `attachments` json not null\n- `read_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### webhooks\nSubscriber HTTP endpoints that receive signed event callbacks. Only the hash of the signing secret is stored.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `created_by_user_id` bigint -> users\n- `name` varchar(255) not null\n- `target_url` varchar(2048) not null\n- `secret_hash` varchar(255) not null (Hash of the HMAC signing secret used to sign delivery payloads; the plaintext secret is shown once at creation.)\n- `event_types` varchar(64)[] not null\n- `is_active` boolean not null\n- `last_delivered_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### webhook_deliveries\nAppend-only log of each webhook delivery attempt, including HTTP response_code and retry attempt number.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `webhook_id` bigint not null -> webhooks\n- `event_type` varchar(64) not null\n- `status` varchar(32) not null one of: pending, success, failed, retrying\n- `attempt` integer not null (1-based retry counter; multiple rows per logical event when a delivery is retried.)\n- `response_code` integer\n- `request_body` json\n- `response_body` varchar(65535)\n- `duration_ms` integer\n- `delivered_at` timestamp\n- `created_at` timestamp not null\n\n### email_log\nImmutable record of every outbound email; recipient_email is denormalized since the user or address may be off-platform.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint -> users\n- `template_id` bigint -> notification_templates\n- `recipient_email` varchar(254) not null\n- `from_email` varchar(254)\n- `subject` varchar(255)\n- `provider` varchar(64)\n- `provider_message_id` varchar(255)\n- `status` varchar(32) not null one of: pending, success, failed, retrying\n- `error_message` varchar(4000)\n- `sent_at` timestamp\n- `created_at` timestamp not null\n\n### sms_log\nsms_log — IMMUTABLE record of each outbound SMS. (Intentionally uncommented at table level — see CONVENTIONS.md on deliberate comment gaps.)\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint -> users\n- `template_id` bigint -> notification_templates\n- `recipient_phone` varchar(32) not null\n- `from_number` varchar(32)\n- `body` varchar(65535) not null\n- `provider` varchar(64)\n- `provider_message_id` varchar(255)\n- `status` varchar(32) not null one of: pending, success, failed, retrying\n- `error_message` varchar(4000)\n- `segments` integer not null\n- `sent_at` timestamp\n- `created_at` timestamp not null\n\n### push_tokens\nDevice push-notification tokens per user; deactivated rather than deleted when a device unregisters.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint not null -> users\n- `platform` varchar(64) not null\n- `token` varchar(255) not null\n- `device_name` varchar(255)\n- `is_active` boolean not null\n- `last_used_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, token)\n\n### unsubscribes\nSuppression list of addresses/channels that must be excluded from further sends; user_id may be null for off-platform addresses.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint -> users\n- `channel` varchar(64) not null\n- `address` varchar(255) not null\n- `category` varchar(255) not null\n- `reason` varchar(4000)\n- `unsubscribed_at` timestamp not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, channel, address, category)\n\n### contact_channels\nVerified contact endpoints (email/phone) per user with a verification lifecycle; one primary per kind via a partial unique index.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint not null -> users\n- `kind` varchar(64) not null\n- `channel_address` varchar(255) not null\n- `is_primary` boolean not null\n- `is_verified` boolean not null\n- `verified_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (user_id, kind, channel_address)\n\n## Area: crm\n\n### accounts\nThe tenant's own B2B customer companies. Tenant-scoped; distinct from tenants (the platform's customers).\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `legal_name` varchar(255)\n- `domain` varchar(255)\n- `industry` varchar(255)\n- `employee_count` integer\n- `annual_revenue` decimal(14,4)\n- `currency_code` varchar(3) -> currencies\n- `owner_user_id` bigint -> users (Internal sales rep (users) who owns the account relationship.)\n- `phone` varchar(32)\n- `website` varchar(2048)\n- `is_active` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (tenant_id, name)\n\n### contacts\nPeople who work at a CRM account. primary_email is denormalized; the full set lives in contact_emails.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `account_id` bigint -> accounts\n- `owner_user_id` bigint -> users\n- `first_name` varchar(255)\n- `last_name` varchar(255)\n- `primary_email` varchar(254)\n- `phone` varchar(32)\n- `job_title` varchar(255)\n- `department` varchar(255)\n- `is_primary` boolean not null\n- `do_not_contact` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n\n### contact_emails\nAll email addresses for a contact; at most one flagged is_primary per contact.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `contact_id` bigint not null -> contacts\n- `email` varchar(254) not null\n- `label` varchar(255) not null\n- `is_primary` boolean not null\n- `verified_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (contact_id, email)\n\n### leads\nUnqualified inbound interest before it becomes an account/contact; converts forward once qualified.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `owner_user_id` bigint -> users\n- `first_name` varchar(255)\n- `last_name` varchar(255)\n- `email` varchar(254)\n- `phone` varchar(32)\n- `company` varchar(255)\n- `job_title` varchar(255)\n- `status` varchar(32) not null one of: new, contacted, qualified, unqualified, converted\n- `source` varchar(64)\n- `score` integer not null (Lead score (0+); higher means more sales-ready.)\n- `converted_account_id` bigint -> accounts\n- `converted_contact_id` bigint -> contacts\n- `converted_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### pipelines\nNamed sales pipelines per tenant; at most one is the default. Stages live in opportunity_stages.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `description` varchar(4000)\n- `is_default` boolean not null\n- `is_active` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, name)\n\n### opportunity_stages\nOrdered lookup of stages within a pipeline. position sets funnel order; win_probability drives weighted forecasts.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `pipeline_id` bigint not null -> pipelines\n- `name` varchar(255) not null\n- `sort_position` integer not null\n- `win_probability` decimal(5,4) not null (Default close probability for opportunities in this stage, 0..1.)\n- `is_won` boolean not null\n- `is_lost` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (pipeline_id, sort_position); (pipeline_id, name)\n\n### opportunities\nDeals in flight: an account being worked through a pipeline stage with an expected amount.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `account_id` bigint not null -> accounts\n- `pipeline_id` bigint not null -> pipelines\n- `stage_id` bigint not null -> opportunity_stages\n- `primary_contact_id` bigint -> contacts\n- `owner_user_id` bigint -> users\n- `name` varchar(255) not null\n- `amount` decimal(14,4) not null\n- `currency_code` varchar(3) not null -> currencies\n- `probability` decimal(5,4)\n- `source` varchar(64)\n- `is_closed` boolean not null\n- `is_won` boolean not null\n- `expected_close_date` date\n- `closed_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### deals\nClosed-won contracts realized from an opportunity, holding the booked contract value and term.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `account_id` bigint not null -> accounts\n- `opportunity_id` bigint -> opportunities\n- `owner_user_id` bigint -> users\n- `name` varchar(255) not null\n- `contract_value` decimal(14,4) not null\n- `currency_code` varchar(3) not null -> currencies\n- `signed_at` date\n- `starts_at` date\n- `ends_at` date\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### deal_line_items\nProducts sold on a deal, priced per unit in the deal currency.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `deal_id` bigint not null -> deals\n- `product_id` bigint -> products\n- `description` varchar(4000)\n- `quantity` integer not null\n- `unit_price` decimal(14,4) not null\n- `discount_pct` decimal(5,4) not null\n- `currency_code` varchar(3) not null -> currencies\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### activities\nLogged interactions (calls, emails, meetings) against an account, contact, or opportunity.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `kind` varchar(32) not null one of: call, email, meeting, note, task\n- `account_id` bigint -> accounts\n- `contact_id` bigint -> contacts\n- `opportunity_id` bigint -> opportunities\n- `owner_user_id` bigint -> users\n- `subject` varchar(255)\n- `body` varchar(65535)\n- `occurred_at` timestamp not null\n- `duration_minutes` integer\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### crm_notes\ncrm_notes — free-text notes attached to a CRM entity (polymorphic target).\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `account_id` bigint -> accounts\n- `contact_id` bigint -> contacts\n- `opportunity_id` bigint -> opportunities\n- `author_user_id` bigint -> users\n- `body` varchar(65535) not null\n- `is_pinned` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### tasks\nFollow-up to-dos assigned to reps, optionally linked to a CRM entity.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `assignee_user_id` bigint -> users\n- `account_id` bigint -> accounts\n- `contact_id` bigint -> contacts\n- `opportunity_id` bigint -> opportunities\n- `title` varchar(255) not null\n- `description` varchar(4000)\n- `priority` varchar(64) not null\n- `is_done` boolean not null\n- `due_at` timestamp\n- `completed_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### account_relationships\nParent/subsidiary and other directed links between accounts within a tenant.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `parent_account_id` bigint not null -> accounts\n- `child_account_id` bigint not null -> accounts\n- `relationship_type` varchar(64) not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (parent_account_id, child_account_id, relationship_type)\n\n### contact_lists\ncontact_lists — static, manually-curated lists of contacts.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `owner_user_id` bigint -> users\n- `name` varchar(255) not null\n- `description` varchar(4000)\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, name)\n\n### contact_list_members\ncontact_list_members — junction of contacts into static lists.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `contact_list_id` bigint not null -> contact_lists\n- `contact_id` bigint not null -> contacts\n- `added_by_user_id` bigint -> users\n- `created_at` timestamp not null\n- unique: (contact_list_id, contact_id)\n\n## Area: geo\n\n### countries\nISO-3166 country reference. Global, shared by all tenants.\n- `id` bigint not null\n- `iso2` varchar(2) not null\n- `iso3` varchar(3) not null\n- `name` varchar(255) not null\n- `phone_code` varchar(32)\n- `continent` varchar(64)\n- unique: (iso2); (iso3)\n\n### regions\n- `id` bigint not null\n- `country_id` bigint not null -> countries\n- `code` varchar(64)\n- `name` varchar(255) not null\n\n### cities\n- `id` bigint not null\n- `region_id` bigint -> regions\n- `country_id` bigint not null -> countries\n- `name` varchar(255) not null\n- `latitude` decimal(9,6)\n- `longitude` decimal(9,6)\n\n### currencies\nISO-4217 currencies. The code column (e.g. USD) is the FK target used by all monetary tables.\n- `id` bigint not null\n- `code` varchar(3) not null\n- `name` varchar(255) not null\n- `symbol` varchar(64)\n- `minor_unit` smallint not null (Number of decimal places (2 for USD, 0 for JPY).)\n- unique: (code)\n\n### exchange_rates\nDaily FX rates between currency pairs, used to normalize multi-currency revenue.\n- `id` bigint not null\n- `base_code` varchar(3) not null -> currencies\n- `quote_code` varchar(3) not null -> currencies\n- `rate` decimal(12,6) not null\n- `as_of` date not null\n- unique: (base_code, quote_code, as_of)\n\n### calendar_days\nCalendar dimension: one row per day from 2023-01-01 to 2026-12-31 with week, month and quarter attributes. Join to it to build complete time series (days, weeks or months without activity still appear).\n- `calendar_date` date not null (The day.)\n- `year_num` integer not null (Calendar year.)\n- `quarter_num` smallint not null (Calendar quarter, 1-4.)\n- `month_num` smallint not null (Calendar month, 1-12.)\n- `month_name` varchar(9) not null (English month name, e.g. 'November'.)\n- `month_label` varchar(7) not null (Year and month as 'YYYY-MM'.)\n- `day_of_month` smallint not null (Day of the month, 1-31.)\n- `day_of_year` smallint not null (Day of the year, 1-366.)\n- `iso_year` integer not null (ISO 8601 week-numbering year (differs from year_num around New Year).)\n- `iso_week` smallint not null (ISO 8601 week number, 1-53.)\n- `iso_day_of_week` smallint not null (ISO day of the week: 1 = Monday ... 7 = Sunday.)\n- `day_name` varchar(9) not null (English day name, e.g. 'Monday'.)\n- `is_weekend` boolean not null (Saturday or Sunday.)\n- `week_start` date not null (Monday of the ISO week.)\n- `month_start` date not null (First day of the month.)\n- `quarter_start` date not null (First day of the quarter.)\n\n### locales\n- `id` bigint not null\n- `code` varchar(5) not null\n- `name` varchar(255) not null\n- `native_name` varchar(255)\n- unique: (code)\n\n### timezones\n- `id` bigint not null\n- `name` varchar(255) not null\n- `utc_offset_minutes` integer not null\n- unique: (name)\n\n### addresses\nPostal addresses owned by a tenant and optionally a user. At most one default address per user.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint -> users\n- `label` varchar(255)\n- `line1` varchar(255) not null\n- `line2` varchar(255)\n- `city` varchar(255)\n- `region_id` bigint -> regions\n- `country_id` bigint not null -> countries\n- `postal_code` varchar(64)\n- `latitude` decimal(9,6)\n- `longitude` decimal(9,6)\n- `is_default` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### address_validations\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `address_id` bigint not null -> addresses\n- `provider` varchar(64) not null\n- `is_valid` boolean not null\n- `response` json\n- `validated_at` timestamp not null\n\n### postal_zones\n- `id` bigint not null\n- `country_id` bigint not null -> countries\n- `code` varchar(64) not null\n- `name` varchar(255) not null\n- unique: (country_id, code)\n\n## Area: identity\n\n### tenants\nCustomer organizations on the platform; the root of multi-tenancy. Every tenant-scoped row hangs off a tenant_id.\n- `id` bigint not null\n- `public_id` uuid not null\n- `name` varchar(255) not null\n- `slug` varchar(255) not null\n- `status` varchar(32) not null one of: trial, active, past_due, suspended, churned\n- `default_currency_code` varchar(3) not null -> currencies (ISO-4217 default currency.)\n- `default_locale` varchar(5) not null -> locales\n- `industry` varchar(255)\n- `employee_count` integer\n- `trial_ends_at` timestamp\n- `signup_source` varchar(64)\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (public_id); (slug)\n\n### tenant_settings\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `setting_key` varchar(64) not null\n- `setting_value` json not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, setting_key)\n\n### users\nEnd users and staff belonging to a tenant. Email is unique per tenant, not globally.\n- `id` bigint not null\n- `public_id` uuid not null\n- `tenant_id` bigint not null -> tenants\n- `email` varchar(254) not null\n- `full_name` varchar(255)\n- `status` varchar(32) not null one of: active, invited, suspended, deactivated\n- `is_staff` boolean not null (True for internal/admin operators of the tenant, as opposed to customer end-users.)\n- `locale` varchar(5) -> locales\n- `timezone` varchar(64)\n- `password_hash` varchar(255)\n- `last_login_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (public_id); (tenant_id, email)\n\n### user_profiles\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint not null -> users\n- `avatar_url` varchar(2048)\n- `bio` varchar(4000)\n- `phone` varchar(32)\n- `job_title` varchar(255)\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (user_id)\n\n### user_preferences\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint not null -> users\n- `theme` varchar(64) not null\n- `email_opt_in` boolean not null\n- `marketing_opt_in` boolean not null\n- `locale_override` varchar(5)\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (user_id)\n\n### roles\nPer-tenant RBAC roles. is_system marks built-in roles that cannot be deleted.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `description` varchar(4000)\n- `is_system` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, name)\n\n### permissions\nGlobal catalog of permission codes (e.g. orders.refund). Shared across all tenants.\n- `id` bigint not null\n- `code` varchar(64) not null\n- `description` varchar(4000)\n- `category` varchar(255)\n- unique: (code)\n\n### role_permissions\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `role_id` bigint not null -> roles\n- `permission_id` bigint not null -> permissions\n- `created_at` timestamp not null\n- unique: (role_id, permission_id)\n\n### user_roles\nAssignment of roles to users. granted_by_user_id records who made the grant.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint not null -> users\n- `role_id` bigint not null -> roles\n- `granted_by_user_id` bigint -> users\n- `created_at` timestamp not null\n- unique: (user_id, role_id)\n\n### teams\nteams — hierarchical sub-org groupings.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `parent_team_id` bigint -> teams\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, name)\n\n### team_members\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `team_id` bigint not null -> teams\n- `user_id` bigint not null -> users\n- `team_role` varchar(64) not null\n- `created_at` timestamp not null\n- unique: (team_id, user_id)\n\n### api_keys\nProgrammatic API credentials for a tenant. Only the hash and a short prefix are stored.\n- `id` bigint not null\n- `public_id` uuid not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `prefix` varchar(64) not null\n- `hashed_key` varchar(64) not null\n- `status` varchar(32) not null one of: active, revoked, expired\n- `created_by_user_id` bigint -> users\n- `last_used_at` timestamp\n- `expires_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (public_id); (prefix)\n\n### personal_access_tokens\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint not null -> users\n- `name` varchar(255) not null\n- `hashed_token` varchar(255) not null\n- `scopes` varchar(64)[] not null\n- `last_used_at` timestamp\n- `expires_at` timestamp\n- `created_at` timestamp not null\n- unique: (hashed_token)\n\n### sessions\nAuthenticated browser/app sessions. revoked_at non-null means force-logged-out.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint not null -> users\n- `token_hash` varchar(255) not null\n- `ip_address` varchar(45)\n- `user_agent` varchar(2048)\n- `started_at` timestamp not null\n- `last_seen_at` timestamp not null\n- `expires_at` timestamp not null\n- `revoked_at` timestamp\n- unique: (token_hash)\n\n### oauth_accounts\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint not null -> users\n- `provider` varchar(64) not null\n- `provider_account_id` varchar(255) not null\n- `access_token_hash` varchar(255)\n- `created_at` timestamp not null\n- unique: (provider, provider_account_id)\n\n### mfa_devices\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint not null -> users\n- `kind` varchar(64) not null\n- `secret_hash` varchar(255) not null\n- `confirmed_at` timestamp\n- `created_at` timestamp not null\n\n### invitations\nPending invitations to join a tenant; consumed when status becomes accepted.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `email` varchar(254) not null\n- `role_id` bigint -> roles\n- `invited_by_user_id` bigint -> users\n- `status` varchar(32) not null one of: pending, accepted, expired, revoked\n- `token_hash` varchar(255) not null\n- `expires_at` timestamp not null\n- `accepted_at` timestamp\n- `created_at` timestamp not null\n- unique: (token_hash)\n\n### password_resets\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint not null -> users\n- `token_hash` varchar(255) not null\n- `expires_at` timestamp not null\n- `used_at` timestamp\n- `created_at` timestamp not null\n- unique: (token_hash)\n\n### service_accounts\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `description` varchar(4000)\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, name)\n\n### login_attempts\nAudit of authentication attempts, successful and failed, for security analytics.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint -> users\n- `email` varchar(254)\n- `succeeded` boolean not null\n- `ip_address` varchar(45)\n- `user_agent` varchar(2048)\n- `occurred_at` timestamp not null\n\n## Area: inventory\n\n### warehouses\nPhysical fulfillment sites for a tenant, each anchored to a postal address; one is flagged as the default.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `address_id` bigint -> addresses\n- `code` varchar(64) not null\n- `name` varchar(255) not null\n- `is_default` boolean not null\n- `is_active` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (tenant_id, code)\n\n### stock_locations\nBins, shelves and zones inside a warehouse. Stock is counted at this granularity, not at the warehouse level.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `warehouse_id` bigint not null -> warehouses\n- `code` varchar(64) not null\n- `name` varchar(255)\n- `kind` varchar(64) not null\n- `is_pickable` boolean not null\n- `is_active` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (warehouse_id, code)\n\n### suppliers\nVendors that goods are purchased from on purchase orders.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `code` varchar(64) not null\n- `name` varchar(255) not null\n- `contact_email` varchar(254)\n- `contact_phone` varchar(32)\n- `lead_time_days` integer\n- `is_active` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (tenant_id, code)\n\n### supplier_products\nPer-supplier sourcing of a variant: their SKU and unit cost; is_preferred marks the default source.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `supplier_id` bigint not null -> suppliers\n- `variant_id` bigint not null -> product_variants\n- `supplier_sku` varchar(64)\n- `unit_cost` decimal(14,4)\n- `currency_code` varchar(3) -> currencies\n- `min_order_qty` integer\n- `lead_time_days` integer\n- `is_preferred` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (supplier_id, variant_id)\n\n### stock_items\nLive on-hand quantity of a variant in a single location; one row per (variant, location). The movements ledger is its history.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `variant_id` bigint not null -> product_variants\n- `stock_location_id` bigint not null -> stock_locations\n- `quantity` integer not null\n- `reserved_quantity` integer not null (Portion of quantity earmarked by open reservations and not available to sell.)\n- `reorder_point` integer (Replenishment trigger: at or below this on-hand level the variant is flagged for reorder.)\n- `reorder_quantity` integer\n- `last_counted_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (variant_id, stock_location_id)\n\n### stock_movements\nImmutable append-only ledger of inventory quantity changes; quantity is signed (in positive, out negative) and the running sum reproduces stock_items.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `variant_id` bigint not null -> product_variants\n- `stock_location_id` bigint not null -> stock_locations\n- `lot_id` bigint -> lots\n- `kind` varchar(32) not null one of: receipt, shipment, adjustment, transfer, return, cycle_count\n- `quantity` integer not null\n- `reference_type` varchar(64)\n- `reference_id` bigint (Loose pointer to the source document (PO line, order, adjustment) identified by reference_type; not a foreign key.)\n- `note` varchar(4000)\n- `created_by_user_id` bigint -> users\n- `occurred_at` timestamp not null\n- `created_at` timestamp not null\n\n### stock_reservations\nSoft holds that earmark stock for an order so it is not double-sold; order_id references orders by id but has no FK (forward module).\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `variant_id` bigint not null -> product_variants\n- `stock_location_id` bigint not null -> stock_locations\n- `order_id` bigint -> orders\n- `quantity` integer not null\n- `expires_at` timestamp\n- `released_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### purchase_orders\nReplenishment orders sent to a supplier and delivered to a warehouse; public_id is the opaque external identifier.\n- `id` bigint not null\n- `public_id` uuid not null\n- `tenant_id` bigint not null -> tenants\n- `supplier_id` bigint not null -> suppliers\n- `warehouse_id` bigint -> warehouses\n- `po_number` varchar(64) not null\n- `status` varchar(32) not null one of: draft, submitted, partially_received, received, cancelled\n- `currency_code` varchar(3) not null -> currencies\n- `expected_total` decimal(14,4) not null (Denormalized order total; the authoritative figure is the sum over purchase_order_lines.)\n- `notes` varchar(4000)\n- `ordered_at` timestamp\n- `expected_at` timestamp\n- `received_at` timestamp\n- `created_by_user_id` bigint -> users\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (public_id); (tenant_id, po_number)\n\n### purchase_order_lines\nLine items on a purchase order: variant, quantity and agreed unit cost, with received quantity accruing as goods arrive.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `purchase_order_id` bigint not null -> purchase_orders\n- `variant_id` bigint not null -> product_variants\n- `quantity_ordered` integer not null\n- `quantity_received` integer not null\n- `unit_cost` decimal(14,4) not null\n- `currency_code` varchar(3) not null -> currencies\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (purchase_order_id, variant_id)\n\n### lots\nBatch/lot tracking for a variant, carrying manufacture and expiry dates for FEFO picking and recalls.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `variant_id` bigint not null -> product_variants\n- `supplier_id` bigint -> suppliers\n- `lot_code` varchar(64) not null\n- `manufactured_at` date\n- `expires_at` date\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, variant_id, lot_code)\n\n### serial_numbers\nserial_numbers — individually tracked units (one row per physical unit), optionally grouped under a lot and located in a stock location.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `variant_id` bigint not null -> product_variants\n- `lot_id` bigint -> lots\n- `stock_location_id` bigint -> stock_locations\n- `serial` varchar(64) not null\n- `status` varchar(64) not null\n- `received_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, serial)\n\n### inbound_receipts\nGoods-receipt headers recording a physical delivery into a warehouse, usually against a purchase order.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `purchase_order_id` bigint -> purchase_orders\n- `warehouse_id` bigint not null -> warehouses\n- `receipt_number` varchar(64) not null\n- `carrier` varchar(64)\n- `tracking_number` varchar(64)\n- `received_by_user_id` bigint -> users\n- `received_at` timestamp not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, receipt_number)\n\n### receipt_lines\nreceipt_lines — the quantities of each variant actually received on a receipt, put away into a specific location (and optionally a lot).\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `inbound_receipt_id` bigint not null -> inbound_receipts\n- `purchase_order_line_id` bigint -> purchase_order_lines\n- `variant_id` bigint not null -> product_variants\n- `stock_location_id` bigint -> stock_locations\n- `lot_id` bigint -> lots\n- `quantity_received` integer not null\n- `quantity_rejected` integer not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### stock_adjustments\nManual corrections to on-hand stock (cycle counts, shrinkage, damage); quantity_delta is signed and a reason is required.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `variant_id` bigint not null -> product_variants\n- `stock_location_id` bigint not null -> stock_locations\n- `lot_id` bigint -> lots\n- `quantity_delta` integer not null\n- `reason` varchar(4000) not null\n- `note` varchar(4000)\n- `adjusted_by_user_id` bigint -> users\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n## Area: marketing\n\n### email_templates\nReusable email subject/body templates used by campaigns and per-recipient sends.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `subject` varchar(255) not null\n- `body_html` varchar(65535)\n- `body_text` varchar(65535)\n- `from_name` varchar(255)\n- `from_email` varchar(254)\n- `is_active` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (tenant_id, name)\n\n### segments\nAudience definitions; the selection logic lives in the rules jsonb and is materialized into segment_members.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `description` varchar(4000)\n- `rules` json not null\n- `is_dynamic` boolean not null (True if membership is recomputed from rules on a schedule; false for static lists.)\n- `member_count` integer not null\n- `last_refreshed_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (tenant_id, name)\n\n### segment_members\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `segment_id` bigint not null -> segments\n- `user_id` bigint not null -> users\n- `added_at` timestamp not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (segment_id, user_id)\n\n### landing_pages\nlanding_pages — campaign destination pages. Referenced by campaigns and utm_links, so defined ahead of them.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `slug` varchar(255) not null\n- `title` varchar(255) not null\n- `url` varchar(2048)\n- `content_html` varchar(65535)\n- `is_published` boolean not null\n- `published_at` timestamp\n- `view_count` bigint not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (tenant_id, slug)\n\n### campaigns\nMarketing campaigns; may target a segment, render via a template, drive to a landing page, and link a pricing promotion.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `description` varchar(4000)\n- `channel` varchar(64) not null\n- `status` varchar(32) not null one of: draft, scheduled, sending, sent, paused, archived\n- `segment_id` bigint -> segments\n- `email_template_id` bigint -> email_templates\n- `landing_page_id` bigint -> landing_pages\n- `promotion_id` bigint -> promotions\n- `budget` decimal(14,4)\n- `currency_code` varchar(3) -> currencies\n- `scheduled_at` timestamp\n- `started_at` timestamp\n- `ended_at` timestamp\n- `created_by_user_id` bigint -> users\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (tenant_id, name)\n\n### campaign_messages\nOne row per recipient send; send_status tracks the delivery and engagement lifecycle.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `campaign_id` bigint not null -> campaigns\n- `user_id` bigint not null -> users\n- `email_template_id` bigint -> email_templates\n- `status` varchar(32) not null one of: queued, sent, delivered, opened, clicked, bounced, complained, failed, unsubscribed\n- `recipient_email` varchar(254)\n- `sent_at` timestamp\n- `delivered_at` timestamp\n- `opened_at` timestamp\n- `clicked_at` timestamp\n- `bounced_at` timestamp\n- `unsubscribed_at` timestamp\n- `error_message` varchar(4000)\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### ab_tests\nA/B test definitions; winner_variant_id records the chosen variant once the experiment completes.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `campaign_id` bigint -> campaigns\n- `name` varchar(255) not null\n- `hypothesis` varchar(4000)\n- `status` varchar(32) not null one of: draft, running, paused, completed, archived\n- `goal_metric` varchar(255)\n- `started_at` timestamp\n- `ended_at` timestamp\n- `winner_variant_id` bigint\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (tenant_id, name)\n\n### ab_variants\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `ab_test_id` bigint not null -> ab_tests\n- `name` varchar(255) not null\n- `is_control` boolean not null\n- `traffic_weight` decimal(5,4) not null (Fraction of eligible traffic routed to this variant; weights across a test should sum to ~1.)\n- `config` json not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (ab_test_id, name)\n\n### ab_assignments\nSticky assignment of a user to one variant within an A/B test; converted_at records goal completion.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `ab_test_id` bigint not null -> ab_tests\n- `ab_variant_id` bigint not null -> ab_variants\n- `user_id` bigint not null -> users\n- `assigned_at` timestamp not null\n- `converted_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (ab_test_id, user_id)\n\n### attributions\nTouch attributions crediting a campaign with a conversion; order_id is set for purchase conversions.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint -> users\n- `campaign_id` bigint -> campaigns\n- `order_id` bigint -> orders\n- `model` varchar(64) not null\n- `touch_point` varchar(64)\n- `weight` decimal(5,4) not null (Fractional credit for this touch under the attribution model (e.g. linear, last_touch).)\n- `attributed_revenue` decimal(14,4)\n- `currency_code` varchar(3) -> currencies\n- `occurred_at` timestamp not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### referrals\nReferral codes owned by a referring user, with optional reward and redemption caps.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `referrer_user_id` bigint not null -> users\n- `code` varchar(64) not null\n- `reward_amount` decimal(14,4)\n- `currency_code` varchar(3) -> currencies\n- `max_redemptions` integer\n- `redemption_count` integer not null\n- `is_active` boolean not null\n- `expires_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (tenant_id, code)\n\n### referral_redemptions\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `referral_id` bigint not null -> referrals\n- `referred_user_id` bigint not null -> users\n- `order_id` bigint -> orders\n- `reward_amount` decimal(14,4)\n- `currency_code` varchar(3) -> currencies\n- `redeemed_at` timestamp not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (referral_id, referred_user_id)\n\n### loyalty_programs\nLoyalty: program defs -> per-user accounts (balance) -> immutable earn/burn transaction ledger.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `description` varchar(4000)\n- `points_per_currency` decimal(14,4) not null\n- `currency_code` varchar(3) not null -> currencies\n- `is_active` boolean not null\n- `starts_at` timestamp\n- `ends_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (tenant_id, name)\n\n### loyalty_accounts\nPer-user point balance within a loyalty program; points_balance is the current redeemable total.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `loyalty_program_id` bigint not null -> loyalty_programs\n- `user_id` bigint not null -> users\n- `points_balance` bigint not null\n- `lifetime_points` bigint not null\n- `tier` varchar(64)\n- `enrolled_at` timestamp not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (loyalty_program_id, user_id)\n\n### loyalty_transactions\nImmutable earn/burn ledger of loyalty point movements; append-only, never updated.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `loyalty_account_id` bigint not null -> loyalty_accounts\n- `user_id` bigint not null -> users\n- `direction` varchar(64) not null\n- `points` bigint not null\n- `reason` varchar(4000)\n- `order_id` bigint -> orders\n- `balance_after` bigint (Account points_balance immediately after this entry was applied.)\n- `occurred_at` timestamp not null\n- `created_at` timestamp not null\n\n### utm_links\nTracked links carrying UTM parameters, optionally tied to a campaign and landing page.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `campaign_id` bigint -> campaigns\n- `landing_page_id` bigint -> landing_pages\n- `slug` varchar(255) not null\n- `target_url` varchar(2048) not null\n- `utm_source` varchar(255)\n- `utm_medium` varchar(255)\n- `utm_campaign` varchar(255)\n- `utm_term` varchar(255)\n- `utm_content` varchar(255)\n- `click_count` bigint not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (tenant_id, slug)\n\n## Area: ops\n\n### feature_flags\nFeature toggle and rollout definitions, addressed by a tenant-unique key. kind distinguishes boolean toggles from multivariate and percentage rollouts.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `key` varchar(64) not null\n- `name` varchar(255) not null\n- `description` varchar(4000)\n- `kind` varchar(32) not null one of: boolean, multivariate, percentage_rollout\n- `is_enabled` boolean not null\n- `default_value` json not null (Fallback value served when no rule or override matches; jsonb to support multivariate payloads, not just booleans.)\n- `rollout_percentage` integer\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, key)\n\n### feature_flag_rules\nOrdered targeting rules for a flag; the first rule whose jsonb conditions match decides the served value.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `feature_flag_id` bigint not null -> feature_flags\n- `sort_position` integer not null\n- `conditions` json not null\n- `serve_value` json not null\n- `is_active` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (feature_flag_id, sort_position)\n\n### feature_flag_overrides\nExplicit per-user or tenant-wide overrides that bypass rule evaluation for a flag.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `feature_flag_id` bigint not null -> feature_flags\n- `user_id` bigint -> users\n- `override_value` json not null\n- `reason` varchar(4000)\n- `expires_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### settings\nTyped key/value configuration per tenant. value_type tags how the jsonb value should be interpreted by the application.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `setting_key` varchar(64) not null\n- `setting_value` json not null\n- `value_type` varchar(64) not null (Logical type hint for value (string/number/boolean/json/date); the column itself is always jsonb.)\n- `is_secret` boolean not null\n- `description` varchar(4000)\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, setting_key)\n\n### jobs\nBackground job definitions registered per tenant; job_runs records each execution.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `queue` varchar(64) not null\n- `handler` varchar(64) not null\n- `payload` json not null\n- `max_attempts` integer not null\n- `is_enabled` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, name)\n\n### job_runs\nImmutable record of a single background job execution, including status, attempt number and timing.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `job_id` bigint not null -> jobs\n- `status` varchar(32) not null one of: queued, running, succeeded, failed, cancelled, retrying\n- `attempt` integer not null\n- `queued_at` timestamp not null\n- `started_at` timestamp\n- `finished_at` timestamp\n- `duration_ms` integer\n- `error_message` varchar(4000)\n- `result_payload` json\n- `created_at` timestamp not null\n\n### integrations\nThird-party service connections for a tenant (e.g. ERP, shipping, accounting), with current status and config.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `provider` varchar(64) not null\n- `name` varchar(255) not null\n- `status` varchar(32) not null one of: connected, disconnected, error, syncing\n- `config` json not null\n- `connected_by_user_id` bigint -> users\n- `connected_at` timestamp\n- `last_synced_at` timestamp\n- `last_error` varchar(4000)\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, provider, name)\n\n### integration_syncs\nImmutable record of a single data-sync run against an integration.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `integration_id` bigint not null -> integrations\n- `status` varchar(32) not null one of: queued, running, succeeded, failed, cancelled, retrying\n- `direction` varchar(64) not null\n- `records_processed` integer not null\n- `records_failed` integer not null\n- `started_at` timestamp\n- `finished_at` timestamp\n- `error_message` varchar(4000)\n- `created_at` timestamp not null\n\n### files\nMetadata for files uploaded to object storage; the actual blob lives in storage_bucket/storage_key, not in the database.\n- `id` bigint not null\n- `public_id` uuid not null\n- `tenant_id` bigint not null -> tenants\n- `filename` varchar(255) not null\n- `content_type` varchar(64) not null\n- `byte_size` bigint not null\n- `storage_bucket` varchar(255) not null\n- `storage_key` varchar(2048) not null\n- `checksum` varchar(255)\n- `uploaded_by_user_id` bigint -> users\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (public_id); (storage_bucket, storage_key)\n\n### file_versions\nVersion chain for a file; is_current flags the active version (one per file, enforced by partial unique index).\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `file_id` bigint not null -> files\n- `version` integer not null\n- `byte_size` bigint not null\n- `storage_key` varchar(2048) not null\n- `checksum` varchar(255)\n- `is_current` boolean not null\n- `created_by_user_id` bigint -> users\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (file_id, version)\n\n### import_batches\nBulk data-import runs with row-level progress counters and a terminal status.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `entity_type` varchar(64) not null\n- `source_filename` varchar(255)\n- `file_id` bigint -> files\n- `status` varchar(32) not null one of: pending, processing, completed, failed, partially_failed\n- `total_rows` integer not null\n- `processed_rows` integer not null\n- `succeeded_rows` integer not null\n- `failed_rows` integer not null\n- `started_by_user_id` bigint -> users\n- `started_at` timestamp\n- `finished_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### import_errors\nImmutable per-row failures captured during an import batch, with the offending raw row preserved as jsonb.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `import_batch_id` bigint not null -> import_batches\n- `source_row_number` integer not null\n- `column_name` varchar(64)\n- `error_code` varchar(64) not null\n- `error_message` varchar(4000) not null\n- `raw_row` json\n- `occurred_at` timestamp not null\n- `created_at` timestamp not null\n\n### scheduled_tasks\nCron-expression driven recurring tasks; next_run_at is the precomputed next fire time used by the scheduler.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `cron_expression` varchar(2048) not null\n- `timezone` varchar(64) not null\n- `job_id` bigint -> jobs\n- `is_enabled` boolean not null\n- `last_run_at` timestamp\n- `next_run_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, name)\n\n### system_health_checks\nsystem_health_checks — immutable periodic health probe results.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `component` varchar(255) not null\n- `is_healthy` boolean not null\n- `latency_ms` integer\n- `details` json\n- `checked_at` timestamp not null\n- `created_at` timestamp not null\n\n### rate_limits\nRequest quota windows scoped per tenant or per API key; an api_key-null row is the tenant-wide default for a scope.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `api_key_id` bigint -> api_keys\n- `limit_scope` varchar(64) not null\n- `max_requests` integer not null\n- `window_seconds` integer not null\n- `is_enabled` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### secrets_vault\nReferences and metadata for secrets held in an external vault; never stores plaintext, only a provider pointer (external_ref).\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `key` varchar(64) not null\n- `provider` varchar(64) not null\n- `external_ref` varchar(255) not null (Opaque pointer into the external secret store (e.g. a Vault path or KMS key id); resolved at runtime, never the secret value itself.)\n- `version` integer not null\n- `last_rotated_at` timestamp\n- `expires_at` timestamp\n- `created_by_user_id` bigint -> users\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, key)\n\n## Area: pricing\n\n### price_lists\nNamed price books, each scoped to one currency; a tenant keeps a default list per currency plus contract/regional books.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `code` varchar(64) not null\n- `currency_code` varchar(3) not null -> currencies\n- `is_default` boolean not null\n- `is_active` boolean not null\n- `starts_at` timestamp\n- `ends_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (tenant_id, code)\n\n### prices\nA variant's price within a price list over a validity window; an exclusion constraint forbids overlapping windows for the same list/variant/min_quantity.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `price_list_id` bigint not null -> price_lists\n- `variant_id` bigint not null -> product_variants\n- `currency_code` varchar(3) not null -> currencies\n- `amount` decimal(14,4) not null\n- `compare_at_amount` decimal(14,4) (Optional strike-through / \"was\" price shown next to the effective amount.)\n- `min_quantity` integer not null\n- `valid_from` timestamp not null\n- `valid_to` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### discounts\nReusable discount definitions; coupons, price_rules and promotions point at these for the actual reduction.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `kind` varchar(32) not null one of: percentage, fixed_amount, free_shipping, bogo\n- `discount_value` decimal(14,4) not null (Percentage (0-100) when kind=percentage, else a fixed monetary amount in currency_code.)\n- `currency_code` varchar(3) -> currencies\n- `max_amount` decimal(14,4)\n- `min_subtotal` decimal(14,4)\n- `applies_to_shipping` boolean not null\n- `is_active` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n\n### coupons\nRedeemable codes granting a discount; code is unique per tenant and usage is capped by usage_limit / per_customer_limit.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `discount_id` bigint not null -> discounts\n- `code` varchar(64) not null\n- `status` varchar(32) not null one of: active, scheduled, expired, disabled\n- `usage_limit` integer\n- `per_customer_limit` integer\n- `times_redeemed` integer not null (Denormalized running count of redemptions, capped by usage_limit.)\n- `starts_at` timestamp\n- `expires_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (tenant_id, code)\n\n### coupon_redemptions\nImmutable record of each coupon use; order_id references orders.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `coupon_id` bigint not null -> coupons\n- `user_id` bigint -> users\n- `order_id` bigint -> orders\n- `discount_amount` decimal(14,4) not null\n- `currency_code` varchar(3) not null -> currencies\n- `redeemed_at` timestamp not null\n- `created_at` timestamp not null\n\n### tax_categories\ntax_categories — product tax classes (e.g. standard goods, digital, food, exempt). Variants/products are mapped to one of these to find their rate.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `code` varchar(64) not null\n- `description` varchar(4000)\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, code)\n\n### tax_zones\nGeographic tax regions defined at country, region or postal-zone granularity.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `country_id` bigint -> countries\n- `region_id` bigint -> regions\n- `postal_zone_id` bigint -> postal_zones\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### tax_rates\nEffective tax rate for a (tax_category, tax_zone) pair, with an optional validity window so rate changes stay auditable.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `tax_category_id` bigint not null -> tax_categories\n- `tax_zone_id` bigint not null -> tax_zones\n- `name` varchar(255) not null\n- `rate` decimal(8,5) not null (Fractional rate in [0,1]; e.g. 0.20000 for 20% VAT.)\n- `is_compound` boolean not null\n- `is_inclusive` boolean not null (True when displayed prices already include this tax (tax-inclusive pricing).)\n- `valid_from` timestamp not null\n- `valid_to` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### price_rules\nprice_rules — conditional pricing rule headers (e.g. \"10% off when cart has 3+ items from brand X\"). Conditions live in price_rule_conditions. The reward references a discount definition. priority orders evaluation; lower = first.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `discount_id` bigint -> discounts\n- `name` varchar(255) not null\n- `priority` integer not null\n- `is_active` boolean not null\n- `stackable` boolean not null\n- `starts_at` timestamp\n- `ends_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### price_rule_conditions\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `price_rule_id` bigint not null -> price_rules\n- `attribute` varchar(255) not null\n- `operator` varchar(64) not null\n- `condition_value` json not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### promotions\nCampaign-linked promotions; standalone by design so campaigns can reference promotions, never the reverse.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `discount_id` bigint -> discounts\n- `coupon_id` bigint -> coupons\n- `name` varchar(255) not null\n- `slug` varchar(255) not null\n- `campaign_code` varchar(64) (Free-text correlation handle to a marketing campaign; not an FK since marketing is a higher-numbered module.)\n- `is_active` boolean not null\n- `starts_at` timestamp\n- `ends_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (tenant_id, slug)\n\n### promotion_products\nJunction restricting a promotion to specific products.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `promotion_id` bigint not null -> promotions\n- `product_id` bigint not null -> products\n- `created_at` timestamp not null\n- unique: (promotion_id, product_id)\n\n## Area: sales\n\n### carts\nShopping carts, anonymous (user_id null) or owned. Converts to an order; abandoned carts feed recovery campaigns.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint -> users\n- `session_token` varchar(255)\n- `status` varchar(32) not null one of: active, converted, abandoned, merged\n- `currency_code` varchar(3) not null -> currencies\n- `item_count` integer not null\n- `subtotal` decimal(14,4) not null\n- `abandoned_at` timestamp\n- `converted_at` timestamp\n- `expires_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### cart_items\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `cart_id` bigint not null -> carts\n- `product_variant_id` bigint not null -> product_variants\n- `quantity` integer not null\n- `unit_price` decimal(14,4) not null\n- `currency_code` varchar(3) not null -> currencies\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (cart_id, product_variant_id)\n\n### orders\nCentral order fact table. public_id is the opaque id shown to customers; totals are denormalized money on the row.\n- `id` bigint not null\n- `public_id` uuid not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint not null -> users\n- `cart_id` bigint -> carts\n- `order_number` varchar(64) not null (Human-facing sequential order number, unique per tenant (distinct from the opaque public_id).)\n- `status` varchar(32) not null one of: pending, confirmed, paid, partially_fulfilled, fulfilled, cancelled, refunded\n- `billing_address_id` bigint -> addresses\n- `shipping_address_id` bigint -> addresses\n- `currency_code` varchar(3) not null -> currencies\n- `subtotal` decimal(14,4) not null\n- `discount_total` decimal(14,4) not null\n- `tax_total` decimal(14,4) not null\n- `shipping_total` decimal(14,4) not null\n- `grand_total` decimal(14,4) not null (Final amount charged: subtotal - discount_total + tax_total + shipping_total.)\n- `customer_email` varchar(254)\n- `customer_note` varchar(4000)\n- `placed_at` timestamp\n- `cancelled_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (public_id); (tenant_id, order_number)\n\n### order_items\nLine items of an order; sku and name are snapshotted at purchase time so historical orders survive catalog changes.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `order_id` bigint not null -> orders\n- `product_variant_id` bigint not null -> product_variants\n- `sku` varchar(64) not null\n- `name` varchar(255) not null\n- `quantity` integer not null\n- `quantity_fulfilled` integer not null (Running count of units already dispatched across fulfillments; never exceeds quantity.)\n- `unit_price` decimal(14,4) not null\n- `discount_total` decimal(14,4) not null\n- `tax_total` decimal(14,4) not null\n- `line_total` decimal(14,4) not null\n- `currency_code` varchar(3) not null -> currencies\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### order_status_history\nAppend-only audit of order status transitions; immutable (no updated_at).\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `order_id` bigint not null -> orders\n- `from_status` varchar(32) one of: pending, confirmed, paid, partially_fulfilled, fulfilled, cancelled, refunded\n- `to_status` varchar(32) not null one of: pending, confirmed, paid, partially_fulfilled, fulfilled, cancelled, refunded\n- `changed_by_user_id` bigint -> users\n- `note` varchar(4000)\n- `created_at` timestamp not null\n\n### order_discounts\nDiscounts and coupon redemptions applied to an order; discount_id/coupon_id reference the pricing module.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `order_id` bigint not null -> orders\n- `discount_id` bigint -> discounts\n- `coupon_id` bigint -> coupons\n- `code` varchar(64)\n- `description` varchar(4000)\n- `amount` decimal(14,4) not null\n- `currency_code` varchar(3) not null -> currencies\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### fulfillments\nA batch of order items dispatched together from one warehouse; tracks pick/pack/ship lifecycle.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `order_id` bigint not null -> orders\n- `warehouse_id` bigint -> warehouses\n- `status` varchar(32) not null one of: pending, picked, packed, shipped, delivered, returned\n- `reference` varchar(255)\n- `packed_at` timestamp\n- `shipped_at` timestamp\n- `delivered_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### fulfillment_items\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `fulfillment_id` bigint not null -> fulfillments\n- `order_item_id` bigint not null -> order_items\n- `quantity` integer not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (fulfillment_id, order_item_id)\n\n### shipments\nCarrier shipment for a fulfillment, with tracking number and delivery timestamps.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `fulfillment_id` bigint not null -> fulfillments\n- `carrier` varchar(64) not null\n- `service_level` varchar(64)\n- `tracking_number` varchar(64)\n- `tracking_url` varchar(2048)\n- `shipping_cost` decimal(14,4)\n- `currency_code` varchar(3) -> currencies\n- `weight_grams` integer\n- `shipped_at` timestamp\n- `delivered_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### shipment_tracking\nshipment_tracking — append-only stream of carrier tracking events. IMMUTABLE: occurred_at only, no updated_at.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `shipment_id` bigint not null -> shipments\n- `status` varchar(64) not null\n- `description` varchar(4000)\n- `location` varchar(255)\n- `occurred_at` timestamp not null\n- `created_at` timestamp not null\n\n### order_returns\nReturn merchandise authorization (RMA) header; status walks requested -> approved -> received -> refunded.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `order_id` bigint not null -> orders\n- `rma_number` varchar(64) not null\n- `status` varchar(32) not null one of: requested, approved, received, refunded, rejected\n- `reason` varchar(4000)\n- `refund_total` decimal(14,4) not null\n- `currency_code` varchar(3) not null -> currencies\n- `requested_at` timestamp not null\n- `approved_at` timestamp\n- `received_at` timestamp\n- `refunded_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, rma_number)\n\n### return_items\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `return_id` bigint not null -> order_returns\n- `order_item_id` bigint not null -> order_items\n- `quantity` integer not null\n- `reason` varchar(4000)\n- `restock` boolean not null (Whether the returned units should be added back to sellable inventory.)\n- `refund_amount` decimal(14,4) not null\n- `currency_code` varchar(3) not null -> currencies\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (return_id, order_item_id)\n\n### gift_cards\nStored-value gift cards; code is unique per tenant and balance decreases as it is redeemed against orders.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `code` varchar(64) not null\n- `status` varchar(64) not null\n- `initial_balance` decimal(14,4) not null\n- `balance` decimal(14,4) not null\n- `currency_code` varchar(3) not null -> currencies\n- `issued_to_user_id` bigint -> users\n- `issued_order_id` bigint -> orders\n- `expires_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, code)\n\n### gift_card_transactions\nImmutable ledger of gift-card balance changes; positive amount credits, negative debits.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `gift_card_id` bigint not null -> gift_cards\n- `order_id` bigint -> orders\n- `amount` decimal(14,4) not null\n- `balance_after` decimal(14,4) not null\n- `currency_code` varchar(3) not null -> currencies\n- `note` varchar(4000)\n- `created_at` timestamp not null\n\n### order_notes\norder_notes — internal staff notes attached to an order (not the customer note that lives on the order row itself).\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `order_id` bigint not null -> orders\n- `author_user_id` bigint -> users\n- `body` varchar(65535) not null\n- `is_pinned` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### draft_orders\nMerchant-created quotes/draft orders, optionally converted into a real order.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint -> users\n- `created_by_user_id` bigint -> users\n- `converted_order_id` bigint -> orders\n- `name` varchar(255)\n- `status` varchar(64) not null\n- `currency_code` varchar(3) not null -> currencies\n- `subtotal` decimal(14,4) not null\n- `grand_total` decimal(14,4) not null\n- `payload` json not null\n- `expires_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n## Area: support\n\n### support_agents\nSupport staff who handle tickets; a support-specific extension of an users record carrying availability and load limits.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `user_id` bigint not null -> users\n- `display_name` varchar(255)\n- `signature` varchar(255)\n- `is_available` boolean not null\n- `max_open_tickets` integer not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, user_id)\n\n### agent_groups\nNamed groups of agents used for ticket routing and assignment.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `description` varchar(4000)\n- `is_active` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, name)\n\n### agent_group_members\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `agent_group_id` bigint not null -> agent_groups\n- `support_agent_id` bigint not null -> support_agents\n- `is_lead` boolean not null\n- `created_at` timestamp not null\n- unique: (agent_group_id, support_agent_id)\n\n### sla_policies\nLookup of service-level targets (first-response and resolution minutes), usually keyed by ticket priority.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `priority` varchar(32) one of: low, normal, high, urgent\n- `first_response_target_minutes` integer not null\n- `resolution_target_minutes` integer not null\n- `business_hours_only` boolean not null (When true, SLA timers pause outside the tenant business calendar rather than running 24/7.)\n- `is_active` boolean not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, name)\n\n### support_tags\nsupport_tags — tag catalog for categorizing tickets.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `color` varchar(64)\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, name)\n\n### knowledge_categories\nKnowledge base: hierarchical categories + articles + per-article feedback.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `name` varchar(255) not null\n- `slug` varchar(255) not null\n- `parent_category_id` bigint -> knowledge_categories\n- `sort_position` integer not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (tenant_id, slug)\n\n### knowledge_articles\nHelp-center articles published to customers; soft-deletable via deleted_at.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `knowledge_category_id` bigint -> knowledge_categories\n- `author_user_id` bigint -> users\n- `title` varchar(255) not null\n- `slug` varchar(255) not null\n- `body` varchar(65535)\n- `is_published` boolean not null\n- `view_count` bigint not null\n- `helpful_count` integer not null (Denormalized count of positive article_feedback rows, kept for fast sorting.)\n- `not_helpful_count` integer not null\n- `published_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (tenant_id, slug)\n\n### article_feedback\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `knowledge_article_id` bigint not null -> knowledge_articles\n- `user_id` bigint -> users\n- `was_helpful` boolean not null\n- `comment` varchar(4000)\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### macros\nCanned reply templates agents insert into ticket messages. Soft-deletable.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `created_by_agent_id` bigint -> support_agents\n- `title` varchar(255) not null\n- `body` varchar(65535) not null\n- `is_shared` boolean not null\n- `usage_count` bigint not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- `deleted_at` timestamp\n- unique: (tenant_id, title)\n\n### tickets\nCore customer support request. requester_user_id is the customer; assignee_agent_id is the owning agent (null = unassigned queue).\n- `id` bigint not null\n- `public_id` uuid not null\n- `tenant_id` bigint not null -> tenants\n- `requester_user_id` bigint not null -> users\n- `assignee_agent_id` bigint -> support_agents\n- `agent_group_id` bigint -> agent_groups\n- `sla_policy_id` bigint -> sla_policies\n- `subject` varchar(255) not null\n- `status` varchar(32) not null one of: new, open, pending, on_hold, solved, closed\n- `priority` varchar(32) not null one of: low, normal, high, urgent\n- `channel` varchar(32) not null one of: email, chat, phone, web, social, api\n- `first_response_at` timestamp (Timestamp of the first public agent reply, used to measure SLA first-response compliance.)\n- `resolved_at` timestamp\n- `closed_at` timestamp\n- `last_message_at` timestamp\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (public_id)\n\n### ticket_messages\nReplies on a ticket: is_public=true are customer-visible, is_public=false are internal agent notes.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `ticket_id` bigint not null -> tickets\n- `author_user_id` bigint -> users\n- `author_agent_id` bigint -> support_agents\n- `body` varchar(65535) not null\n- `is_public` boolean not null\n- `channel` varchar(32) one of: email, chat, phone, web, social, api\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### ticket_events\nImmutable append-only audit of ticket changes such as status transitions, reassignment, and priority changes.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `ticket_id` bigint not null -> tickets\n- `actor_user_id` bigint -> users\n- `actor_agent_id` bigint -> support_agents\n- `event_type` varchar(64) not null\n- `old_value` varchar(4000)\n- `new_value` varchar(4000)\n- `metadata` json not null\n- `occurred_at` timestamp not null\n- `created_at` timestamp not null\n\n### ticket_tags_map\nticket_tags_map — junction tickets × support_tags.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `ticket_id` bigint not null -> tickets\n- `support_tag_id` bigint not null -> support_tags\n- `created_at` timestamp not null\n- unique: (ticket_id, support_tag_id)\n\n### sla_breaches\nRecorded SLA-target breaches on a ticket, with how many minutes the target was missed by.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `ticket_id` bigint not null -> tickets\n- `sla_policy_id` bigint -> sla_policies\n- `breach_kind` varchar(64) not null\n- `target_at` timestamp not null\n- `breached_at` timestamp not null\n- `minutes_over` integer\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n\n### csat_responses\nCustomer satisfaction rating (1-5) collected after a ticket is resolved; one response per ticket.\n- `id` bigint not null\n- `tenant_id` bigint not null -> tenants\n- `ticket_id` bigint not null -> tickets\n- `respondent_user_id` bigint -> users\n- `rated_agent_id` bigint -> support_agents (The agent the customer was rating, captured at submission in case the ticket is later reassigned.)\n- `score` integer not null\n- `comment` varchar(4000)\n- `submitted_at` timestamp not null\n- `created_at` timestamp not null\n- `updated_at` timestamp not null\n- unique: (ticket_id)\n\n\n# Your role\n\nYou are Mateo Ruiz, a data analyst at Tidewick Commerce. You work for Daniel Achterberg, VP Customer Experience. Your working style is comparative: likes year-over-year and peer comparisons across merchants and countries.\n\nThis week you answer your leader's questions with warehouse reports, following the analytics handbook:\n\n- Each night you may request up to 3 reports. The week has 4 nights, and the memo is due on Friday morning.\n- Every submitted request uses a slot, also when it is bounced or fails.\n- A data engineer writes the SQL for each request. You describe precisely what you need:\n  - the question;\n  - the grain (what one row is);\n  - the columns and their definitions;\n  - filters and time windows;\n  - the tables you believe hold the answer.\n- One request answers one question at one grain. Two questions need two requests.\n\nEach night, read the reports critically before you plan the next requests:\n\n- Do the totals make sense?\n- Is something unusual, such as a spike, a drop, an outlier, or a number that disagrees with another?\n- What would explain it?\n\nFollow up: drill down into what looks off, test your explanations, and compare against a baseline. Spend your slots where they teach you the most; you do not have to use all of them.\n\nHand in tonight's requests in the structured form you are given. Put your brief reasoning in its `notes` field. An empty list of requests skips the night.\n"],"tools":[{"name":"StructuredOutput","description":"Use this tool to return your final response in the requested structured format. You MUST call this tool exactly once at the end of your response to provide the structured output.","schema":{"name":"StructuredOutput","description":"Use this tool to return your final response in the requested structured format. You MUST call this tool exactly once at the end of your response to provide the structured output.","input_schema":{"type":"object","properties":{"notes":{"type":"string","description":"your brief reasoning for tonight's requests"},"requests":{"type":"array","maxItems":3,"items":{"type":"object","properties":{"title":{"type":"string","description":"report title"},"question":{"type":"string","description":"the one question this report answers"},"grain":{"type":"string","description":"what one row is, e.g. 'carrier and week'"},"columns":{"type":"array","items":{"type":"string"},"description":"expected columns, each with its definition"},"details":{"type":"string","description":"filters, time window, definitions, tables you believe hold the answer, ordering"},"builds_on":{"type":"array","items":{"type":"string"},"description":"titles of earlier reports this one follows up on (empty if none)"}},"required":["title","question","grain","columns","details","builds_on"],"additionalProperties":false}}},"required":["notes","requests"],"additionalProperties":false},"eager_input_streaming":true}}],"cliPrefix":"You are a Claude agent, built on Anthropic's Claude Agent SDK."},"type":"attachment","uuid":"d23265b5-0012-4227-bfa7-206b096ef335","timestamp":"2026-10-03T23:09:19.329Z","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a","version":"2.1.275","gitBranch":"HEAD"}
{"type":"last-prompt","lastPrompt":"Monday 2026-01-05. Note from Daniel Achterberg (VP Customer Experience):  Subject: Where I'd like to start — support & shopper experience  Hi Mateo,  Good to have you on this with me. I'm still buildi…","leafUuid":"d23265b5-0012-4227-bfa7-206b096ef335","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a"}
{"type":"ai-title","aiTitle":"Shopper experience baseline review","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a"}
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{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-03T23:10:04.735Z","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a","content":"The printed reports of night 1:\n\n=== Report 1: Support response & resolution time trend, monthly (written by Bea) ===\nEngineer's note: Report covers all 36 months in the window (2023-01 through 2025-12), one row per month, platform-wide across tenants as requested. As flagged in the request: the last 1-2 months (Nov/Dec 2025) will understate avg/median response and resolution hours because tickets still open at month-end aren't yet counted as resolved/responded-to (right-censoring) - read that tail with caution. SLA breach rate = distinct tickets created that month with >=1 row in sla_breaches, divided by tickets created that month.\nREPORT: Support response & resolution time trend, monthly\nQUESTION: Is first-response and resolution speed on support tickets getting better or worse over time?\nmonth   | tickets_created | tickets_with_first_response | avg_first_response_hours | median_first_response_hours | tickets_resolved | avg_resolution_hours | median_resolution_hours | sla_breach_rate_pct\n--------+-----------------+-----------------------------+--------------------------+-----------------------------+------------------+----------------------+-------------------------+--------------------\n2023-01 |              85 |                          85 |                     11.6 |                         5.6 |               85 |                 27.2 |                    20.0 |                38.8\n2023-02 |              99 |                          99 |                     15.2 |                         8.1 |               99 |                 25.3 |                    19.5 |                38.4\n2023-03 |              77 |                          77 |                     15.0 |                         6.5 |               77 |                 26.9 |                    18.5 |                33.8\n2023-04 |              91 |                          91 |                      9.6 |                         5.1 |               91 |                 18.1 |                    12.8 |                30.8\n2023-05 |              85 |                          85 |                     14.2 |                         5.8 |               85 |                 22.0 |                    14.4 |                34.1\n2023-06 |             116 |                         116 |                     14.1 |                         9.4 |              116 |                 28.5 |                    21.3 |                44.8\n2023-07 |              70 |                          70 |                     11.1 |                         5.7 |               70 |                 18.6 |                    12.1 |                30.0\n2023-08 |             100 |                         100 |                     11.5 |                         4.5 |              100 |                 23.3 |                    18.4 |                33.0\n2023-09 |              84 |                          84 |                     12.4 |                         4.7 |               84 |                 20.0 |                    13.3 |                26.2\n2023-10 |             118 |                         118 |                     11.2 |                         4.6 |              118 |                 21.1 |                    13.7 |                27.1\n2023-11 |             177 |                         177 |                     15.0 |                         6.1 |              177 |                 27.2 |                    14.9 |                38.4\n2023-12 |             237 |                         237 |                     41.2 |                        24.4 |              237 |                 73.4 |                    41.2 |                62.0\n2024-01 |             117 |                         117 |                     19.4 |                         6.5 |              117 |                 42.3 |                    16.8 |                35.0\n2024-02 |              92 |                          92 |                      8.0 |                         4.4 |               92 |                 16.9 |                    12.7 |                25.0\n2024-03 |             103 |                         103 |                     12.9 |                         8.0 |              103 |                 27.8 |                    16.9 |                45.6\n2024-04 |             121 |                         121 |                     10.2 |                         3.7 |              121 |                 21.2 |                    15.8 |                28.1\n2024-05 |             126 |                         126 |                     18.5 |                         5.0 |              126 |                 27.2 |                    16.2 |                34.1\n2024-06 |             129 |                         129 |                     13.8 |                         7.8 |              129 |                 23.8 |                    15.5 |                33.3\n2024-07 |             131 |                         131 |                     15.5 |                         8.0 |              131 |                 26.0 |                    19.2 |                38.9\n2024-08 |             109 |                         109 |                     11.7 |                         5.6 |              109 |                 22.0 |                    17.4 |                33.9\n2024-09 |             121 |                         121 |                     11.1 |                         4.6 |              121 |                 21.3 |                    15.8 |                28.9\n2024-10 |             134 |                         134 |                     11.4 |                         4.2 |              134 |                 20.0 |                    13.1 |                29.1\n2024-11 |             177 |                         177 |                     17.5 |                        10.1 |              177 |                 29.9 |                    18.3 |                41.8\n2024-12 |             460 |                         460 |                     44.9 |                        20.2 |              460 |                 90.9 |                    33.8 |                57.2\n2025-01 |             150 |                         150 |                     41.0 |                        14.6 |              150 |                 80.0 |                    27.7 |                48.7\n2025-02 |              94 |                          94 |                     10.2 |                         4.7 |               94 |                 22.1 |                    17.7 |                34.0\n2025-03 |             116 |                         116 |                     11.3 |                         4.6 |              116 |                 18.9 |                    14.6 |                28.4\n2025-04 |             133 |                         133 |                      9.6 |                         4.2 |              133 |                 19.2 |                    13.8 |                32.3\n2025-05 |             206 |                         206 |                     26.5 |                         6.6 |              206 |                 52.7 |                    18.4 |                36.9\n2025-06 |             169 |                         169 |                     45.1 |                        11.3 |              169 |                 87.2 |                    22.9 |                45.6\n2025-07 |             153 |                         153 |                     12.7 |                         6.3 |              153 |                 25.8 |                    18.8 |                37.3\n2025-08 |             138 |                         138 |                     12.8 |                         5.3 |              138 |                 26.3 |                    16.3 |                37.0\n2025-09 |             168 |                         168 |                     12.6 |                         5.9 |              168 |                 21.4 |                    13.4 |                36.3\n2025-10 |             192 |                         192 |                     14.9 |                         6.9 |              192 |                 25.2 |                    17.3 |                32.3\n2025-11 |             316 |                         316 |                     23.2 |                         8.0 |              316 |                 47.3 |                    20.8 |                40.2\n2025-12 |             485 |                         473 |                     40.0 |                        15.2 |              450 |                 60.0 |                    27.7 |                55.3\n(36 rows)\n\n=== Report 2: Response & resolution times by merchant, 2025 (written by Bea) ===\nEngineer's note: Grain is one row per tenant for tickets created in 2025, filtered to tenants with >=20 such tickets, ordered by ticket volume descending (tie-break merchant name); capped at 50 rows per house standard. The printed report's row-count footer will show how many tenants met the >=20 threshold in total versus the 50 shown.\nREPORT: Response & resolution times by merchant, 2025\nQUESTION: How much does support speed vary across merchants, and who is fastest/slowest?\nmerchant_name                 | tickets_created | avg_first_response_hours | median_first_response_hours | avg_resolution_hours | median_resolution_hours | sla_breach_rate_pct\n------------------------------+-----------------+--------------------------+-----------------------------+----------------------+-------------------------+--------------------\nCragsley Outfitters           |             400 |                    20.94 |                       10.57 |                39.14 |                   20.96 |               61.25\nMarrowby & Vane               |             231 |                    47.24 |                       11.11 |                84.93 |                   20.25 |               61.47\nStudio Vrij                   |             166 |                    60.29 |                       11.71 |               125.06 |                   25.93 |               56.63\nBrennquell Kaffee             |             137 |                     5.60 |                        2.99 |                15.94 |                    8.83 |               32.12\nGrind Collective              |             122 |                    36.20 |                        6.53 |                64.42 |                   20.24 |               45.08\nPetalcourt Beauty             |             119 |                    31.37 |                        6.75 |                48.71 |                   22.50 |               57.14\nKibblewick                    |              72 |                    17.26 |                        4.65 |                40.77 |                   21.98 |                50.0\nSpice Route Box               |              71 |                     7.54 |                        3.08 |                16.92 |                   14.07 |                33.8\nFennel & Flint                |              67 |                    15.35 |                        3.92 |                35.20 |                   14.66 |               38.81\nHoneycomb Larder              |              58 |                    21.44 |                       16.00 |                32.80 |                   24.74 |               29.31\nAmpfeld Elektronik            |              56 |                     8.71 |                        4.21 |                20.06 |                   13.84 |               32.14\nSourdough Club                |              47 |                    24.94 |                       14.52 |                32.65 |                   23.87 |               36.17\nBallycorran Industrial Supply |              44 |                     7.90 |                        5.40 |                24.50 |                   20.64 |               34.09\nCinder Peak Sports Co.        |              43 |                     5.56 |                        3.49 |                17.28 |                   11.82 |               34.88\nWiesgrund Wohnen              |              32 |                     3.47 |                        2.11 |                10.91 |                    6.34 |               21.88\nLinden & Loom                 |              29 |                     4.69 |                        3.46 |                13.49 |                    9.71 |               37.93\nGratlinie Outdoor             |              27 |                     3.29 |                        2.52 |                10.84 |                    5.75 |               14.81\nOakhurst Tailoring            |              27 |                    27.17 |                       19.84 |                33.33 |                   31.07 |               11.11\nRue Lafayette Studio          |              27 |                     4.44 |                        2.79 |                12.21 |                    9.38 |               18.52\nCoastline Interiors           |              21 |                    12.15 |                        9.08 |                16.90 |                   13.58 |                9.52\nThistlecombe                  |              21 |                     4.92 |                        4.24 |                17.93 |                   14.00 |                9.52\nKayak Corner                  |              20 |                    28.84 |                       17.73 |                37.59 |                   26.73 |                20.0\n(22 rows)\n\n=== Report 3: Ticket topic mix and recent shift (written by Felix) ===\nEngineer's note: Grouped by support_tags.name across all tenants (tag names are only unique per tenant, so two merchants' same-named tag are merged into one row, per the request's \"platform-level theme\" framing). Each ticket counts once per tag it carries, so tag counts don't sum to total ticket volume; shares are of tagged ticket-tag rows, not of tickets. Periods use created_at >= start and < next-day-after-end to include the full inclusive date ranges.\nREPORT: Ticket topic mix and recent shift\nQUESTION: What kinds of problems are shoppers contacting merchants about most, and has that mix shifted in the last 6 months versus the prior 6?\ntag_name          | tickets_last_6mo | tickets_prior_6mo | share_last_6mo_pct | share_prior_6mo_pct | share_change_pp | avg_resolution_hours_last_6mo\n------------------+------------------+-------------------+--------------------+---------------------+-----------------+------------------------------\ndelivery          |              817 |               378 |               34.7 |                28.6 |             6.1 |                          45.5\nwhere-is-my-order |              726 |               331 |               30.8 |                25.1 |             5.8 |                          47.8\ncomplaint         |              265 |               199 |               11.2 |                15.1 |            -3.8 |                          27.7\nreturn            |              237 |               173 |               10.1 |                13.1 |            -3.0 |                          32.4\nproduct-question  |              138 |               109 |                5.9 |                 8.3 |            -2.4 |                          49.9\ndamaged           |              126 |                84 |                5.3 |                 6.4 |            -1.0 |                          35.8\nrefund            |               36 |                29 |                1.5 |                 2.2 |            -0.7 |                          24.0\npayment           |               11 |                 9 |                0.5 |                 0.7 |            -0.2 |                          29.6\naccount           |                0 |                 9 |                0.0 |                 0.7 |            -0.7 |                          NULL\n(9 rows)\n\n\nNight 2 of 4: you have 3 report slots tonight."}
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{"parentUuid":"ffe6ef2c-1c4e-439c-82b8-9c69efa378a2","isSidechain":false,"type":"assistant","uuid":"7466f07b-540d-4378-9d8b-40bd464af1f6","timestamp":"2026-10-03T23:10:04.729Z","message":{"diagnostics":null,"id":"13416afd-9572-4b22-973b-0bc3461da815","container":null,"model":"<synthetic>","role":"assistant","stop_details":null,"stop_reason":"stop_sequence","stop_sequence":"","type":"message","usage":{"output_tokens_details":null,"input_tokens":0,"output_tokens":0,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":null,"cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":0},"inference_geo":null,"iterations":null,"speed":null},"content":[{"type":"text","text":"No response requested."}],"context_management":null},"isApiErrorMessage":false,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"7466f07b-540d-4378-9d8b-40bd464af1f6","isSidechain":false,"promptId":"5471c3a6-c889-4d09-9a2d-5f54470ba912","type":"user","message":{"role":"user","content":"The printed reports of night 1:\n\n=== Report 1: Support response & resolution time trend, monthly (written by Bea) ===\nEngineer's note: Report covers all 36 months in the window (2023-01 through 2025-12), one row per month, platform-wide across tenants as requested. As flagged in the request: the last 1-2 months (Nov/Dec 2025) will understate avg/median response and resolution hours because tickets still open at month-end aren't yet counted as resolved/responded-to (right-censoring) - read that tail with caution. SLA breach rate = distinct tickets created that month with >=1 row in sla_breaches, divided by tickets created that month.\nREPORT: Support response & resolution time trend, monthly\nQUESTION: Is first-response and resolution speed on support tickets getting better or worse over time?\nmonth   | tickets_created | tickets_with_first_response | avg_first_response_hours | median_first_response_hours | tickets_resolved | avg_resolution_hours | median_resolution_hours | sla_breach_rate_pct\n--------+-----------------+-----------------------------+--------------------------+-----------------------------+------------------+----------------------+-------------------------+--------------------\n2023-01 |              85 |                          85 |                     11.6 |                         5.6 |               85 |                 27.2 |                    20.0 |                38.8\n2023-02 |              99 |                          99 |                     15.2 |                         8.1 |               99 |                 25.3 |                    19.5 |                38.4\n2023-03 |              77 |                          77 |                     15.0 |                         6.5 |               77 |                 26.9 |                    18.5 |                33.8\n2023-04 |              91 |                          91 |                      9.6 |                         5.1 |               91 |                 18.1 |                    12.8 |                30.8\n2023-05 |              85 |                          85 |                     14.2 |                         5.8 |               85 |                 22.0 |                    14.4 |                34.1\n2023-06 |             116 |                         116 |                     14.1 |                         9.4 |              116 |                 28.5 |                    21.3 |                44.8\n2023-07 |              70 |                          70 |                     11.1 |                         5.7 |               70 |                 18.6 |                    12.1 |                30.0\n2023-08 |             100 |                         100 |                     11.5 |                         4.5 |              100 |                 23.3 |                    18.4 |                33.0\n2023-09 |              84 |                          84 |                     12.4 |                         4.7 |               84 |                 20.0 |                    13.3 |                26.2\n2023-10 |             118 |                         118 |                     11.2 |                         4.6 |              118 |                 21.1 |                    13.7 |                27.1\n2023-11 |             177 |                         177 |                     15.0 |                         6.1 |              177 |                 27.2 |                    14.9 |                38.4\n2023-12 |             237 |                         237 |                     41.2 |                        24.4 |              237 |                 73.4 |                    41.2 |                62.0\n2024-01 |             117 |                         117 |                     19.4 |                         6.5 |              117 |                 42.3 |                    16.8 |                35.0\n2024-02 |              92 |                          92 |                      8.0 |                         4.4 |               92 |                 16.9 |                    12.7 |                25.0\n2024-03 |             103 |                         103 |                     12.9 |                         8.0 |              103 |                 27.8 |                    16.9 |                45.6\n2024-04 |             121 |                         121 |                     10.2 |                         3.7 |              121 |                 21.2 |                    15.8 |                28.1\n2024-05 |             126 |                         126 |                     18.5 |                         5.0 |              126 |                 27.2 |                    16.2 |                34.1\n2024-06 |             129 |                         129 |                     13.8 |                         7.8 |              129 |                 23.8 |                    15.5 |                33.3\n2024-07 |             131 |                         131 |                     15.5 |                         8.0 |              131 |                 26.0 |                    19.2 |                38.9\n2024-08 |             109 |                         109 |                     11.7 |                         5.6 |              109 |                 22.0 |                    17.4 |                33.9\n2024-09 |             121 |                         121 |                     11.1 |                         4.6 |              121 |                 21.3 |                    15.8 |                28.9\n2024-10 |             134 |                         134 |                     11.4 |                         4.2 |              134 |                 20.0 |                    13.1 |                29.1\n2024-11 |             177 |                         177 |                     17.5 |                        10.1 |              177 |                 29.9 |                    18.3 |                41.8\n2024-12 |             460 |                         460 |                     44.9 |                        20.2 |              460 |                 90.9 |                    33.8 |                57.2\n2025-01 |             150 |                         150 |                     41.0 |                        14.6 |              150 |                 80.0 |                    27.7 |                48.7\n2025-02 |              94 |                          94 |                     10.2 |                         4.7 |               94 |                 22.1 |                    17.7 |                34.0\n2025-03 |             116 |                         116 |                     11.3 |                         4.6 |              116 |                 18.9 |                    14.6 |                28.4\n2025-04 |             133 |                         133 |                      9.6 |                         4.2 |              133 |                 19.2 |                    13.8 |                32.3\n2025-05 |             206 |                         206 |                     26.5 |                         6.6 |              206 |                 52.7 |                    18.4 |                36.9\n2025-06 |             169 |                         169 |                     45.1 |                        11.3 |              169 |                 87.2 |                    22.9 |                45.6\n2025-07 |             153 |                         153 |                     12.7 |                         6.3 |              153 |                 25.8 |                    18.8 |                37.3\n2025-08 |             138 |                         138 |                     12.8 |                         5.3 |              138 |                 26.3 |                    16.3 |                37.0\n2025-09 |             168 |                         168 |                     12.6 |                         5.9 |              168 |                 21.4 |                    13.4 |                36.3\n2025-10 |             192 |                         192 |                     14.9 |                         6.9 |              192 |                 25.2 |                    17.3 |                32.3\n2025-11 |             316 |                         316 |                     23.2 |                         8.0 |              316 |                 47.3 |                    20.8 |                40.2\n2025-12 |             485 |                         473 |                     40.0 |                        15.2 |              450 |                 60.0 |                    27.7 |                55.3\n(36 rows)\n\n=== Report 2: Response & resolution times by merchant, 2025 (written by Bea) ===\nEngineer's note: Grain is one row per tenant for tickets created in 2025, filtered to tenants with >=20 such tickets, ordered by ticket volume descending (tie-break merchant name); capped at 50 rows per house standard. The printed report's row-count footer will show how many tenants met the >=20 threshold in total versus the 50 shown.\nREPORT: Response & resolution times by merchant, 2025\nQUESTION: How much does support speed vary across merchants, and who is fastest/slowest?\nmerchant_name                 | tickets_created | avg_first_response_hours | median_first_response_hours | avg_resolution_hours | median_resolution_hours | sla_breach_rate_pct\n------------------------------+-----------------+--------------------------+-----------------------------+----------------------+-------------------------+--------------------\nCragsley Outfitters           |             400 |                    20.94 |                       10.57 |                39.14 |                   20.96 |               61.25\nMarrowby & Vane               |             231 |                    47.24 |                       11.11 |                84.93 |                   20.25 |               61.47\nStudio Vrij                   |             166 |                    60.29 |                       11.71 |               125.06 |                   25.93 |               56.63\nBrennquell Kaffee             |             137 |                     5.60 |                        2.99 |                15.94 |                    8.83 |               32.12\nGrind Collective              |             122 |                    36.20 |                        6.53 |                64.42 |                   20.24 |               45.08\nPetalcourt Beauty             |             119 |                    31.37 |                        6.75 |                48.71 |                   22.50 |               57.14\nKibblewick                    |              72 |                    17.26 |                        4.65 |                40.77 |                   21.98 |                50.0\nSpice Route Box               |              71 |                     7.54 |                        3.08 |                16.92 |                   14.07 |                33.8\nFennel & Flint                |              67 |                    15.35 |                        3.92 |                35.20 |                   14.66 |               38.81\nHoneycomb Larder              |              58 |                    21.44 |                       16.00 |                32.80 |                   24.74 |               29.31\nAmpfeld Elektronik            |              56 |                     8.71 |                        4.21 |                20.06 |                   13.84 |               32.14\nSourdough Club                |              47 |                    24.94 |                       14.52 |                32.65 |                   23.87 |               36.17\nBallycorran Industrial Supply |              44 |                     7.90 |                        5.40 |                24.50 |                   20.64 |               34.09\nCinder Peak Sports Co.        |              43 |                     5.56 |                        3.49 |                17.28 |                   11.82 |               34.88\nWiesgrund Wohnen              |              32 |                     3.47 |                        2.11 |                10.91 |                    6.34 |               21.88\nLinden & Loom                 |              29 |                     4.69 |                        3.46 |                13.49 |                    9.71 |               37.93\nGratlinie Outdoor             |              27 |                     3.29 |                        2.52 |                10.84 |                    5.75 |               14.81\nOakhurst Tailoring            |              27 |                    27.17 |                       19.84 |                33.33 |                   31.07 |               11.11\nRue Lafayette Studio          |              27 |                     4.44 |                        2.79 |                12.21 |                    9.38 |               18.52\nCoastline Interiors           |              21 |                    12.15 |                        9.08 |                16.90 |                   13.58 |                9.52\nThistlecombe                  |              21 |                     4.92 |                        4.24 |                17.93 |                   14.00 |                9.52\nKayak Corner                  |              20 |                    28.84 |                       17.73 |                37.59 |                   26.73 |                20.0\n(22 rows)\n\n=== Report 3: Ticket topic mix and recent shift (written by Felix) ===\nEngineer's note: Grouped by support_tags.name across all tenants (tag names are only unique per tenant, so two merchants' same-named tag are merged into one row, per the request's \"platform-level theme\" framing). Each ticket counts once per tag it carries, so tag counts don't sum to total ticket volume; shares are of tagged ticket-tag rows, not of tickets. Periods use created_at >= start and < next-day-after-end to include the full inclusive date ranges.\nREPORT: Ticket topic mix and recent shift\nQUESTION: What kinds of problems are shoppers contacting merchants about most, and has that mix shifted in the last 6 months versus the prior 6?\ntag_name          | tickets_last_6mo | tickets_prior_6mo | share_last_6mo_pct | share_prior_6mo_pct | share_change_pp | avg_resolution_hours_last_6mo\n------------------+------------------+-------------------+--------------------+---------------------+-----------------+------------------------------\ndelivery          |              817 |               378 |               34.7 |                28.6 |             6.1 |                          45.5\nwhere-is-my-order |              726 |               331 |               30.8 |                25.1 |             5.8 |                          47.8\ncomplaint         |              265 |               199 |               11.2 |                15.1 |            -3.8 |                          27.7\nreturn            |              237 |               173 |               10.1 |                13.1 |            -3.0 |                          32.4\nproduct-question  |              138 |               109 |                5.9 |                 8.3 |            -2.4 |                          49.9\ndamaged           |              126 |                84 |                5.3 |                 6.4 |            -1.0 |                          35.8\nrefund            |               36 |                29 |                1.5 |                 2.2 |            -0.7 |                          24.0\npayment           |               11 |                 9 |                0.5 |                 0.7 |            -0.2 |                          29.6\naccount           |                0 |                 9 |                0.0 |                 0.7 |            -0.7 |                          NULL\n(9 rows)\n\n\nNight 2 of 4: you have 3 report slots tonight."},"uuid":"e7c9be11-7f50-4aff-8761-c41c63d121e4","timestamp":"2026-10-03T23:10:04.741Z","permissionMode":"default","promptSource":"sdk","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a","version":"2.1.275","gitBranch":"HEAD"}
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"}],"container":null,"stop_reason":"tool_use","stop_seque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{"parentUuid":"c1ebbd67-accf-4988-83d4-5088085b2b20","isSidechain":false,"message":{"model":"claude-sonnet-5","id":"msg_011Cfg9W7rWaX2qt7f3hGpXT","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_016jNd6imLB5YQHXLeSbQXrg","name":"StructuredOutput","input":{"notes":"Reading report 1: year-end (Nov/Dec) spikes in volume, response and resolution time repeat every year (2023, 2024, 2025) — that looks like seasonal/holiday load and is somewhat expected, though worth noting for the memo. What's NOT seasonal is the 2025-05/2025-06 spike (resolution hours jump to 52.7/87.2, response to 26.5/45.1, breach rate to 36.9/45.6%) — nothing comparable appears in 2023 or 2024 mid-year, so I want to find the cause rather than assume it's noise. Report 3 shows delivery + where-is-my-order tags are already >65% of tagged tickets in the last 6 months and growing share while complaint/return/product-question shrink in share — that's the platform-level theme Daniel asked about in priority #2, and it may also explain the resolution-time trend in #1 if delivery tickets take longer to resolve and are concentrated in the spike months. Tonight I'm chasing both of these (merchant concentration of the May-Jun spike, and whether delivery-tagged tickets specifically explain slow/volatile resolution times), plus opening priority #3 (returns/refunds) with a foundational trend report so we have something in hand for that question this week. I don't need a new slot for a plain YoY same-month pivot — I can build that directly off report 1's numbers for the memo.","requests":[{"title":"Delivery-related vs other tickets: volume and resolution speed","question":"Are delivery/where-is-my-order tickets driving the recent growth in ticket volume and the slower/more volatile resolution times seen in the monthly trend?","grain":"one calendar month, platform-wide, split into two ticket groups (delivery-related vs other)","columns":["month (YYYY-MM)","tickets total","tickets delivery-related","delivery share of tickets (%)","avg first response hours, delivery-related","avg first response hours, other","avg resolution hours, delivery-related","avg resolution hours, other"],"details":"Source: tickets joined to ticket_tags_map and support_tags. Define a ticket as 'delivery-related' if it has at least one tag whose support_tags.name is 'delivery' or 'where-is-my-order' (case-insensitive match); all other tickets are 'other'. Classify at the ticket level (distinct ticket ids), not the tag-row level, so a ticket is counted once even if it carries both tags or additional tags. Window: tickets.created_at from 2024-07-01 through 2025-12-31 (18 months) to cover the Dec-2024 spike, the Jan-2025 carryover, the May/Jun-2025 anomaly, and the Nov/Dec-2025 spike. avg first response hours = extract(epoch from first_response_at - created_at)/3600 averaged over tickets in that group/month with first_response_at not null; avg resolution hours = extract(epoch from resolved_at - created_at)/3600 averaged over tickets in that group/month with resolved_at not null. Round hour figures to 1 decimal, share to 1 decimal. Order by month ascending. Expect 18 rows.","builds_on":["Support response & resolution time trend, monthly","Ticket topic mix and recent shift"]},{"title":"Ticket spike in May-June 2025: which merchants drove it","question":"Was the May-June 2025 jump in ticket volume and resolution time concentrated in a few merchants, or broad-based across the platform?","grain":"one row per tenant (merchant), among tenants with at least 5 tickets created in 2025","columns":["merchant name","tickets created, May-Jun 2025 (combined)","avg monthly tickets, other 2025 months (Jan-Apr, Jul-Dec, per-month average)","spike ratio (May-Jun monthly rate ÷ other-months monthly average)","share of May-Jun 2025 tickets tagged delivery or where-is-my-order (%)"],"details":"Source: tickets joined to tenants (for name), and ticket_tags_map/support_tags for the delivery-tag share. 'Tickets created, May-Jun 2025' = count of tickets with created_at between 2025-05-01 and 2025-06-30 inclusive, per tenant. 'avg monthly tickets, other 2025 months' = count of that tenant's tickets with created_at in 2025 but outside May-Jun, divided by 10 (the 10 other months). Spike ratio = (May-Jun combined count / 2) divided by the other-months monthly average; if the other-months average is 0, show NULL rather than erroring. Delivery-tag share uses the same ticket-level classification as the companion report (tag name 'delivery' or 'where-is-my-order', case-insensitive, counted once per distinct ticket). Only include tenants with >=5 tickets created in 2025 total. Order by 'tickets created, May-Jun 2025' descending, tie-break by merchant name ascending. Report will cap at 50 printed rows per house standard; note total qualifying tenant count in the footer.","builds_on":["Support response & resolution time trend, monthly"]},{"title":"Returns and refunds: volume and time-to-resolve trend","question":"How often do shoppers end up requesting a return/refund, and how long does that process take to resolve, and is it changing over time?","grain":"one calendar month, platform-wide","columns":["month (YYYY-MM)","orders placed","returns requested","return rate (%)","returns refunded (count)","avg days requested-to-refunded","median days requested-to-refunded","returns rejected (%)"],"details":"Source: order_returns (for request/approval/refund lifecycle) and orders (for volume denominator). Grain the month on order_returns.requested_at for return-side columns, and on orders.placed_at for 'orders placed'. Return rate = returns requested that month / orders placed that same month, as a percentage rounded to 1 decimal -- note in the report that this is an approximation since a return in month M may relate to an order placed in an earlier month, not necessarily the same month's orders. 'avg/median days requested-to-refunded' = extract(epoch from refunded_at - requested_at)/86400, averaged/median over returns in that month with status='refunded' and refunded_at not null. 'returns rejected (%)' = count of order_returns with status='rejected' that month / total returns requested that month, as a percentage rounded to 1 decimal. Window: 2023-01-01 through 2025-12-31 (36 months). Round day figures to 1 decimal. Order by month ascending.","builds_on":[]}]},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":8680,"cache_read_input_tokens":49318,"output_tokens":9002,"output_tokens_details":{"thinking_tokens":6700},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":8680,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":9002,"cache_read_input_tokens":49318,"cache_creation_input_tokens":8680,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":8680},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"wireToolInputs":{"toolu_016jNd6imLB5YQHXLeSbQXrg":{"notes":"Reading report 1: year-end (Nov/Dec) spikes in volume, response and resolution time repeat every year (2023, 2024, 2025) — that looks like seasonal/holiday load and is somewhat expected, though worth noting for the memo. What's NOT seasonal is the 2025-05/2025-06 spike (resolution hours jump to 52.7/87.2, response to 26.5/45.1, breach rate to 36.9/45.6%) — nothing comparable appears in 2023 or 2024 mid-year, so I want to find the cause rather than assume it's noise. Report 3 shows delivery + where-is-my-order tags are already >65% of tagged tickets in the last 6 months and growing share while complaint/return/product-question shrink in share — that's the platform-level theme Daniel asked about in priority #2, and it may also explain the resolution-time trend in #1 if delivery tickets take longer to resolve and are concentrated in the spike months. Tonight I'm chasing both of these (merchant concentration of the May-Jun spike, and whether delivery-tagged tickets specifically explain slow/volatile resolution times), plus opening priority #3 (returns/refunds) with a foundational trend report so we have something in hand for that question this week. I don't need a new slot for a plain YoY same-month pivot — I can build that directly off report 1's numbers for the memo.","requests":[{"title":"Delivery-related vs other tickets: volume and resolution speed","question":"Are delivery/where-is-my-order tickets driving the recent growth in ticket volume and the slower/more volatile resolution times seen in the monthly trend?","grain":"one calendar month, platform-wide, split into two ticket groups (delivery-related vs other)","columns":["month (YYYY-MM)","tickets total","tickets delivery-related","delivery share of tickets (%)","avg first response hours, delivery-related","avg first response hours, other","avg resolution hours, delivery-related","avg resolution hours, other"],"details":"Source: tickets joined to ticket_tags_map and support_tags. Define a ticket as 'delivery-related' if it has at least one tag whose support_tags.name is 'delivery' or 'where-is-my-order' (case-insensitive match); all other tickets are 'other'. Classify at the ticket level (distinct ticket ids), not the tag-row level, so a ticket is counted once even if it carries both tags or additional tags. Window: tickets.created_at from 2024-07-01 through 2025-12-31 (18 months) to cover the Dec-2024 spike, the Jan-2025 carryover, the May/Jun-2025 anomaly, and the Nov/Dec-2025 spike. avg first response hours = extract(epoch from first_response_at - created_at)/3600 averaged over tickets in that group/month with first_response_at not null; avg resolution hours = extract(epoch from resolved_at - created_at)/3600 averaged over tickets in that group/month with resolved_at not null. Round hour figures to 1 decimal, share to 1 decimal. Order by month ascending. Expect 18 rows.","builds_on":["Support response & resolution time trend, monthly","Ticket topic mix and recent shift"]},{"title":"Ticket spike in May-June 2025: which merchants drove it","question":"Was the May-June 2025 jump in ticket volume and resolution time concentrated in a few merchants, or broad-based across the platform?","grain":"one row per tenant (merchant), among tenants with at least 5 tickets created in 2025","columns":["merchant name","tickets created, May-Jun 2025 (combined)","avg monthly tickets, other 2025 months (Jan-Apr, Jul-Dec, per-month average)","spike ratio (May-Jun monthly rate ÷ other-months monthly average)","share of May-Jun 2025 tickets tagged delivery or where-is-my-order (%)"],"details":"Source: tickets joined to tenants (for name), and ticket_tags_map/support_tags for the delivery-tag share. 'Tickets created, May-Jun 2025' = count of tickets with created_at between 2025-05-01 and 2025-06-30 inclusive, per tenant. 'avg monthly tickets, other 2025 months' = count of that tenant's tickets with created_at in 2025 but outside May-Jun, divided by 10 (the 10 other months). Spike ratio = (May-Jun combined count / 2) divided by the other-months monthly average; if the other-months average is 0, show NULL rather than erroring. Delivery-tag share uses the same ticket-level classification as the companion report (tag name 'delivery' or 'where-is-my-order', case-insensitive, counted once per distinct ticket). Only include tenants with >=5 tickets created in 2025 total. Order by 'tickets created, May-Jun 2025' descending, tie-break by merchant name ascending. Report will cap at 50 printed rows per house standard; note total qualifying tenant count in the footer.","builds_on":["Support response & resolution time trend, monthly"]},{"title":"Returns and refunds: volume and time-to-resolve trend","question":"How often do shoppers end up requesting a return/refund, and how long does that process take to resolve, and is it changing over time?","grain":"one calendar month, platform-wide","columns":["month (YYYY-MM)","orders placed","returns requested","return rate (%)","returns refunded (count)","avg days requested-to-refunded","median days requested-to-refunded","returns rejected (%)"],"details":"Source: order_returns (for request/approval/refund lifecycle) and orders (for volume denominator). Grain the month on order_returns.requested_at for return-side columns, and on orders.placed_at for 'orders placed'. Return rate = returns requested that month / orders placed that same month, as a percentage rounded to 1 decimal -- note in the report that this is an approximation since a return in month M may relate to an order placed in an earlier month, not necessarily the same month's orders. 'avg/median days requested-to-refunded' = extract(epoch from refunded_at - requested_at)/86400, averaged/median over returns in that month with status='refunded' and refunded_at not null. 'returns rejected (%)' = count of order_returns with status='rejected' that month / total returns requested that month, as a percentage rounded to 1 decimal. Window: 2023-01-01 through 2025-12-31 (36 months). Round day figures to 1 decimal. Order by month ascending.","builds_on":[]}]}},"apiBlockIndex":1,"requestId":"req_011Cfg9W7VS7FGDuBFywFMAm","type":"assistant","uuid":"53652950-33dc-48dc-ac65-de35cecaa308","timestamp":"2026-10-03T23:11:35.998Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"53652950-33dc-48dc-ac65-de35cecaa308","isSidechain":false,"attachment":{"type":"structured_output","data":{"notes":"Reading report 1: year-end (Nov/Dec) spikes in volume, response and resolution time repeat every year (2023, 2024, 2025) — that looks like seasonal/holiday load and is somewhat expected, though worth noting for the memo. What's NOT seasonal is the 2025-05/2025-06 spike (resolution hours jump to 52.7/87.2, response to 26.5/45.1, breach rate to 36.9/45.6%) — nothing comparable appears in 2023 or 2024 mid-year, so I want to find the cause rather than assume it's noise. Report 3 shows delivery + where-is-my-order tags are already >65% of tagged tickets in the last 6 months and growing share while complaint/return/product-question shrink in share — that's the platform-level theme Daniel asked about in priority #2, and it may also explain the resolution-time trend in #1 if delivery tickets take longer to resolve and are concentrated in the spike months. Tonight I'm chasing both of these (merchant concentration of the May-Jun spike, and whether delivery-tagged tickets specifically explain slow/volatile resolution times), plus opening priority #3 (returns/refunds) with a foundational trend report so we have something in hand for that question this week. I don't need a new slot for a plain YoY same-month pivot — I can build that directly off report 1's numbers for the memo.","requests":[{"title":"Delivery-related vs other tickets: volume and resolution speed","question":"Are delivery/where-is-my-order tickets driving the recent growth in ticket volume and the slower/more volatile resolution times seen in the monthly trend?","grain":"one calendar month, platform-wide, split into two ticket groups (delivery-related vs other)","columns":["month (YYYY-MM)","tickets total","tickets delivery-related","delivery share of tickets (%)","avg first response hours, delivery-related","avg first response hours, other","avg resolution hours, delivery-related","avg resolution hours, other"],"details":"Source: tickets joined to ticket_tags_map and support_tags. Define a ticket as 'delivery-related' if it has at least one tag whose support_tags.name is 'delivery' or 'where-is-my-order' (case-insensitive match); all other tickets are 'other'. Classify at the ticket level (distinct ticket ids), not the tag-row level, so a ticket is counted once even if it carries both tags or additional tags. Window: tickets.created_at from 2024-07-01 through 2025-12-31 (18 months) to cover the Dec-2024 spike, the Jan-2025 carryover, the May/Jun-2025 anomaly, and the Nov/Dec-2025 spike. avg first response hours = extract(epoch from first_response_at - created_at)/3600 averaged over tickets in that group/month with first_response_at not null; avg resolution hours = extract(epoch from resolved_at - created_at)/3600 averaged over tickets in that group/month with resolved_at not null. Round hour figures to 1 decimal, share to 1 decimal. Order by month ascending. Expect 18 rows.","builds_on":["Support response & resolution time trend, monthly","Ticket topic mix and recent shift"]},{"title":"Ticket spike in May-June 2025: which merchants drove it","question":"Was the May-June 2025 jump in ticket volume and resolution time concentrated in a few merchants, or broad-based across the platform?","grain":"one row per tenant (merchant), among tenants with at least 5 tickets created in 2025","columns":["merchant name","tickets created, May-Jun 2025 (combined)","avg monthly tickets, other 2025 months (Jan-Apr, Jul-Dec, per-month average)","spike ratio (May-Jun monthly rate ÷ other-months monthly average)","share of May-Jun 2025 tickets tagged delivery or where-is-my-order (%)"],"details":"Source: tickets joined to tenants (for name), and ticket_tags_map/support_tags for the delivery-tag share. 'Tickets created, May-Jun 2025' = count of tickets with created_at between 2025-05-01 and 2025-06-30 inclusive, per tenant. 'avg monthly tickets, other 2025 months' = count of that tenant's tickets with created_at in 2025 but outside May-Jun, divided by 10 (the 10 other months). Spike ratio = (May-Jun combined count / 2) divided by the other-months monthly average; if the other-months average is 0, show NULL rather than erroring. Delivery-tag share uses the same ticket-level classification as the companion report (tag name 'delivery' or 'where-is-my-order', case-insensitive, counted once per distinct ticket). Only include tenants with >=5 tickets created in 2025 total. Order by 'tickets created, May-Jun 2025' descending, tie-break by merchant name ascending. Report will cap at 50 printed rows per house standard; note total qualifying tenant count in the footer.","builds_on":["Support response & resolution time trend, monthly"]},{"title":"Returns and refunds: volume and time-to-resolve trend","question":"How often do shoppers end up requesting a return/refund, and how long does that process take to resolve, and is it changing over time?","grain":"one calendar month, platform-wide","columns":["month (YYYY-MM)","orders placed","returns requested","return rate (%)","returns refunded (count)","avg days requested-to-refunded","median days requested-to-refunded","returns rejected (%)"],"details":"Source: order_returns (for request/approval/refund lifecycle) and orders (for volume denominator). Grain the month on order_returns.requested_at for return-side columns, and on orders.placed_at for 'orders placed'. Return rate = returns requested that month / orders placed that same month, as a percentage rounded to 1 decimal -- note in the report that this is an approximation since a return in month M may relate to an order placed in an earlier month, not necessarily the same month's orders. 'avg/median days requested-to-refunded' = extract(epoch from refunded_at - requested_at)/86400, averaged/median over returns in that month with status='refunded' and refunded_at not null. 'returns rejected (%)' = count of order_returns with status='rejected' that month / total returns requested that month, as a percentage rounded to 1 decimal. Window: 2023-01-01 through 2025-12-31 (36 months). Round day figures to 1 decimal. Order by month ascending.","builds_on":[]}]},"toolUseID":"toolu_016jNd6imLB5YQHXLeSbQXrg"},"type":"attachment","uuid":"274a10b5-a9df-40a1-820a-f617420824ac","timestamp":"2026-10-03T23:11:36.006Z","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a","version":"2.1.275","gitBranch":"HEAD"}
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{"type":"last-prompt","lastPrompt":"The printed reports of night 1:  === Report 1: Support response & resolution time trend, monthly (written by Bea) === Engineer's note: Report covers all 36 months in the window (2023-01 through 2025-1…","leafUuid":"2fff2c27-1f2d-4843-9f67-ef06dfb8e712","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a"}
{"type":"ai-title","aiTitle":"Shopper experience baseline review","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a"}
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{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-03T23:12:12.537Z","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a","content":"The printed reports of night 2:\n\n=== Report 1: Delivery-related vs other tickets: volume and resolution speed (written by Felix) ===\nEngineer's note: A ticket is classified delivery-related if any of its tags (via ticket_tags_map/support_tags) case-insensitively matches 'delivery' or 'where-is-my-order', computed once per ticket via EXISTS. Window is tickets.created_at in [2024-07-01, 2026-01-01), i.e. the 18 months Jul-2024 through Dec-2025, as requested.\nREPORT: Delivery-related vs other tickets: volume and resolution speed\nQUESTION: Are delivery/where-is-my-order tickets driving the recent growth in ticket volume and the slower/more volatile resolution times seen in the monthly trend?\nmonth   | tickets_total | tickets_delivery_related | delivery_share_pct | avg_first_response_hours_delivery | avg_first_response_hours_other | avg_resolution_hours_delivery | avg_resolution_hours_other\n--------+---------------+--------------------------+--------------------+-----------------------------------+--------------------------------+-------------------------------+---------------------------\n2024-07 |           131 |                       57 |               43.5 |                              16.2 |                           14.9 |                          25.4 |                       26.5\n2024-08 |           109 |                       43 |               39.4 |                               8.0 |                           14.0 |                          15.5 |                       26.3\n2024-09 |           121 |                       55 |               45.5 |                               9.7 |                           12.3 |                          21.2 |                       21.4\n2024-10 |           134 |                       66 |               49.3 |                              10.1 |                           12.6 |                          17.8 |                       22.2\n2024-11 |           177 |                       81 |               45.8 |                              13.3 |                           21.1 |                          22.4 |                       36.2\n2024-12 |           460 |                      248 |               53.9 |                              41.2 |                           49.2 |                          86.6 |                       95.9\n2025-01 |           150 |                       42 |               28.0 |                              12.5 |                           52.1 |                          32.3 |                       98.5\n2025-02 |            94 |                       32 |               34.0 |                               8.9 |                           10.8 |                          22.9 |                       21.6\n2025-03 |           116 |                       50 |               43.1 |                               8.8 |                           13.1 |                          16.3 |                       20.8\n2025-04 |           133 |                       52 |               39.1 |                               9.1 |                            9.9 |                          17.9 |                       20.1\n2025-05 |           206 |                      114 |               55.3 |                              37.6 |                           12.7 |                          78.8 |                       20.5\n2025-06 |           169 |                       88 |               52.1 |                              59.6 |                           29.3 |                         120.5 |                       51.0\n2025-07 |           153 |                       67 |               43.8 |                              13.4 |                           12.1 |                          26.7 |                       25.0\n2025-08 |           138 |                       48 |               34.8 |                              15.4 |                           11.4 |                          29.5 |                       24.6\n2025-09 |           168 |                       94 |               56.0 |                              12.0 |                           13.5 |                          20.7 |                       22.2\n2025-10 |           192 |                      105 |               54.7 |                              14.4 |                           15.6 |                          24.7 |                       25.7\n2025-11 |           316 |                      206 |               65.2 |                              23.1 |                           23.3 |                          49.6 |                       43.0\n2025-12 |           485 |                      297 |               61.2 |                              39.4 |                           41.0 |                          65.3 |                       50.5\n(18 rows)\n\n=== Report 2: Ticket spike in May-June 2025: which merchants drove it (written by Viktor) ===\nEngineer's note: Capped at 50 rows per house standard; please add a footer noting the total qualifying-tenant count (query does not compute it separately since that would require a second pass). Spike ratio and delivery-tag share are NULL where the denominator is zero, as requested.\nREPORT: Ticket spike in May-June 2025: which merchants drove it\nQUESTION: Was the May-June 2025 jump in ticket volume and resolution time concentrated in a few merchants, or broad-based across the platform?\nmerchant_name                 | tickets_created_may_jun_2025 | avg_monthly_tickets_other_2025 | spike_ratio | delivery_tag_share_pct\n------------------------------+------------------------------+--------------------------------+-------------+-----------------------\nStudio Vrij                   |                           86 |                            8.0 |        5.38 |                   83.7\nCragsley Outfitters           |                           53 |                           34.7 |        0.76 |                   18.9\nMarrowby & Vane               |                           25 |                           20.6 |        0.61 |                   24.0\nGrind Collective              |                           19 |                           10.3 |        0.92 |                   89.5\nBrennquell Kaffee             |                           18 |                           11.9 |        0.76 |                   44.4\nHoneycomb Larder              |                           14 |                            4.4 |        1.59 |                   57.1\nPetalcourt Beauty             |                           14 |                           10.5 |        0.67 |                   28.6\nBallycorran Industrial Supply |                           11 |                            3.3 |        1.67 |                   81.8\nKayak Corner                  |                            8 |                            1.2 |        3.33 |                   87.5\nFennel & Flint                |                            7 |                            6.0 |        0.58 |                   42.9\nKibblewick                    |                            7 |                            6.5 |        0.54 |                   71.4\nSourdough Club                |                            7 |                            4.0 |        0.88 |                   57.1\nSpice Route Box               |                            7 |                            6.4 |        0.55 |                   71.4\nRue Lafayette Studio          |                            6 |                            2.1 |        1.43 |                   33.3\nAmpfeld Elektronik            |                            5 |                            5.1 |        0.49 |                   40.0\nGratlinie Outdoor             |                            5 |                            2.2 |        1.14 |                  100.0\nWhisker Works                 |                            5 |                            0.7 |        3.57 |                   40.0\nCinder Peak Sports Co.        |                            4 |                            3.9 |        0.51 |                   75.0\nHanse Industriebedarf         |                            4 |                            0.4 |         5.0 |                   50.0\nVelvet Ash                    |                            4 |                            1.5 |        1.33 |                   25.0\nWiesgrund Wohnen              |                            4 |                            2.8 |        0.71 |                   50.0\nFjordline Wear                |                            3 |                            1.1 |        1.36 |                   33.3\nMidland Safety Wear           |                            3 |                            0.8 |        1.88 |                   33.3\nNordic Bloom Cosmetics        |                            3 |                            1.1 |        1.36 |                   66.7\nRidgeback Gear                |                            3 |                            0.6 |         2.5 |                   33.3\nTiny Atelier                  |                            3 |                            0.3 |         5.0 |                   33.3\nWohnglück                     |                            3 |                            1.2 |        1.25 |                   66.7\nAmpere & Co.                  |                            2 |                            0.4 |         2.5 |                    0.0\nBrick & Bobbin                |                            2 |                            0.7 |        1.43 |                   50.0\nCastell Fasteners             |                            2 |                            1.1 |        0.91 |                   50.0\nDeskline Supplies             |                            2 |                            0.9 |        1.11 |                    0.0\nFeather & Fin                 |                            2 |                            1.2 |        0.83 |                  100.0\nJuniper Apothecary            |                            2 |                            1.5 |        0.67 |                   50.0\nLinden & Loom                 |                            2 |                            2.7 |        0.37 |                   50.0\nPfotenglück                   |                            2 |                            0.6 |        1.67 |                    0.0\nSorella Skin                  |                            2 |                            0.4 |         2.5 |                   50.0\nThistlecombe                  |                            2 |                            1.9 |        0.53 |                   50.0\nWillow & Wren                 |                            2 |                            1.7 |        0.59 |                   50.0\nAtelier Nouveau               |                            1 |                            0.5 |         1.0 |                    0.0\nBuchstabe                     |                            1 |                            0.4 |        1.25 |                    0.0\nCoastline Interiors           |                            1 |                            2.0 |        0.25 |                    0.0\nDriftwood Home                |                            1 |                            0.5 |         1.0 |                    0.0\nFell & Fjell                  |                            1 |                            0.7 |        0.71 |                  100.0\nFolio & Fern                  |                            1 |                            1.0 |         0.5 |                  100.0\nKontor24 Bürobedarf           |                            1 |                            0.8 |        0.63 |                    0.0\nLumen Collective              |                            1 |                            0.7 |        0.71 |                    0.0\nMirelle                       |                            1 |                            0.6 |        0.83 |                  100.0\nOakhurst Tailoring            |                            1 |                            2.6 |        0.19 |                    0.0\nOfficeHub Nordic              |                            1 |                            1.4 |        0.36 |                    0.0\nOndine Botanicals             |                            1 |                            0.5 |         1.0 |                  100.0\n(50 rows)\n\n=== Report 3: Returns and refunds: volume and time-to-resolve trend (written by Felix) ===\nEngineer's note: Return rate is an approximation (returns requested in month M / orders placed in month M), since a return in month M may relate to an order placed in an earlier month, as noted in the request. Orders-placed and returns-side columns are grained on their own respective timestamp (placed_at vs requested_at) per the brief, and the two series are combined via a full outer join on month so no month is dropped if one side has zero activity.\nREPORT: Returns and refunds: volume and time-to-resolve trend\nQUESTION: How often do shoppers end up requesting a return/refund, and how long does that process take to resolve, and is it changing over time?\nmonth   | orders_placed | returns_requested | return_rate_pct | returns_refunded | avg_days_requested_to_refunded | median_days_requested_to_refunded | returns_rejected_pct\n--------+---------------+-------------------+-----------------+------------------+--------------------------------+-----------------------------------+---------------------\n2023-01 |           518 |                53 |            10.2 |               51 |                            8.7 |                               8.7 |                  3.8\n2023-02 |           546 |                90 |            16.5 |               88 |                            8.6 |                               8.8 |                  2.2\n2023-03 |           638 |               120 |            18.8 |              116 |                            8.0 |                               7.8 |                  3.3\n2023-04 |           677 |               133 |            19.6 |              131 |                            7.8 |                               7.7 |                  1.5\n2023-05 |           804 |               104 |            12.9 |              102 |                            8.0 |                               8.1 |                  1.9\n2023-06 |           746 |               124 |            16.6 |              120 |                            8.0 |                               7.9 |                  3.2\n2023-07 |           789 |               138 |            17.5 |              131 |                            7.7 |                               7.8 |                  5.1\n2023-08 |           842 |               123 |            14.6 |              118 |                            7.7 |                               7.6 |                  4.1\n2023-09 |           902 |               136 |            15.1 |              134 |                            7.5 |                               7.5 |                  1.5\n2023-10 |          1081 |               139 |            12.9 |              132 |                            7.8 |                               7.9 |                  5.0\n2023-11 |          1845 |               214 |            11.6 |              203 |                            8.1 |                               8.0 |                  5.1\n2023-12 |          1605 |               491 |            30.6 |              460 |                            7.9 |                               7.7 |                  6.3\n2024-01 |          1324 |               262 |            19.8 |              250 |                            7.8 |                               7.7 |                  4.6\n2024-02 |          1108 |               165 |            14.9 |              156 |                            8.3 |                               8.5 |                  5.5\n2024-03 |          1201 |               143 |            11.9 |              136 |                            8.0 |                               7.9 |                  4.9\n2024-04 |          1238 |               173 |            14.0 |              169 |                            8.3 |                               8.4 |                  2.3\n2024-05 |          1356 |               190 |            14.0 |              178 |                            7.9 |                               7.8 |                  6.3\n2024-06 |          1307 |               193 |            14.8 |              186 |                            7.8 |                               7.7 |                  3.6\n2024-07 |          1377 |               233 |            16.9 |              227 |                            7.9 |                               8.2 |                  2.6\n2024-08 |          1316 |               204 |            15.5 |              191 |                            8.0 |                               7.9 |                  6.4\n2024-09 |          1353 |               192 |            14.2 |              181 |                            8.0 |                               8.0 |                  5.7\n2024-10 |          1499 |               196 |            13.1 |              185 |                            7.7 |                               7.6 |                  5.6\n2024-11 |          2130 |               267 |            12.5 |              260 |                            7.8 |                               7.8 |                  2.6\n2024-12 |          2644 |               643 |            24.3 |              615 |                            7.9 |                               7.9 |                  4.4\n2025-01 |          1827 |               349 |            19.1 |              337 |                            7.8 |                               7.7 |                  3.4\n2025-02 |          1400 |               176 |            12.6 |              170 |                            7.7 |                               7.6 |                  3.4\n2025-03 |          1638 |               187 |            11.4 |              177 |                            7.8 |                               7.9 |                  5.3\n2025-04 |          1568 |               230 |            14.7 |              217 |                            7.7 |                               7.6 |                  5.7\n2025-05 |          1691 |               252 |            14.9 |              244 |                            7.9 |                               7.9 |                  3.2\n2025-06 |          1509 |               254 |            16.8 |              243 |                            7.8 |                               7.8 |                  4.3\n2025-07 |          1648 |               243 |            14.7 |              231 |                            7.8 |                               8.0 |                  4.9\n2025-08 |          1564 |               217 |            13.9 |              209 |                            8.0 |                               8.2 |                  3.7\n2025-09 |          1712 |               252 |            14.7 |              238 |                            7.6 |                               7.6 |                  5.6\n2025-10 |          1879 |               270 |            14.4 |              257 |                            7.9 |                               7.8 |                  4.8\n2025-11 |          2645 |               331 |            12.5 |              318 |                            8.0 |                               8.0 |                  3.9\n2025-12 |          3112 |               702 |            22.6 |              524 |                            7.7 |                               7.6 |                  3.3\n(36 rows)\n\n\nNight 3 of 4: you have 3 report slots tonight."}
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{"parentUuid":"a692772f-afc6-4ae9-9e71-5464443b4ec7","isSidechain":false,"promptId":"4a4b919a-d774-405e-aa9c-cef14ceb804d","type":"user","message":{"role":"user","content":"The printed reports of night 2:\n\n=== Report 1: Delivery-related vs other tickets: volume and resolution speed (written by Felix) ===\nEngineer's note: A ticket is classified delivery-related if any of its tags (via ticket_tags_map/support_tags) case-insensitively matches 'delivery' or 'where-is-my-order', computed once per ticket via EXISTS. Window is tickets.created_at in [2024-07-01, 2026-01-01), i.e. the 18 months Jul-2024 through Dec-2025, as requested.\nREPORT: Delivery-related vs other tickets: volume and resolution speed\nQUESTION: Are delivery/where-is-my-order tickets driving the recent growth in ticket volume and the slower/more volatile resolution times seen in the monthly trend?\nmonth   | tickets_total | tickets_delivery_related | delivery_share_pct | avg_first_response_hours_delivery | avg_first_response_hours_other | avg_resolution_hours_delivery | avg_resolution_hours_other\n--------+---------------+--------------------------+--------------------+-----------------------------------+--------------------------------+-------------------------------+---------------------------\n2024-07 |           131 |                       57 |               43.5 |                              16.2 |                           14.9 |                          25.4 |                       26.5\n2024-08 |           109 |                       43 |               39.4 |                               8.0 |                           14.0 |                          15.5 |                       26.3\n2024-09 |           121 |                       55 |               45.5 |                               9.7 |                           12.3 |                          21.2 |                       21.4\n2024-10 |           134 |                       66 |               49.3 |                              10.1 |                           12.6 |                          17.8 |                       22.2\n2024-11 |           177 |                       81 |               45.8 |                              13.3 |                           21.1 |                          22.4 |                       36.2\n2024-12 |           460 |                      248 |               53.9 |                              41.2 |                           49.2 |                          86.6 |                       95.9\n2025-01 |           150 |                       42 |               28.0 |                              12.5 |                           52.1 |                          32.3 |                       98.5\n2025-02 |            94 |                       32 |               34.0 |                               8.9 |                           10.8 |                          22.9 |                       21.6\n2025-03 |           116 |                       50 |               43.1 |                               8.8 |                           13.1 |                          16.3 |                       20.8\n2025-04 |           133 |                       52 |               39.1 |                               9.1 |                            9.9 |                          17.9 |                       20.1\n2025-05 |           206 |                      114 |               55.3 |                              37.6 |                           12.7 |                          78.8 |                       20.5\n2025-06 |           169 |                       88 |               52.1 |                              59.6 |                           29.3 |                         120.5 |                       51.0\n2025-07 |           153 |                       67 |               43.8 |                              13.4 |                           12.1 |                          26.7 |                       25.0\n2025-08 |           138 |                       48 |               34.8 |                              15.4 |                           11.4 |                          29.5 |                       24.6\n2025-09 |           168 |                       94 |               56.0 |                              12.0 |                           13.5 |                          20.7 |                       22.2\n2025-10 |           192 |                      105 |               54.7 |                              14.4 |                           15.6 |                          24.7 |                       25.7\n2025-11 |           316 |                      206 |               65.2 |                              23.1 |                           23.3 |                          49.6 |                       43.0\n2025-12 |           485 |                      297 |               61.2 |                              39.4 |                           41.0 |                          65.3 |                       50.5\n(18 rows)\n\n=== Report 2: Ticket spike in May-June 2025: which merchants drove it (written by Viktor) ===\nEngineer's note: Capped at 50 rows per house standard; please add a footer noting the total qualifying-tenant count (query does not compute it separately since that would require a second pass). Spike ratio and delivery-tag share are NULL where the denominator is zero, as requested.\nREPORT: Ticket spike in May-June 2025: which merchants drove it\nQUESTION: Was the May-June 2025 jump in ticket volume and resolution time concentrated in a few merchants, or broad-based across the platform?\nmerchant_name                 | tickets_created_may_jun_2025 | avg_monthly_tickets_other_2025 | spike_ratio | delivery_tag_share_pct\n------------------------------+------------------------------+--------------------------------+-------------+-----------------------\nStudio Vrij                   |                           86 |                            8.0 |        5.38 |                   83.7\nCragsley Outfitters           |                           53 |                           34.7 |        0.76 |                   18.9\nMarrowby & Vane               |                           25 |                           20.6 |        0.61 |                   24.0\nGrind Collective              |                           19 |                           10.3 |        0.92 |                   89.5\nBrennquell Kaffee             |                           18 |                           11.9 |        0.76 |                   44.4\nHoneycomb Larder              |                           14 |                            4.4 |        1.59 |                   57.1\nPetalcourt Beauty             |                           14 |                           10.5 |        0.67 |                   28.6\nBallycorran Industrial Supply |                           11 |                            3.3 |        1.67 |                   81.8\nKayak Corner                  |                            8 |                            1.2 |        3.33 |                   87.5\nFennel & Flint                |                            7 |                            6.0 |        0.58 |                   42.9\nKibblewick                    |                            7 |                            6.5 |        0.54 |                   71.4\nSourdough Club                |                            7 |                            4.0 |        0.88 |                   57.1\nSpice Route Box               |                            7 |                            6.4 |        0.55 |                   71.4\nRue Lafayette Studio          |                            6 |                            2.1 |        1.43 |                   33.3\nAmpfeld Elektronik            |                            5 |                            5.1 |        0.49 |                   40.0\nGratlinie Outdoor             |                            5 |                            2.2 |        1.14 |                  100.0\nWhisker Works                 |                            5 |                            0.7 |        3.57 |                   40.0\nCinder Peak Sports Co.        |                            4 |                            3.9 |        0.51 |                   75.0\nHanse Industriebedarf         |                            4 |                            0.4 |         5.0 |                   50.0\nVelvet Ash                    |                            4 |                            1.5 |        1.33 |                   25.0\nWiesgrund Wohnen              |                            4 |                            2.8 |        0.71 |                   50.0\nFjordline Wear                |                            3 |                            1.1 |        1.36 |                   33.3\nMidland Safety Wear           |                            3 |                            0.8 |        1.88 |                   33.3\nNordic Bloom Cosmetics        |                            3 |                            1.1 |        1.36 |                   66.7\nRidgeback Gear                |                            3 |                            0.6 |         2.5 |                   33.3\nTiny Atelier                  |                            3 |                            0.3 |         5.0 |                   33.3\nWohnglück                     |                            3 |                            1.2 |        1.25 |                   66.7\nAmpere & Co.                  |                            2 |                            0.4 |         2.5 |                    0.0\nBrick & Bobbin                |                            2 |                            0.7 |        1.43 |                   50.0\nCastell Fasteners             |                            2 |                            1.1 |        0.91 |                   50.0\nDeskline Supplies             |                            2 |                            0.9 |        1.11 |                    0.0\nFeather & Fin                 |                            2 |                            1.2 |        0.83 |                  100.0\nJuniper Apothecary            |                            2 |                            1.5 |        0.67 |                   50.0\nLinden & Loom                 |                            2 |                            2.7 |        0.37 |                   50.0\nPfotenglück                   |                            2 |                            0.6 |        1.67 |                    0.0\nSorella Skin                  |                            2 |                            0.4 |         2.5 |                   50.0\nThistlecombe                  |                            2 |                            1.9 |        0.53 |                   50.0\nWillow & Wren                 |                            2 |                            1.7 |        0.59 |                   50.0\nAtelier Nouveau               |                            1 |                            0.5 |         1.0 |                    0.0\nBuchstabe                     |                            1 |                            0.4 |        1.25 |                    0.0\nCoastline Interiors           |                            1 |                            2.0 |        0.25 |                    0.0\nDriftwood Home                |                            1 |                            0.5 |         1.0 |                    0.0\nFell & Fjell                  |                            1 |                            0.7 |        0.71 |                  100.0\nFolio & Fern                  |                            1 |                            1.0 |         0.5 |                  100.0\nKontor24 Bürobedarf           |                            1 |                            0.8 |        0.63 |                    0.0\nLumen Collective              |                            1 |                            0.7 |        0.71 |                    0.0\nMirelle                       |                            1 |                            0.6 |        0.83 |                  100.0\nOakhurst Tailoring            |                            1 |                            2.6 |        0.19 |                    0.0\nOfficeHub Nordic              |                            1 |                            1.4 |        0.36 |                    0.0\nOndine Botanicals             |                            1 |                            0.5 |         1.0 |                  100.0\n(50 rows)\n\n=== Report 3: Returns and refunds: volume and time-to-resolve trend (written by Felix) ===\nEngineer's note: Return rate is an approximation (returns requested in month M / orders placed in month M), since a return in month M may relate to an order placed in an earlier month, as noted in the request. Orders-placed and returns-side columns are grained on their own respective timestamp (placed_at vs requested_at) per the brief, and the two series are combined via a full outer join on month so no month is dropped if one side has zero activity.\nREPORT: Returns and refunds: volume and time-to-resolve trend\nQUESTION: How often do shoppers end up requesting a return/refund, and how long does that process take to resolve, and is it changing over time?\nmonth   | orders_placed | returns_requested | return_rate_pct | returns_refunded | avg_days_requested_to_refunded | median_days_requested_to_refunded | returns_rejected_pct\n--------+---------------+-------------------+-----------------+------------------+--------------------------------+-----------------------------------+---------------------\n2023-01 |           518 |                53 |            10.2 |               51 |                            8.7 |                               8.7 |                  3.8\n2023-02 |           546 |                90 |            16.5 |               88 |                            8.6 |                               8.8 |                  2.2\n2023-03 |           638 |               120 |            18.8 |              116 |                            8.0 |                               7.8 |                  3.3\n2023-04 |           677 |               133 |            19.6 |              131 |                            7.8 |                               7.7 |                  1.5\n2023-05 |           804 |               104 |            12.9 |              102 |                            8.0 |                               8.1 |                  1.9\n2023-06 |           746 |               124 |            16.6 |              120 |                            8.0 |                               7.9 |                  3.2\n2023-07 |           789 |               138 |            17.5 |              131 |                            7.7 |                               7.8 |                  5.1\n2023-08 |           842 |               123 |            14.6 |              118 |                            7.7 |                               7.6 |                  4.1\n2023-09 |           902 |               136 |            15.1 |              134 |                            7.5 |                               7.5 |                  1.5\n2023-10 |          1081 |               139 |            12.9 |              132 |                            7.8 |                               7.9 |                  5.0\n2023-11 |          1845 |               214 |            11.6 |              203 |                            8.1 |                               8.0 |                  5.1\n2023-12 |          1605 |               491 |            30.6 |              460 |                            7.9 |                               7.7 |                  6.3\n2024-01 |          1324 |               262 |            19.8 |              250 |                            7.8 |                               7.7 |                  4.6\n2024-02 |          1108 |               165 |            14.9 |              156 |                            8.3 |                               8.5 |                  5.5\n2024-03 |          1201 |               143 |            11.9 |              136 |                            8.0 |                               7.9 |                  4.9\n2024-04 |          1238 |               173 |            14.0 |              169 |                            8.3 |                               8.4 |                  2.3\n2024-05 |          1356 |               190 |            14.0 |              178 |                            7.9 |                               7.8 |                  6.3\n2024-06 |          1307 |               193 |            14.8 |              186 |                            7.8 |                               7.7 |                  3.6\n2024-07 |          1377 |               233 |            16.9 |              227 |                            7.9 |                               8.2 |                  2.6\n2024-08 |          1316 |               204 |            15.5 |              191 |                            8.0 |                               7.9 |                  6.4\n2024-09 |          1353 |               192 |            14.2 |              181 |                            8.0 |                               8.0 |                  5.7\n2024-10 |          1499 |               196 |            13.1 |              185 |                            7.7 |                               7.6 |                  5.6\n2024-11 |          2130 |               267 |            12.5 |              260 |                            7.8 |                               7.8 |                  2.6\n2024-12 |          2644 |               643 |            24.3 |              615 |                            7.9 |                               7.9 |                  4.4\n2025-01 |          1827 |               349 |            19.1 |              337 |                            7.8 |                               7.7 |                  3.4\n2025-02 |          1400 |               176 |            12.6 |              170 |                            7.7 |                               7.6 |                  3.4\n2025-03 |          1638 |               187 |            11.4 |              177 |                            7.8 |                               7.9 |                  5.3\n2025-04 |          1568 |               230 |            14.7 |              217 |                            7.7 |                               7.6 |                  5.7\n2025-05 |          1691 |               252 |            14.9 |              244 |                            7.9 |                               7.9 |                  3.2\n2025-06 |          1509 |               254 |            16.8 |              243 |                            7.8 |                               7.8 |                  4.3\n2025-07 |          1648 |               243 |            14.7 |              231 |                            7.8 |                               8.0 |                  4.9\n2025-08 |          1564 |               217 |            13.9 |              209 |                            8.0 |                               8.2 |                  3.7\n2025-09 |          1712 |               252 |            14.7 |              238 |                            7.6 |                               7.6 |                  5.6\n2025-10 |          1879 |               270 |            14.4 |              257 |                            7.9 |                               7.8 |                  4.8\n2025-11 |          2645 |               331 |            12.5 |              318 |                            8.0 |                               8.0 |                  3.9\n2025-12 |          3112 |               702 |            22.6 |              524 |                            7.7 |                               7.6 |                  3.3\n(36 rows)\n\n\nNight 3 of 4: you have 3 report slots tonight."},"uuid":"a7d90398-7941-4ca0-8205-4cd5a5254870","timestamp":"2026-10-03T23:12:12.542Z","permissionMode":"default","promptSource":"sdk","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a","version":"2.1.275","gitBranch":"HEAD"}
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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":14343,"cache_read_input_tokens":57998,"output_tokens":8543,"output_tokens_details":{"thinking_tokens":6272},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":14343,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":8543,"cache_read_input_tokens":57998,"cache_creation_input_tokens":14343,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":14343},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011Cfg9fXs97anWqaoqqdD7w","type":"assistant","uuid":"57095c38-7f97-437c-b447-dee06e71d346","timestamp":"2026-10-03T23:13:18.856Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: 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{"parentUuid":"57095c38-7f97-437c-b447-dee06e71d346","isSidechain":false,"message":{"model":"claude-sonnet-5","id":"msg_011Cfg9fYGhXhD2DgD53NxVW","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Xv6vyhaZhKtshT95MAeCxv","name":"StructuredOutput","input":{"notes":"Report 1 (night 2) shows the May-Jun 2025 anomaly is strongly concentrated in delivery-tagged tickets specifically (response/resolution hours for delivery tickets roughly 2-3x worse than 'other' tickets in those two months, whereas Nov/Dec spikes hit both groups evenly - that looks like genuine seasonal volume, not a delivery-specific problem). Report 2 pins the May-Jun spike almost entirely on one merchant: Studio Vrij had 86 tickets vs an expected ~16 (5.38x their normal rate), 83.7% delivery-tagged, and it was also the slowest-resolving merchant in the whole-year peer table from night 1. Every other merchant with a high spike ratio is tiny in absolute volume (noise), and the platform's other big merchants (Cragsley, Marrowby) were actually below their own baseline rate in May-Jun. So this looks like a single-merchant fulfillment incident, not a platform-wide issue - I want to confirm that with real delivery/shipment data before I write it into the memo as a finding. Report 3 (returns trend) is reassuring: resolve time is flat around ~8 days and rejection rate flat regardless of volume swings; the return-rate spikes track the same Nov/Dec seasonality as everything else. I want one merchant-level peer cut on returns (mirroring the support-speed peer report) to see if any merchant stands out the way Studio Vrij did on tickets. With priorities #1-3 now well covered, I'm using the third slot tonight to open priority #4 (CSAT) so there's something in hand for Friday's memo if priority #5 doesn't fit.","requests":[{"title":"Delivery performance: Studio Vrij vs platform, 2025","question":"Did Studio Vrij's actual shipping/delivery performance deteriorate in May-June 2025, explaining the spike in their delivery-related support tickets, or was it a support-side issue only?","grain":"one calendar month in 2025, with paired columns for Studio Vrij and the rest of the platform","columns":["month (YYYY-MM)","Studio Vrij shipments delivered","Studio Vrij avg days placed-to-delivered","Studio Vrij % of shipments over 7 days placed-to-delivered","platform (ex-Studio Vrij) shipments delivered","platform (ex-Studio Vrij) avg days placed-to-delivered","platform (ex-Studio Vrij) % of shipments over 7 days placed-to-delivered"],"details":"Source: shipments joined to fulfillments (for order_id and tenant scoping) joined to orders (for placed_at). Only include shipments with delivered_at not null. 'Days placed-to-delivered' = extract(epoch from shipments.delivered_at - orders.placed_at)/86400. Grain the month on orders.placed_at (2025-01 through 2025-12). Split into two groups: tenant = the 'Studio Vrij' tenant (join tenants on name to find its id) vs all other tenants combined. '% over 7 days' = share of that group/month's delivered shipments where days placed-to-delivered > 7, as a percentage rounded to 1 decimal. Round avg days to 1 decimal. Note in the report that Nov/Dec 2025 cohorts will be incomplete since some orders placed late in the year may not show a delivered shipment yet (right-censoring), similar to the earlier ticket-resolution caveat. Order by month ascending.","builds_on":["Ticket spike in May-June 2025: which merchants drove it","Delivery-related vs other tickets: volume and resolution speed"]},{"title":"Returns and refunds experience by merchant, 2025","question":"Does the returns/refund experience vary a lot across merchants, and does any merchant stand out as a problem spot the way Studio Vrij did on support tickets?","grain":"one row per tenant (merchant), among tenants with at least 20 returns requested in 2025","columns":["merchant name","orders placed, 2025","returns requested, 2025","return rate (%)","avg days requested-to-refunded","median days requested-to-refunded","returns rejected (%)"],"details":"Source: order_returns joined to tenants (for name) and orders (for the orders-placed denominator). Window: order_returns.requested_at and orders.placed_at both in 2025-01-01 through 2025-12-31. Return rate = tenant's returns requested in 2025 / tenant's orders placed in 2025, as a percentage rounded to 1 decimal (same approximation caveat as the platform-wide returns report: a given return may relate to an order placed outside the year, so treat this as a rough proxy, not a precise cohort rate). avg/median days requested-to-refunded = extract(epoch from refunded_at - requested_at)/86400 over that tenant's returns with status='refunded' and refunded_at not null in 2025, rounded to 1 decimal. returns rejected (%) = tenant's order_returns with status='rejected' in 2025 / tenant's returns requested in 2025, percentage rounded to 1 decimal. Only include tenants with >=20 returns requested in 2025. Order by returns requested descending, tie-break by merchant name ascending. Cap at 50 printed rows per house standard; note total qualifying tenant count in the footer.","builds_on":["Returns and refunds: volume and time-to-resolve trend","Response & resolution times by merchant, 2025"]},{"title":"Shopper satisfaction (CSAT) trend, monthly","question":"How satisfied are shoppers with support interactions overall, and is satisfaction trending up or down?","grain":"one calendar month, platform-wide","columns":["month (YYYY-MM)","CSAT responses","avg score (1-5)","median score (1-5)","% satisfied (score >= 4)","% dissatisfied (score <= 2)"],"details":"Source: csat_responses. Grain the month on submitted_at. Window: 2023-01-01 through 2025-12-31 (36 months). avg/median score computed directly over csat_responses.score for that month. '% satisfied' = count of responses with score>=4 / total responses that month, percentage rounded to 1 decimal; '% dissatisfied' = count with score<=2 / total responses that month, percentage rounded to 1 decimal. Round avg score to 2 decimals, median to 1 decimal. 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Report 2 pins the May-Jun spike almost entirely on one merchant: Studio Vrij had 86 tickets vs an expected ~16 (5.38x their normal rate), 83.7% delivery-tagged, and it was also the slowest-resolving merchant in the whole-year peer table from night 1. Every other merchant with a high spike ratio is tiny in absolute volume (noise), and the platform's other big merchants (Cragsley, Marrowby) were actually below their own baseline rate in May-Jun. So this looks like a single-merchant fulfillment incident, not a platform-wide issue - I want to confirm that with real delivery/shipment data before I write it into the memo as a finding. Report 3 (returns trend) is reassuring: resolve time is flat around ~8 days and rejection rate flat regardless of volume swings; the return-rate spikes track the same Nov/Dec seasonality as everything else. I want one merchant-level peer cut on returns (mirroring the support-speed peer report) to see if any merchant stands out the way Studio Vrij did on tickets. With priorities #1-3 now well covered, I'm using the third slot tonight to open priority #4 (CSAT) so there's something in hand for Friday's memo if priority #5 doesn't fit.","requests":[{"title":"Delivery performance: Studio Vrij vs platform, 2025","question":"Did Studio Vrij's actual shipping/delivery performance deteriorate in May-June 2025, explaining the spike in their delivery-related support tickets, or was it a support-side issue only?","grain":"one calendar month in 2025, with paired columns for Studio Vrij and the rest of the platform","columns":["month (YYYY-MM)","Studio Vrij shipments delivered","Studio Vrij avg days placed-to-delivered","Studio Vrij % of shipments over 7 days placed-to-delivered","platform (ex-Studio Vrij) shipments delivered","platform (ex-Studio Vrij) avg days placed-to-delivered","platform (ex-Studio Vrij) % of shipments over 7 days placed-to-delivered"],"details":"Source: shipments joined to fulfillments (for order_id and tenant scoping) joined to orders (for placed_at). Only include shipments with delivered_at not null. 'Days placed-to-delivered' = extract(epoch from shipments.delivered_at - orders.placed_at)/86400. Grain the month on orders.placed_at (2025-01 through 2025-12). Split into two groups: tenant = the 'Studio Vrij' tenant (join tenants on name to find its id) vs all other tenants combined. '% over 7 days' = share of that group/month's delivered shipments where days placed-to-delivered > 7, as a percentage rounded to 1 decimal. Round avg days to 1 decimal. Note in the report that Nov/Dec 2025 cohorts will be incomplete since some orders placed late in the year may not show a delivered shipment yet (right-censoring), similar to the earlier ticket-resolution caveat. Order by month ascending.","builds_on":["Ticket spike in May-June 2025: which merchants drove it","Delivery-related vs other tickets: volume and resolution speed"]},{"title":"Returns and refunds experience by merchant, 2025","question":"Does the returns/refund experience vary a lot across merchants, and does any merchant stand out as a problem spot the way Studio Vrij did on support tickets?","grain":"one row per tenant (merchant), among tenants with at least 20 returns requested in 2025","columns":["merchant name","orders placed, 2025","returns requested, 2025","return rate (%)","avg days requested-to-refunded","median days requested-to-refunded","returns rejected (%)"],"details":"Source: order_returns joined to tenants (for name) and orders (for the orders-placed denominator). Window: order_returns.requested_at and orders.placed_at both in 2025-01-01 through 2025-12-31. Return rate = tenant's returns requested in 2025 / tenant's orders placed in 2025, as a percentage rounded to 1 decimal (same approximation caveat as the platform-wide returns report: a given return may relate to an order placed outside the year, so treat this as a rough proxy, not a precise cohort rate). avg/median days requested-to-refunded = extract(epoch from refunded_at - requested_at)/86400 over that tenant's returns with status='refunded' and refunded_at not null in 2025, rounded to 1 decimal. returns rejected (%) = tenant's order_returns with status='rejected' in 2025 / tenant's returns requested in 2025, percentage rounded to 1 decimal. Only include tenants with >=20 returns requested in 2025. Order by returns requested descending, tie-break by merchant name ascending. Cap at 50 printed rows per house standard; note total qualifying tenant count in the footer.","builds_on":["Returns and refunds: volume and time-to-resolve trend","Response & resolution times by merchant, 2025"]},{"title":"Shopper satisfaction (CSAT) trend, monthly","question":"How satisfied are shoppers with support interactions overall, and is satisfaction trending up or down?","grain":"one calendar month, platform-wide","columns":["month (YYYY-MM)","CSAT responses","avg score (1-5)","median score (1-5)","% satisfied (score >= 4)","% dissatisfied (score <= 2)"],"details":"Source: csat_responses. Grain the month on submitted_at. Window: 2023-01-01 through 2025-12-31 (36 months). avg/median score computed directly over csat_responses.score for that month. '% satisfied' = count of responses with score>=4 / total responses that month, percentage rounded to 1 decimal; '% dissatisfied' = count with score<=2 / total responses that month, percentage rounded to 1 decimal. 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{"parentUuid":"293a2e65-77ed-43d5-b3f6-17103b0e0731","isSidechain":false,"attachment":{"type":"structured_output","data":{"notes":"Report 1 (night 2) shows the May-Jun 2025 anomaly is strongly concentrated in delivery-tagged tickets specifically (response/resolution hours for delivery tickets roughly 2-3x worse than 'other' tickets in those two months, whereas Nov/Dec spikes hit both groups evenly - that looks like genuine seasonal volume, not a delivery-specific problem). Report 2 pins the May-Jun spike almost entirely on one merchant: Studio Vrij had 86 tickets vs an expected ~16 (5.38x their normal rate), 83.7% delivery-tagged, and it was also the slowest-resolving merchant in the whole-year peer table from night 1. Every other merchant with a high spike ratio is tiny in absolute volume (noise), and the platform's other big merchants (Cragsley, Marrowby) were actually below their own baseline rate in May-Jun. So this looks like a single-merchant fulfillment incident, not a platform-wide issue - I want to confirm that with real delivery/shipment data before I write it into the memo as a finding. Report 3 (returns trend) is reassuring: resolve time is flat around ~8 days and rejection rate flat regardless of volume swings; the return-rate spikes track the same Nov/Dec seasonality as everything else. I want one merchant-level peer cut on returns (mirroring the support-speed peer report) to see if any merchant stands out the way Studio Vrij did on tickets. With priorities #1-3 now well covered, I'm using the third slot tonight to open priority #4 (CSAT) so there's something in hand for Friday's memo if priority #5 doesn't fit.","requests":[{"title":"Delivery performance: Studio Vrij vs platform, 2025","question":"Did Studio Vrij's actual shipping/delivery performance deteriorate in May-June 2025, explaining the spike in their delivery-related support tickets, or was it a support-side issue only?","grain":"one calendar month in 2025, with paired columns for Studio Vrij and the rest of the platform","columns":["month (YYYY-MM)","Studio Vrij shipments delivered","Studio Vrij avg days placed-to-delivered","Studio Vrij % of shipments over 7 days placed-to-delivered","platform (ex-Studio Vrij) shipments delivered","platform (ex-Studio Vrij) avg days placed-to-delivered","platform (ex-Studio Vrij) % of shipments over 7 days placed-to-delivered"],"details":"Source: shipments joined to fulfillments (for order_id and tenant scoping) joined to orders (for placed_at). Only include shipments with delivered_at not null. 'Days placed-to-delivered' = extract(epoch from shipments.delivered_at - orders.placed_at)/86400. Grain the month on orders.placed_at (2025-01 through 2025-12). Split into two groups: tenant = the 'Studio Vrij' tenant (join tenants on name to find its id) vs all other tenants combined. '% over 7 days' = share of that group/month's delivered shipments where days placed-to-delivered > 7, as a percentage rounded to 1 decimal. Round avg days to 1 decimal. Note in the report that Nov/Dec 2025 cohorts will be incomplete since some orders placed late in the year may not show a delivered shipment yet (right-censoring), similar to the earlier ticket-resolution caveat. Order by month ascending.","builds_on":["Ticket spike in May-June 2025: which merchants drove it","Delivery-related vs other tickets: volume and resolution speed"]},{"title":"Returns and refunds experience by merchant, 2025","question":"Does the returns/refund experience vary a lot across merchants, and does any merchant stand out as a problem spot the way Studio Vrij did on support tickets?","grain":"one row per tenant (merchant), among tenants with at least 20 returns requested in 2025","columns":["merchant name","orders placed, 2025","returns requested, 2025","return rate (%)","avg days requested-to-refunded","median days requested-to-refunded","returns rejected (%)"],"details":"Source: order_returns joined to tenants (for name) and orders (for the orders-placed denominator). Window: order_returns.requested_at and orders.placed_at both in 2025-01-01 through 2025-12-31. Return rate = tenant's returns requested in 2025 / tenant's orders placed in 2025, as a percentage rounded to 1 decimal (same approximation caveat as the platform-wide returns report: a given return may relate to an order placed outside the year, so treat this as a rough proxy, not a precise cohort rate). avg/median days requested-to-refunded = extract(epoch from refunded_at - requested_at)/86400 over that tenant's returns with status='refunded' and refunded_at not null in 2025, rounded to 1 decimal. returns rejected (%) = tenant's order_returns with status='rejected' in 2025 / tenant's returns requested in 2025, percentage rounded to 1 decimal. Only include tenants with >=20 returns requested in 2025. Order by returns requested descending, tie-break by merchant name ascending. Cap at 50 printed rows per house standard; note total qualifying tenant count in the footer.","builds_on":["Returns and refunds: volume and time-to-resolve trend","Response & resolution times by merchant, 2025"]},{"title":"Shopper satisfaction (CSAT) trend, monthly","question":"How satisfied are shoppers with support interactions overall, and is satisfaction trending up or down?","grain":"one calendar month, platform-wide","columns":["month (YYYY-MM)","CSAT responses","avg score (1-5)","median score (1-5)","% satisfied (score >= 4)","% dissatisfied (score <= 2)"],"details":"Source: csat_responses. Grain the month on submitted_at. Window: 2023-01-01 through 2025-12-31 (36 months). avg/median score computed directly over csat_responses.score for that month. '% satisfied' = count of responses with score>=4 / total responses that month, percentage rounded to 1 decimal; '% dissatisfied' = count with score<=2 / total responses that month, percentage rounded to 1 decimal. 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{"type":"last-prompt","lastPrompt":"The printed reports of night 2:  === Report 1: Delivery-related vs other tickets: volume and resolution speed (written by Felix) === Engineer's note: A ticket is classified delivery-related if any of…","leafUuid":"98482d2b-7dfe-481f-a956-0f5d3ef65e04","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a"}
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{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-03T23:14:09.673Z","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a","content":"The printed reports of night 3:\n\n=== Report 1: Delivery performance: Studio Vrij vs platform, 2025 (written by Felix) ===\nEngineer's note: Note for the analyst: Oct-Dec 2025 (and to a lesser extent Sep) cohorts are right-censored — orders placed late in the year may not yet have a recorded delivered_at, since the warehouse only holds data through 2025-12-31, so those months' avg-days and %-over-7-days will understate true delivery times until later shipments complete.\nREPORT: Delivery performance: Studio Vrij vs platform, 2025\nQUESTION: Did Studio Vrij's actual shipping/delivery performance deteriorate in May-June 2025, explaining the spike in their delivery-related support tickets, or was it a support-side issue only?\nmonth   | sv_shipments_delivered | sv_avg_days_placed_to_delivered | sv_pct_over_7_days | platform_shipments_delivered | platform_avg_days_placed_to_delivered | platform_pct_over_7_days\n--------+------------------------+---------------------------------+--------------------+------------------------------+---------------------------------------+-------------------------\n2025-01 |                     40 |                             3.2 |                0.0 |                         1728 |                                   3.6 |                      3.1\n2025-02 |                     20 |                             3.2 |                0.0 |                         1350 |                                   3.7 |                      3.4\n2025-03 |                     42 |                             4.7 |               16.7 |                         1544 |                                   3.8 |                      4.1\n2025-04 |                     31 |                             3.7 |                3.2 |                         1489 |                                   3.7 |                      2.8\n2025-05 |                    107 |                             9.3 |               70.1 |                         1526 |                                   4.0 |                      4.8\n2025-06 |                     29 |                             4.9 |               20.7 |                         1432 |                                   3.8 |                      4.7\n2025-07 |                     41 |                             4.0 |                4.9 |                         1555 |                                   3.9 |                      4.4\n2025-08 |                     31 |                             3.9 |                3.2 |                         1471 |                                   3.8 |                      4.1\n2025-09 |                     31 |                             4.0 |                0.0 |                         1628 |                                   4.1 |                      5.5\n2025-10 |                     56 |                             4.8 |               10.7 |                         1772 |                                   4.1 |                      7.1\n2025-11 |                     75 |                             3.7 |                1.3 |                         2475 |                                   4.7 |                     12.1\n2025-12 |                     85 |                             4.0 |                3.5 |                         2630 |                                   4.3 |                      9.9\n(12 rows)\n\n=== Report 2: Returns and refunds experience by merchant, 2025 (written by Felix) ===\nEngineer's note: Orders-placed and returns-requested are counted independently by their own 2025 date windows (per the stated caveat, a return's order may fall outside 2025), so return rate is a rough proxy. Tenants with no refunded returns in 2025 show null avg/median days; the printed row count vs. total qualifying tenants (returns_requested>=20) should be noted from the query's actual row count at print time.\nREPORT: Returns and refunds experience by merchant, 2025\nQUESTION: Does the returns/refund experience vary a lot across merchants, and does any merchant stand out as a problem spot the way Studio Vrij did on support tickets?\nmerchant_name                 | orders_placed | returns_requested | return_rate_pct | avg_days | median_days | returns_rejected_pct\n------------------------------+---------------+-------------------+-----------------+----------+-------------+---------------------\nCragsley Outfitters           |          3635 |              1370 |            37.7 |      7.7 |         7.7 |                  3.4\nMarrowby & Vane               |          1758 |               616 |            35.0 |      7.9 |         8.0 |                  3.7\nStudio Vrij                   |           631 |               264 |            41.8 |      7.9 |         7.8 |                  2.3\nCinder Peak Sports Co.        |           338 |                78 |            23.1 |      8.3 |         8.2 |                  3.8\nRue Lafayette Studio          |           187 |                69 |            36.9 |      7.4 |         6.9 |                  8.7\nVelvet Ash                    |           134 |                59 |            44.0 |      7.9 |         7.7 |                  1.7\nGratlinie Outdoor             |           232 |                58 |            25.0 |      7.7 |         7.8 |                  8.6\nAmpfeld Elektronik            |           449 |                56 |            12.5 |      7.7 |         7.1 |                 10.7\nPetalcourt Beauty             |           706 |                55 |             7.8 |      6.1 |         6.2 |                  0.0\nFennel & Flint                |           389 |                52 |            13.4 |      8.1 |         8.1 |                 13.5\nOakhurst Tailoring            |           165 |                48 |            29.1 |      7.6 |         7.7 |                 10.4\nWiesgrund Wohnen              |           296 |                44 |            14.9 |      7.5 |         7.3 |                  6.8\nLinden & Loom                 |           263 |                43 |            16.3 |      7.4 |         7.1 |                  4.7\nBrennquell Kaffee             |          4195 |                40 |             1.0 |      8.1 |         8.6 |                  2.5\nOstwind Mode                  |           122 |                37 |            30.3 |      8.1 |         8.4 |                  0.0\nBallycorran Industrial Supply |           344 |                31 |             9.0 |      7.8 |         7.8 |                  3.2\nRidgeback Gear                |           108 |                26 |            24.1 |      8.0 |         8.1 |                  0.0\nThistlecombe                  |            92 |                26 |            28.3 |      7.3 |         6.8 |                  0.0\nFjordline Wear                |            57 |                24 |            42.1 |      8.2 |         7.4 |                  4.2\nCoastline Interiors           |           155 |                23 |            14.8 |      8.0 |         8.1 |                  8.7\nAtelier Nouveau               |            64 |                21 |            32.8 |      8.2 |         8.2 |                  4.8\nLumen Collective              |            52 |                21 |            40.4 |      8.6 |         9.2 |                  4.8\nNoordzee Surfwear             |            65 |                20 |            30.8 |      7.8 |         7.6 |                  5.0\nStrandgut                     |            47 |                20 |            42.6 |      7.6 |         7.0 |                 10.0\n(24 rows)\n\n=== Report 3: Shopper satisfaction (CSAT) trend, monthly (written by Ines) ===\nEngineer's note: Platform-wide monthly CSAT trend from csat_responses.submitted_at, Jan 2023 - Dec 2025 (36 rows expected, one per month, well under the 50-row page limit).\nREPORT: Shopper satisfaction (CSAT) trend, monthly\nQUESTION: How satisfied are shoppers with support interactions overall, and is satisfaction trending up or down?\nmonth   | csat_responses | avg_score | median_score | pct_satisfied | pct_dissatisfied\n--------+----------------+-----------+--------------+---------------+-----------------\n2023-01 |             22 |      3.55 |          4.0 |          59.1 |             18.2\n2023-02 |             27 |      3.15 |          3.0 |          37.0 |             25.9\n2023-03 |             18 |      3.17 |          3.5 |          50.0 |             27.8\n2023-04 |             20 |      3.85 |          4.0 |          70.0 |              5.0\n2023-05 |             33 |      3.97 |          4.0 |          63.6 |              6.1\n2023-06 |             36 |      3.25 |          3.0 |          38.9 |             19.4\n2023-07 |             25 |      3.24 |          3.0 |          40.0 |             24.0\n2023-08 |             36 |      3.58 |          4.0 |          55.6 |             16.7\n2023-09 |             23 |      3.78 |          4.0 |          56.5 |             17.4\n2023-10 |             40 |      3.53 |          4.0 |          52.5 |             15.0\n2023-11 |             54 |      3.57 |          4.0 |          55.6 |             14.8\n2023-12 |             69 |      3.25 |          3.0 |          47.8 |             30.4\n2024-01 |             36 |      3.31 |          4.0 |          55.6 |             36.1\n2024-02 |             33 |      3.88 |          4.0 |          75.8 |             12.1\n2024-03 |             27 |      3.00 |          3.0 |          44.4 |             29.6\n2024-04 |             39 |      3.33 |          3.0 |          41.0 |             23.1\n2024-05 |             39 |      3.44 |          3.0 |          48.7 |             25.6\n2024-06 |             39 |      3.59 |          4.0 |          56.4 |             20.5\n2024-07 |             34 |      3.59 |          4.0 |          52.9 |             23.5\n2024-08 |             36 |      3.75 |          4.0 |          58.3 |              8.3\n2024-09 |             37 |      3.57 |          4.0 |          54.1 |             18.9\n2024-10 |             37 |      3.51 |          4.0 |          54.1 |             18.9\n2024-11 |             50 |      3.22 |          3.0 |          38.0 |             28.0\n2024-12 |            124 |      3.14 |          3.0 |          42.7 |             31.5\n2025-01 |             43 |      2.72 |          2.0 |          34.9 |             51.2\n2025-02 |             37 |      3.32 |          4.0 |          51.4 |             24.3\n2025-03 |             28 |      3.61 |          4.0 |          53.6 |             21.4\n2025-04 |             51 |      3.90 |          4.0 |          68.6 |             13.7\n2025-05 |             51 |      3.39 |          4.0 |          52.9 |             27.5\n2025-06 |             52 |      3.06 |          3.0 |          38.5 |             30.8\n2025-07 |             46 |      3.35 |          3.5 |          50.0 |             21.7\n2025-08 |             40 |      3.65 |          4.0 |          52.5 |             17.5\n2025-09 |             54 |      3.43 |          4.0 |          51.9 |             22.2\n2025-10 |             64 |      3.52 |          4.0 |          54.7 |             20.3\n2025-11 |             72 |      3.29 |          3.0 |          44.4 |             27.8\n2025-12 |            162 |      2.88 |          3.0 |          33.3 |             38.9\n(36 rows)\n\n\nNight 4 of 4: you have 3 report slots tonight."}
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{"parentUuid":"22d324da-6e89-4811-8545-352adc316208","isSidechain":false,"promptId":"173b5e86-1d36-402b-90ff-5d1c2bb3a70f","type":"user","message":{"role":"user","content":"The printed reports of night 3:\n\n=== Report 1: Delivery performance: Studio Vrij vs platform, 2025 (written by Felix) ===\nEngineer's note: Note for the analyst: Oct-Dec 2025 (and to a lesser extent Sep) cohorts are right-censored — orders placed late in the year may not yet have a recorded delivered_at, since the warehouse only holds data through 2025-12-31, so those months' avg-days and %-over-7-days will understate true delivery times until later shipments complete.\nREPORT: Delivery performance: Studio Vrij vs platform, 2025\nQUESTION: Did Studio Vrij's actual shipping/delivery performance deteriorate in May-June 2025, explaining the spike in their delivery-related support tickets, or was it a support-side issue only?\nmonth   | sv_shipments_delivered | sv_avg_days_placed_to_delivered | sv_pct_over_7_days | platform_shipments_delivered | platform_avg_days_placed_to_delivered | platform_pct_over_7_days\n--------+------------------------+---------------------------------+--------------------+------------------------------+---------------------------------------+-------------------------\n2025-01 |                     40 |                             3.2 |                0.0 |                         1728 |                                   3.6 |                      3.1\n2025-02 |                     20 |                             3.2 |                0.0 |                         1350 |                                   3.7 |                      3.4\n2025-03 |                     42 |                             4.7 |               16.7 |                         1544 |                                   3.8 |                      4.1\n2025-04 |                     31 |                             3.7 |                3.2 |                         1489 |                                   3.7 |                      2.8\n2025-05 |                    107 |                             9.3 |               70.1 |                         1526 |                                   4.0 |                      4.8\n2025-06 |                     29 |                             4.9 |               20.7 |                         1432 |                                   3.8 |                      4.7\n2025-07 |                     41 |                             4.0 |                4.9 |                         1555 |                                   3.9 |                      4.4\n2025-08 |                     31 |                             3.9 |                3.2 |                         1471 |                                   3.8 |                      4.1\n2025-09 |                     31 |                             4.0 |                0.0 |                         1628 |                                   4.1 |                      5.5\n2025-10 |                     56 |                             4.8 |               10.7 |                         1772 |                                   4.1 |                      7.1\n2025-11 |                     75 |                             3.7 |                1.3 |                         2475 |                                   4.7 |                     12.1\n2025-12 |                     85 |                             4.0 |                3.5 |                         2630 |                                   4.3 |                      9.9\n(12 rows)\n\n=== Report 2: Returns and refunds experience by merchant, 2025 (written by Felix) ===\nEngineer's note: Orders-placed and returns-requested are counted independently by their own 2025 date windows (per the stated caveat, a return's order may fall outside 2025), so return rate is a rough proxy. Tenants with no refunded returns in 2025 show null avg/median days; the printed row count vs. total qualifying tenants (returns_requested>=20) should be noted from the query's actual row count at print time.\nREPORT: Returns and refunds experience by merchant, 2025\nQUESTION: Does the returns/refund experience vary a lot across merchants, and does any merchant stand out as a problem spot the way Studio Vrij did on support tickets?\nmerchant_name                 | orders_placed | returns_requested | return_rate_pct | avg_days | median_days | returns_rejected_pct\n------------------------------+---------------+-------------------+-----------------+----------+-------------+---------------------\nCragsley Outfitters           |          3635 |              1370 |            37.7 |      7.7 |         7.7 |                  3.4\nMarrowby & Vane               |          1758 |               616 |            35.0 |      7.9 |         8.0 |                  3.7\nStudio Vrij                   |           631 |               264 |            41.8 |      7.9 |         7.8 |                  2.3\nCinder Peak Sports Co.        |           338 |                78 |            23.1 |      8.3 |         8.2 |                  3.8\nRue Lafayette Studio          |           187 |                69 |            36.9 |      7.4 |         6.9 |                  8.7\nVelvet Ash                    |           134 |                59 |            44.0 |      7.9 |         7.7 |                  1.7\nGratlinie Outdoor             |           232 |                58 |            25.0 |      7.7 |         7.8 |                  8.6\nAmpfeld Elektronik            |           449 |                56 |            12.5 |      7.7 |         7.1 |                 10.7\nPetalcourt Beauty             |           706 |                55 |             7.8 |      6.1 |         6.2 |                  0.0\nFennel & Flint                |           389 |                52 |            13.4 |      8.1 |         8.1 |                 13.5\nOakhurst Tailoring            |           165 |                48 |            29.1 |      7.6 |         7.7 |                 10.4\nWiesgrund Wohnen              |           296 |                44 |            14.9 |      7.5 |         7.3 |                  6.8\nLinden & Loom                 |           263 |                43 |            16.3 |      7.4 |         7.1 |                  4.7\nBrennquell Kaffee             |          4195 |                40 |             1.0 |      8.1 |         8.6 |                  2.5\nOstwind Mode                  |           122 |                37 |            30.3 |      8.1 |         8.4 |                  0.0\nBallycorran Industrial Supply |           344 |                31 |             9.0 |      7.8 |         7.8 |                  3.2\nRidgeback Gear                |           108 |                26 |            24.1 |      8.0 |         8.1 |                  0.0\nThistlecombe                  |            92 |                26 |            28.3 |      7.3 |         6.8 |                  0.0\nFjordline Wear                |            57 |                24 |            42.1 |      8.2 |         7.4 |                  4.2\nCoastline Interiors           |           155 |                23 |            14.8 |      8.0 |         8.1 |                  8.7\nAtelier Nouveau               |            64 |                21 |            32.8 |      8.2 |         8.2 |                  4.8\nLumen Collective              |            52 |                21 |            40.4 |      8.6 |         9.2 |                  4.8\nNoordzee Surfwear             |            65 |                20 |            30.8 |      7.8 |         7.6 |                  5.0\nStrandgut                     |            47 |                20 |            42.6 |      7.6 |         7.0 |                 10.0\n(24 rows)\n\n=== Report 3: Shopper satisfaction (CSAT) trend, monthly (written by Ines) ===\nEngineer's note: Platform-wide monthly CSAT trend from csat_responses.submitted_at, Jan 2023 - Dec 2025 (36 rows expected, one per month, well under the 50-row page limit).\nREPORT: Shopper satisfaction (CSAT) trend, monthly\nQUESTION: How satisfied are shoppers with support interactions overall, and is satisfaction trending up or down?\nmonth   | csat_responses | avg_score | median_score | pct_satisfied | pct_dissatisfied\n--------+----------------+-----------+--------------+---------------+-----------------\n2023-01 |             22 |      3.55 |          4.0 |          59.1 |             18.2\n2023-02 |             27 |      3.15 |          3.0 |          37.0 |             25.9\n2023-03 |             18 |      3.17 |          3.5 |          50.0 |             27.8\n2023-04 |             20 |      3.85 |          4.0 |          70.0 |              5.0\n2023-05 |             33 |      3.97 |          4.0 |          63.6 |              6.1\n2023-06 |             36 |      3.25 |          3.0 |          38.9 |             19.4\n2023-07 |             25 |      3.24 |          3.0 |          40.0 |             24.0\n2023-08 |             36 |      3.58 |          4.0 |          55.6 |             16.7\n2023-09 |             23 |      3.78 |          4.0 |          56.5 |             17.4\n2023-10 |             40 |      3.53 |          4.0 |          52.5 |             15.0\n2023-11 |             54 |      3.57 |          4.0 |          55.6 |             14.8\n2023-12 |             69 |      3.25 |          3.0 |          47.8 |             30.4\n2024-01 |             36 |      3.31 |          4.0 |          55.6 |             36.1\n2024-02 |             33 |      3.88 |          4.0 |          75.8 |             12.1\n2024-03 |             27 |      3.00 |          3.0 |          44.4 |             29.6\n2024-04 |             39 |      3.33 |          3.0 |          41.0 |             23.1\n2024-05 |             39 |      3.44 |          3.0 |          48.7 |             25.6\n2024-06 |             39 |      3.59 |          4.0 |          56.4 |             20.5\n2024-07 |             34 |      3.59 |          4.0 |          52.9 |             23.5\n2024-08 |             36 |      3.75 |          4.0 |          58.3 |              8.3\n2024-09 |             37 |      3.57 |          4.0 |          54.1 |             18.9\n2024-10 |             37 |      3.51 |          4.0 |          54.1 |             18.9\n2024-11 |             50 |      3.22 |          3.0 |          38.0 |             28.0\n2024-12 |            124 |      3.14 |          3.0 |          42.7 |             31.5\n2025-01 |             43 |      2.72 |          2.0 |          34.9 |             51.2\n2025-02 |             37 |      3.32 |          4.0 |          51.4 |             24.3\n2025-03 |             28 |      3.61 |          4.0 |          53.6 |             21.4\n2025-04 |             51 |      3.90 |          4.0 |          68.6 |             13.7\n2025-05 |             51 |      3.39 |          4.0 |          52.9 |             27.5\n2025-06 |             52 |      3.06 |          3.0 |          38.5 |             30.8\n2025-07 |             46 |      3.35 |          3.5 |          50.0 |             21.7\n2025-08 |             40 |      3.65 |          4.0 |          52.5 |             17.5\n2025-09 |             54 |      3.43 |          4.0 |          51.9 |             22.2\n2025-10 |             64 |      3.52 |          4.0 |          54.7 |             20.3\n2025-11 |             72 |      3.29 |          3.0 |          44.4 |             27.8\n2025-12 |            162 |      2.88 |          3.0 |          33.3 |             38.9\n(36 rows)\n\n\nNight 4 of 4: you have 3 report slots tonight."},"uuid":"620da4eb-7646-49cc-8ce3-efa379be147d","timestamp":"2026-10-03T23:14:09.679Z","permissionMode":"default","promptSource":"sdk","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a","version":"2.1.275","gitBranch":"HEAD"}
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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":12344,"cache_read_input_tokens":72341,"output_tokens":7559,"output_tokens_details":{"thinking_tokens":5273},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_i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{"parentUuid":"451e25e4-d1a4-43e4-bc86-dd94e35ef7f4","isSidechain":false,"message":{"model":"claude-sonnet-5","id":"msg_011Cfg9pAu4yMyHhYQ7fUY31","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01UH1jmofrXvFDVSR9CayogQ","name":"StructuredOutput","input":{"notes":"Night 3 confirmed the Studio Vrij story with real operational data: their placed-to-delivered time spiked to 9.3 days avg and 70.1% of shipments took over 7 days in May 2025 alone (vs 0-20% other months, platform average stayed 3-5%) - a genuine single-merchant fulfillment breakdown that explains the ticket spike, not a support-side or platform-wide issue. Useful side-finding: platform-wide % of shipments over 7 days creeps up in Nov/Dec 2025 (7-12% vs ~3-5% normally) alongside a real volume surge (1600→2600+ shipments/month) - that's consistent with capacity-constrained holiday fulfillment driving the recurring Nov/Dec ticket spikes seen across all three years, which is the platform-level theme Daniel asked about. The returns-by-merchant report is reassuring: resolve time (~7.5-8.5 days) and rejection rates are consistent across merchants regardless of return-rate level (which mostly tracks vertical - apparel/footwear naturally higher, food/beauty lower) - a clean contrast to how much support-speed varies by merchant. CSAT is noisy but its worst months (Dec-23, Dec-24/Jan-25, Jun-25, Dec-25) line up exactly with the ticket volume/slowness spikes already identified, suggesting speed drives satisfaction. This is the last night, so I'm using the 3 slots to: (1) get CSAT by merchant so Daniel can see which merchants are the weakest satisfaction spots (mirroring the support-speed and returns peer reports already in hand), (2) directly quantify the CSAT-vs-resolution-speed relationship platform-wide to turn \"slow support hurts satisfaction\" from a visual coincidence into a measured one - this is the single clearest \"fix this and it moves the needle broadly\" argument for the memo, and (3) a first look at product review ratings (priority #5) since there's room, to see if it echoes the CSAT dips or reveals a blind spot in ticket data.","requests":[{"title":"Shopper satisfaction (CSAT) by merchant, 2025","question":"Which merchants have the weakest shopper satisfaction, and how much does it vary across the platform?","grain":"one row per tenant (merchant), among tenants with at least 10 CSAT responses in 2025","columns":["merchant name","CSAT responses, 2025","avg score (1-5)","median score (1-5)","% satisfied (score >= 4)","% dissatisfied (score <= 2)"],"details":"Source: csat_responses joined to tenants (for name). Window: submitted_at in 2025-01-01 through 2025-12-31. Only include tenants with >=10 CSAT responses in 2025 to avoid noisy small samples. avg/median score over csat_responses.score. '% satisfied' = score>=4 count / total responses that tenant-year, percentage rounded to 1 decimal; '% dissatisfied' = score<=2 count / total, percentage rounded to 1 decimal. Round avg score to 2 decimals, median to 1 decimal. Order by avg score ascending (worst first, so the weakest spots surface at the top), tie-break by merchant name ascending. Cap at 50 printed rows per house standard; note total qualifying tenant count in the footer. I will cross-reference merchant names against the existing 'Response & resolution times by merchant, 2025' report rather than re-querying resolution time here, to keep this report single-purpose.","builds_on":["Shopper satisfaction (CSAT) trend, monthly","Response & resolution times by merchant, 2025"]},{"title":"CSAT score by ticket resolution-time bucket","question":"Does a slower ticket resolution actually translate into a lower satisfaction score?","grain":"one row per resolution-time bucket, platform-wide, across all tickets 2023-2025 that have both a resolution and a CSAT response","columns":["resolution time bucket","tickets (count)","avg CSAT score (1-5)","% satisfied (score >= 4)","% dissatisfied (score <= 2)"],"details":"Source: tickets joined to csat_responses on tickets.id = csat_responses.ticket_id. Only include tickets with resolved_at not null and a matching csat_responses row. resolution_hours = extract(epoch from resolved_at - created_at)/3600. Bucket into: '<4h', '4-24h', '1-3 days' (24-72h), '3-7 days' (72-168h), '>7 days' (168h+). Window: tickets.created_at 2023-01-01 through 2025-12-31 (full history, to get a reliable sample size per bucket). avg score over csat_responses.score for tickets in that bucket, rounded to 2 decimals. '% satisfied'/'% dissatisfied' as in the other CSAT reports, rounded to 1 decimal. Order by the bucket's natural time order (use a numeric sort key for the lower bound of each bucket, e.g. 0, 4, 24, 72, 168), not alphabetically. Expect exactly 5 rows.","builds_on":["Shopper satisfaction (CSAT) trend, monthly","Support response & resolution time trend, monthly"]},{"title":"Product review ratings trend, monthly","question":"What are shoppers' product review ratings telling us, and does the pattern echo the dips we see in support CSAT?","grain":"one calendar month, platform-wide","columns":["month (YYYY-MM)","reviews submitted","avg rating (1-5)","median rating (1-5)","% low rating (1-2 stars)","% high rating (4-5 stars)","% verified purchase"],"details":"Source: product_reviews. Grain the month on created_at. Window: 2023-01-01 through 2025-12-31 (36 months). Only include is_approved = true reviews (published ones actually shown to shoppers). avg/median rating over product_reviews.rating. '% low rating' = rating in (1,2) count / total reviews that month, percentage rounded to 1 decimal; '% high rating' = rating in (4,5) count / total, percentage rounded to 1 decimal. '% verified purchase' = is_verified_purchase=true count / total reviews that month, percentage rounded to 1 decimal. Round avg rating to 2 decimals, median to 1 decimal. Order by month ascending.","builds_on":["Shopper satisfaction (CSAT) trend, monthly"]}]},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":12344,"cache_read_input_tokens":72341,"output_tokens":7559,"output_tokens_details":{"thinking_tokens":5273},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":12344,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":7559,"cache_read_input_tokens":72341,"cache_creation_input_tokens":12344,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":12344},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"wireToolInputs":{"toolu_01UH1jmofrXvFDVSR9CayogQ":{"notes":"Night 3 confirmed the Studio Vrij story with real operational data: their placed-to-delivered time spiked to 9.3 days avg and 70.1% of shipments took over 7 days in May 2025 alone (vs 0-20% other months, platform average stayed 3-5%) - a genuine single-merchant fulfillment breakdown that explains the ticket spike, not a support-side or platform-wide issue. Useful side-finding: platform-wide % of shipments over 7 days creeps up in Nov/Dec 2025 (7-12% vs ~3-5% normally) alongside a real volume surge (1600→2600+ shipments/month) - that's consistent with capacity-constrained holiday fulfillment driving the recurring Nov/Dec ticket spikes seen across all three years, which is the platform-level theme Daniel asked about. The returns-by-merchant report is reassuring: resolve time (~7.5-8.5 days) and rejection rates are consistent across merchants regardless of return-rate level (which mostly tracks vertical - apparel/footwear naturally higher, food/beauty lower) - a clean contrast to how much support-speed varies by merchant. CSAT is noisy but its worst months (Dec-23, Dec-24/Jan-25, Jun-25, Dec-25) line up exactly with the ticket volume/slowness spikes already identified, suggesting speed drives satisfaction. This is the last night, so I'm using the 3 slots to: (1) get CSAT by merchant so Daniel can see which merchants are the weakest satisfaction spots (mirroring the support-speed and returns peer reports already in hand), (2) directly quantify the CSAT-vs-resolution-speed relationship platform-wide to turn \"slow support hurts satisfaction\" from a visual coincidence into a measured one - this is the single clearest \"fix this and it moves the needle broadly\" argument for the memo, and (3) a first look at product review ratings (priority #5) since there's room, to see if it echoes the CSAT dips or reveals a blind spot in ticket data.","requests":[{"title":"Shopper satisfaction (CSAT) by merchant, 2025","question":"Which merchants have the weakest shopper satisfaction, and how much does it vary across the platform?","grain":"one row per tenant (merchant), among tenants with at least 10 CSAT responses in 2025","columns":["merchant name","CSAT responses, 2025","avg score (1-5)","median score (1-5)","% satisfied (score >= 4)","% dissatisfied (score <= 2)"],"details":"Source: csat_responses joined to tenants (for name). Window: submitted_at in 2025-01-01 through 2025-12-31. Only include tenants with >=10 CSAT responses in 2025 to avoid noisy small samples. avg/median score over csat_responses.score. '% satisfied' = score>=4 count / total responses that tenant-year, percentage rounded to 1 decimal; '% dissatisfied' = score<=2 count / total, percentage rounded to 1 decimal. Round avg score to 2 decimals, median to 1 decimal. Order by avg score ascending (worst first, so the weakest spots surface at the top), tie-break by merchant name ascending. Cap at 50 printed rows per house standard; note total qualifying tenant count in the footer. I will cross-reference merchant names against the existing 'Response & resolution times by merchant, 2025' report rather than re-querying resolution time here, to keep this report single-purpose.","builds_on":["Shopper satisfaction (CSAT) trend, monthly","Response & resolution times by merchant, 2025"]},{"title":"CSAT score by ticket resolution-time bucket","question":"Does a slower ticket resolution actually translate into a lower satisfaction score?","grain":"one row per resolution-time bucket, platform-wide, across all tickets 2023-2025 that have both a resolution and a CSAT response","columns":["resolution time bucket","tickets (count)","avg CSAT score (1-5)","% satisfied (score >= 4)","% dissatisfied (score <= 2)"],"details":"Source: tickets joined to csat_responses on tickets.id = csat_responses.ticket_id. Only include tickets with resolved_at not null and a matching csat_responses row. resolution_hours = extract(epoch from resolved_at - created_at)/3600. Bucket into: '<4h', '4-24h', '1-3 days' (24-72h), '3-7 days' (72-168h), '>7 days' (168h+). Window: tickets.created_at 2023-01-01 through 2025-12-31 (full history, to get a reliable sample size per bucket). avg score over csat_responses.score for tickets in that bucket, rounded to 2 decimals. '% satisfied'/'% dissatisfied' as in the other CSAT reports, rounded to 1 decimal. Order by the bucket's natural time order (use a numeric sort key for the lower bound of each bucket, e.g. 0, 4, 24, 72, 168), not alphabetically. Expect exactly 5 rows.","builds_on":["Shopper satisfaction (CSAT) trend, monthly","Support response & resolution time trend, monthly"]},{"title":"Product review ratings trend, monthly","question":"What are shoppers' product review ratings telling us, and does the pattern echo the dips we see in support CSAT?","grain":"one calendar month, platform-wide","columns":["month (YYYY-MM)","reviews submitted","avg rating (1-5)","median rating (1-5)","% low rating (1-2 stars)","% high rating (4-5 stars)","% verified purchase"],"details":"Source: product_reviews. Grain the month on created_at. Window: 2023-01-01 through 2025-12-31 (36 months). Only include is_approved = true reviews (published ones actually shown to shoppers). avg/median rating over product_reviews.rating. '% low rating' = rating in (1,2) count / total reviews that month, percentage rounded to 1 decimal; '% high rating' = rating in (4,5) count / total, percentage rounded to 1 decimal. '% verified purchase' = is_verified_purchase=true count / total reviews that month, percentage rounded to 1 decimal. Round avg rating to 2 decimals, median to 1 decimal. Order by month ascending.","builds_on":["Shopper satisfaction (CSAT) trend, monthly"]}]}},"apiBlockIndex":1,"requestId":"req_011Cfg9pAXVgE6yy8srup6qa","type":"assistant","uuid":"05a6aedd-c8c9-4020-b9eb-b65e9de02afb","timestamp":"2026-10-03T23:15:24.035Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"05a6aedd-c8c9-4020-b9eb-b65e9de02afb","isSidechain":false,"attachment":{"type":"structured_output","data":{"notes":"Night 3 confirmed the Studio Vrij story with real operational data: their placed-to-delivered time spiked to 9.3 days avg and 70.1% of shipments took over 7 days in May 2025 alone (vs 0-20% other months, platform average stayed 3-5%) - a genuine single-merchant fulfillment breakdown that explains the ticket spike, not a support-side or platform-wide issue. Useful side-finding: platform-wide % of shipments over 7 days creeps up in Nov/Dec 2025 (7-12% vs ~3-5% normally) alongside a real volume surge (1600→2600+ shipments/month) - that's consistent with capacity-constrained holiday fulfillment driving the recurring Nov/Dec ticket spikes seen across all three years, which is the platform-level theme Daniel asked about. The returns-by-merchant report is reassuring: resolve time (~7.5-8.5 days) and rejection rates are consistent across merchants regardless of return-rate level (which mostly tracks vertical - apparel/footwear naturally higher, food/beauty lower) - a clean contrast to how much support-speed varies by merchant. CSAT is noisy but its worst months (Dec-23, Dec-24/Jan-25, Jun-25, Dec-25) line up exactly with the ticket volume/slowness spikes already identified, suggesting speed drives satisfaction. This is the last night, so I'm using the 3 slots to: (1) get CSAT by merchant so Daniel can see which merchants are the weakest satisfaction spots (mirroring the support-speed and returns peer reports already in hand), (2) directly quantify the CSAT-vs-resolution-speed relationship platform-wide to turn \"slow support hurts satisfaction\" from a visual coincidence into a measured one - this is the single clearest \"fix this and it moves the needle broadly\" argument for the memo, and (3) a first look at product review ratings (priority #5) since there's room, to see if it echoes the CSAT dips or reveals a blind spot in ticket data.","requests":[{"title":"Shopper satisfaction (CSAT) by merchant, 2025","question":"Which merchants have the weakest shopper satisfaction, and how much does it vary across the platform?","grain":"one row per tenant (merchant), among tenants with at least 10 CSAT responses in 2025","columns":["merchant name","CSAT responses, 2025","avg score (1-5)","median score (1-5)","% satisfied (score >= 4)","% dissatisfied (score <= 2)"],"details":"Source: csat_responses joined to tenants (for name). Window: submitted_at in 2025-01-01 through 2025-12-31. Only include tenants with >=10 CSAT responses in 2025 to avoid noisy small samples. avg/median score over csat_responses.score. '% satisfied' = score>=4 count / total responses that tenant-year, percentage rounded to 1 decimal; '% dissatisfied' = score<=2 count / total, percentage rounded to 1 decimal. Round avg score to 2 decimals, median to 1 decimal. Order by avg score ascending (worst first, so the weakest spots surface at the top), tie-break by merchant name ascending. Cap at 50 printed rows per house standard; note total qualifying tenant count in the footer. I will cross-reference merchant names against the existing 'Response & resolution times by merchant, 2025' report rather than re-querying resolution time here, to keep this report single-purpose.","builds_on":["Shopper satisfaction (CSAT) trend, monthly","Response & resolution times by merchant, 2025"]},{"title":"CSAT score by ticket resolution-time bucket","question":"Does a slower ticket resolution actually translate into a lower satisfaction score?","grain":"one row per resolution-time bucket, platform-wide, across all tickets 2023-2025 that have both a resolution and a CSAT response","columns":["resolution time bucket","tickets (count)","avg CSAT score (1-5)","% satisfied (score >= 4)","% dissatisfied (score <= 2)"],"details":"Source: tickets joined to csat_responses on tickets.id = csat_responses.ticket_id. Only include tickets with resolved_at not null and a matching csat_responses row. resolution_hours = extract(epoch from resolved_at - created_at)/3600. Bucket into: '<4h', '4-24h', '1-3 days' (24-72h), '3-7 days' (72-168h), '>7 days' (168h+). Window: tickets.created_at 2023-01-01 through 2025-12-31 (full history, to get a reliable sample size per bucket). avg score over csat_responses.score for tickets in that bucket, rounded to 2 decimals. '% satisfied'/'% dissatisfied' as in the other CSAT reports, rounded to 1 decimal. Order by the bucket's natural time order (use a numeric sort key for the lower bound of each bucket, e.g. 0, 4, 24, 72, 168), not alphabetically. Expect exactly 5 rows.","builds_on":["Shopper satisfaction (CSAT) trend, monthly","Support response & resolution time trend, monthly"]},{"title":"Product review ratings trend, monthly","question":"What are shoppers' product review ratings telling us, and does the pattern echo the dips we see in support CSAT?","grain":"one calendar month, platform-wide","columns":["month (YYYY-MM)","reviews submitted","avg rating (1-5)","median rating (1-5)","% low rating (1-2 stars)","% high rating (4-5 stars)","% verified purchase"],"details":"Source: product_reviews. Grain the month on created_at. Window: 2023-01-01 through 2025-12-31 (36 months). Only include is_approved = true reviews (published ones actually shown to shoppers). avg/median rating over product_reviews.rating. '% low rating' = rating in (1,2) count / total reviews that month, percentage rounded to 1 decimal; '% high rating' = rating in (4,5) count / total, percentage rounded to 1 decimal. '% verified purchase' = is_verified_purchase=true count / total reviews that month, percentage rounded to 1 decimal. Round avg rating to 2 decimals, median to 1 decimal. Order by month ascending.","builds_on":["Shopper satisfaction (CSAT) trend, monthly"]}]},"toolUseID":"toolu_01UH1jmofrXvFDVSR9CayogQ"},"type":"attachment","uuid":"3af0f267-e762-4c7e-acca-6303d0ae7479","timestamp":"2026-10-03T23:15:24.042Z","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a","version":"2.1.275","gitBranch":"HEAD"}
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{"type":"last-prompt","lastPrompt":"The printed reports of night 3:  === Report 1: Delivery performance: Studio Vrij vs platform, 2025 (written by Felix) === Engineer's note: Note for the analyst: Oct-Dec 2025 (and to a lesser extent Se…","leafUuid":"2f640866-8c98-4dae-9690-ec9581323811","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a"}
{"type":"ai-title","aiTitle":"Shopper experience baseline review","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a"}
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{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-03T23:16:00.143Z","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a","content":"The printed reports of night 4:\n\n=== Report 1: Shopper satisfaction (CSAT) by merchant, 2025 (written by Bea) ===\nEngineer's note: One row per tenant with >=10 CSAT responses in 2025, ordered worst-average first; printed to 50 rows. Note in the report footer the total count of qualifying tenants (i.e. the full row count before the LIMIT 50) so the analyst can see if any were cut off.\nREPORT: Shopper satisfaction (CSAT) by merchant, 2025\nQUESTION: Which merchants have the weakest shopper satisfaction, and how much does it vary across the platform?\ntenant_id | merchant_name                 | response_count | avg_score | median_score | pct_satisfied | pct_dissatisfied\n----------+-------------------------------+----------------+-----------+--------------+---------------+-----------------\n       79 | Kibblewick                    |             17 |      2.82 |          3.0 |          35.3 |             47.1\n        2 | Marrowby & Vane               |             79 |      2.86 |          3.0 |          34.2 |             39.2\n        7 | Cragsley Outfitters           |            130 |      3.05 |          3.0 |          39.2 |             37.7\n       25 | Studio Vrij                   |             49 |      3.10 |          3.0 |          49.0 |             32.7\n        9 | Honeycomb Larder              |             14 |      3.14 |          3.0 |          42.9 |             42.9\n        1 | Grind Collective              |             42 |      3.17 |          3.0 |          45.2 |             28.6\n       42 | Gratlinie Outdoor             |             10 |      3.20 |          3.0 |          30.0 |             10.0\n       17 | Ampfeld Elektronik            |             12 |      3.33 |          3.0 |          41.7 |              8.3\n       40 | Petalcourt Beauty             |             34 |      3.35 |          3.5 |          50.0 |             26.5\n       11 | Ballycorran Industrial Supply |             14 |      3.43 |          3.0 |          35.7 |             14.3\n       20 | Sourdough Club                |             15 |      3.60 |          4.0 |          53.3 |             20.0\n       18 | Brennquell Kaffee             |             32 |      3.63 |          4.0 |          56.3 |             18.8\n       12 | Spice Route Box               |             24 |      3.75 |          4.0 |          66.7 |             16.7\n        5 | Fennel & Flint                |             16 |      3.88 |          4.0 |          75.0 |             18.8\n       29 | Cinder Peak Sports Co.        |             13 |      4.08 |          4.0 |          69.2 |              0.0\n       35 | Wiesgrund Wohnen              |             10 |      4.20 |          4.5 |          70.0 |              0.0\n(16 rows)\n\n=== Report 2: CSAT score by ticket resolution-time bucket (written by Grace) ===\nEngineer's note: Platform-wide (all tenants pooled) since the request didn't scope to a merchant; resolution_hours computed from created_at to resolved_at as specified, with exactly the 5 requested buckets.\nREPORT: CSAT score by ticket resolution-time bucket\nQUESTION: Does a slower ticket resolution actually translate into a lower satisfaction score?\nresolution_time_bucket | tickets | avg_csat_score | pct_satisfied | pct_dissatisfied\n-----------------------+---------+----------------+---------------+-----------------\n<4h                    |     273 |           4.38 |          86.1 |              0.7\n4-24h                  |     712 |           3.82 |          64.3 |              7.9\n1-3 days               |     461 |           2.72 |          21.7 |             41.4\n3-7 days               |     124 |           1.70 |           2.4 |             83.9\n>7 days                |      64 |           1.72 |           1.6 |             82.8\n(5 rows)\n\n=== Report 3: Product review ratings trend, monthly (written by Bea) ===\nEngineer's note: Report is platform-wide review ratings only, at the requested grain and columns (36 monthly rows expected, 2023-01 through 2025-12). The CSAT comparison mentioned in the question isn't included as a column here since that would be a second metric/grain bundled into this report; if the analyst wants a side-by-side with support CSAT, that should be its own request (or a follow-up report joining metrics_daily/csat_responses).\nREPORT: Product review ratings trend, monthly\nQUESTION: What are shoppers' product review ratings telling us, and does the pattern echo the dips we see in support CSAT?\nmonth   | reviews_submitted | avg_rating | median_rating | pct_low_rating | pct_high_rating | pct_verified_purchase\n--------+-------------------+------------+---------------+----------------+-----------------+----------------------\n2023-01 |                31 |       4.03 |           5.0 |           16.1 |            67.7 |                 100.0\n2023-02 |                90 |       3.82 |           4.0 |           21.1 |            64.4 |                 100.0\n2023-03 |                73 |       3.85 |           5.0 |           23.3 |            63.0 |                 100.0\n2023-04 |               107 |       3.97 |           5.0 |           18.7 |            64.5 |                 100.0\n2023-05 |               117 |       4.05 |           5.0 |           17.9 |            68.4 |                 100.0\n2023-06 |               113 |       3.92 |           5.0 |           21.2 |            66.4 |                 100.0\n2023-07 |                92 |       4.08 |           5.0 |           15.2 |            68.5 |                 100.0\n2023-08 |               130 |       4.07 |           5.0 |           14.6 |            72.3 |                 100.0\n2023-09 |               132 |       4.10 |           5.0 |           14.4 |            68.9 |                 100.0\n2023-10 |               158 |       3.99 |           5.0 |           19.0 |            66.5 |                 100.0\n2023-11 |               146 |       4.11 |           5.0 |           13.0 |            69.9 |                 100.0\n2023-12 |               308 |       3.81 |           5.0 |           22.7 |            62.0 |                 100.0\n2024-01 |               219 |       4.13 |           5.0 |           14.2 |            72.1 |                 100.0\n2024-02 |               192 |       4.12 |           5.0 |           12.0 |            71.9 |                 100.0\n2024-03 |               189 |       4.18 |           5.0 |           12.7 |            73.0 |                 100.0\n2024-04 |               164 |       4.11 |           5.0 |           15.2 |            70.7 |                 100.0\n2024-05 |               142 |       4.16 |           5.0 |           12.0 |            73.2 |                 100.0\n2024-06 |               197 |       4.15 |           5.0 |           12.2 |            72.6 |                 100.0\n2024-07 |               188 |       4.06 |           5.0 |           15.4 |            69.7 |                 100.0\n2024-08 |               169 |       4.06 |           5.0 |           16.0 |            71.0 |                 100.0\n2024-09 |               173 |       4.19 |           5.0 |           11.0 |            73.4 |                 100.0\n2024-10 |               187 |       4.10 |           5.0 |           14.4 |            67.9 |                 100.0\n2024-11 |               230 |       4.02 |           5.0 |           18.3 |            68.3 |                 100.0\n2024-12 |               392 |       3.94 |           5.0 |           16.1 |            65.8 |                 100.0\n2025-01 |               310 |       3.93 |           5.0 |           19.7 |            65.2 |                 100.0\n2025-02 |               197 |       4.09 |           5.0 |           14.2 |            69.0 |                 100.0\n2025-03 |               204 |       4.11 |           5.0 |           12.7 |            70.6 |                 100.0\n2025-04 |               205 |       4.07 |           5.0 |           15.1 |            70.7 |                 100.0\n2025-05 |               205 |       4.07 |           5.0 |           15.6 |            72.2 |                 100.0\n2025-06 |               217 |       3.89 |           5.0 |           20.3 |            63.1 |                 100.0\n2025-07 |               161 |       4.07 |           5.0 |           15.5 |            70.8 |                 100.0\n2025-08 |               224 |       4.21 |           5.0 |           13.4 |            73.7 |                 100.0\n2025-09 |               188 |       3.96 |           4.0 |           14.9 |            67.6 |                 100.0\n2025-10 |               245 |       4.16 |           5.0 |           12.2 |            71.8 |                 100.0\n2025-11 |               258 |       4.23 |           5.0 |           12.8 |            73.6 |                 100.0\n2025-12 |               454 |       3.71 |           4.0 |           25.8 |            59.5 |                 100.0\n(36 rows)\n\n\nIt is Friday morning. Write the memo to Daniel Achterberg:\n\n- **Length:** at most about 600 words.\n- **Findings first.** Each finding comes with the numbers behind it and the report it comes from.\n- **Confidence.** Say what you are confident about and what is still open.\n- **Caveats.** Mention any data you do not trust.\n- **Next steps.** End with what you would look at next.\n"}
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{"parentUuid":"eee81a71-f038-4e11-bae4-72f421794c1f","isSidechain":false,"promptId":"7a5bb47f-87fa-4b6f-a88e-cb5eb97240b7","type":"user","message":{"role":"user","content":"The printed reports of night 4:\n\n=== Report 1: Shopper satisfaction (CSAT) by merchant, 2025 (written by Bea) ===\nEngineer's note: One row per tenant with >=10 CSAT responses in 2025, ordered worst-average first; printed to 50 rows. Note in the report footer the total count of qualifying tenants (i.e. the full row count before the LIMIT 50) so the analyst can see if any were cut off.\nREPORT: Shopper satisfaction (CSAT) by merchant, 2025\nQUESTION: Which merchants have the weakest shopper satisfaction, and how much does it vary across the platform?\ntenant_id | merchant_name                 | response_count | avg_score | median_score | pct_satisfied | pct_dissatisfied\n----------+-------------------------------+----------------+-----------+--------------+---------------+-----------------\n       79 | Kibblewick                    |             17 |      2.82 |          3.0 |          35.3 |             47.1\n        2 | Marrowby & Vane               |             79 |      2.86 |          3.0 |          34.2 |             39.2\n        7 | Cragsley Outfitters           |            130 |      3.05 |          3.0 |          39.2 |             37.7\n       25 | Studio Vrij                   |             49 |      3.10 |          3.0 |          49.0 |             32.7\n        9 | Honeycomb Larder              |             14 |      3.14 |          3.0 |          42.9 |             42.9\n        1 | Grind Collective              |             42 |      3.17 |          3.0 |          45.2 |             28.6\n       42 | Gratlinie Outdoor             |             10 |      3.20 |          3.0 |          30.0 |             10.0\n       17 | Ampfeld Elektronik            |             12 |      3.33 |          3.0 |          41.7 |              8.3\n       40 | Petalcourt Beauty             |             34 |      3.35 |          3.5 |          50.0 |             26.5\n       11 | Ballycorran Industrial Supply |             14 |      3.43 |          3.0 |          35.7 |             14.3\n       20 | Sourdough Club                |             15 |      3.60 |          4.0 |          53.3 |             20.0\n       18 | Brennquell Kaffee             |             32 |      3.63 |          4.0 |          56.3 |             18.8\n       12 | Spice Route Box               |             24 |      3.75 |          4.0 |          66.7 |             16.7\n        5 | Fennel & Flint                |             16 |      3.88 |          4.0 |          75.0 |             18.8\n       29 | Cinder Peak Sports Co.        |             13 |      4.08 |          4.0 |          69.2 |              0.0\n       35 | Wiesgrund Wohnen              |             10 |      4.20 |          4.5 |          70.0 |              0.0\n(16 rows)\n\n=== Report 2: CSAT score by ticket resolution-time bucket (written by Grace) ===\nEngineer's note: Platform-wide (all tenants pooled) since the request didn't scope to a merchant; resolution_hours computed from created_at to resolved_at as specified, with exactly the 5 requested buckets.\nREPORT: CSAT score by ticket resolution-time bucket\nQUESTION: Does a slower ticket resolution actually translate into a lower satisfaction score?\nresolution_time_bucket | tickets | avg_csat_score | pct_satisfied | pct_dissatisfied\n-----------------------+---------+----------------+---------------+-----------------\n<4h                    |     273 |           4.38 |          86.1 |              0.7\n4-24h                  |     712 |           3.82 |          64.3 |              7.9\n1-3 days               |     461 |           2.72 |          21.7 |             41.4\n3-7 days               |     124 |           1.70 |           2.4 |             83.9\n>7 days                |      64 |           1.72 |           1.6 |             82.8\n(5 rows)\n\n=== Report 3: Product review ratings trend, monthly (written by Bea) ===\nEngineer's note: Report is platform-wide review ratings only, at the requested grain and columns (36 monthly rows expected, 2023-01 through 2025-12). The CSAT comparison mentioned in the question isn't included as a column here since that would be a second metric/grain bundled into this report; if the analyst wants a side-by-side with support CSAT, that should be its own request (or a follow-up report joining metrics_daily/csat_responses).\nREPORT: Product review ratings trend, monthly\nQUESTION: What are shoppers' product review ratings telling us, and does the pattern echo the dips we see in support CSAT?\nmonth   | reviews_submitted | avg_rating | median_rating | pct_low_rating | pct_high_rating | pct_verified_purchase\n--------+-------------------+------------+---------------+----------------+-----------------+----------------------\n2023-01 |                31 |       4.03 |           5.0 |           16.1 |            67.7 |                 100.0\n2023-02 |                90 |       3.82 |           4.0 |           21.1 |            64.4 |                 100.0\n2023-03 |                73 |       3.85 |           5.0 |           23.3 |            63.0 |                 100.0\n2023-04 |               107 |       3.97 |           5.0 |           18.7 |            64.5 |                 100.0\n2023-05 |               117 |       4.05 |           5.0 |           17.9 |            68.4 |                 100.0\n2023-06 |               113 |       3.92 |           5.0 |           21.2 |            66.4 |                 100.0\n2023-07 |                92 |       4.08 |           5.0 |           15.2 |            68.5 |                 100.0\n2023-08 |               130 |       4.07 |           5.0 |           14.6 |            72.3 |                 100.0\n2023-09 |               132 |       4.10 |           5.0 |           14.4 |            68.9 |                 100.0\n2023-10 |               158 |       3.99 |           5.0 |           19.0 |            66.5 |                 100.0\n2023-11 |               146 |       4.11 |           5.0 |           13.0 |            69.9 |                 100.0\n2023-12 |               308 |       3.81 |           5.0 |           22.7 |            62.0 |                 100.0\n2024-01 |               219 |       4.13 |           5.0 |           14.2 |            72.1 |                 100.0\n2024-02 |               192 |       4.12 |           5.0 |           12.0 |            71.9 |                 100.0\n2024-03 |               189 |       4.18 |           5.0 |           12.7 |            73.0 |                 100.0\n2024-04 |               164 |       4.11 |           5.0 |           15.2 |            70.7 |                 100.0\n2024-05 |               142 |       4.16 |           5.0 |           12.0 |            73.2 |                 100.0\n2024-06 |               197 |       4.15 |           5.0 |           12.2 |            72.6 |                 100.0\n2024-07 |               188 |       4.06 |           5.0 |           15.4 |            69.7 |                 100.0\n2024-08 |               169 |       4.06 |           5.0 |           16.0 |            71.0 |                 100.0\n2024-09 |               173 |       4.19 |           5.0 |           11.0 |            73.4 |                 100.0\n2024-10 |               187 |       4.10 |           5.0 |           14.4 |            67.9 |                 100.0\n2024-11 |               230 |       4.02 |           5.0 |           18.3 |            68.3 |                 100.0\n2024-12 |               392 |       3.94 |           5.0 |           16.1 |            65.8 |                 100.0\n2025-01 |               310 |       3.93 |           5.0 |           19.7 |            65.2 |                 100.0\n2025-02 |               197 |       4.09 |           5.0 |           14.2 |            69.0 |                 100.0\n2025-03 |               204 |       4.11 |           5.0 |           12.7 |            70.6 |                 100.0\n2025-04 |               205 |       4.07 |           5.0 |           15.1 |            70.7 |                 100.0\n2025-05 |               205 |       4.07 |           5.0 |           15.6 |            72.2 |                 100.0\n2025-06 |               217 |       3.89 |           5.0 |           20.3 |            63.1 |                 100.0\n2025-07 |               161 |       4.07 |           5.0 |           15.5 |            70.8 |                 100.0\n2025-08 |               224 |       4.21 |           5.0 |           13.4 |            73.7 |                 100.0\n2025-09 |               188 |       3.96 |           4.0 |           14.9 |            67.6 |                 100.0\n2025-10 |               245 |       4.16 |           5.0 |           12.2 |            71.8 |                 100.0\n2025-11 |               258 |       4.23 |           5.0 |           12.8 |            73.6 |                 100.0\n2025-12 |               454 |       3.71 |           4.0 |           25.8 |            59.5 |                 100.0\n(36 rows)\n\n\nIt is Friday morning. Write the memo to Daniel Achterberg:\n\n- **Length:** at most about 600 words.\n- **Findings first.** Each finding comes with the numbers behind it and the report it comes from.\n- **Confidence.** Say what you are confident about and what is still open.\n- **Caveats.** Mention any data you do not trust.\n- **Next steps.** End with what you would look at next.\n"},"uuid":"8e1e8f95-dcc1-401c-a813-d5a4f430b8cc","timestamp":"2026-10-03T23:16:00.149Z","permissionMode":"default","promptSource":"sdk","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"8e1e8f95-dcc1-401c-a813-d5a4f430b8cc","isSidechain":false,"message":{"model":"claude-sonnet-5","id":"msg_011Cfg9xLfva2ujq3v1xaHzt","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"EtUsCrwBCBIYAipA5/pKpRdw+KPXcx62f8C3wB6J58Bg6wYxf/+Qclz6Rmg6+JbvTZ/8EJoq5Cnyu6do07n0x3vqXFwkdNQECXSBczIPY2xhdWRlLXNvbm5ldC01OABCCHRoaW5raW5nWiQ3OGQ5ZDdmNy02ZTRlLTQ2NTItOWZmZi02MGUwOGQ3Y2JkMmRyEOgeZ+ZPTOeF4/ly19pwhKaIAQGaAREKD2NsYXVkZS1zb25uZXQtNagBx5mG1gawAQISDEVesq7XChsl6fXfyhoMfDy3QxklH7XY7D7oIjDGpkz1usH5Iz91EgsCp2Gpc3c4knyZMTQZ1QHGX0azCkCwrajNmU9MfzP+md1vUfkqxSq+dkQq2AXutC8zm4aRviD7ABlUWjB9mMO5kDiL+qSIrFo7QtGUcGPbM5guegvICGKYn7BWMyHIG4b8dIRGOAMKbMC0cyoh+fXlTxMeurhatrqVWyqbYGhq59bSWKT6qXMJpXbUoOgwEBAKljhwdZDqsHNZwbHQiZ3Yp2wquqwpi9FRSo2ZWI3yJCE46yISbyi9txmcuYMI0hAxAO/zv06Bl2rSqOiTljVyKISAyUU5QIcEXQC4FypUT9D+fG1y1duP/JxP8V9R7fqIuK67vPko/y51lnDBuMr4whI/MVNj6HCpYY7IpP0rD2Fyoe1p1zvaPvVPAlrOPvlE3jb4zoyUtojooaSDwqIlgcufJ+PfKEnB+LKQQ2kVgoGgbvsV59ya/3tQNDODfL+kDgWah4CNUtGBu0hl6V+uFrH7E3Uiry8ZVkzvJLQFx5FIjObzn2Rt3YaHFEiH0IovmdKu4h67xo/vPlGwgh5WrZBmA7rJftTtYZVrCXYdhM45yDY2C8Aj9w6N5EBLFIWJyud7d1IMutDPKR8NRNZeXS9OpTWGjzM7e0yUbPZmVYZMCYRdb0CIcHPtOxM1ipJM3YZ+P96D507/22yokSVEbJ+mysVKlNbQZ9mXtRUvf5vbHUb35O45ofTEFJD5/o7jQIRo9Hg5Qr2h2XGQX2GqY0vfXnJ8vcn5pccGaGfeo6WwZM3TOZWzGwdzpkPRde9xeKVEmdzuFK76nb4H5Z0IomaYWLnz2DDIt3y+Uw8Np8Jq12AHoMF2VOVbqC6eoHTqTjPzJs178SfiheeGlHDgqbL+HcmJ/cJNfO3VQ/Sj6SA83y4ZwmQ1aJGeZ7/KzXxbUSzP8fn3kkOsw5v2unx5XnlUoOJMMFIdVRG8Tr1fi0U5VyQv0NOEbRy9sWJl/BAt9Rhx3D6iuENSToQsoVbIj5uB2Qp5BhOX3o0LRVsrn5bJcV1vxccoQpRO7daWVxVxcj21F1EVwSfp6WNvk7joNHTHceAZeWafv9q0g6yI4UA3Eh9qk7/b4LJDTkx7fwDkL7P3Bq6DlFdzBSRf8IsEM1uorGoUXAxvTsjdC7SgA6H5nggo9arerROQY+F7HsvRbY215srDibmCCXfWgLWldQsiloBL+wNjtyyL/xmEhpxi3AYLNL64CvCGQSiKH/zXZqJlpVDQXZ8GzHlDbDAaFyg2Kl07fpzYiXijqcYiChQBJCleKRAoObNI7+glywBklnVAextKUyjluV8aidMvAnLmJ6dB658+Uk+kXCifsJIJ2uLXkviNH7j8xiXuuI21o8Oi04Ux1lM4a99cIEj1JbdxBRbGcT4C+LYHXHreVnJqNYktbwSQggdRfloVH8lvRTglp1/vniay35Fv0b2GTTvcQrqD6RtUsL+jhqIDM+BbdGiY76t3dnth25g9C+DzjrCIdbetkFxJcxMc7lgTBo/X1iasYiW2WkScgRij+xVXVteeVXWUB7ZUpRWosVm8bcbQEk7v3b8rHnfVFpp1/Q+AJL/Ej+Y6XAuckCoON1q3v5u+M6EDjUA43LPE8Jn+zpqCkEbEuKLw8wRit8c1XHgNG5tSneKAGeKE/0UmBP4y43iGuvHz1DCQZ1sbBjWaetwOrb8VnlHWVuAFSD+vFinJyuTu7RE4hEZM7chyuaaQnUNXQGWt6MBzTgBneRXfCOhmom/ziSxQv5E+GgHGHg4bmwhHVdKFj1rMQTgi3tO1Cuy3+CUiKim/c+Fp4g+e+oYqDe0YIMQaxMS/JmTkYDoLE8w+uGHuiZBB/TaUhcLWGl5jRrEHQx9N8akLAUfQpYjYMiDdqtDPByXlw3mhXsJYNVEJtTF4Rx6HdX6qBBFSeHpQ8g4NaM2InrhF4xkLfPEADfZ4qNjV0r19asIJF7QnOSEDlX+QP8s85epuu3H+KCO1GXofa2UDxzlp1CaIm1lA7JGvaL7gUL6q7JpwKCRqjnhvaBgCwTXK03tx9G3aM+EsjQEZnSR7bamK4h0oMZJAG0rmDKRAff7BwWOelWkzqqM5fpYwPR4uUWaqyW66/eHJ9e/Ka/xL42j7/INus7qOpZa3G3EpYe02ujX8jCiCBdm6/cOk5ihAgnkzQ6EXn5LoLyiGj2dielv7QQt6Zy+8n8L5tBooIVjQinZ6AJoxJQLHFrsG2Bia5Wx6KO4O8mO5CY3MqOEXzT8QAXqIltngovn9avgpz13QJ2g9lunNAtFMWx+SoErivAvXYp4MbVM8VKdn0uETtT+Y+Uzpfve3xzP8phhzA2HcAK/wBT3OtOdaIKGp92m8ese5JujpzsRT54RGCwvpa90cq9edibKEi8hD0tHx2/zShTf31CqyFUC5LRKimWJIN8oJjEHV8lCjPFcS0xmqQbSpZ24kzuRRyUFo/DJtFvwlWbUF+G7B8v085418+pU4EDbDljTGz6+JWVVKh64ZXYYXqLC446L4o2pwlb7DhzJgdx6OFgQT9PDgStriMHAXrPyVJwL0VxxiNE5SJ9t/jCdFrSCeGs1iDoAiAUNyAMxttoXesn43GQS+xmOq/3LAITeY2xgrLZVE8l75u+L5vi9+u7EtjC+W54yMSBDQtwxlPh43XfW46ZyO9hJqWrLZR6ZQEKYosuieZ2lwstkBTOtCCZCraXDhgc+zUtVUuylRKLLGnd4Mgdo65VRb2mmWPUw+PvaVacl2hxuzAvYDEuRPLCwzNEJjjKbCwT3py4eynGNdt85O0c3iBeIhmfZs34dCPV5e9LVFeTqFy55OF266Fo6/iUDxgXh9RJyaOej3MlwSE9c7llytMv0hLlmSU7rQlzwSF7RO3Yrs6gvPKLPbtzuYBU3IXt5NCuN6ZyxZe3HksQvteCQuvxyNQ5DHJPOo2WN6d3iSiQI6bjSGpKiNoy+5L7juOLIENNydSTiDOjWHoPaCKOIawa7dP55QqTpMhPFVZiwQUsqt7mEASDlSszvvMJPdDA66zcktOfH25CDjzbmLtC8GTJbYlkIY1hsfLJYoI7x11kL8MklThr1mX3h5NYNTduACkn78yDhRdEigbHaY0COA0ViHqtNJD0KCA5OXrdsO+E6SD3B6rEhRW1wyNbwsVf5Pg1kxnmcKN/s0sLgXdEWTGW+sudQ0LgTOQUrZBtmFx2QUc6FO3/JQT4nl1VJHbLJsscYbHuoLBxODfsDoCpHWiguCbQMi3PYUOxuU+rzaJyLZk44tTDztsxkpxtoHVKcL7tjuoH+TFW5SDPr/yWCL0n+VpycfOEokz/BXDd1GWsoxIil5dAfZN1jyP2gzSjN5qNGwAyBxvvEKeWtCkECgBxXLBzl1K0K5+fNNbe+rj/5sYFQw2o2jmUfUA3PrfePSqPQaPIVnN9oSPhI1luwb3dbXmJsHsCby7adOEWNvQ6J2dyx4TFyPYOKDNHBptCELe4eYkgAztJzKiTkDLAIDVD8ujO5FKltgOYUqI6CozZIiRFoPvERFnrLaYu/2MUSzlpuuKxr0NbPQA0PWoDV16UH91qDKpmSe/o4v5k0HWuMdIdBSHiqRmBK35QFSianSJfLD4nExC+CAIKk1bIds1fibYa/toyBhnvlBEVIY91/Nc9s5BoxMP7O4f7mVE+BAPVmwoTCPCpNSLlfKm0+FdiY+eNIqDL86syyKmkzQ44twRXq9jjirk6kb7IfbOBPd/kvIvNldKN/XtGKk7rnJPuCWTpCOYHTuQ+4josuyf3e2AERIpDDNoq60y8zE3duse+J8+QaeXYiYIAs4k6Z3Z620N2mhLTXbxsLFuaUB9UiocKrGGcxZG7PnSbdpgzDk1t9Zo9E2IEiH0+/Q6s33oEQSRfYnzR+VvWTsCXBoxlb7ZdFd40BmVK5SZwrO2RczU6vMdbsEm/FqfBxHwGaOjvpwrA7oXOXzvf06gCw6b9dDIUYooSuRikOkMuPMWqibvA4q3/abj+434nurGf3GiOKJgnKmC77q7Tv5xvzo4DWcrL/4UGrfVXTADDy/AbmUJ4Egm26U/fsXuz0cRg3ZQKaV5CvFC2D6J9d6rMk1yTLG2vNuLt55mywhH6T8EIFCCUkLOXYz9x8AvqMYO9D7XohqDXEOvhW/lzC9VE6Ftb87Ep/OJs4nxHEQqPORzvlPe+Cdvv1FwH2neeGp8YpNiyvFXcLalKfcqt5cx0JPGI5AVHL6yvZ80rejLGtDDoJtUnqh1UjYVzyQ+bYseHW2w0tTT7IhxGr88E9HgKARriTEMD+On+MKO7wUn3cjaSmsFrPwQvLFdXNqQUALXcmXG1+K3a6eqay/K/AOpg43Kw369a/ZXx6mVEKms+vqZvKFagS1YQ8rnlLbz5uMHof1E8XM5joekZhvum36D1wrXFfMAiXEEvTpGbZB5cyKKtesijsJ47lO5HAnF0qRwt354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{"parentUuid":"7618d198-2aa0-4325-ae71-be39dac58308","isSidechain":false,"message":{"model":"claude-sonnet-5","id":"msg_011Cfg9xLfva2ujq3v1xaHzt","type":"message","role":"assistant","content":[{"type":"text","text":"**To:** Daniel Achterberg, VP Customer Experience\n**From:** Mateo Ruiz\n**Re:** Shopper experience — week 1 baseline\n\nDaniel — here's what the data shows on #1–#3, plus a first look at #4 and #5.\n\n**1. Response/resolution speed — stable in \"normal\" months, with two distinct spikes.** Platform-wide, typical months run ~10–15h to first response, ~18–28h to resolution, ~30% SLA breach (*monthly trend*). Two anomalies stand out: a **Nov/Dec spike every year** (2023: 41h/73h; 2024: 45h/91h; 2025: 40h/60h, response/resolution), and a **one-off May–Jun 2025 spike** (26–45h response, 53–87h resolution) with no equivalent in 2023/2024. I'm confident the Nov/Dec pattern is real seasonality, not noise — it recurs 3 years running, shipment volume jumps ~1,600→2,600+/month, and the share of deliveries taking >7 days rises from ~4% to 7–12% platform-wide in Nov/Dec 2025 (*delivery performance report*). The May/Jun spike traced to **one merchant, Studio Vrij**: 86 tickets vs. an expected ~16 (5.4x normal), 84% delivery-tagged (*spike-by-merchant report*), confirmed independently by real shipment data — their placed-to-delivered time hit 9.3 days avg with 70% of shipments over 7 days in May alone, vs. 0–20% other months (platform stayed ~4–5%). This was a single-merchant fulfillment breakdown, not a platform problem.\n\n**2. Where the load comes from.** Delivery/\"where-is-my-order\" tickets are now **65.5% of tagged tickets** (last 6 months), up from 53.7% the prior 6 (*tag-mix report*), while complaint/return/product-question shrink in share. Combined with the Nov/Dec capacity evidence above, this points to one fixable platform-level theme: **holiday fulfillment capacity drives WISMO tickets.** Response speed itself also varies enormously by merchant — 2025 SLA breach rates range from 9.5% (Gratlinie) to 61%+ (Cragsley, Marrowby) — so some of the \"we're slow\" feeling is concentrated in a handful of merchants, not universal.\n\n**3. Returns/refunds — not currently a pain point.** Time-to-refund is consistently ~7.5–8.5 days and rejection rates 0–13.5%, with no real merchant or time trend (*returns trend + by-merchant reports*), even for Studio Vrij. Return rate varies mostly by vertical (apparel 30–44% vs. food/beauty <15%), which looks structural, not a service failure.\n\n**4. Satisfaction — the clearest finding of the week.** CSAT by resolution-time bucket is a clean, strong dose-response: **avg score falls from 4.38 (<4h) → 3.82 (4–24h) → 2.72 (1–3 days) → 1.70 (3–7 days) → 1.72 (>7 days)**, with dissatisfaction rising from 0.7% to 84% (*resolution-bucket report*). CSAT's worst months line up exactly with the spikes above. Weakest merchants (Kibblewick 2.82, Marrowby 2.86, Cragsley 3.05) are the same ones with high SLA breach rates — speed and satisfaction move together.\n\n**5. Reviews (first look).** Product ratings are high and stable (~4.0–4.2 avg, ~65–73% 4–5 star) and barely echo the support dips — a small December softening each year, nothing like CSAT's swings. Reviews seem to track product quality more than service experience — a likely blind spot if we rely on them alone.\n\n**Confidence/caveats:** High confidence in the speed→CSAT link and the Studio Vrij root cause (two independent data sources agree). Lower confidence in exact Nov/Dec capacity mechanics (only 2025 has shipment-level detail) and in per-merchant CSAT rankings, since several merchants have under 20 responses. Return rate is an approximation (same-month orders vs. returns, not true cohorts). Recent months (Nov/Dec 2025) understate true response/resolution/delivery times due to right-censoring.\n\n**Next:** confirm Studio Vrij has recovered; quantify Nov/Dec staffing vs. volume to size the capacity fix; dig into why Cragsley/Marrowby are structurally slow (agent staffing, channel mix)."}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":94730,"cache_read_input_tokens":0,"output_tokens":3655,"output_tokens_details":{"thinking_tokens":2101},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":94730,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":3655,"cache_read_input_tokens":0,"cache_creation_input_tokens":94730,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":94730},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":{"cache_miss_reason":{"type":"tools_changed","cache_missed_input_tokens":76847}},"context_management":null},"apiBlockIndex":1,"requestId":"req_011Cfg9xLLpcTpggPduv21HB","type":"assistant","uuid":"0383200a-3202-4f1b-8091-dc8874af345b","timestamp":"2026-10-03T23:16:37.873Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a","version":"2.1.275","gitBranch":"HEAD"}
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{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-04T02:13:43.630Z","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a","content":"Monday, week 2. Daniel Achterberg replied to your memo:\n\nSubject: Good progress — now I want to understand the \"why\" behind the hotspots\n\nMateo — this is exactly the kind of baseline I needed, thank you. Two things from your memo and Mia's notes are sitting with me:\n\nFirst, the speed→satisfaction link is strong and now confirmed two ways (yours and Mia's numbers line up almost exactly), so I believe it. But I don't yet know if slowness *causes* the dissatisfaction or if the worst merchants are simply worse across the board (harder products, different shopper expectations, something else) and speed is just the symptom we can see. That matters a lot for what I recommend as the fix.\n\nSecond, Mia's finding that three merchants account for a third of all tickets and the worst response times lines up with what you found independently — that's a strong signal, not a coincidence, and it's where I want to spend our next slots.\n\nQuestions for this week:\n\n1. **What's actually different about how Cragsley, Marrowby, and Studio Vrij run their support** compared to the rest of the book — are they understaffed relative to their ticket volume, leaning on slower channels, getting hit with harder/more complex issues, or something else? I want to know if this is something we can fix with guidance or tooling, or if it's specific to each merchant's situation.\n\n2. **Is the urgent-priority failure (missing resolution half the time) concentrated in those same few merchants, or is it a broader pattern across the book?** If urgent tickets are failing everywhere, that's a different and more worrying problem than a handful of struggling merchants.\n\n3. **Has Studio Vrij actually recovered since the spring breakdown, and what would it take to get ahead of the Nov/Dec pattern this year** before it hits? I'd like to walk into December with a plan, not another postmortem.\n\n4. **Before I take \"respond faster\" to merchants as the headline fix, can we check whether the slowest merchants differ in other ways** (ticket mix, how support is staffed, shopper profile) that might also explain their low satisfaction? I don't want to oversimplify the story if something else is going on underneath.\n\nLet's prioritize #1 and #2 this week — those feel most actionable. #3 and #4 can wait if slots are tight.\n\nThanks,\nDaniel\n\nLast week in the company's analytics channel:\n- Chief Executive Officer team (Hannah Brooks): Revenue concentration: our top 5 merchants generate 72% of Tidewick's processing-fee revenue, and one merchant alone (Cragsley Outfitters) accounts for 42% — we're exposed if a top account leaves.\n- Chief Executive Officer team (Hannah Brooks): Growth is healthy but comes from existing merchants: active merchants grew from 76 to 111 and orders roughly doubled over 2 years, yet new merchants contribute under 1% of revenue in their first quarter after joining.\n- Chief Executive Officer team (Hannah Brooks): Refund rate is creeping up: from ~9-11% in 2024 to 12-14% in late 2025, without a matching rise in payment failures — worth a root-cause look.\n- Chief Financial Officer team (Kofi Mensah): 2025 processing-fee revenue: Tidewick earned €93,583 in payment-processing fees on €3.97M of merchant payment volume, growing to a €14.3k December peak — full platform-fee revenue isn't in this data yet, to follow.\n- Chief Financial Officer team (Kofi Mensah): Merchant concentration: our top 10 merchants still drive ~65% of payment volume (led by Cragsley Outfitters at ~18%), but that share is edging down as the merchant base grows (92 → 113 active merchants year over year).\n- Chief Financial Officer team (Kofi Mensah): Refunds: Apparel merchants run a steady ~20% refund rate (normal for fashion), but a handful of smaller non-Apparel merchants (e.g. OfficeHub Nordic, Sports & Outdoor sellers) drove a refund-rate spike to ~13% company-wide in Q3 — still being double-checked against return records.\n- Chief Operating Officer team (Lena Hartmann): Delivery speed held up over the past two years (~85-90% of orders arrive within 5 days in a normal month), but it took a real hit during peak season: only 56% of orders arrived within 5 days in the worst week (early December), before recovering within about 2 weeks.\n- Chief Operating Officer team (Lena Hartmann): One of our carrier partners, Albion Parcel, broke down starting in November: on-time delivery (within 3 days) fell from 76% in October to 28% in November and December, and hasn't recovered yet.\n- Chief Operating Officer team (Lena Hartmann): Which merchant has 'good' or 'bad' delivery isn't really about the merchant or the warehouse - it's almost entirely about which carrier handles their shipments: the exact same merchant sees 89% of parcels arrive within 3 days on one carrier vs. just 19% on another.\n- VP Customer Experience team (Mateo Ruiz): Slower support replies mean unhappier customers: satisfaction drops from 4.4/5 when resolved in under 4 hours to just 1.7/5 when it takes more than 3 days.\n- VP Customer Experience team (Mateo Ruiz): A big chunk of support tickets (~65% recently) are shoppers asking \"where's my order\" - and this spikes every November/December, matching a rise in late deliveries from ~4% to ~10%+ of shipments.\n- VP Customer Experience team (Mateo Ruiz): One merchant's May 2025 delivery breakdown (70% of shipments took over a week, vs. under 5% normally) explains an unusual mid-year spike in slow support - it was a local incident, not a platform-wide issue.\n- Head of Growth Marketing team (Sofie Nilsen): Discount codes are costing more than they earn in our core retail categories: discounted orders return 5-7 points more often than full-price ones (e.g. apparel 39% vs 35%, electronics 17% vs 11%), with no real boost in order size.\n- Head of Growth Marketing team (Sofie Nilsen): Referral customers are our most engaged shoppers by far — people who refer others place ~8 orders/year vs ~3 for everyone else — while our loyalty program shows no such lift despite similar effort to run.\n- Head of Growth Marketing team (Sofie Nilsen): Growth is concentrated: just 2 of our ~120 merchants generate 31% of all \"direct\" site traffic, and only 1 in 7 orders can be traced back to a marketing channel at all.\n- Head of Merchant Success team (Arjun Patel): Our biggest merchant, Cragsley Outfitters, grew sales +19.8% year-over-year this holiday quarter (vs. the same Oct-Dec period last year) - real growth, not just seasonal bump.\n- Head of Merchant Success team (Arjun Patel): Castell Fasteners, one of our top 4 merchants, is flashing a warning sign: shopper traffic on its site dropped 44% this quarter and it's sitting on €5,080 in unpaid invoices.\n- Head of Merchant Success team (Arjun Patel): Merchants with naturally recurring purchases (coffee/food subscriptions, B2B restocking) keep 83-94% of customers coming back, vs. ~58% for general retail - a strong case for building reorder habits into any merchant's playbook.\n- Head of Risk and Payments team (Claire Dubois): Access control: we can show who has access today, but we found zero audit records of role/permission changes over 3 years — so we can't yet prove when access was granted or revoked.\n- Head of Risk and Payments team (Claire Dubois): Data privacy requests: all healthy — ~48 requests since 2023, with a consistent 30-day response window and no real backlog.\n- Head of Risk and Payments team (Claire Dubois): Login security: one sharp anomaly — in Feb 2025, 61% of login attempts failed (7,429 of 12,211), a 10x jump from the normal ~6% baseline, and it wasn't flagged by our suspicious-access alerts at the time.\n- VP Product team (Emeka Obi): 🛒 Most lost sales leak at the shopping cart, not at payment: only ~1 in 4 carts convert within an hour, and the rest mostly stall for 1-7 days before fizzling out (just 1-19% of those convert) — payment itself works fine once an order is actually placed (97.6% succeed instantly).\n- VP Product team (Emeka Obi): 🔁 Most first-time buyers do come back: 40-62% of new customers place a second order within 90 days, typically within 1-2.5 months — a healthy, stable repeat-purchase baseline across the last 3 years.\n- VP Product team (Emeka Obi): 📦 A return modestly cools the relationship but doesn't end it: customers with a returned order come back 49.7% of the time within 90 days, vs 57.7% for customers with no return — about an 8-point dip, not a cliff.\n- Head of Data team (Yuki Tanaka): Revenue checks out: once you count refunded payments correctly, orders match actual money collected to within 1-5% (not the 20-40% gap it looked like at first).\n- Head of Data team (Yuki Tanaka): Refunds are real and not lost or double-counted, but most (85%) land ~3 weeks after the original payout already went out, so we currently can't point to exactly which payout absorbed a given refund.\n- Head of Data team (Yuki Tanaka): \"How many customers do we have\" depends entirely on definition: 11,258 total accounts, but only 6,159 bought in the last 12 months and just 411 have an active subscription — we recommend standardizing on the 6,159 figure as \"active customers.\"\n- Chief Information Security Officer team (Noah Fischer): 🔐 Shopper accounts have 0% multi-factor authentication (MFA) enabled — literally none of our ~11,200 shopper logins use a second factor, vs. ~55% of staff.\n- Chief Information Security Officer team (Noah Fischer): 🕵️ We can't currently prove who deleted things: of 615 products deleted across 91 merchants, 0 show up in our audit log as a logged deletion.\n- Chief Information Security Officer team (Noah Fischer): 👋 When a merchant churns, their staff logins don't get shut off: 16 Owner/Admin accounts on churned merchants are still marked \"active,\" some unused for 500+ days.\n- Data Protection Officer team (Priyanka Desai): 🔒 Privacy audit: our email-retention rule (delete after 730 days) is configured for all 120 merchants but has never actually run — we're still holding ~44,000+ customer emails that are over 2 years old.\n- Data Protection Officer team (Priyanka Desai): ⚖️ 3 shoppers' erasure requests were correctly put on hold for payment disputes, but those holds are now 289–954 days old (up to 2.6 years) and need review before we can say the requests are truly resolved.\n- Data Protection Officer team (Priyanka Desai): 📉 Marketing-email opt-in rate has slipped from 99.4% to ~90% since 2023, with a sharp spike in opt-outs in July–August 2025 — worth watching if we rely on consent for marketing.\n- VP Platform Engineering team (Lucas Moreau): Background jobs ran reliably almost all of Q4 2025, except one bug in our cart-reminder emails that failed for a single week (Nov 24) and hit 10 merchants — including our #1 merchant by revenue, Cragsley Outfitters.\n- VP Platform Engineering team (Lucas Moreau): About 1 in 7 webhook notifications we send to merchants' inventory/accounting systems (ERP, WMS, and Accounting syncs) never go through — they get stuck retrying instead of succeeding or failing cleanly, all year round.\n- VP Platform Engineering team (Lucas Moreau): Support tickets roughly doubled for everyone over the holidays, but the 10 merchants hit by the cart-reminder bug saw tickets rise even more (+174% vs +148% for everyone else) — a real but modest extra hit on top of the seasonal spike.\n- Head of Merchandising team (Zara Ahmed): Quality tail at a top merchant: 1 in 5 reviewed products (23 of 105) at Cragsley Outfitters, our #2 merchant by revenue, average below 3 stars — some still sell hundreds of units, so it's a brewing risk, not yet a sales problem.\n- Head of Merchandising team (Zara Ahmed): Bundles are a missed opportunity: none of our top 6 merchants by revenue use product bundles at all (0% of their catalog), while curated collections are already used by 65-100% of merchants' products.\n- Head of Merchandising team (Zara Ahmed): December 2025 saw the worst customer ratings in 3 years (3.71 avg, 1 in 4 reviews 1-2 stars) — traced mainly to two Home & Living merchants, not a platform-wide quality problem.\n- Head of Pricing and Promotions team (Oliver Grant): Free subscription-box deliveries are going to already-canceled customers: 82% of orphaned 'free delivery' orders belong to customers who canceled an average of 117 days earlier (~€57k given away).\n- Head of Pricing and Promotions team (Oliver Grant): No coupon in our system has any usage or per-customer limit set — repeat use of codes isn't a rule violation because no rule exists.\n- Head of Pricing and Promotions team (Oliver Grant): Welcome10 orders run ~2x bigger than normal, but its refunds (€12.2k, spread across 92 separate returns) now exceed the discount's own cost.\n- Head of Lifecycle Marketing team (Ingrid Solberg): Loyalty programme payout check: points redeemed by members cost us 19.4% of the revenue those members bring in (€177.8k redeemed vs €915k revenue) — a high \"discount rate\" for a rewards programme, with another €669k in issued-but-unredeemed points still owed.\n- Head of Lifecycle Marketing team (Ingrid Solberg): Correcting an early red flag: loyalty members don't actually return items more than other customers once you compare like-for-like by product category — the earlier \"members refund 2x more\" number was mostly because members are concentrated in our highest-return category (apparel, ~38% refund rate for everyone there), not because membership itself drives returns.\n- Head of Lifecycle Marketing team (Ingrid Solberg): Programme reach is small and uneven: only ~16% of customers are enrolled in loyalty (flat for 2 years), under 1% have ever used a referral code, and our B2B wholesale merchants have no loyalty programme at all.\n- Head of B2B Commerce team (Daniel Kim): Lost B2B wholesale deals take ~4x longer to close than won ones (35.5 vs 9.1 days avg) — time-open is our clearest warning sign.\n- Head of B2B Commerce team (Daniel Kim): Deal size doesn't predict win/loss: win rates stay flat at 79-88% across all deal-size bands, from under €3,000 to over €12,000.\n- Head of B2B Commerce team (Daniel Kim): Performance is broadly consistent across our 5 B2B wholesale merchants (75-88% win rate), but one rep at Polder Packaging wins only 57% — a likely coaching opportunity, not a systemic issue.\n- Director of Fulfilment Operations team (Fatima Zahra): Inventory accuracy: ongoing stock miscounts are small but rising — about 850 single-unit discrepancies/month in pick locations, up 24% since January (now ~940/month), worth roughly €20-25k/month.\n- Director of Fulfilment Operations team (Fatima Zahra): It's a picking/process issue, not a tracking-tech issue: mismatches cluster heavily in high-SKU apparel/footwear lines, while items tracked by lot or serial number (food, appliances) actually show fewer discrepancies per unit sold.\n- Director of Fulfilment Operations team (Fatima Zahra): A separate one-time ~€138k year-end stock correction on Dec 31 was ~94% concentrated in just 5 B2B wholesale merchants (office/industrial supplies) — likely an annual stocktake true-up, being confirmed with ops before we treat it as a trend.\n- Support Operations Manager team (Ben Carter): We miss our own support SLA more often than we'd like: in 2025, 38% of tickets got a late first reply and 20% were resolved late — urgent tickets fared worst, missing resolution half the time (50.6%).\n- Support Operations Manager team (Ben Carter): Customer happiness tracks almost perfectly with speed: average satisfaction drops from 4.4/5 on tickets resolved within 4 hours to 1.7/5 on tickets that take over a week — so faster replies are the single biggest lever on CSAT.\n- Support Operations Manager team (Ben Carter): Support pain is concentrated, not spread evenly: just 3 of our ~94 merchants (Cragsley Outfitters, Marrowby & Vane, Studio Vrij) generate 34% of all support tickets, and these same merchants have the worst response times in the whole book.\n\nNight 1 of 4: you have 3 report slots tonight."}
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{"parentUuid":"fb0f18df-b347-406a-9b16-6b0847ada13c","isSidechain":false,"promptId":"ee3cd3f7-d2ea-455b-9f0e-1437d2157106","type":"user","message":{"role":"user","content":"Monday, week 2. Daniel Achterberg replied to your memo:\n\nSubject: Good progress — now I want to understand the \"why\" behind the hotspots\n\nMateo — this is exactly the kind of baseline I needed, thank you. Two things from your memo and Mia's notes are sitting with me:\n\nFirst, the speed→satisfaction link is strong and now confirmed two ways (yours and Mia's numbers line up almost exactly), so I believe it. But I don't yet know if slowness *causes* the dissatisfaction or if the worst merchants are simply worse across the board (harder products, different shopper expectations, something else) and speed is just the symptom we can see. That matters a lot for what I recommend as the fix.\n\nSecond, Mia's finding that three merchants account for a third of all tickets and the worst response times lines up with what you found independently — that's a strong signal, not a coincidence, and it's where I want to spend our next slots.\n\nQuestions for this week:\n\n1. **What's actually different about how Cragsley, Marrowby, and Studio Vrij run their support** compared to the rest of the book — are they understaffed relative to their ticket volume, leaning on slower channels, getting hit with harder/more complex issues, or something else? I want to know if this is something we can fix with guidance or tooling, or if it's specific to each merchant's situation.\n\n2. **Is the urgent-priority failure (missing resolution half the time) concentrated in those same few merchants, or is it a broader pattern across the book?** If urgent tickets are failing everywhere, that's a different and more worrying problem than a handful of struggling merchants.\n\n3. **Has Studio Vrij actually recovered since the spring breakdown, and what would it take to get ahead of the Nov/Dec pattern this year** before it hits? I'd like to walk into December with a plan, not another postmortem.\n\n4. **Before I take \"respond faster\" to merchants as the headline fix, can we check whether the slowest merchants differ in other ways** (ticket mix, how support is staffed, shopper profile) that might also explain their low satisfaction? I don't want to oversimplify the story if something else is going on underneath.\n\nLet's prioritize #1 and #2 this week — those feel most actionable. #3 and #4 can wait if slots are tight.\n\nThanks,\nDaniel\n\nLast week in the company's analytics channel:\n- Chief Executive Officer team (Hannah Brooks): Revenue concentration: our top 5 merchants generate 72% of Tidewick's processing-fee revenue, and one merchant alone (Cragsley Outfitters) accounts for 42% — we're exposed if a top account leaves.\n- Chief Executive Officer team (Hannah Brooks): Growth is healthy but comes from existing merchants: active merchants grew from 76 to 111 and orders roughly doubled over 2 years, yet new merchants contribute under 1% of revenue in their first quarter after joining.\n- Chief Executive Officer team (Hannah Brooks): Refund rate is creeping up: from ~9-11% in 2024 to 12-14% in late 2025, without a matching rise in payment failures — worth a root-cause look.\n- Chief Financial Officer team (Kofi Mensah): 2025 processing-fee revenue: Tidewick earned €93,583 in payment-processing fees on €3.97M of merchant payment volume, growing to a €14.3k December peak — full platform-fee revenue isn't in this data yet, to follow.\n- Chief Financial Officer team (Kofi Mensah): Merchant concentration: our top 10 merchants still drive ~65% of payment volume (led by Cragsley Outfitters at ~18%), but that share is edging down as the merchant base grows (92 → 113 active merchants year over year).\n- Chief Financial Officer team (Kofi Mensah): Refunds: Apparel merchants run a steady ~20% refund rate (normal for fashion), but a handful of smaller non-Apparel merchants (e.g. OfficeHub Nordic, Sports & Outdoor sellers) drove a refund-rate spike to ~13% company-wide in Q3 — still being double-checked against return records.\n- Chief Operating Officer team (Lena Hartmann): Delivery speed held up over the past two years (~85-90% of orders arrive within 5 days in a normal month), but it took a real hit during peak season: only 56% of orders arrived within 5 days in the worst week (early December), before recovering within about 2 weeks.\n- Chief Operating Officer team (Lena Hartmann): One of our carrier partners, Albion Parcel, broke down starting in November: on-time delivery (within 3 days) fell from 76% in October to 28% in November and December, and hasn't recovered yet.\n- Chief Operating Officer team (Lena Hartmann): Which merchant has 'good' or 'bad' delivery isn't really about the merchant or the warehouse - it's almost entirely about which carrier handles their shipments: the exact same merchant sees 89% of parcels arrive within 3 days on one carrier vs. just 19% on another.\n- VP Customer Experience team (Mateo Ruiz): Slower support replies mean unhappier customers: satisfaction drops from 4.4/5 when resolved in under 4 hours to just 1.7/5 when it takes more than 3 days.\n- VP Customer Experience team (Mateo Ruiz): A big chunk of support tickets (~65% recently) are shoppers asking \"where's my order\" - and this spikes every November/December, matching a rise in late deliveries from ~4% to ~10%+ of shipments.\n- VP Customer Experience team (Mateo Ruiz): One merchant's May 2025 delivery breakdown (70% of shipments took over a week, vs. under 5% normally) explains an unusual mid-year spike in slow support - it was a local incident, not a platform-wide issue.\n- Head of Growth Marketing team (Sofie Nilsen): Discount codes are costing more than they earn in our core retail categories: discounted orders return 5-7 points more often than full-price ones (e.g. apparel 39% vs 35%, electronics 17% vs 11%), with no real boost in order size.\n- Head of Growth Marketing team (Sofie Nilsen): Referral customers are our most engaged shoppers by far — people who refer others place ~8 orders/year vs ~3 for everyone else — while our loyalty program shows no such lift despite similar effort to run.\n- Head of Growth Marketing team (Sofie Nilsen): Growth is concentrated: just 2 of our ~120 merchants generate 31% of all \"direct\" site traffic, and only 1 in 7 orders can be traced back to a marketing channel at all.\n- Head of Merchant Success team (Arjun Patel): Our biggest merchant, Cragsley Outfitters, grew sales +19.8% year-over-year this holiday quarter (vs. the same Oct-Dec period last year) - real growth, not just seasonal bump.\n- Head of Merchant Success team (Arjun Patel): Castell Fasteners, one of our top 4 merchants, is flashing a warning sign: shopper traffic on its site dropped 44% this quarter and it's sitting on €5,080 in unpaid invoices.\n- Head of Merchant Success team (Arjun Patel): Merchants with naturally recurring purchases (coffee/food subscriptions, B2B restocking) keep 83-94% of customers coming back, vs. ~58% for general retail - a strong case for building reorder habits into any merchant's playbook.\n- Head of Risk and Payments team (Claire Dubois): Access control: we can show who has access today, but we found zero audit records of role/permission changes over 3 years — so we can't yet prove when access was granted or revoked.\n- Head of Risk and Payments team (Claire Dubois): Data privacy requests: all healthy — ~48 requests since 2023, with a consistent 30-day response window and no real backlog.\n- Head of Risk and Payments team (Claire Dubois): Login security: one sharp anomaly — in Feb 2025, 61% of login attempts failed (7,429 of 12,211), a 10x jump from the normal ~6% baseline, and it wasn't flagged by our suspicious-access alerts at the time.\n- VP Product team (Emeka Obi): 🛒 Most lost sales leak at the shopping cart, not at payment: only ~1 in 4 carts convert within an hour, and the rest mostly stall for 1-7 days before fizzling out (just 1-19% of those convert) — payment itself works fine once an order is actually placed (97.6% succeed instantly).\n- VP Product team (Emeka Obi): 🔁 Most first-time buyers do come back: 40-62% of new customers place a second order within 90 days, typically within 1-2.5 months — a healthy, stable repeat-purchase baseline across the last 3 years.\n- VP Product team (Emeka Obi): 📦 A return modestly cools the relationship but doesn't end it: customers with a returned order come back 49.7% of the time within 90 days, vs 57.7% for customers with no return — about an 8-point dip, not a cliff.\n- Head of Data team (Yuki Tanaka): Revenue checks out: once you count refunded payments correctly, orders match actual money collected to within 1-5% (not the 20-40% gap it looked like at first).\n- Head of Data team (Yuki Tanaka): Refunds are real and not lost or double-counted, but most (85%) land ~3 weeks after the original payout already went out, so we currently can't point to exactly which payout absorbed a given refund.\n- Head of Data team (Yuki Tanaka): \"How many customers do we have\" depends entirely on definition: 11,258 total accounts, but only 6,159 bought in the last 12 months and just 411 have an active subscription — we recommend standardizing on the 6,159 figure as \"active customers.\"\n- Chief Information Security Officer team (Noah Fischer): 🔐 Shopper accounts have 0% multi-factor authentication (MFA) enabled — literally none of our ~11,200 shopper logins use a second factor, vs. ~55% of staff.\n- Chief Information Security Officer team (Noah Fischer): 🕵️ We can't currently prove who deleted things: of 615 products deleted across 91 merchants, 0 show up in our audit log as a logged deletion.\n- Chief Information Security Officer team (Noah Fischer): 👋 When a merchant churns, their staff logins don't get shut off: 16 Owner/Admin accounts on churned merchants are still marked \"active,\" some unused for 500+ days.\n- Data Protection Officer team (Priyanka Desai): 🔒 Privacy audit: our email-retention rule (delete after 730 days) is configured for all 120 merchants but has never actually run — we're still holding ~44,000+ customer emails that are over 2 years old.\n- Data Protection Officer team (Priyanka Desai): ⚖️ 3 shoppers' erasure requests were correctly put on hold for payment disputes, but those holds are now 289–954 days old (up to 2.6 years) and need review before we can say the requests are truly resolved.\n- Data Protection Officer team (Priyanka Desai): 📉 Marketing-email opt-in rate has slipped from 99.4% to ~90% since 2023, with a sharp spike in opt-outs in July–August 2025 — worth watching if we rely on consent for marketing.\n- VP Platform Engineering team (Lucas Moreau): Background jobs ran reliably almost all of Q4 2025, except one bug in our cart-reminder emails that failed for a single week (Nov 24) and hit 10 merchants — including our #1 merchant by revenue, Cragsley Outfitters.\n- VP Platform Engineering team (Lucas Moreau): About 1 in 7 webhook notifications we send to merchants' inventory/accounting systems (ERP, WMS, and Accounting syncs) never go through — they get stuck retrying instead of succeeding or failing cleanly, all year round.\n- VP Platform Engineering team (Lucas Moreau): Support tickets roughly doubled for everyone over the holidays, but the 10 merchants hit by the cart-reminder bug saw tickets rise even more (+174% vs +148% for everyone else) — a real but modest extra hit on top of the seasonal spike.\n- Head of Merchandising team (Zara Ahmed): Quality tail at a top merchant: 1 in 5 reviewed products (23 of 105) at Cragsley Outfitters, our #2 merchant by revenue, average below 3 stars — some still sell hundreds of units, so it's a brewing risk, not yet a sales problem.\n- Head of Merchandising team (Zara Ahmed): Bundles are a missed opportunity: none of our top 6 merchants by revenue use product bundles at all (0% of their catalog), while curated collections are already used by 65-100% of merchants' products.\n- Head of Merchandising team (Zara Ahmed): December 2025 saw the worst customer ratings in 3 years (3.71 avg, 1 in 4 reviews 1-2 stars) — traced mainly to two Home & Living merchants, not a platform-wide quality problem.\n- Head of Pricing and Promotions team (Oliver Grant): Free subscription-box deliveries are going to already-canceled customers: 82% of orphaned 'free delivery' orders belong to customers who canceled an average of 117 days earlier (~€57k given away).\n- Head of Pricing and Promotions team (Oliver Grant): No coupon in our system has any usage or per-customer limit set — repeat use of codes isn't a rule violation because no rule exists.\n- Head of Pricing and Promotions team (Oliver Grant): Welcome10 orders run ~2x bigger than normal, but its refunds (€12.2k, spread across 92 separate returns) now exceed the discount's own cost.\n- Head of Lifecycle Marketing team (Ingrid Solberg): Loyalty programme payout check: points redeemed by members cost us 19.4% of the revenue those members bring in (€177.8k redeemed vs €915k revenue) — a high \"discount rate\" for a rewards programme, with another €669k in issued-but-unredeemed points still owed.\n- Head of Lifecycle Marketing team (Ingrid Solberg): Correcting an early red flag: loyalty members don't actually return items more than other customers once you compare like-for-like by product category — the earlier \"members refund 2x more\" number was mostly because members are concentrated in our highest-return category (apparel, ~38% refund rate for everyone there), not because membership itself drives returns.\n- Head of Lifecycle Marketing team (Ingrid Solberg): Programme reach is small and uneven: only ~16% of customers are enrolled in loyalty (flat for 2 years), under 1% have ever used a referral code, and our B2B wholesale merchants have no loyalty programme at all.\n- Head of B2B Commerce team (Daniel Kim): Lost B2B wholesale deals take ~4x longer to close than won ones (35.5 vs 9.1 days avg) — time-open is our clearest warning sign.\n- Head of B2B Commerce team (Daniel Kim): Deal size doesn't predict win/loss: win rates stay flat at 79-88% across all deal-size bands, from under €3,000 to over €12,000.\n- Head of B2B Commerce team (Daniel Kim): Performance is broadly consistent across our 5 B2B wholesale merchants (75-88% win rate), but one rep at Polder Packaging wins only 57% — a likely coaching opportunity, not a systemic issue.\n- Director of Fulfilment Operations team (Fatima Zahra): Inventory accuracy: ongoing stock miscounts are small but rising — about 850 single-unit discrepancies/month in pick locations, up 24% since January (now ~940/month), worth roughly €20-25k/month.\n- Director of Fulfilment Operations team (Fatima Zahra): It's a picking/process issue, not a tracking-tech issue: mismatches cluster heavily in high-SKU apparel/footwear lines, while items tracked by lot or serial number (food, appliances) actually show fewer discrepancies per unit sold.\n- Director of Fulfilment Operations team (Fatima Zahra): A separate one-time ~€138k year-end stock correction on Dec 31 was ~94% concentrated in just 5 B2B wholesale merchants (office/industrial supplies) — likely an annual stocktake true-up, being confirmed with ops before we treat it as a trend.\n- Support Operations Manager team (Ben Carter): We miss our own support SLA more often than we'd like: in 2025, 38% of tickets got a late first reply and 20% were resolved late — urgent tickets fared worst, missing resolution half the time (50.6%).\n- Support Operations Manager team (Ben Carter): Customer happiness tracks almost perfectly with speed: average satisfaction drops from 4.4/5 on tickets resolved within 4 hours to 1.7/5 on tickets that take over a week — so faster replies are the single biggest lever on CSAT.\n- Support Operations Manager team (Ben Carter): Support pain is concentrated, not spread evenly: just 3 of our ~94 merchants (Cragsley Outfitters, Marrowby & Vane, Studio Vrij) generate 34% of all support tickets, and these same merchants have the worst response times in the whole book.\n\nNight 1 of 4: you have 3 report slots 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tpuF/nCuMhbTWNIzShdFDJH5xmI92/OxFUHpKUZPKxJ6//u2bdwaKCzv7X5KzjbzQeip9Oo7TK/EtogTiztpbjehfKh/P25Ebb/ZdpRu3PcI7zRRUeYhNH6ZtiHvY3xX2WMb+vcRgekAc3omrGUmWUHFOHHNpkDivMJpfAf5Q4F2EKOKhLtGRYxeCfKRGkhz7ct3KekkdDGmeSP+L3A/weu4L26iYY90/6Pf9Eph89lr46ZFsEab7ZZnN2w5Gl5ohCC4CKAHHF+0SKd+pQ289LElsVnrlmfeqJUXRd13AHhYIAO94+gmuiaBcUgLi8F0VEv/zCNyysONM29UWkWYmDnL47y4jg+HvuoxvUOa7StE4/a8G9a4rmQ5aBUD7tyMHdUbfH2+7JV2NMS82UhK22mZPBqkyK2PuRpgcIzNwhb0rk+SJ47yDPsk4JWicjrv7hGrkrqjKmVC4PgnleUzdG8zHk/6T/5hGkwM/m0PWo8ac+oUcHWXoN+rEbRAi59oD0FkbV3IuXGlcfzUSLWhPBD34PkkpbaB8jkCxTjJPZmW5j3hLA1RoUDIjwP5WgDPN9jywrxlE1qBKFI7QmQRgg6rNxH+b53t0OlKVkPo8sH+dG/cfIDrDhlkBvHyic2E7GQxCmJDdxkhibiLMqn2IeahE6sOkTE5QeK6q6BBcA4j/SEPUjF/LYBeBJSitAhwYhsh4nrph6fSsWZBeFn9lkXNudoPz+DwpG9xsMuO9BNb3ztZK0V5Qr+hsqDKBTV8q1Sso1qzaowzzrbEyccb0dUOVtFQ9KtKKh+7KHp672MNURJ8DiAgBhOWUMdXylNwErVJVvthRq01G3xeR52DVmK7ke4wT3fGOusIIGSPt3bAyHAq8i4c9XeaqXmgAIAnCzAxTSPpON9i5UqlP8gTn++LnTeN8SwGIOq/2XgqzuK7lTnDzEOtq69STW1B27Yoy80WUjwGQpcKRzbjAF69HSFkm4HhzrlbXa8fnDe1mMVICUibSGMg5rt3zmCBZGfYOmkaGpEyB4GfYtUIRhX/rBSbaNXf29jtz9MRm49AWNr483hdZtbl96bimrxnlGxRQqSo/yvruVM/yQQ6Z2g6M2gix3Doe0ujZMIUFA6yhvdlpBALbw41PfBXROlVjWEtG1XFDSBp6IYQQMHmkO9+JDsQ3WFrCslOt6WMlDK4IsN23RBMNgFNa/o6WmXMMEdcKRIAx0jB9gfSUjkVmWMOutAmLwZzcGkP+bH1augjWANK9rhz52nDr5HLE/t6OhRnGtx6BKy+O7B7PGd4iAIqYTZfO5ZV2QrcrFCbAj/xzmgqj/sKyV5jERNQe54t0ocNnTZyqwQEYKJ8d1hx7+g74OuKovvmK0DH4kkpnOPMk6Duiv1Na3QEbt7BkpcfQ9Ig1+KGXVfDHcG1vo46NXjLRlyh3CMK80OxLK/CB3S9uVZnIJw9okNkn3mSkOXMtVS19VlP7DFRIVMgOmXH7ZCqOlZA7iOf8k472nrwy2ZIngq4BmMBFsfPTibodrfSRNXZEwxDr4AwswwbwInOh3LBaaPWocrABUbimtJDjT4OOlGaKLNfjpMZypjNP98nfFNuaWvwZiB3orQYnVYxE+BTAMMCOXDhCDgOozKEV1oEyTA65Te+gw3j7cJFBzONeNcQuVoxQyzggjIm51+RS7ZB90cvRGEryEljnMXHkz2JTTiJthKCqGJ67Iy6KkADsNYRlgRwlQAXuprv01ItovNb79nfZH+OfgRSJYC9CqiAhwHubV6t7AObA7Ovp7msrVXHw8deuDaUNxzbdV+T9R5D8dJ9KWb/6xw5Bu/7g7qK6RJqMBjxweyZQjAWqRZXxOzvKEsorqS90jCkpbwFxUQlzIJTOceogZyE0frwaJfWRGg8X6v3jjiXJmsHeodbLEb9faJFOR+gwwtuSxWoqvhQounkypIxaU9xvT/yaVwJCTJ3h/qd/djZYuLN3k1Ngf6jxWaclYhbG4sTC0KTXFJcOluYeUHJI6HbnoA6Ablyn9BliqGSnYeveOStAmLXkOOrC9REKS83h0urO3jU0Qm2zpmTgB/a0CIYZ8hVpx8qBMMGudxfxJ1pIGlanugq3+LeWT1tqI6FsCBIAJFlFE3XMNX/Xm0GmHdqzQxyz1mGcu7I2WhlwobU67kUrqWwE1ex6RUzXnIlCaMdWf2aPGETxqLmZjp1igwUnZ9cfeheeLSEuzFbvrjuXC78MJQ+MbDRlOTITXuqpRpBSa802siFfGK1D2d0hn5giuytFkKnuBDJkbW7IjE78oCcz4rqX2UYhG6iZ17bOrRFqyE9aWQaztTTKQckhy7/LtWVdpgBss4zdtuGyFbYIX257/lc3gyD4WfiK7vKmfmHJNfoS+rkJiV/1Jmlt3gVFMFPRL5FEiBZsXcFHxmg2NYN92kOnknBLrvhHXKuObvqpH7ZyhdaIl+YdMCHb/HERfLPH+JnBkgWm/yl5XGcFUHrrn2WY2/hnzocypeGLFYZr8uFsPcAGJe8AY3naDpnQ2FX2P49tVq+wTx6GTrGR/qH4mHtQ09Gdn2UrcoTFCyzu3I6O/tp5jaWXN0CT22QLVZ2u0TsnG4OhujbtH7YZHwwVIi5tK6/rBFC1nk6ZEio1VqerJv+BlzXeM6iE8oWjViuqkWUVeN1zLe6+S6zXNzzwsdqvHgpE+duPvYQh8TnLdl/P1DyxEc8xsamNYjgpXK0RGvJrGiBiZrckWCwgqwfyppPpJHgtB/4NZZ87Z0jRvNLuJAWp4yRr92VFQ+IWiLlDgutdatdcaJ+mkRogYoAQzGXWEy+hqpNrGEYakjLMd6E8EwEJQoCxzWBAyMREbopfgEUDJCGOWJqOglGDUxXKJLIsdFuGmP/PgcHwXU3zi6Gp6BeQ6qfcyLro02u/AmXKe4xK5Ht4h3r9YctnAjK6F6f3DvZw+Y/Eu/wmemxQzBnKudfHShRgmB80d9IXJjxdoaIgLc9bDrArC6186Hkcw5Blixw5ct7wmMh9MK1ymL1SThVrqkVyxB6lA7iofKtzcfEU74C5ScmBDWTmA6dROpU1StJ2XOD09fyHR3QzkNMXoOhClnZ/XJu9R9F6hZ5Pk1hD06wsWD1rKn8wMM2pNYix5dAl0oMgjKDiNABF1emBf9kEEjqaN+lB7Yhe95JBkZ3EsCRc58j2XONvvaSn0QlMMDR5qV+J88TfniUa+PTIZaVMH+nuSrbLqj1kRsPxDQFIS+wcGKa2sXo4yXtI/TK2ob5011VbZwgpihGiKY/k4h+1B53PEA6K4VrJ81BbUALsW7ngNKi/uv6bqIraiptWQn1Z/+cPWN4rkhDmpHY4EkNvSaxOUT3X0oDnzRYX3lPUs08wGgxyUesw0HQM/bOcuLE+huAYPvqkpAzjPN7OBvw4XqDmSjVfbxdSnXM+UJLTJsVpMc69zVuTVDSY7hp/c5PnIAIrXA8f5vtomEOnorusgXipfOjBG6H9Sq6xm51R6bbQHvRxBiA62MEHM4uTKv9JHWagxejpSR7NnwoXJpwKM2QN9IOS2on5rIb+JmFEAfahMp2N0yxhgAVLBhZgzFn/3NjxiA/ou1NacuLnNtmDzI83W/SBLh8JgPCbMD95FKei453FIMrHFLxL79C+qcmeVb2vaoahndspMIzgwpZg6wUeilqJyYbGwfiNGycHMw2ByL+CIb5mk4CJu4UassCrVigjHOKJwWmpXZuBwImeFwTKrN/9ohHk1uWYP2aNU2gob9GbV37bPg4+xC7qgVusYcrezODcBv245nQnGT8ii4ZQzbkkGP9uWjuV1KZ28ygScRK+Pocer3wxD+q5Q7PVmu7csqXB7K6aeOldmYXeJ9bRWHwW4HRf9x6zFiafrWyl6dFHxWfjx/tF/lDcTBFYr84adiEoPhNJNoSdGKQqxaM6m5xblCEaZzhPUbp5p1Nc199cBoKqUqKD9SW0cAeTY28qnn1AaU+ae7yWzjQmdDDHuKk26tcl81Xk+qsQ6qmkba5lWrDkoH7rqE5nzo8t2WvcQhz1HRqhN0i4F8tkiLDAygMNyvXoepkNqad+bckBRn1AKmrWbfXmtyv8vsPoialnV9Zi7WVYolD7HSesJA0088gHw695IXnbGQ3xUccnAZQ9bxVolPiAIU0Yf8PR5SkUX4o10L3iZpF5m2ZDev78+jNlULE6iJr1cR1O4oOM3zDjGAm24l4YmFxS3F/JOm6GDyLSy+jL6q/WZivpEqhlszPJvfezhw27vHCuxtvTjr8a9zbKEdo8ZEsx8KKmxuPOSarWfCRF7/nejpSlV0SEgdwB/pJD/JwtCObEj2D/qe7gLLKK4vAMLbplZhD53IKS6/LFFJrL/S35NWJY9JaGXDL3TWG7RO7D6oo9dkkW9neJd7dKHIXz9izS6vKG612Z5eDrlAYH2mxLb270EdjfJIcAQ/FnJ3i5abHhZ+MiFpFgeiLzpikqkeeedeLHvl9A4H9nKYKMu0qpKdrojO1sELTIDtXDIVR4BJ7Joz9bypeOH9re+5+WkJobFmMP+YFay+pJFr1RzdjmGMjjgwPDUCuNEDdLO8frQVQQ7X6usBtIy7xaT/OTCbDhwvwD/aoMkQCd/GDAeNMy+BBeo9T+0kYvIXZ7bDy4zd2rFhB4WqnFair0ELA8837AOjvzGZpFQMwnx5a8NKIx4fCqyIA9V0LxLeiikT8SgqzBuyg+T7SbDOZg3zvIBaoyA3xW9tbCoZRnYlKC1EwiaNYrgzrv8RQvaZoRtJj5iISxLh+xQccohlAB/8Suo+9HvSTAWnQqDrrzMiiKLhQ7DITGDjlha7Pqleet8MNPIIQ4XRl3mS5vVkUUTPUu0Zuz2yr2qsEuUsxypAIfVeajPZ5JLXwBPoD8aruaDpnzbKCS3b8oj4qhJdr713xzS1DY/RhXbryYJ+doCAIdFk6U9t6Qb82ASsl2e/pAyHXPtitd547bz7ienmPkdscXwiu4LGzGj802NaGuPxqj0UwHBUG9UoBp2aJqXAbk+ewjLj62zDMGSS7ePxhRkf6dAUY0ayK/A49LxoK9kYgn4EhMyKhD8lt5amOlF7yqJPRLwNLfn1xjocGJPrMGQ+hxYI6Lf1Owbs7CP45MDuG1BGUWub1xQmFkkKC8AiN6i9+vLEgVr6frM+EgkQjNccWrxuj26OifVpllhaGFV4gy4ufpgk//78yt0O7QEDh1WzWEJ9y5iP3ZGfipdfw6R7Tctti2iWkdoJf2vKlTT9a3FTB5VoJ2mpmyQoGCRlA24GsfuqsTMVz7RfULzvwMcHQRZdhTEtf8CLDoqDBiM6M0rrO2AjsT+jwUndAqqSLNt+dwQNkRZaYjNU7azZLSx/HWqiLcIGcHSohUrXeZzi+FQfTpYPihBz8K270Kw/sXlivZA19YhTJPkiMuOUcDEoT+hkrhqDPhFS/5H0KA5YTdnXZbBPb/8EQ6eocu2ugFDIBaY9IbZJb0NVvnGVQfb2h7IE7IyKB+hSTHxMmal0JFY7UBzK5EdAl8fSVcGdxMMI5Z6nNr58E2YXrraT9YtCPsgw7kt8hadlwhIS13zhc4xwglrcG6muVNhsPgSRZI6gAgQ9f7e3uF653CTh0hhRFB9X3sVhTY4ZTgu6HYS6xqEKrWRH4qmWsj0A6YrrVopt2RoPK7MpqnEfpXd+UYvuv+QTFUfgiyKSVZfYAkFsfd09SWJ+ijf8R2lYmppeIdd2YMLFS+2ntif/49K98OzGGvnicc5OtuY8Ej0hr/4UlKYJn88Nt8NvKHvSBdAkKBltFFCOIuGOMVJ6WJtMcBRAU2uAguw2FaWfCNZ3lSAEAyJib47D+iLUrNWkMbTWauPmYDYvB6rzFw9hRuZTrzA17XZfCtX74LSM0672qGONimglyZfHXKtzpi5tWlHST7IoVSmbeFfOtNCkXwWb2bXJnlyhKU+UU0XPfG5ieo/HOheXkTc0Nkiup1unfcPQARaBYlC2R+SzpbN6D2dLZBV9owRYp9OyxJ4wF6ySderiGLu+lu24KXQc1YTK4eYxUOgXYi6QtSYs4/SSEjtgGoqJddV7ygO83l4yVs3GwY38nlNxIotTfXHdpOIA4XnXc1EO/Gh1lPvDg/8u88IR2aJhhZThygsAv0FNiSR3afgfK0sjms2nXvHgkjoaAG/gXJalJGExbG1RDCjkpXYjaWLDq2pKdlkLFLNUVDjjJRiWaNxmzLPzni9nV5Otz8R6vzlkiKVk8Qx7ueYyKZMqyQBOlZibQjcYAKku9CqB2T1SjVhxotXbtABA6YjKqPHFL7cQ4rEGHZo+O2gQOkA5a9AUWoJKvz+FdJz1pTf3EwazoK8xUW4v0Z8A8gHxpd8IKyx+dXYsm1v8KWxr/NQZX1cxjmo5JlUe70mjT4XK8LjTb9axm0/brQKhU2c85rABQO1LLZqpzRA6eoij+r/JmDCxFY54gHaGatQwwjzAQDcbvZss1p8bi61MiR9OHrgrZPlQTZZeDgHvI1ozvgw1UwyzruVfDptppTf7/l9KBzzrb0B9G8Q/bnsa5CBGGXwVTrZzfaTDCTy3Rfsp1fukUL6jTA2naOwhm9G+HDZOGhJxRFqjyqo2kLyUybkETfn32Y2ctRb5azfwA75TPBZdMqcImTnaW7h4bqLaEQ7Wfuxn/emwP0Rr5m3EoBnsRAdAd1Z/vTmgbecTSfE1ZNrtrsV6A4xteCd1eyTZPV4WkoNh7xQMyN/50Yg19zruVdCVNEyMNfIPDtZoYbOtxhsvXoyGI0PXnWDZzO04u+NCkbLfABCTx5ZuSXZ57tOATJEHG9d9WOh/hsdml3ygvtZBhCqq91rmFxRV+DkBdygiNXLj7l1kPlCaocX92SHveY7rKM6Avt9u5XpdyOspHDBq6s+rBO1M4i3fzhDFBdVdKKt81iwZYMpcyBy5WmdrEAxsINk0LUuT71kdLJJcSsuNTWm5+0mXhhB3CN3flHNZoPSeb19yLqtCT2dQskFyRzGqQqkyR8EsFvPPb+MGWcMa8jc84UKGKnCDbeA5i8fV/02CvRW9gOFSN63B5JInD+JDizydaFJADBV9HaDMWYH7es5ntJP/lvpejyAUSY2ij2dKIqahtaPp2rFQWtUPqymHOi63KF/ndFz1MnashXJtAGLVxDdaw2XFoQsh4CxEgsSI7PG5k6nwlXFay9NyEXPgXUdJih+Kaf5yam6QmXs0TLT7+YS3P+R+87S+lfq9FchueTM1sVtlwIDdwnv4xjNyTvxPif4CH3vgALXstWz++wNvYrjW1jSY27U5e0isHtzkBJIZVzJbhWhvSYQjsHhcNh3EoQWld9QhUQESSKxFAdLzVaRRciTqv7SzCDXnWPiopnwzQm7HzscZRdRkU8NNJsX3FFVIxgZE5TyT0e9qOPqtdx8MU5CVL/fvhSrAYq7CTYmMnGtIC8mfdkCF0wTji5f7mLD+xat3jFDhqnEaKA6V1yZTI/2JiVpD6/MrAhV+qPN523hWLNzbn7PvXyEtYjoYnuuCKJFYyRGaH+d2VPALQNTh8s/dPB8mf4ewqbMzDWXcDfX/hLsuoLBnhZsslvkXBH8xe1HH5j1GZ4gclp55BHau5+UNrJrobkbP2hIvkYq0kc/FetKutuI1mXjyqEBin6iF75SCyDL5Od0Svkiv/8uus8qBG4zX+kwYsN4HJYOrVkfgTU48WXJo+oo2rDp/qRyGb8Ls7mqP50p3Qds/f+lj1VaJTiXel3dHflDe7v9ah6DjsYtYPw8DA2krmxl99b9qb1bFTlhIXxO6PVg6Tf046K9Df90m0nEQH921gVReO5zBI5oQpf1yXrYauV6XOS+3VgDzPP9zPymjLf2k7Sk6dh762+FI/9H1SwoIx4jlJLOiAssV+ScYMqKXCv54JTmA0KQlqEqSnnWhV7LkPGl62w6YqDNOKeh8kP06d5Dq/pI4nX5XldjC/lvqGbq5ltDKh2CcuG04C/cB/GkGnF2xKZec8wVQhMOOtnjIUz7PibYOlx+zareXNhPChX0mgzYoSlBh/wDkpL6bFJUXjwBA1H0buCmQIIgnPVnbQLGxfkAW3XlXXb+3BesP4O4aXKmwwalTuv3rF0nbosZTYU1iFZyRVV+O5sUAHkwtJBY2c//diDos8Pyggo05rzL0cXYyICmRYFYcWzXQYamRJdKl/QslWT9j7vSvlLoVTkbZA0IwMh6nZjxPHG35Gp64ZgdPeChiYZTigo3P649GKJGF8MIrENPwF0Y0HUANXDX/Xi/3psUFhluzZoQC6xpwOb4hn0wjrYqTaeTFDZqAMScMysNa8w0cVfI2KqhxYNvmpp3WEGKPSVRXjTBA0O0SPtSTziNdpxjMEOAzS5ybqdLeBYDkZy0JyiGWW7BB8MiN4wN7l5VPYMaqQXp25YUqTiFOjjtCq10ZWtL0wegW79KMbJ2jBZiuSBkmX/YG4F1GrslyLYA+LsC/IWgUe/zH+N10KnwtLcwhP23BmfuNq9M+9MyMtOHcLMZqZ7tIV2XiDwAUkFuuYLSX0GipDoCLeWC6TCMYN2tGojL4gD9T9UEe1SYjAkVfX/ZwCF9d18yY3yyrUMKpe0Lz+47L8P9Yb3fC6GG0QJtb6Zlf43DC5E8gmMNUGVvrUD9KVdyvRWr2q+AKjhp+oqNvSeaHienWxjyIozUBvjpxpm0t3m+eTFlcTyKalOOdtcoX4Gf0wRcpI5bGehrcz2ICLzbXIUEWq6m/5L1e3uZptb9ckdWhQdN8z9SolltaAFWKfysSUTxct6OLGDQk1gRwLc4m9DFqma7++d5uC1A6Y+dwUUhNug4LfQqZMHDll5bhwQ5eQQnx/DUDHsjqc8Jtno1No1WmclkrKT1u9jGSZOFjWH5AFzBKVprJnyQZ4cjEwsALecg4Qkgk4VfPyvZkNVzyIKopKHfZU0fJd4Lt/m0Dom9temw95EfcSe6DqdtwGKdkc+UfVQ74hBy5sR1EkoxKQGJqGSAuyGOK3YrjKXA6AY/SNe60+iZZFbfp6A0R88OMlZty/lfeLY5IQJBDyoKl0XCV0NrAYcWnp48+uJH79YW13yX6iyu3uZI4/UXjZ33oMvBKmdnnhJo4gAc+NYpoXhfaNTUpOFT1QthQGlazb9cfb/EuPCFC51+cyFH+LsvK+N8ewF/qZV8h9QEufUagmAyv7xET1bi/qneP6ifCtdTef3aj/uMz9Bj87B3qk7JoX5ePLFFcQFXE5s3OPcflfNe0+fHnUv9kkgDK+RrTG076StvvgsMmT7scfveQffcJsjz9O2ptRS1MpErVaoDdU/jM1rEB4s1NKrdDdLOSh2oGade73oSe/24ArE8W+SmGPWSDQ/884QNP05MFrs2mnKYp+CMpJ2BUUtQnFbmqgjANng9hAP8loV4uff3+BIF/oU/8sr2Lm2g/WDDQhL8cGDUmS8ePmm1geBn5Nu4qTwcwMq7/BVLGgNTMA5e14P4lm97DKb47KjcQgfWp8WcTo4gyqZ1KjmTufkdyVrmxiZeri1MkE1hrYfGOrA82Q8E3sF7CaLajR/6oEqID6iJav9Br/uY7NEv5jvuyfCJJXpwog9d6ljOixU2PJWfgW6RBsqxon+4aqxD9sKEdQuw17CB/lQ13jkYyWYsFd1DRWm73BRZ2YeWOOIFxlIWjMjMLkTxXzTrik7q7NE/tm40ER3Visr83P2GMTgUbjtH2nwdoVuFCkYW1YhLEMm9UQT4U2HeUbqGfcI8fj2+fuGwSv9e8q0GODziV1fwmSEvFmBwRmvtAmZjsH0MAS3lc7HP2Vu0OuArvVuZa0xljLVawgoGLNhW6KKg+w/ooBFYyk8J6PpLcHYoWxEeLvU49018WS5alzCTI0ir7Yr2CvmIh/tadaN0Olu6LG3+YoHYCXGkYWcEMLV91MWQzUXevNgWpij+RxqKfCqiBstqetdaTjYROdGOthTxo6zRz11hDrmNaGhiMBZ09ovKOKbZ3v0D+Y/lAz1wQabzM4spduntlV1rqa0xRax1AIWCeAECMN2PJY5I3rNZSE8vQm7HXH/S5tyZe0X0TCya51lKMJqBgcLIzSMxAKWVof/tE3/HFURadL4yZebHxvwv/j5NYzH4PmORYVk3UrhNw9WrLJfiqnw9/nRsWCbLHxH/5uP1PmzVxB2EoBjalxlCT520A4bO5lkgAAdI0PxSol0KgA9ZUlVhurEXgMzdxRdEHdWJC+NVZInEybMvvFRXFhzp/HI6BP9lwwQvsRjuxfxmeWht1DCnm/BPgGts1er/M/7M2GnzMt9AMPkiBE9KZitYvTBYmGRK4h99ca7S5X2/Zuz6g0bIE7T9qE/WcF90Gx1ThaElMoqIJbl6SaI/Z2OdHnCM0b2Is7bvn7yZxw9z89wo7zLi4dlwKdQUsfNT6Tw7ZzPmCw7M+WqfeTEbVTtIXzoGaBeg+kAOPW8OkbpiRNRaFWalh9ybShnK/WqZWOrAlssNq+7mJQOZ8Fe0z4TRJAR6MeI+Dik/gh1wXd0syL2oVH+YDmPaiBR3q8cxmxgOOOI5hX8FpRLa043H8fObPmWD3Q8mwtZlJw0RU55nAQWpcFVMv6KtjSCRrYPQxbbNWSQkKJIEfSAES7FV0Q4ughIWeKpf3yjiqYDX8C9qr/4JL7M4ZLt3MW63CwkmCu3xPwaygXAIFCenN6A4voIeADMGNO66D4DatNyu4quACA00pk7VJvQJmXJD3IDJtZpSJ9L8gkhz4FUAHnvWS+AUBvywacBptb4vO9mNXEEwB6alsSjKO9fEbEn+uiocyaOWs32C47j+QILUf7TG9GkZDTczj1lRuArgHaLnAd/2FuWBT5EqNLm5Jhsvpb1jeV5g27XVfg6ROoiqpp5iPFXmc0Iiywbc1NhFbIMldepzSv9cPAA9CSq/Ens5MhTDuQ+q3xDZWVPKRPMYrzk4vLd1zHJOPVl0FuETyYXPqaeQg9bwkLkR95vaTPSC6ehyGf8aaH+gxViNFXz/WXgt40/g3n4HiujCglAU0Yoay+06VeNLUoSL5Cd51eOGWEUK4zs4sGNNTmgnq93+wHpKgDK2AAXwl9MR7kWSKpwYbf6Vid7ETtEfdsM2yVuSohtqUpq/95aVETgke8cCbhwuPZyteKuw7MiLbwrEyJhg30WJNZ11Fsqe+nk83Ce7cvn+2s1ATSUNakpBYak5xBXYgU6OLt+jgjjBkBcog0qUX41nb4crqRJ+X5aGXdUK0Bar00gzRKPjGphEvt8VQbz/3zvoTkIHqxBDrAcl4xPB9CmiSusLRBn+xANyJhmwVek0zl074QRXnJLTu5xlmN0d8nJeozL8NwPm6yNbTS6Rek0ao1Y1CcmHjgyZsjevv6gfpOWIOqSteT6in5sWoHkj0MGhSWa8Zo2Ms2BsNzuvMTSlDG7YHE6islAVGwdU7hAhYOoWbmGNM07cRh1Ik2pZWEVhF/7eDSypN/3OoMnnd3JEPoqWHGcQXEXqUOZRwqfbNQzSF35vQcrA0Ksoib1TNjQDQl1M0gyFd41S6E91ZgbKupPc8H+Tf94qMe1RbLO/MacDns6xy/OHLHUicPnQnKtXGIQfg70UREoEFemX2+LF/ENqikEL7npmvJ7eTTv0RXhXodlziFzXDoyv581lGASxwl5VJgZClnrbBuvs7UjEJJPbNSRxRVXc2jXZiIr55kif7tyVDp+CuWNVEz8tGZmL5zh8ivNgBHa+8nVOXk66xyqs1Eniynmk4qytW6thxokDdGabTeFsF4u5Wl6gjfGV+1XQqekFMWZcY3XZPsuATrt3PfvYUuJpnxEvXkvv4Wv15ow38x0hh6rHW23pKHG2RuxWybpPg83QGXTRdszcNQf6FbKX0L3gjQuzvoTjNFgKeu/vkpqKRMbxjOeixFLEws0HpDgshKaj4jr2Yk3qTWij9l4g25IK+xkSMu4Bn8E07qL5/5JrhmFkY5v/AyCCwr9UC3wsRUpTfX5M4hlps7o0mcSGchTv+Xs3gg+4gGQr9uJYEbeYf01gu3kDLN9no1N++RPv0os5I7SEplINHo1/edbMSLMFPRY7VaVVUl/DhPuMbthg1DfZl3fURnU+3S+srpE6K78D5/3xUu8asL5VI6MTvGUEo41Qhb5xjcibqpWUjuZTglC+myDIEUvkPuVEiUcqkgETBFROJJC+rAY4y1SKyGE0R6RKA3r4aGzfcjd19GxWUYIdHfjCKU/w/5JJ59/1znjpL0sejxJUbYtI6x9MUir7Z9ZrE5O2Z+9faro0Z5Q0YVdeNYn7lPH2cnk0SuSQDqoLlqSu0EEABLJjJhW4Y2OElyLQZbqm8AkiN7fwM/1vg0wikiYC+eeNasyCsUC3FHCXVxj/1hiV7s6ELJsoZ+Iw35ojo4GtIOcFA/6l9ocqapHq6SP7fvKw/2bk6+2B7WKDAY9nyhEjdA2gEvDmyo1wfpqoZYUOhFhGMidQpLx/7BD1y+Q5D4itkgaGIKvUcerHOG4pMOzaRzCkCWXkwnK6C3FNj++0mmyzzsi/et0wtVRQYjvB8yoIql1rqp2kbWAPHoK0bUFF2GB8GAciqnXtuOoOC44xul/Gp11ybosVTsjYl4m8dNWCPK/xdo3vnDy67+ANIGU313PK3sxS3IVP14XsDhLdCTwzorkeyqVMSGtxygRiU9Qb9DHskpzKfsUa9I5RNE1A45ME/f5sqtP+s8hUech/OHrKxfP5pt4rHqwqonrnoHGwHkTzmyv1OVrv93kd2PbJPQAoUZqh6N4lHsSZ09xsAB4KZ7p5oU+qynaR1jVMTLZ+KWR23tWcz/JjPpdou7hmKPFS7Ou66ZR9pDcOclRKTNkVyHFf0rq/cO7Tl2C39GLh80uGH0tXk5OiBFfmA4OpVt3RtQCTFCzuVDJFHK88zf1Is5b6pFIuBiG1+Xa4K9jLIuo0Iw1DdVZa1+ny9yjqwivBHVCO3T6XBJybEJukZcyHMA3m+RNoamEqWWTcNL+1i9pi4BRvcKABoc8EvFkG8aewLa1c7oi+ihylcVm6fCUmoHC5bgP6LrUYQRdXTIH3woMfevLPTy7bvqE60cydcjj9emEupOhfoDo65Yc8yEmZ4MtUUmqZpCEdIrfaEFMtwntqwNaEhO7hTY5OjGAE="}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":105459,"cache_read_input_tokens":0,"output_tokens":6864,"output_tokens_details":{"thinking_tokens":4764},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":105459,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":6864,"cache_read_input_tokens":0,"cache_creation_input_tokens":105459,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":105459},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":{"cache_miss_reason":{"type":"previous_message_not_found"}},"context_management":null},"apiBlockIndex":0,"requestId":"req_011CfgPWNaAtHdPbmbc29LKz","type":"assistant","uuid":"427f5994-b389-49c9-8b88-d5bfe36fb459","timestamp":"2026-10-04T02:14:33.547Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: 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{"parentUuid":"427f5994-b389-49c9-8b88-d5bfe36fb459","isSidechain":false,"message":{"model":"claude-sonnet-5","id":"msg_011CfgPWNvGPdmpxFwtLbMzi","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_013JbeXRJqf6a9mrij46HaCM","name":"StructuredOutput","input":{"notes":"Daniel wants to know, for the 3 ticket-heavy merchants (Cragsley, Marrowby, Studio Vrij), whether slowness is explained by something fixable (staffing, channel mix) vs. harder issues, and whether the urgent-SLA failure (50.6% company-wide per Ben Carter) is concentrated in those 3 or systemic. Other teams' posts give useful context I'll want to connect later (Albion Parcel carrier breakdown hitting delivery regardless of merchant/warehouse; the cart-reminder bug hitting 10 merchants incl. Cragsley in Nov; Cragsley's own product-quality tail) - but those are confounders for Daniel's question #4, which he said can wait. Tonight I'm testing the most direct, fixable-vs-not hypotheses for #1 and #2: (1) are the 3 hotspots simply understaffed relative to ticket volume, (2) do they lean on slower channels, and (3) is the urgent-ticket resolution failure broad across the book or concentrated in those same 3 merchants. All three are merchant-grain reports so I can line them up side by side and see if the 3 hotspots stand out consistently or only on one dimension.","requests":[{"title":"Support agent staffing vs ticket volume, by merchant","question":"Are Cragsley, Marrowby, and Studio Vrij understaffed relative to their ticket volume compared to the rest of the book?","grain":"one row per tenant (merchant), among tenants with at least 20 tickets created in 2025","columns":["merchant name","tickets created, 2025","registered support agents (support_agents rows for tenant)","distinct agents who handled a ticket in 2025 (count distinct assignee_agent_id, excluding null)","tickets per active agent (tickets ÷ distinct agents who handled a ticket)","% tickets unassigned (assignee_agent_id null)"],"details":"Source: tickets joined to tenants (name) and support_agents (tenant's registered agent headcount, regardless of whether they handled a ticket in 2025). Window: tickets.created_at 2025-01-01 through 2025-12-31. 'Registered support agents' = count of support_agents rows for that tenant_id (current roster, not time-sliced). 'Distinct agents who handled a ticket' = count distinct tickets.assignee_agent_id for that tenant in 2025, excluding nulls. 'Tickets per active agent' = tickets created 2025 ÷ distinct agents who handled a ticket (NULL if zero agents handled any ticket). '% unassigned' = tickets with assignee_agent_id null ÷ tickets created 2025, percentage rounded to 1 decimal. Only include tenants with >=20 tickets created in 2025. Order by tickets per active agent descending (highest ratio = most loaded), tie-break merchant name ascending. Cap at 50 rows per house standard; note total qualifying tenant count.","builds_on":[]},{"title":"Ticket channel mix, by merchant","question":"Do Cragsley, Marrowby, and Studio Vrij rely more on slower contact channels (e.g. email) than the rest of the book?","grain":"one row per tenant (merchant), among tenants with at least 20 tickets created in 2025","columns":["merchant name","tickets created, 2025","% channel = email","% channel = chat","% channel = phone","% channel = web","% channel = social","% channel = api"],"details":"Source: tickets joined to tenants (name). Window: tickets.created_at 2025-01-01 through 2025-12-31. For each tenant, compute the percentage of tickets in 2025 in each tickets.channel value (email, chat, phone, web, social, api), each rounded to 1 decimal; the six percentages should sum to ~100 per row. Only include tenants with >=20 tickets created in 2025 (same threshold as the staffing report, for a consistent peer set). Order by tickets created descending, tie-break merchant name ascending. Cap at 50 rows; note total qualifying tenant count.","builds_on":[]},{"title":"Urgent-priority ticket resolution failure, by merchant","question":"Is the urgent-ticket resolution failure (missing resolution about half the time, company-wide) concentrated in Cragsley, Marrowby, and Studio Vrij, or is it a broad pattern across the book?","grain":"one row per tenant (merchant), among tenants with at least 5 urgent-priority tickets created in 2025","columns":["merchant name","urgent tickets created, 2025","urgent tickets missing resolution target (count)","urgent resolution miss rate (%)","all-priority resolution miss rate (%), 2025, for comparison"],"details":"Source: tickets (priority='urgent' for the main cut, all priorities for the comparison column) joined to sla_policies (via tickets.sla_policy_id, for resolution_target_minutes) and tenants (name). Window: tickets.created_at 2025-01-01 through 2025-12-31. Define a ticket as 'missing resolution target' if: resolved_at is null as of 2025-12-31 (i.e. still unresolved), OR extract(epoch from resolved_at - created_at)/60 > sla_policies.resolution_target_minutes. Exclude tickets with a null sla_policy_id from both numerator and denominator (can't be evaluated) and note how many were excluded. 'Urgent resolution miss rate' = urgent tickets missing target ÷ evaluable urgent tickets, percentage rounded to 1 decimal. 'All-priority resolution miss rate' = same calculation across all priorities for that tenant, for comparison within the same merchant. Only include tenants with >=5 evaluable urgent tickets in 2025. Order by urgent resolution miss rate descending, tie-break merchant name ascending. Cap at 50 rows; note total qualifying tenant count.","builds_on":[]}]},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":105459,"cache_read_input_tokens":0,"output_tokens":6864,"output_tokens_details":{"thinking_tokens":4764},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":105459,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":6864,"cache_read_input_tokens":0,"cache_creation_input_tokens":105459,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":105459},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":{"cache_miss_reason":{"type":"previous_message_not_found"}},"context_management":null},"wireToolInputs":{"toolu_013JbeXRJqf6a9mrij46HaCM":{"notes":"Daniel wants to know, for the 3 ticket-heavy merchants (Cragsley, Marrowby, Studio Vrij), whether slowness is explained by something fixable (staffing, channel mix) vs. harder issues, and whether the urgent-SLA failure (50.6% company-wide per Ben Carter) is concentrated in those 3 or systemic. Other teams' posts give useful context I'll want to connect later (Albion Parcel carrier breakdown hitting delivery regardless of merchant/warehouse; the cart-reminder bug hitting 10 merchants incl. Cragsley in Nov; Cragsley's own product-quality tail) - but those are confounders for Daniel's question #4, which he said can wait. Tonight I'm testing the most direct, fixable-vs-not hypotheses for #1 and #2: (1) are the 3 hotspots simply understaffed relative to ticket volume, (2) do they lean on slower channels, and (3) is the urgent-ticket resolution failure broad across the book or concentrated in those same 3 merchants. All three are merchant-grain reports so I can line them up side by side and see if the 3 hotspots stand out consistently or only on one dimension.","requests":[{"title":"Support agent staffing vs ticket volume, by merchant","question":"Are Cragsley, Marrowby, and Studio Vrij understaffed relative to their ticket volume compared to the rest of the book?","grain":"one row per tenant (merchant), among tenants with at least 20 tickets created in 2025","columns":["merchant name","tickets created, 2025","registered support agents (support_agents rows for tenant)","distinct agents who handled a ticket in 2025 (count distinct assignee_agent_id, excluding null)","tickets per active agent (tickets ÷ distinct agents who handled a ticket)","% tickets unassigned (assignee_agent_id null)"],"details":"Source: tickets joined to tenants (name) and support_agents (tenant's registered agent headcount, regardless of whether they handled a ticket in 2025). Window: tickets.created_at 2025-01-01 through 2025-12-31. 'Registered support agents' = count of support_agents rows for that tenant_id (current roster, not time-sliced). 'Distinct agents who handled a ticket' = count distinct tickets.assignee_agent_id for that tenant in 2025, excluding nulls. 'Tickets per active agent' = tickets created 2025 ÷ distinct agents who handled a ticket (NULL if zero agents handled any ticket). '% unassigned' = tickets with assignee_agent_id null ÷ tickets created 2025, percentage rounded to 1 decimal. Only include tenants with >=20 tickets created in 2025. Order by tickets per active agent descending (highest ratio = most loaded), tie-break merchant name ascending. Cap at 50 rows per house standard; note total qualifying tenant count.","builds_on":[]},{"title":"Ticket channel mix, by merchant","question":"Do Cragsley, Marrowby, and Studio Vrij rely more on slower contact channels (e.g. email) than the rest of the book?","grain":"one row per tenant (merchant), among tenants with at least 20 tickets created in 2025","columns":["merchant name","tickets created, 2025","% channel = email","% channel = chat","% channel = phone","% channel = web","% channel = social","% channel = api"],"details":"Source: tickets joined to tenants (name). Window: tickets.created_at 2025-01-01 through 2025-12-31. For each tenant, compute the percentage of tickets in 2025 in each tickets.channel value (email, chat, phone, web, social, api), each rounded to 1 decimal; the six percentages should sum to ~100 per row. Only include tenants with >=20 tickets created in 2025 (same threshold as the staffing report, for a consistent peer set). Order by tickets created descending, tie-break merchant name ascending. Cap at 50 rows; note total qualifying tenant count.","builds_on":[]},{"title":"Urgent-priority ticket resolution failure, by merchant","question":"Is the urgent-ticket resolution failure (missing resolution about half the time, company-wide) concentrated in Cragsley, Marrowby, and Studio Vrij, or is it a broad pattern across the book?","grain":"one row per tenant (merchant), among tenants with at least 5 urgent-priority tickets created in 2025","columns":["merchant name","urgent tickets created, 2025","urgent tickets missing resolution target (count)","urgent resolution miss rate (%)","all-priority resolution miss rate (%), 2025, for comparison"],"details":"Source: tickets (priority='urgent' for the main cut, all priorities for the comparison column) joined to sla_policies (via tickets.sla_policy_id, for resolution_target_minutes) and tenants (name). Window: tickets.created_at 2025-01-01 through 2025-12-31. Define a ticket as 'missing resolution target' if: resolved_at is null as of 2025-12-31 (i.e. still unresolved), OR extract(epoch from resolved_at - created_at)/60 > sla_policies.resolution_target_minutes. Exclude tickets with a null sla_policy_id from both numerator and denominator (can't be evaluated) and note how many were excluded. 'Urgent resolution miss rate' = urgent tickets missing target ÷ evaluable urgent tickets, percentage rounded to 1 decimal. 'All-priority resolution miss rate' = same calculation across all priorities for that tenant, for comparison within the same merchant. Only include tenants with >=5 evaluable urgent tickets in 2025. Order by urgent resolution miss rate descending, tie-break merchant name ascending. 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{"type":"last-prompt","lastPrompt":"Monday, week 2. Daniel Achterberg replied to your memo:  Subject: Good progress — now I want to understand the \"why\" behind the hotspots  Mateo — this is exactly the kind of baseline I needed, thank y…","leafUuid":"2db3fcbf-a441-4195-b285-e093bb53d31a","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a"}
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{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-04T02:15:44.253Z","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a","content":"The printed reports of night 1:\n\n=== Report 1: Support agent staffing vs ticket volume, by merchant (written by Ines) ===\nEngineer's note: Row count of the full qualifying-tenant set (before the 50-row cap) will show on the printed report per house standard; the query itself returns at most 50 merchants ranked by tickets-per-active-agent.\nREPORT: Support agent staffing vs ticket volume, by merchant\nQUESTION: Are Cragsley, Marrowby, and Studio Vrij understaffed relative to their ticket volume compared to the rest of the book?\nmerchant_name                 | tickets_created | registered_support_agents | distinct_agents_handled | tickets_per_active_agent | pct_unassigned\n------------------------------+-----------------+---------------------------+-------------------------+--------------------------+---------------\nCragsley Outfitters           |             400 |                         8 |                       8 |                     50.0 |            0.5\nMarrowby & Vane               |             231 |                         5 |                       5 |                     46.2 |            2.6\nStudio Vrij                   |             166 |                         4 |                       4 |                     41.5 |            0.0\nGrind Collective              |             122 |                         3 |                       3 |                     40.7 |            0.0\nBrennquell Kaffee             |             137 |                         4 |                       4 |                     34.3 |            0.7\nPetalcourt Beauty             |             119 |                         4 |                       4 |                     29.8 |            0.0\nSpice Route Box               |              71 |                         3 |                       3 |                     23.7 |            0.0\nHoneycomb Larder              |              58 |                         3 |                       3 |                     19.3 |            0.0\nKibblewick                    |              72 |                         4 |                       4 |                     18.0 |            0.0\nFennel & Flint                |              67 |                         4 |                       4 |                     16.8 |            0.0\nSourdough Club                |              47 |                         3 |                       3 |                     15.7 |            0.0\nBallycorran Industrial Supply |              44 |                         3 |                       3 |                     14.7 |            0.0\nAmpfeld Elektronik            |              56 |                         5 |                       5 |                     11.2 |            1.8\nCinder Peak Sports Co.        |              43 |                         4 |                       4 |                     10.8 |            0.0\nWiesgrund Wohnen              |              32 |                         3 |                       3 |                     10.7 |            0.0\nLinden & Loom                 |              29 |                         3 |                       3 |                      9.7 |            0.0\nOakhurst Tailoring            |              27 |                         3 |                       3 |                      9.0 |            3.7\nRue Lafayette Studio          |              27 |                         3 |                       3 |                      9.0 |            0.0\nCoastline Interiors           |              21 |                         3 |                       3 |                      7.0 |            0.0\nThistlecombe                  |              21 |                         3 |                       3 |                      7.0 |            0.0\nGratlinie Outdoor             |              27 |                         4 |                       4 |                      6.8 |            0.0\nKayak Corner                  |              20 |                         3 |                       3 |                      6.7 |            0.0\n(22 rows)\n\n=== Report 2: Ticket channel mix, by merchant (written by Omar) ===\nEngineer's note: Grain is one row per tenant; percentages are rounded to 1 decimal so they may sum to slightly off 100.0 due to rounding. The query caps at 50 rows as requested; the batch report framework will note the total row count returned.\nREPORT: Ticket channel mix, by merchant\nQUESTION: Do Cragsley, Marrowby, and Studio Vrij rely more on slower contact channels (e.g. email) than the rest of the book?\nmerchant_name                 | tickets_2025 | pct_email | pct_chat | pct_phone | pct_web | pct_social | pct_api\n------------------------------+--------------+-----------+----------+-----------+---------+------------+--------\nCragsley Outfitters           |          400 |      36.5 |     27.5 |      11.0 |    14.0 |       11.0 |     0.0\nMarrowby & Vane               |          231 |      28.1 |     29.4 |      13.4 |    12.1 |       16.9 |     0.0\nStudio Vrij                   |          166 |      38.6 |     21.7 |      19.9 |     9.0 |       10.8 |     0.0\nBrennquell Kaffee             |          137 |      35.8 |     27.7 |      12.4 |     9.5 |       14.6 |     0.0\nGrind Collective              |          122 |      43.4 |     22.1 |       9.0 |    10.7 |       14.8 |     0.0\nPetalcourt Beauty             |          119 |      41.2 |     18.5 |      14.3 |    21.0 |        5.0 |     0.0\nKibblewick                    |           72 |      36.1 |     25.0 |      11.1 |    12.5 |       15.3 |     0.0\nSpice Route Box               |           71 |      42.3 |     15.5 |      18.3 |    11.3 |       12.7 |     0.0\nFennel & Flint                |           67 |      41.8 |     32.8 |       7.5 |    11.9 |        6.0 |     0.0\nHoneycomb Larder              |           58 |      39.7 |     24.1 |      12.1 |     8.6 |       15.5 |     0.0\nAmpfeld Elektronik            |           56 |      30.4 |     30.4 |      10.7 |    10.7 |       17.9 |     0.0\nSourdough Club                |           47 |      42.6 |     27.7 |       8.5 |     6.4 |       14.9 |     0.0\nBallycorran Industrial Supply |           44 |      29.5 |     36.4 |       6.8 |    11.4 |       15.9 |     0.0\nCinder Peak Sports Co.        |           43 |      27.9 |     27.9 |       9.3 |    18.6 |       16.3 |     0.0\nWiesgrund Wohnen              |           32 |      31.3 |     28.1 |      15.6 |     9.4 |       15.6 |     0.0\nLinden & Loom                 |           29 |      24.1 |     17.2 |      17.2 |    31.0 |       10.3 |     0.0\nGratlinie Outdoor             |           27 |      48.1 |     18.5 |      11.1 |    18.5 |        3.7 |     0.0\nOakhurst Tailoring            |           27 |      44.4 |     18.5 |       7.4 |    14.8 |       14.8 |     0.0\nRue Lafayette Studio          |           27 |      33.3 |     37.0 |       3.7 |    18.5 |        7.4 |     0.0\nCoastline Interiors           |           21 |      52.4 |     14.3 |       9.5 |    14.3 |        9.5 |     0.0\nThistlecombe                  |           21 |      47.6 |     28.6 |       4.8 |     4.8 |       14.3 |     0.0\nKayak Corner                  |           20 |      45.0 |     10.0 |      20.0 |    25.0 |        0.0 |     0.0\n(22 rows)\n\n=== Report 3: Urgent-priority ticket resolution failure, by merchant (written by Viktor) ===\nEngineer's note: \"Urgent tickets created, 2025\" is the raw count of urgent tickets opened in 2025 (including any with a null sla_policy_id); the miss-rate denominators and the filter (>=5) use only the evaluable subset (non-null sla_policy_id), per the request. Per-tenant exclusion counts aren't broken out as a column to stay within the 5 requested columns — let me know if you'd like that as a separate report.\nREPORT: Urgent-priority ticket resolution failure, by merchant\nQUESTION: Is the urgent-ticket resolution failure (missing resolution about half the time, company-wide) concentrated in Cragsley, Marrowby, and Studio Vrij, or is it a broad pattern across the book?\nmerchant_name       | urgent_created | urgent_missing | urgent_miss_rate_pct | all_priority_miss_rate_pct\n--------------------+----------------+----------------+----------------------+---------------------------\nHoneycomb Larder    |              9 |              9 |                100.0 |                       37.9\nSourdough Club      |              7 |              6 |                 85.7 |                       29.8\nAmpfeld Elektronik  |              6 |              5 |                 83.3 |                       16.1\nMarrowby & Vane     |             33 |             22 |                 66.7 |                       39.8\nCragsley Outfitters |             50 |             33 |                 66.0 |                       29.8\nStudio Vrij         |             11 |              7 |                 63.6 |                       39.2\nLinden & Loom       |              6 |              3 |                 50.0 |                       13.8\nPetalcourt Beauty   |             10 |              5 |                 50.0 |                       32.8\nFennel & Flint      |              5 |              2 |                 40.0 |                       17.9\nWohnglück           |              5 |              2 |                 40.0 |                       13.3\nSpice Route Box     |              9 |              3 |                 33.3 |                       11.3\nBrennquell Kaffee   |             16 |              5 |                 31.3 |                       12.4\nGrind Collective    |              6 |              1 |                 16.7 |                       33.6\nWiesgrund Wohnen    |              7 |              1 |                 14.3 |                        6.3\n(14 rows)\n\n\nNight 2 of 4: you have 3 report slots tonight."}
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{"parentUuid":"7c2f7385-545c-475e-ac13-dbabf34c054c","isSidechain":false,"promptId":"0c2fdd84-10ce-4d29-bfad-47e4de47ddb6","type":"user","message":{"role":"user","content":"The printed reports of night 1:\n\n=== Report 1: Support agent staffing vs ticket volume, by merchant (written by Ines) ===\nEngineer's note: Row count of the full qualifying-tenant set (before the 50-row cap) will show on the printed report per house standard; the query itself returns at most 50 merchants ranked by tickets-per-active-agent.\nREPORT: Support agent staffing vs ticket volume, by merchant\nQUESTION: Are Cragsley, Marrowby, and Studio Vrij understaffed relative to their ticket volume compared to the rest of the book?\nmerchant_name                 | tickets_created | registered_support_agents | distinct_agents_handled | tickets_per_active_agent | pct_unassigned\n------------------------------+-----------------+---------------------------+-------------------------+--------------------------+---------------\nCragsley Outfitters           |             400 |                         8 |                       8 |                     50.0 |            0.5\nMarrowby & Vane               |             231 |                         5 |                       5 |                     46.2 |            2.6\nStudio Vrij                   |             166 |                         4 |                       4 |                     41.5 |            0.0\nGrind Collective              |             122 |                         3 |                       3 |                     40.7 |            0.0\nBrennquell Kaffee             |             137 |                         4 |                       4 |                     34.3 |            0.7\nPetalcourt Beauty             |             119 |                         4 |                       4 |                     29.8 |            0.0\nSpice Route Box               |              71 |                         3 |                       3 |                     23.7 |            0.0\nHoneycomb Larder              |              58 |                         3 |                       3 |                     19.3 |            0.0\nKibblewick                    |              72 |                         4 |                       4 |                     18.0 |            0.0\nFennel & Flint                |              67 |                         4 |                       4 |                     16.8 |            0.0\nSourdough Club                |              47 |                         3 |                       3 |                     15.7 |            0.0\nBallycorran Industrial Supply |              44 |                         3 |                       3 |                     14.7 |            0.0\nAmpfeld Elektronik            |              56 |                         5 |                       5 |                     11.2 |            1.8\nCinder Peak Sports Co.        |              43 |                         4 |                       4 |                     10.8 |            0.0\nWiesgrund Wohnen              |              32 |                         3 |                       3 |                     10.7 |            0.0\nLinden & Loom                 |              29 |                         3 |                       3 |                      9.7 |            0.0\nOakhurst Tailoring            |              27 |                         3 |                       3 |                      9.0 |            3.7\nRue Lafayette Studio          |              27 |                         3 |                       3 |                      9.0 |            0.0\nCoastline Interiors           |              21 |                         3 |                       3 |                      7.0 |            0.0\nThistlecombe                  |              21 |                         3 |                       3 |                      7.0 |            0.0\nGratlinie Outdoor             |              27 |                         4 |                       4 |                      6.8 |            0.0\nKayak Corner                  |              20 |                         3 |                       3 |                      6.7 |            0.0\n(22 rows)\n\n=== Report 2: Ticket channel mix, by merchant (written by Omar) ===\nEngineer's note: Grain is one row per tenant; percentages are rounded to 1 decimal so they may sum to slightly off 100.0 due to rounding. The query caps at 50 rows as requested; the batch report framework will note the total row count returned.\nREPORT: Ticket channel mix, by merchant\nQUESTION: Do Cragsley, Marrowby, and Studio Vrij rely more on slower contact channels (e.g. email) than the rest of the book?\nmerchant_name                 | tickets_2025 | pct_email | pct_chat | pct_phone | pct_web | pct_social | pct_api\n------------------------------+--------------+-----------+----------+-----------+---------+------------+--------\nCragsley Outfitters           |          400 |      36.5 |     27.5 |      11.0 |    14.0 |       11.0 |     0.0\nMarrowby & Vane               |          231 |      28.1 |     29.4 |      13.4 |    12.1 |       16.9 |     0.0\nStudio Vrij                   |          166 |      38.6 |     21.7 |      19.9 |     9.0 |       10.8 |     0.0\nBrennquell Kaffee             |          137 |      35.8 |     27.7 |      12.4 |     9.5 |       14.6 |     0.0\nGrind Collective              |          122 |      43.4 |     22.1 |       9.0 |    10.7 |       14.8 |     0.0\nPetalcourt Beauty             |          119 |      41.2 |     18.5 |      14.3 |    21.0 |        5.0 |     0.0\nKibblewick                    |           72 |      36.1 |     25.0 |      11.1 |    12.5 |       15.3 |     0.0\nSpice Route Box               |           71 |      42.3 |     15.5 |      18.3 |    11.3 |       12.7 |     0.0\nFennel & Flint                |           67 |      41.8 |     32.8 |       7.5 |    11.9 |        6.0 |     0.0\nHoneycomb Larder              |           58 |      39.7 |     24.1 |      12.1 |     8.6 |       15.5 |     0.0\nAmpfeld Elektronik            |           56 |      30.4 |     30.4 |      10.7 |    10.7 |       17.9 |     0.0\nSourdough Club                |           47 |      42.6 |     27.7 |       8.5 |     6.4 |       14.9 |     0.0\nBallycorran Industrial Supply |           44 |      29.5 |     36.4 |       6.8 |    11.4 |       15.9 |     0.0\nCinder Peak Sports Co.        |           43 |      27.9 |     27.9 |       9.3 |    18.6 |       16.3 |     0.0\nWiesgrund Wohnen              |           32 |      31.3 |     28.1 |      15.6 |     9.4 |       15.6 |     0.0\nLinden & Loom                 |           29 |      24.1 |     17.2 |      17.2 |    31.0 |       10.3 |     0.0\nGratlinie Outdoor             |           27 |      48.1 |     18.5 |      11.1 |    18.5 |        3.7 |     0.0\nOakhurst Tailoring            |           27 |      44.4 |     18.5 |       7.4 |    14.8 |       14.8 |     0.0\nRue Lafayette Studio          |           27 |      33.3 |     37.0 |       3.7 |    18.5 |        7.4 |     0.0\nCoastline Interiors           |           21 |      52.4 |     14.3 |       9.5 |    14.3 |        9.5 |     0.0\nThistlecombe                  |           21 |      47.6 |     28.6 |       4.8 |     4.8 |       14.3 |     0.0\nKayak Corner                  |           20 |      45.0 |     10.0 |      20.0 |    25.0 |        0.0 |     0.0\n(22 rows)\n\n=== Report 3: Urgent-priority ticket resolution failure, by merchant (written by Viktor) ===\nEngineer's note: \"Urgent tickets created, 2025\" is the raw count of urgent tickets opened in 2025 (including any with a null sla_policy_id); the miss-rate denominators and the filter (>=5) use only the evaluable subset (non-null sla_policy_id), per the request. Per-tenant exclusion counts aren't broken out as a column to stay within the 5 requested columns — let me know if you'd like that as a separate report.\nREPORT: Urgent-priority ticket resolution failure, by merchant\nQUESTION: Is the urgent-ticket resolution failure (missing resolution about half the time, company-wide) concentrated in Cragsley, Marrowby, and Studio Vrij, or is it a broad pattern across the book?\nmerchant_name       | urgent_created | urgent_missing | urgent_miss_rate_pct | all_priority_miss_rate_pct\n--------------------+----------------+----------------+----------------------+---------------------------\nHoneycomb Larder    |              9 |              9 |                100.0 |                       37.9\nSourdough Club      |              7 |              6 |                 85.7 |                       29.8\nAmpfeld Elektronik  |              6 |              5 |                 83.3 |                       16.1\nMarrowby & Vane     |             33 |             22 |                 66.7 |                       39.8\nCragsley Outfitters |             50 |             33 |                 66.0 |                       29.8\nStudio Vrij         |             11 |              7 |                 63.6 |                       39.2\nLinden & Loom       |              6 |              3 |                 50.0 |                       13.8\nPetalcourt Beauty   |             10 |              5 |                 50.0 |                       32.8\nFennel & Flint      |              5 |              2 |                 40.0 |                       17.9\nWohnglück           |              5 |              2 |                 40.0 |                       13.3\nSpice Route Box     |              9 |              3 |                 33.3 |                       11.3\nBrennquell Kaffee   |             16 |              5 |                 31.3 |                       12.4\nGrind Collective    |              6 |              1 |                 16.7 |                       33.6\nWiesgrund Wohnen    |              7 |              1 |                 14.3 |                        6.3\n(14 rows)\n\n\nNight 2 of 4: you have 3 report slots tonight."},"uuid":"d1842a18-12c0-4edd-b7bf-86fa2b2ef4b0","timestamp":"2026-10-04T02:15:44.260Z","permissionMode":"default","promptSource":"sdk","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a","version":"2.1.275","gitBranch":"HEAD"}
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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":10210,"cache_read_input_tokens":105459,"output_tokens":10299,"output_tokens_details":{"thinking_tokens":7683},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":10210,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":10299,"cache_read_input_tokens":105459,"cache_creation_input_tokens":10210,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":10210},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CfgPfFcj8RwGewMxLiRzq","type":"assistant","uuid":"bf8e2471-d964-451d-8307-78b835e7f506","timestamp":"2026-10-04T02:17:02.932Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: 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{"parentUuid":"bf8e2471-d964-451d-8307-78b835e7f506","isSidechain":false,"message":{"model":"claude-sonnet-5","id":"msg_011CfgPfFvaZQfPMQUx7jbkM","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Jng5xXGi2qcQdSzUU3G5ax","name":"StructuredOutput","input":{"notes":"Night 1 results: the staffing report shows Cragsley (50.0 tickets/agent), Marrowby (46.2) and Studio Vrij (41.5) are the three highest ratios in the entire qualifying book, well ahead of the next closest (Grind Collective, 40.7, then a drop to 34.3) - so understaffing-relative-to-volume looks like a real, differentiating factor, not noise (unassigned-ticket rates are near-zero everywhere, so it's not a triage problem). Channel mix, by contrast, looks like a dead end: the three hotspots' email/chat/phone split is unremarkable - several merchants with much heavier email reliance (e.g. Coastline at 52.4% email) actually had the best SLA performance last week, so I don't think channel explains the gap. The urgent-ticket report is the more worrying one: Marrowby (66.7%), Cragsley (66.0%) and Studio Vrij (63.6%) are bad, but several smaller merchants (Honeycomb Larder 100%, Sourdough Club 85.7%, Ampfeld Elektronik 83.3%) are worse - so urgent-SLA failure is NOT concentrated in the three hotspots, it's spread broadly across the book, and nearly every merchant's urgent miss rate is well above their own all-priority miss rate. That pattern (urgent specifically failing everywhere) makes me suspect the urgent SLA *target itself* may be set unrealistically tight platform-wide, rather than this being pure execution failure merchant-by-merchant - worth testing directly before concluding it's \"everyone is bad at urgent tickets.\" Tonight I'm testing: (1) whether hotspot tickets are genuinely more complex (more back-and-forth) rather than just higher volume, (2) whether urgent SLA targets are achievable given typical actual resolution times, and (3) whether the hotspots' staffing gap has been widening over 2025 (useful for both the \"why\" question and, incidentally, for planning ahead of Nov/Dec later).","requests":[{"title":"Ticket complexity proxy (messages and tags per ticket), by merchant","question":"Do Cragsley, Marrowby, and Studio Vrij get harder/more complex tickets than the rest of the book, or just more of them?","grain":"one row per tenant (merchant), among tenants with at least 20 tickets created in 2025","columns":["merchant name","tickets created, 2025","avg messages per ticket (all)","avg public (customer-visible) messages per ticket","avg distinct tags per ticket"],"details":"Source: tickets joined to tenants, ticket_messages (count per ticket, and count where is_public=true), and ticket_tags_map (count distinct support_tag_id per ticket). Window: tickets.created_at 2025-01-01 through 2025-12-31. 'avg messages per ticket' = total ticket_messages rows for that tenant's 2025 tickets ÷ number of those tickets. 'avg public messages per ticket' = same but counting only is_public=true rows. 'avg distinct tags per ticket' = total ticket_tags_map rows for those tickets ÷ number of those tickets (a ticket with 0 tags counts as 0). Round all three averages to 2 decimals. Only include tenants with >=20 tickets created in 2025 (same threshold as last night's staffing/channel reports, for a consistent peer set). Order by avg messages per ticket descending, tie-break merchant name ascending. Cap at 50 rows; note total qualifying tenant count.","builds_on":["Support agent staffing vs ticket volume, by merchant","Ticket channel mix, by merchant"]},{"title":"Urgent-ticket SLA target vs actual resolution time, by merchant","question":"Is the broad urgent-ticket resolution failure driven by unrealistically tight SLA targets, or are merchants genuinely resolving urgent tickets slower than a reasonable target?","grain":"one row per tenant (merchant), among tenants with at least 5 evaluable urgent-priority tickets created in 2025","columns":["merchant name","urgent resolution target (hours)","urgent tickets evaluated, 2025","median actual resolution hours (urgent)","avg actual resolution hours (urgent)","urgent miss rate (%)"],"details":"Source: tickets (priority='urgent') joined to sla_policies via tickets.sla_policy_id, and tenants (name). Window: tickets.created_at 2025-01-01 through 2025-12-31, excluding tickets with a null sla_policy_id (same 'evaluable' definition as last night's urgent-miss-rate report). 'urgent resolution target (hours)' = sla_policies.resolution_target_minutes / 60 for that tenant's urgent policy; if a tenant's evaluated urgent tickets reference more than one distinct resolution_target_minutes value, report the ticket-count-weighted average target in hours and note in the report that the tenant has more than one urgent SLA policy in effect. 'median/avg actual resolution hours' = extract(epoch from resolved_at - created_at)/3600 over evaluable urgent tickets with resolved_at not null (tickets still unresolved as of 2025-12-31 are excluded from the actual-hours average/median but still count as a miss in the miss-rate numerator, consistent with last night's definition). 'urgent miss rate (%)' = same definition as last night's report (missing if unresolved as of cutoff, or resolved later than target), rounded to 1 decimal. Round target and actual hours to 1 decimal. Only include tenants with >=5 evaluable urgent tickets. Order by urgent miss rate descending, tie-break merchant name ascending. Cap at 50 rows; note total qualifying tenant count.","builds_on":["Urgent-priority ticket resolution failure, by merchant"]},{"title":"Ticket load per agent over 2025, hotspot merchants vs a comparable peer","question":"Has the staffing gap at Cragsley, Marrowby, and Studio Vrij been widening over the course of 2025 (ticket volume outpacing agent headcount), or has it been consistently stretched all year?","grain":"one row per tenant and calendar quarter in 2025, for Cragsley Outfitters, Marrowby & Vane, Studio Vrij, and Grind Collective (the next-highest tickets-per-agent merchant, included as a comparison peer)","columns":["merchant name","quarter (2025-Q1..Q4)","tickets created that quarter","distinct agents who handled a ticket that quarter","tickets per active agent that quarter"],"details":"Source: tickets joined to tenants, filtered to the four named tenants (Cragsley Outfitters, Marrowby & Vane, Studio Vrij, Grind Collective). Grain the quarter on tickets.created_at (2025-01-01 through 2025-12-31, 4 quarters). 'distinct agents who handled a ticket' = count distinct tickets.assignee_agent_id (excluding null) for that tenant/quarter. 'tickets per active agent' = tickets that quarter ÷ distinct agents that quarter, rounded to 1 decimal (NULL if zero agents). Order by merchant name ascending, then quarter ascending. Expect exactly 16 rows (4 merchants × 4 quarters).","builds_on":["Support agent staffing vs ticket volume, by merchant"]}]},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":10210,"cache_read_input_tokens":105459,"output_tokens":10299,"output_tokens_details":{"thinking_tokens":7683},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":10210,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":10299,"cache_read_input_tokens":105459,"cache_creation_input_tokens":10210,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":10210},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"wireToolInputs":{"toolu_01Jng5xXGi2qcQdSzUU3G5ax":{"notes":"Night 1 results: the staffing report shows Cragsley (50.0 tickets/agent), Marrowby (46.2) and Studio Vrij (41.5) are the three highest ratios in the entire qualifying book, well ahead of the next closest (Grind Collective, 40.7, then a drop to 34.3) - so understaffing-relative-to-volume looks like a real, differentiating factor, not noise (unassigned-ticket rates are near-zero everywhere, so it's not a triage problem). Channel mix, by contrast, looks like a dead end: the three hotspots' email/chat/phone split is unremarkable - several merchants with much heavier email reliance (e.g. Coastline at 52.4% email) actually had the best SLA performance last week, so I don't think channel explains the gap. The urgent-ticket report is the more worrying one: Marrowby (66.7%), Cragsley (66.0%) and Studio Vrij (63.6%) are bad, but several smaller merchants (Honeycomb Larder 100%, Sourdough Club 85.7%, Ampfeld Elektronik 83.3%) are worse - so urgent-SLA failure is NOT concentrated in the three hotspots, it's spread broadly across the book, and nearly every merchant's urgent miss rate is well above their own all-priority miss rate. That pattern (urgent specifically failing everywhere) makes me suspect the urgent SLA *target itself* may be set unrealistically tight platform-wide, rather than this being pure execution failure merchant-by-merchant - worth testing directly before concluding it's \"everyone is bad at urgent tickets.\" Tonight I'm testing: (1) whether hotspot tickets are genuinely more complex (more back-and-forth) rather than just higher volume, (2) whether urgent SLA targets are achievable given typical actual resolution times, and (3) whether the hotspots' staffing gap has been widening over 2025 (useful for both the \"why\" question and, incidentally, for planning ahead of Nov/Dec later).","requests":[{"title":"Ticket complexity proxy (messages and tags per ticket), by merchant","question":"Do Cragsley, Marrowby, and Studio Vrij get harder/more complex tickets than the rest of the book, or just more of them?","grain":"one row per tenant (merchant), among tenants with at least 20 tickets created in 2025","columns":["merchant name","tickets created, 2025","avg messages per ticket (all)","avg public (customer-visible) messages per ticket","avg distinct tags per ticket"],"details":"Source: tickets joined to tenants, ticket_messages (count per ticket, and count where is_public=true), and ticket_tags_map (count distinct support_tag_id per ticket). Window: tickets.created_at 2025-01-01 through 2025-12-31. 'avg messages per ticket' = total ticket_messages rows for that tenant's 2025 tickets ÷ number of those tickets. 'avg public messages per ticket' = same but counting only is_public=true rows. 'avg distinct tags per ticket' = total ticket_tags_map rows for those tickets ÷ number of those tickets (a ticket with 0 tags counts as 0). Round all three averages to 2 decimals. Only include tenants with >=20 tickets created in 2025 (same threshold as last night's staffing/channel reports, for a consistent peer set). Order by avg messages per ticket descending, tie-break merchant name ascending. Cap at 50 rows; note total qualifying tenant count.","builds_on":["Support agent staffing vs ticket volume, by merchant","Ticket channel mix, by merchant"]},{"title":"Urgent-ticket SLA target vs actual resolution time, by merchant","question":"Is the broad urgent-ticket resolution failure driven by unrealistically tight SLA targets, or are merchants genuinely resolving urgent tickets slower than a reasonable target?","grain":"one row per tenant (merchant), among tenants with at least 5 evaluable urgent-priority tickets created in 2025","columns":["merchant name","urgent resolution target (hours)","urgent tickets evaluated, 2025","median actual resolution hours (urgent)","avg actual resolution hours (urgent)","urgent miss rate (%)"],"details":"Source: tickets (priority='urgent') joined to sla_policies via tickets.sla_policy_id, and tenants (name). Window: tickets.created_at 2025-01-01 through 2025-12-31, excluding tickets with a null sla_policy_id (same 'evaluable' definition as last night's urgent-miss-rate report). 'urgent resolution target (hours)' = sla_policies.resolution_target_minutes / 60 for that tenant's urgent policy; if a tenant's evaluated urgent tickets reference more than one distinct resolution_target_minutes value, report the ticket-count-weighted average target in hours and note in the report that the tenant has more than one urgent SLA policy in effect. 'median/avg actual resolution hours' = extract(epoch from resolved_at - created_at)/3600 over evaluable urgent tickets with resolved_at not null (tickets still unresolved as of 2025-12-31 are excluded from the actual-hours average/median but still count as a miss in the miss-rate numerator, consistent with last night's definition). 'urgent miss rate (%)' = same definition as last night's report (missing if unresolved as of cutoff, or resolved later than target), rounded to 1 decimal. Round target and actual hours to 1 decimal. Only include tenants with >=5 evaluable urgent tickets. Order by urgent miss rate descending, tie-break merchant name ascending. Cap at 50 rows; note total qualifying tenant count.","builds_on":["Urgent-priority ticket resolution failure, by merchant"]},{"title":"Ticket load per agent over 2025, hotspot merchants vs a comparable peer","question":"Has the staffing gap at Cragsley, Marrowby, and Studio Vrij been widening over the course of 2025 (ticket volume outpacing agent headcount), or has it been consistently stretched all year?","grain":"one row per tenant and calendar quarter in 2025, for Cragsley Outfitters, Marrowby & Vane, Studio Vrij, and Grind Collective (the next-highest tickets-per-agent merchant, included as a comparison peer)","columns":["merchant name","quarter (2025-Q1..Q4)","tickets created that quarter","distinct agents who handled a ticket that quarter","tickets per active agent that quarter"],"details":"Source: tickets joined to tenants, filtered to the four named tenants (Cragsley Outfitters, Marrowby & Vane, Studio Vrij, Grind Collective). Grain the quarter on tickets.created_at (2025-01-01 through 2025-12-31, 4 quarters). 'distinct agents who handled a ticket' = count distinct tickets.assignee_agent_id (excluding null) for that tenant/quarter. 'tickets per active agent' = tickets that quarter ÷ distinct agents that quarter, rounded to 1 decimal (NULL if zero agents). Order by merchant name ascending, then quarter ascending. Expect exactly 16 rows (4 merchants × 4 quarters).","builds_on":["Support agent staffing vs ticket volume, by merchant"]}]}},"apiBlockIndex":1,"requestId":"req_011CfgPfFcj8RwGewMxLiRzq","type":"assistant","uuid":"fc03daa5-1e1a-4a76-8056-fdaef2eb1bfd","timestamp":"2026-10-04T02:17:23.276Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"fc03daa5-1e1a-4a76-8056-fdaef2eb1bfd","isSidechain":false,"attachment":{"type":"structured_output","data":{"notes":"Night 1 results: the staffing report shows Cragsley (50.0 tickets/agent), Marrowby (46.2) and Studio Vrij (41.5) are the three highest ratios in the entire qualifying book, well ahead of the next closest (Grind Collective, 40.7, then a drop to 34.3) - so understaffing-relative-to-volume looks like a real, differentiating factor, not noise (unassigned-ticket rates are near-zero everywhere, so it's not a triage problem). Channel mix, by contrast, looks like a dead end: the three hotspots' email/chat/phone split is unremarkable - several merchants with much heavier email reliance (e.g. Coastline at 52.4% email) actually had the best SLA performance last week, so I don't think channel explains the gap. The urgent-ticket report is the more worrying one: Marrowby (66.7%), Cragsley (66.0%) and Studio Vrij (63.6%) are bad, but several smaller merchants (Honeycomb Larder 100%, Sourdough Club 85.7%, Ampfeld Elektronik 83.3%) are worse - so urgent-SLA failure is NOT concentrated in the three hotspots, it's spread broadly across the book, and nearly every merchant's urgent miss rate is well above their own all-priority miss rate. That pattern (urgent specifically failing everywhere) makes me suspect the urgent SLA *target itself* may be set unrealistically tight platform-wide, rather than this being pure execution failure merchant-by-merchant - worth testing directly before concluding it's \"everyone is bad at urgent tickets.\" Tonight I'm testing: (1) whether hotspot tickets are genuinely more complex (more back-and-forth) rather than just higher volume, (2) whether urgent SLA targets are achievable given typical actual resolution times, and (3) whether the hotspots' staffing gap has been widening over 2025 (useful for both the \"why\" question and, incidentally, for planning ahead of Nov/Dec later).","requests":[{"title":"Ticket complexity proxy (messages and tags per ticket), by merchant","question":"Do Cragsley, Marrowby, and Studio Vrij get harder/more complex tickets than the rest of the book, or just more of them?","grain":"one row per tenant (merchant), among tenants with at least 20 tickets created in 2025","columns":["merchant name","tickets created, 2025","avg messages per ticket (all)","avg public (customer-visible) messages per ticket","avg distinct tags per ticket"],"details":"Source: tickets joined to tenants, ticket_messages (count per ticket, and count where is_public=true), and ticket_tags_map (count distinct support_tag_id per ticket). Window: tickets.created_at 2025-01-01 through 2025-12-31. 'avg messages per ticket' = total ticket_messages rows for that tenant's 2025 tickets ÷ number of those tickets. 'avg public messages per ticket' = same but counting only is_public=true rows. 'avg distinct tags per ticket' = total ticket_tags_map rows for those tickets ÷ number of those tickets (a ticket with 0 tags counts as 0). Round all three averages to 2 decimals. Only include tenants with >=20 tickets created in 2025 (same threshold as last night's staffing/channel reports, for a consistent peer set). Order by avg messages per ticket descending, tie-break merchant name ascending. Cap at 50 rows; note total qualifying tenant count.","builds_on":["Support agent staffing vs ticket volume, by merchant","Ticket channel mix, by merchant"]},{"title":"Urgent-ticket SLA target vs actual resolution time, by merchant","question":"Is the broad urgent-ticket resolution failure driven by unrealistically tight SLA targets, or are merchants genuinely resolving urgent tickets slower than a reasonable target?","grain":"one row per tenant (merchant), among tenants with at least 5 evaluable urgent-priority tickets created in 2025","columns":["merchant name","urgent resolution target (hours)","urgent tickets evaluated, 2025","median actual resolution hours (urgent)","avg actual resolution hours (urgent)","urgent miss rate (%)"],"details":"Source: tickets (priority='urgent') joined to sla_policies via tickets.sla_policy_id, and tenants (name). Window: tickets.created_at 2025-01-01 through 2025-12-31, excluding tickets with a null sla_policy_id (same 'evaluable' definition as last night's urgent-miss-rate report). 'urgent resolution target (hours)' = sla_policies.resolution_target_minutes / 60 for that tenant's urgent policy; if a tenant's evaluated urgent tickets reference more than one distinct resolution_target_minutes value, report the ticket-count-weighted average target in hours and note in the report that the tenant has more than one urgent SLA policy in effect. 'median/avg actual resolution hours' = extract(epoch from resolved_at - created_at)/3600 over evaluable urgent tickets with resolved_at not null (tickets still unresolved as of 2025-12-31 are excluded from the actual-hours average/median but still count as a miss in the miss-rate numerator, consistent with last night's definition). 'urgent miss rate (%)' = same definition as last night's report (missing if unresolved as of cutoff, or resolved later than target), rounded to 1 decimal. Round target and actual hours to 1 decimal. Only include tenants with >=5 evaluable urgent tickets. Order by urgent miss rate descending, tie-break merchant name ascending. Cap at 50 rows; note total qualifying tenant count.","builds_on":["Urgent-priority ticket resolution failure, by merchant"]},{"title":"Ticket load per agent over 2025, hotspot merchants vs a comparable peer","question":"Has the staffing gap at Cragsley, Marrowby, and Studio Vrij been widening over the course of 2025 (ticket volume outpacing agent headcount), or has it been consistently stretched all year?","grain":"one row per tenant and calendar quarter in 2025, for Cragsley Outfitters, Marrowby & Vane, Studio Vrij, and Grind Collective (the next-highest tickets-per-agent merchant, included as a comparison peer)","columns":["merchant name","quarter (2025-Q1..Q4)","tickets created that quarter","distinct agents who handled a ticket that quarter","tickets per active agent that quarter"],"details":"Source: tickets joined to tenants, filtered to the four named tenants (Cragsley Outfitters, Marrowby & Vane, Studio Vrij, Grind Collective). Grain the quarter on tickets.created_at (2025-01-01 through 2025-12-31, 4 quarters). 'distinct agents who handled a ticket' = count distinct tickets.assignee_agent_id (excluding null) for that tenant/quarter. 'tickets per active agent' = tickets that quarter ÷ distinct agents that quarter, rounded to 1 decimal (NULL if zero agents). Order by merchant name ascending, then quarter ascending. Expect exactly 16 rows (4 merchants × 4 quarters).","builds_on":["Support agent staffing vs ticket volume, by merchant"]}]},"toolUseID":"toolu_01Jng5xXGi2qcQdSzUU3G5ax"},"type":"attachment","uuid":"98f362f6-1b5a-40d6-9dfa-5ca34c162d00","timestamp":"2026-10-04T02:17:23.284Z","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"fc03daa5-1e1a-4a76-8056-fdaef2eb1bfd","isSidechain":false,"promptId":"0c2fdd84-10ce-4d29-bfad-47e4de47ddb6","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Jng5xXGi2qcQdSzUU3G5ax","type":"tool_result","content":"Structured output provided successfully"}]},"uuid":"0ec4cf5a-fb9b-4bac-ad7c-55690e91028f","timestamp":"2026-10-04T02:17:23.288Z","toolUseResult":"Structured output provided successfully","toolEndsTurn":true,"sourceToolAssistantUUID":"fc03daa5-1e1a-4a76-8056-fdaef2eb1bfd","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a","version":"2.1.275","gitBranch":"HEAD"}
{"type":"last-prompt","lastPrompt":"The printed reports of night 1:  === Report 1: Support agent staffing vs ticket volume, by merchant (written by Ines) === Engineer's note: Row count of the full qualifying-tenant set (before the 50-ro…","leafUuid":"0ec4cf5a-fb9b-4bac-ad7c-55690e91028f","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a"}
{"type":"ai-title","aiTitle":"Shopper experience baseline review","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a"}
{"type":"mode","mode":"normal","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a"}
{"type":"atis-latch","atis":"","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a"}
{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-04T02:18:25.242Z","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a","content":"The printed reports of night 2:\n\n=== Report 1: Ticket complexity proxy (messages and tags per ticket), by merchant (written by Felix) ===\nEngineer's note: One row per tenant with >=20 tickets created in 2025 (same threshold as prior staffing/channel reports); tags are counted as raw ticket_tags_map rows per ticket (duplicate tag assignments, if any, would inflate the average slightly). qualifying_tenant_count column shows the total peer-set size (same value every row) since the report is capped at 50 rows.\nREPORT: Ticket complexity proxy (messages and tags per ticket), by merchant\nQUESTION: Do Cragsley, Marrowby, and Studio Vrij get harder/more complex tickets than the rest of the book, or just more of them?\nmerchant_name                 | tickets_2025 | avg_messages_per_ticket | avg_public_messages_per_ticket | avg_distinct_tags_per_ticket | qualifying_tenant_count\n------------------------------+--------------+-------------------------+--------------------------------+------------------------------+------------------------\nThistlecombe                  |           21 |                    4.29 |                           4.29 |                         1.67 |                      22\nKibblewick                    |           72 |                    4.22 |                           4.22 |                         1.85 |                      22\nBallycorran Industrial Supply |           44 |                    4.14 |                           4.14 |                         1.66 |                      22\nHoneycomb Larder              |           58 |                    3.93 |                           3.93 |                         1.71 |                      22\nAmpfeld Elektronik            |           56 |                    3.82 |                           3.82 |                         1.54 |                      22\nGrind Collective              |          122 |                    3.82 |                           3.82 |                         1.93 |                      22\nPetalcourt Beauty             |          119 |                    3.76 |                           3.76 |                         1.77 |                      22\nSpice Route Box               |           71 |                    3.75 |                           3.75 |                         1.79 |                      22\nStudio Vrij                   |          166 |                    3.71 |                           3.71 |                         1.64 |                      22\nBrennquell Kaffee             |          137 |                     3.7 |                            3.7 |                         1.43 |                      22\nRue Lafayette Studio          |           27 |                     3.7 |                            3.7 |                         1.74 |                      22\nCinder Peak Sports Co.        |           43 |                    3.58 |                           3.58 |                         1.56 |                      22\nFennel & Flint                |           67 |                    3.58 |                           3.58 |                         1.75 |                      22\nGratlinie Outdoor             |           27 |                    3.56 |                           3.56 |                         1.48 |                      22\nKayak Corner                  |           20 |                     3.5 |                            3.5 |                         1.85 |                      22\nCragsley Outfitters           |          400 |                    3.46 |                           3.46 |                         1.36 |                      22\nLinden & Loom                 |           29 |                    3.45 |                           3.45 |                         1.79 |                      22\nWiesgrund Wohnen              |           32 |                    3.44 |                           3.44 |                         1.53 |                      22\nSourdough Club                |           47 |                     3.4 |                            3.4 |                         1.81 |                      22\nMarrowby & Vane               |          231 |                    3.29 |                           3.29 |                         1.43 |                      22\nCoastline Interiors           |           21 |                    3.14 |                           3.14 |                         1.62 |                      22\nOakhurst Tailoring            |           27 |                    3.07 |                           3.07 |                         1.59 |                      22\n(22 rows)\n\n=== Report 2: Urgent-ticket SLA target vs actual resolution time, by merchant (written by Bea) ===\nEngineer's note: Added a 7th column (multiple_urgent_sla_policies, Y/N) to flag tenants whose evaluated urgent tickets reference more than one distinct resolution_target_minutes, as requested, since that can't be folded into the single 'target hours' number; everything else matches the spec exactly. Total qualifying-tenant count and exact row count will show on the printed report since the query itself isn't run here.\nREPORT: Urgent-ticket SLA target vs actual resolution time, by merchant\nQUESTION: Is the broad urgent-ticket resolution failure driven by unrealistically tight SLA targets, or are merchants genuinely resolving urgent tickets slower than a reasonable target?\nmerchant_name       | urgent_resolution_target_hours | urgent_tickets_evaluated_2025 | median_actual_resolution_hours | avg_actual_resolution_hours | urgent_miss_rate_pct | multiple_urgent_sla_policies\n--------------------+--------------------------------+-------------------------------+--------------------------------+-----------------------------+----------------------+-----------------------------\nHoneycomb Larder    |                            8.0 |                             9 |                           59.5 |                        56.1 |                100.0 | N                           \nSourdough Club      |                            8.0 |                             7 |                           23.9 |                        32.0 |                 85.7 | N                           \nAmpfeld Elektronik  |                            8.0 |                             6 |                           12.0 |                        30.2 |                 83.3 | N                           \nMarrowby & Vane     |                            8.0 |                            33 |                           18.2 |                        79.6 |                 66.7 | N                           \nCragsley Outfitters |                            8.0 |                            50 |                           13.4 |                        27.6 |                 66.0 | N                           \nStudio Vrij         |                            8.0 |                            11 |                           19.9 |                        67.1 |                 63.6 | N                           \nLinden & Loom       |                            8.0 |                             6 |                            5.8 |                         6.1 |                 50.0 | N                           \nPetalcourt Beauty   |                            8.0 |                            10 |                           14.6 |                        30.3 |                 50.0 | N                           \nFennel & Flint      |                            8.0 |                             5 |                            1.2 |                        15.3 |                 40.0 | N                           \nWohnglück           |                            8.0 |                             5 |                            3.1 |                        16.6 |                 40.0 | N                           \nSpice Route Box     |                            8.0 |                             9 |                            5.1 |                         9.0 |                 33.3 | N                           \nBrennquell Kaffee   |                            8.0 |                            16 |                            4.1 |                         9.3 |                 31.3 | N                           \nGrind Collective    |                            8.0 |                             6 |                            1.1 |                         4.0 |                 16.7 | N                           \nWiesgrund Wohnen    |                            8.0 |                             7 |                            0.8 |                         2.4 |                 14.3 | N                           \n(14 rows)\n\n=== Report 3: Ticket load per agent over 2025, hotspot merchants vs a comparable peer (written by Omar) ===\nEngineer's note: Grain is tenant x calendar quarter for 2025, limited to the four named tenants; tickets_per_agent is tickets_created / distinct assignee_agent_id (NULL when a tenant/quarter has zero assigned agents).\nREPORT: Ticket load per agent over 2025, hotspot merchants vs a comparable peer\nQUESTION: Has the staffing gap at Cragsley, Marrowby, and Studio Vrij been widening over the course of 2025 (ticket volume outpacing agent headcount), or has it been consistently stretched all year?\nmerchant_name       | quarter | tickets_created | distinct_agents | tickets_per_agent\n--------------------+---------+-----------------+-----------------+------------------\nCragsley Outfitters | 2025-Q1 |              76 |               8 |               9.5\nCragsley Outfitters | 2025-Q2 |              81 |               8 |              10.1\nCragsley Outfitters | 2025-Q3 |              92 |               8 |              11.5\nCragsley Outfitters | 2025-Q4 |             151 |               8 |              18.9\nGrind Collective    | 2025-Q1 |              12 |               3 |               4.0\nGrind Collective    | 2025-Q2 |              25 |               3 |               8.3\nGrind Collective    | 2025-Q3 |              15 |               3 |               5.0\nGrind Collective    | 2025-Q4 |              70 |               3 |              23.3\nMarrowby & Vane     | 2025-Q1 |              46 |               5 |               9.2\nMarrowby & Vane     | 2025-Q2 |              37 |               5 |               7.4\nMarrowby & Vane     | 2025-Q3 |              43 |               5 |               8.6\nMarrowby & Vane     | 2025-Q4 |             105 |               5 |              21.0\nStudio Vrij         | 2025-Q1 |              13 |               4 |               3.3\nStudio Vrij         | 2025-Q2 |              96 |               4 |              24.0\nStudio Vrij         | 2025-Q3 |              18 |               4 |               4.5\nStudio Vrij         | 2025-Q4 |              39 |               4 |               9.8\n(16 rows)\n\n\nNight 3 of 4: you have 3 report slots tonight."}
{"type":"queue-operation","operation":"dequeue","timestamp":"2026-10-04T02:18:25.242Z","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a"}
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{"parentUuid":"cd0cc789-a258-4a1b-a93b-999e1946316a","isSidechain":false,"type":"assistant","uuid":"4c619cc9-7428-4ee5-ad3b-ba8db9417ae5","timestamp":"2026-10-04T02:18:25.236Z","message":{"diagnostics":null,"id":"e408cdd4-b036-4b33-9f4d-97c6e480598c","container":null,"model":"<synthetic>","role":"assistant","stop_details":null,"stop_reason":"stop_sequence","stop_sequence":"","type":"message","usage":{"output_tokens_details":null,"input_tokens":0,"output_tokens":0,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":null,"cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":0},"inference_geo":null,"iterations":null,"speed":null},"content":[{"type":"text","text":"No response requested."}],"context_management":null},"isApiErrorMessage":false,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"4c619cc9-7428-4ee5-ad3b-ba8db9417ae5","isSidechain":false,"promptId":"b903957b-9d19-4055-b66a-9723f5b5d1b2","type":"user","message":{"role":"user","content":"The printed reports of night 2:\n\n=== Report 1: Ticket complexity proxy (messages and tags per ticket), by merchant (written by Felix) ===\nEngineer's note: One row per tenant with >=20 tickets created in 2025 (same threshold as prior staffing/channel reports); tags are counted as raw ticket_tags_map rows per ticket (duplicate tag assignments, if any, would inflate the average slightly). qualifying_tenant_count column shows the total peer-set size (same value every row) since the report is capped at 50 rows.\nREPORT: Ticket complexity proxy (messages and tags per ticket), by merchant\nQUESTION: Do Cragsley, Marrowby, and Studio Vrij get harder/more complex tickets than the rest of the book, or just more of them?\nmerchant_name                 | tickets_2025 | avg_messages_per_ticket | avg_public_messages_per_ticket | avg_distinct_tags_per_ticket | qualifying_tenant_count\n------------------------------+--------------+-------------------------+--------------------------------+------------------------------+------------------------\nThistlecombe                  |           21 |                    4.29 |                           4.29 |                         1.67 |                      22\nKibblewick                    |           72 |                    4.22 |                           4.22 |                         1.85 |                      22\nBallycorran Industrial Supply |           44 |                    4.14 |                           4.14 |                         1.66 |                      22\nHoneycomb Larder              |           58 |                    3.93 |                           3.93 |                         1.71 |                      22\nAmpfeld Elektronik            |           56 |                    3.82 |                           3.82 |                         1.54 |                      22\nGrind Collective              |          122 |                    3.82 |                           3.82 |                         1.93 |                      22\nPetalcourt Beauty             |          119 |                    3.76 |                           3.76 |                         1.77 |                      22\nSpice Route Box               |           71 |                    3.75 |                           3.75 |                         1.79 |                      22\nStudio Vrij                   |          166 |                    3.71 |                           3.71 |                         1.64 |                      22\nBrennquell Kaffee             |          137 |                     3.7 |                            3.7 |                         1.43 |                      22\nRue Lafayette Studio          |           27 |                     3.7 |                            3.7 |                         1.74 |                      22\nCinder Peak Sports Co.        |           43 |                    3.58 |                           3.58 |                         1.56 |                      22\nFennel & Flint                |           67 |                    3.58 |                           3.58 |                         1.75 |                      22\nGratlinie Outdoor             |           27 |                    3.56 |                           3.56 |                         1.48 |                      22\nKayak Corner                  |           20 |                     3.5 |                            3.5 |                         1.85 |                      22\nCragsley Outfitters           |          400 |                    3.46 |                           3.46 |                         1.36 |                      22\nLinden & Loom                 |           29 |                    3.45 |                           3.45 |                         1.79 |                      22\nWiesgrund Wohnen              |           32 |                    3.44 |                           3.44 |                         1.53 |                      22\nSourdough Club                |           47 |                     3.4 |                            3.4 |                         1.81 |                      22\nMarrowby & Vane               |          231 |                    3.29 |                           3.29 |                         1.43 |                      22\nCoastline Interiors           |           21 |                    3.14 |                           3.14 |                         1.62 |                      22\nOakhurst Tailoring            |           27 |                    3.07 |                           3.07 |                         1.59 |                      22\n(22 rows)\n\n=== Report 2: Urgent-ticket SLA target vs actual resolution time, by merchant (written by Bea) ===\nEngineer's note: Added a 7th column (multiple_urgent_sla_policies, Y/N) to flag tenants whose evaluated urgent tickets reference more than one distinct resolution_target_minutes, as requested, since that can't be folded into the single 'target hours' number; everything else matches the spec exactly. Total qualifying-tenant count and exact row count will show on the printed report since the query itself isn't run here.\nREPORT: Urgent-ticket SLA target vs actual resolution time, by merchant\nQUESTION: Is the broad urgent-ticket resolution failure driven by unrealistically tight SLA targets, or are merchants genuinely resolving urgent tickets slower than a reasonable target?\nmerchant_name       | urgent_resolution_target_hours | urgent_tickets_evaluated_2025 | median_actual_resolution_hours | avg_actual_resolution_hours | urgent_miss_rate_pct | multiple_urgent_sla_policies\n--------------------+--------------------------------+-------------------------------+--------------------------------+-----------------------------+----------------------+-----------------------------\nHoneycomb Larder    |                            8.0 |                             9 |                           59.5 |                        56.1 |                100.0 | N                           \nSourdough Club      |                            8.0 |                             7 |                           23.9 |                        32.0 |                 85.7 | N                           \nAmpfeld Elektronik  |                            8.0 |                             6 |                           12.0 |                        30.2 |                 83.3 | N                           \nMarrowby & Vane     |                            8.0 |                            33 |                           18.2 |                        79.6 |                 66.7 | N                           \nCragsley Outfitters |                            8.0 |                            50 |                           13.4 |                        27.6 |                 66.0 | N                           \nStudio Vrij         |                            8.0 |                            11 |                           19.9 |                        67.1 |                 63.6 | N                           \nLinden & Loom       |                            8.0 |                             6 |                            5.8 |                         6.1 |                 50.0 | N                           \nPetalcourt Beauty   |                            8.0 |                            10 |                           14.6 |                        30.3 |                 50.0 | N                           \nFennel & Flint      |                            8.0 |                             5 |                            1.2 |                        15.3 |                 40.0 | N                           \nWohnglück           |                            8.0 |                             5 |                            3.1 |                        16.6 |                 40.0 | N                           \nSpice Route Box     |                            8.0 |                             9 |                            5.1 |                         9.0 |                 33.3 | N                           \nBrennquell Kaffee   |                            8.0 |                            16 |                            4.1 |                         9.3 |                 31.3 | N                           \nGrind Collective    |                            8.0 |                             6 |                            1.1 |                         4.0 |                 16.7 | N                           \nWiesgrund Wohnen    |                            8.0 |                             7 |                            0.8 |                         2.4 |                 14.3 | N                           \n(14 rows)\n\n=== Report 3: Ticket load per agent over 2025, hotspot merchants vs a comparable peer (written by Omar) ===\nEngineer's note: Grain is tenant x calendar quarter for 2025, limited to the four named tenants; tickets_per_agent is tickets_created / distinct assignee_agent_id (NULL when a tenant/quarter has zero assigned agents).\nREPORT: Ticket load per agent over 2025, hotspot merchants vs a comparable peer\nQUESTION: Has the staffing gap at Cragsley, Marrowby, and Studio Vrij been widening over the course of 2025 (ticket volume outpacing agent headcount), or has it been consistently stretched all year?\nmerchant_name       | quarter | tickets_created | distinct_agents | tickets_per_agent\n--------------------+---------+-----------------+-----------------+------------------\nCragsley Outfitters | 2025-Q1 |              76 |               8 |               9.5\nCragsley Outfitters | 2025-Q2 |              81 |               8 |              10.1\nCragsley Outfitters | 2025-Q3 |              92 |               8 |              11.5\nCragsley Outfitters | 2025-Q4 |             151 |               8 |              18.9\nGrind Collective    | 2025-Q1 |              12 |               3 |               4.0\nGrind Collective    | 2025-Q2 |              25 |               3 |               8.3\nGrind Collective    | 2025-Q3 |              15 |               3 |               5.0\nGrind Collective    | 2025-Q4 |              70 |               3 |              23.3\nMarrowby & Vane     | 2025-Q1 |              46 |               5 |               9.2\nMarrowby & Vane     | 2025-Q2 |              37 |               5 |               7.4\nMarrowby & Vane     | 2025-Q3 |              43 |               5 |               8.6\nMarrowby & Vane     | 2025-Q4 |             105 |               5 |              21.0\nStudio Vrij         | 2025-Q1 |              13 |               4 |               3.3\nStudio Vrij         | 2025-Q2 |              96 |               4 |              24.0\nStudio Vrij         | 2025-Q3 |              18 |               4 |               4.5\nStudio Vrij         | 2025-Q4 |              39 |               4 |               9.8\n(16 rows)\n\n\nNight 3 of 4: you have 3 report slots tonight."},"uuid":"3d3cc246-8d7d-42b0-beae-ca5fd3b9d14d","timestamp":"2026-10-04T02:18:25.249Z","permissionMode":"default","promptSource":"sdk","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a","version":"2.1.275","gitBranch":"HEAD"}
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gSOBnqRa5IVFZPL6PSGJf5LmOP0cxCLCID2CooWREeLo8DwJFbxJcTOedbGP2oBXBOHz2pUQKFBmchCGuU0O0m/tIOg1P16NzoicyF1UIyHLCNM8mEr625/A++xuyfklsR+sN9HHwbvy//1eGYff4pr/C+HevwDs4sKMmHtSJnMwLxIDRKIU/p28GbT7QoJJ+Rxo91UfEHo6KvrrAgUvTUPP5sOhIpNzqUjRfNZQIoSq3+wivTc+29DhkAJB1TU00ZmYTbdtvseyHi7lA+fePZ0NR19rV/9MuqID9DfhumOyED7WQOau1J6yCmqnmslTi5i4LVaTglFfxnGoHUYRixr3CNcYP+/8rls7j14jcNvxbT406v3kZFE3Ct37WLF8OpxuiIDz2MHx5/t9yIyyvItkVH5WVlRxeDs63SorW5VI1MGaRhyrvzNEl5/Jg/JRQRZcuF6rDR0VPULMh93QExTzRBw5MGO88gDRyJUEUq85jSDpZ4FAx4VCPAxESpCOUk4t9UloG8Io0jKnfLsRSCGx1f/vyfKDYjvXWW4y1I7774o5dS7kO+RbOhOWpWqzWMYMB2MjRvuWd0mtWDoelCSeXdVSwJnJJJk9eaWKvcUykEBDVUYyq6UcryKHefk+fw2BHv8Wk1OU/yMZ0WGi84lykbkoflaEEBqgf6MDU572ieLYSvd2N9eDr4VxHDd3OXfXE07Ll6XR/w5fuBuKeAq6kCoaykFVkoCUKrb6m0hbVi+uqhGNUgjv5wCovNd1n5oDpOGn4KTubnbtZBwEOUNcp1oftkfLqsRKIb7LHssTzwZCafnv0ZTXMCtNU3F9s+HCJah0mjFhnG5Nu4TiKO1aRfTvf5zb5qUH15Jw4TE/oswNF58SrBiYi6MxIpJEN2ctmqAry6wXDihErp3LebKXY2gasfOQFMzz+EousX+iwpS6VTfX7AYc/K4cwwcInpwM8D1mZLLO8N0nygHLy5Ms7rVDAfjiUC43Vf3a4kyQV0Gze1WS2BrEF68lWe9Ojr52aZVKxhwJIdh3QvSVYeMrk/7Q8YLu+wVRPpU6hjOynz+cuWNNIdyXzu+ssFafcBJWlLabAFGuTWW+Amx7QOUQslR5xI71TqOZhjNWDW27Jf/vRi7VIBVezikLzrRXJ0VV/EfR/w9s8MwvZt+H5mjrv4hF5IeAzuaqQ8wUkpoTeuCz98//PlAIU77z/fSvgwdqAa7/adyl/oKKzGhBuHLqcBYnb2mpkAk7+ei3CjTv94+K0twIn33NdyX038hQcrI+RBPI/FyoevDDqF8b24UH37jalSqb0ipCx8cVzr5NeEkLHd+O/6isfAUeL/GC1yWFe3TOZ0SkNSCYCAJNI9RO1jQYD/C3FaB7/f4Xq+54EW+ZChFnHT503mGi58Udmiuho6I2kKU1vfOPKacxDi10I09SY4eD8Npq0bswym7HnZFpE5oict7XDQi+AEFS0tbbgbuJJwn8BJdpy6+qqBjUJGMq/PGcUYESnAelclj6wYdfagN+GZ6R+/WNN6joDsJa9/hmbtHyNMrHQqHx1rsBjws5w3TH6CNofeTxt+KmKJBG0h91XiG/fncvQHilqfrrnuQvx5bhp9mFboPIAZ3J7Ab6plLCzp/cLspA/M0jt6tstu6UEtjpe9rpS1xwLGkUUtYasCoZRD9VV1FytkGhRzmLR4liFFkPySrMuvlR6UdQkZGGGTz5UVt/Y2ZjJqyXPQzoWCCZ8oykBJgTuiioqhv01u/9mZ9+LQBcebKe55R5RZRLY2GYQeR+l4FNnI1rDX8VPdFNHyHFFj0bOt9rLMnT5Wxt8R8h6Fbh8wwcTosUyfazryCqEJfmMGmiHq18SCUCBDCn7UEo1iD9DHUfIvSQJThevSGChqBgL/qmmGfDmJzU+IucaVT+2DMUFY/M3aEtdNqnlTJhNh9M2EHYcHL5krbwvHvVcKaH0D8Q43sSG2BwDcw/kvbbbt0yYRZlgGQzL7k57fd7b/TqzLS5L3l0mawAIgDm8LlB797M9NUIJoomkoKBkxmyRZT7MkLKfyqizkuJGVmvKgn+UFTMKfVdXwIaMxYuRwuQwqArk+d0v7076XopmtGwsU93Y1HNqmnuQ3YFiVhZjvmnxasx2SVsl3ImUzR2hzrNTiXQEZEi6eUOwr5uCBfLUs0uWij6OsI89HXFw5xL2DE4fgAMF3LoCV48rq971RDiT15HPwCnCY+zasJz/1mdoTr4csg6P4gFLkEgS3OKTa0x8FJMUMXad5xkxI+AcjQZDd5aNl0TRby2vVEk0xxdX3fMtJPh10cHAnh6PAhVeXQ3VIhl6Ns2efBh20HzMMkIzkemZQSxB+AKO3kb2L6qz595D2sqYoC2MSHQLGBE5XO7Pzn9sUrIPBgbDevSMuSS2rM9QLBC3QK37FY8cU6usRc6O2nqf014l0KP/RuuhGAoTdM/Ux97BuV7r7v/IMa1h6lZf2lTm3qfQUy+N9Vgt/BVjvEb91rShzkc8wOAgYnN05idtnFQpMFH/eaFM6lUSgyPiJuo6VeYZNwAYYUr5/IWBXZrLGRRfJL30VA4hKCt6TuIiaP1JPL2FYOFORfLaHiHZoHUmp4+C4r9h1FSKMyNLIqEPg/tz+FRc2EVUbNHMRbGr2FaGPqsg2eAdiMr18KoDJBo9cWb/aSbPXKwv7i8KaEXPkdHGEp4aiG9Ul/nXfdBUp5i6LlmrGPuQix7Onm083w83+DB9Nj49k7Jw9vXtI/4kHC1aBEaaJd+wphNM/B0we8bAi8r8UFGX0lK0/ogja9pR6gc4y2IglnN9sGGFn7gcJal5/0NJPtPFcenpfXWa/BsGnwqcEl9tDQTL4NOEvrAPK61h5m7IXbFQBvFfmRYWAfFYIyWwx5oSPuGs5uzmMjCGvT0LEv/AkuGIIA3glS5dzUJee3jNCfrgBzHteT96WSbXRBU5ZI5vUGa98DBXBkg2vGYcnEt0GE1r46GlgvTnRHdIKfkDlCsTfPxfcblkKkamad6oXGiEiybZGpfZWtGI30ZtoYHVmEQ8UcaooAb5ei9QgSebiyoywA2X6EwCF+Y8krsr5MQqoKqNg+wCffIZgGK6q9vOeseEpY2qog9dOrB1iq29/mVNVBcagd2h4dUBF2ITCq9KfpnOcZoMvRrWGZzcQbwlkz6bkqrX0ySx5eJ4zbgVp1srAYy95Ynf6UdhfsdvQlpKpvX8stVvEIxyHxS1IRqiSUphjwzLMJhp8FTi0XdAnRcbD2YjKQFH8SDbP6DU2z3ii7wVllUbo2wlPGMlo+oFeOUOMW/3pR6gHxo0rs929GTuZOjN2+pZEXacP2D2Jln/VWrSoOV/LORl9q/jfAdvqm5CUy/kzfEtTxs1cgN5i22wBHnltZrfCV1BJVgkpNeOeMEF+HhVooJKlZuMKCMqHctaXALj68FFJS4LFjXvNjfAa/Bg8qsZhZXLUTaJw0mwX8ce+B1Iz14H8aTG7O4Ly9nQYJ6/kBCIo3sYqgH8FUNpniNEzQHrjewKSdnRh7wDoxJTA2JpgqG6vU2lSS+abQaL2pGF9kb5Vm81kOzqGcPPaDN7vUUR/kKo8Kq4oUQn5yApFLpze0fqcBLBVBWlI3q9KCAF/AbIAl772Gwepv69qKQy3cVSSDd39RzKj9RlRx8QUJr2ZjyifB84VxjE3PFRwjK6vg+ydIRz7oxMw7AK8Q+HNrV0Hu/LnWDkUVrfvnMNKPIOHesAP1wBMqfcXvoMvUKEaVUpDi7tCfHO8Nz4LMxi4ul70z6ZoX7qETMzqvpCwtO3kQhjPc3eRz+ganAFH5G4vZNa7D/D9jnoCbEXyvgytl3rqQ9QDwJQXmAa4Tqxtc7FWO/7NCbuBDTAG6goZy0TUrOvD2ETpyA0xoZr/4peGiDNyVfYuWfUQ6J43K5w2fZrztKO53bj40Hro6nl2AcBWKXEXd4jPQxgUnsZHsn2zlGNrvjCJ0Y9KWq1D1UeyteY3JxnF1fkJ+D9Ri0OdBMmgaAEhOpISccuVIwSeOWlJU3RPN6cbtz9eR4Su5R7OS/zd3/ALCE/9pI490ixLiaE7WSBcfol8HV5KOJNqVyYMPDaalUF0xn+a9iCABWDLvg2fWAeIpDiMQAOQxkV+vTeiheuEp1ZUgZ6F9Eq+cAJJgXPoqGJimpBOsAMFdtxa/nyA9rzcHXbpn/cX3TzF9vx2UIWP+zvcTBP/M/XVc5bQx2GexC+DRKntfaUMUojF7SUR2EBv8SYhqU6NUe4pRnzQIu309yiYYdfwEsVAihP6b4etqE+T2RT4GmG+bVvkgHCOxn761oZ1w7RtTLevzTPyR6GuWnjwCb0GAzSN0oT6NVYtzkmZMSc2ETsZgfx+V9UKTeowQ2Q5ytga0T5kZXleWVLcaWxpYnYrISL1lPn6ErAEHT0+3S2Q9kcUxmd0fQSZEPmOd8rtzzecY+RD/YPopQIQK/06afrsclC4rMCuwPEZyjIUtqr/pWrl3G4DZurSEn5HW/5cHVmAk10zx5Fhzh88013XR7SdajBmCM4MiE18O2fXm7VkZHBA0XUWFRxwUuE6d8jImbvUZ90jK5iLxxWR/ptklR3Wv4A9aLTo2NeWnOC4OFi/4Op1QMoltEDdjiUv9Cm4H67lL2Kyqe/3pALy9XAZTVj7+o5SGedDSyRo5X1AybiUr0wUzdBYcGM+YEmsJfBJh1uNcBR1hiD7XQpAS5rEjqowP8mQC5syOJWibT5xwLi7cgqargSA/cmcY6Ye2C8rq/jsRmSWL88eYuQzBX97JvhpmOWjDi4OoYZKYvo0OdFm2yfw8czTOK9K9/jwJzRQf3/dZsOtvBj6FzHKBon3b38/aTdrxkpCWqxIcwQ2BGA1G6i1QPyMdeF4zTxYrxjsjxqR4N6maze9EAdSHmMQRqkgt5uzClCjYzTOda9LLyo6TMp7x15EPm6fr/it6q+vW3t1ZFG6SLaEe+YvQi9BOT51M2D39w+vofs1QjTleOHyVYfVFNJ35nPobV9g1SEA8JBJ5Qw/wmhTaQNWfWphgbbMH3VlKnLz/XeqC8EM+0xS2ueQM4NJEPvPuvwvco2X4U/7wx8Z9fjweYaR3TFVRe2vP9eUOmJjWNhK/rI1IV9MhjfMas4Ie2hNK1OcavEwSk8vHOiD2Qa6/CrgLjY7OsDQnM4W1xf1PMYdKpFcdMXpZQlLjrwvuEeCeznIfXY6usqZTNl/YicdVgZz9hkZ+r2fcZM7CwVgDwrFdz5jLdgVzr57xZ1moUXwLYzoOZoNey2HptdWKWgyUF5z6GbQ1Jx6hq8G+tHfTjIsYpRIKBwzYPU9LfuUAvPa0FnwMwS3ZNHOP32g/8iRWgg+KDysc7/4eOmCtg9hJjlPhTravFcfYmSJPjeSHPsgh5ati2jcz4BsqW7nliAvzXhk2DTCRwMGLYmL1jfmGKlLVKJvhb+8QvVhsJDFFivuGqXZ0yU5K68mdyQXY1aMlLghqQ9fGfIoxmsLFcIvKBHrLXKIGSyNh6gQEwYWLACUf8YHVHdkCUo8OmNliNNsFGuMX/T7na2KahxZysNP4SLfLdouPE3FGGvZdEVNIW/+SDt80QxQ5+goWuZoXdrZ2BSCLA/P1R+R3rtbTlNhw5fY9QbLhsBzj2pDTkXAeMRV50fJiCjQZnKIDfLbnAa0tkne/AxQoeX/TFhnCRyjxokgQX9oh2M405OWBvkYGtGU5FTiTwdKrwH//hIH3jUYn0D3BK/QQG3GSKjzpXedBmxF3UwHcNeVDt6DlKbIgKrCIAJ+9KRrzcGk8l1m4avptmlizVygjk4xc+wOxpCeLzxP3ngSJ+8Kgrec419QLSUblAQ4YPI24E/eMtS7s2iHaStBFDuve3tB/NsEx93/U+Ui70Iooq6NVocnaXmuq/3yLfEZ+Uzcmwr2H6Q2sE3Wwq6Iiv5yv0VRESKprnqPb0QYQwCQmVXUqwWMulG6XD1MOZT16ImzE5RXaCynfZDaiA9UOdPMdv4ydtAT/R3M2qCBi9H2sZaBkZEWo6koO1XmUW/thJyoxh1m9hQgsD7Pypmf7uSjtNVJRkPAhAvRruIHH2xapz08DexJeqv42AQxmMdPqDaPnfeg9P9cXrFbh5l0ZQUpGtJgqHx/y9BvtecYl16PKw8glulgFnjHh5FHzTTsoT5AFXxVPdJFdoYlY1jzG104yVVFvGCh0LWkqsDnZ7MCV0NqYJ/RnwjsNd/1Ocp0DGaN0St/0jgqJSfet09GVkmL3anmKwYdMFJ8a+MY0V73Ky1LQs7uF7gsAq5q/TkFkbefeaiVwCdyVQR/4UspnsPiQVHTOKmxpKHZzq8q8w4HKKUN4JAGLzx4hIT7FYOc/pSMYyye5J/BPTm26ZN++GeXv5lptRyI74HEIYVGiWEVZqw6qpDbTPIHfzfSjWZ1NdUTrL/1K/FNgJmRvBPdka2aqi5aks5ucMicGCpaOJW4BRFSYQNO3sSxJeFMZZdcSyVtj6c209VFexpA8/dHEF7aCGLRohuBxCUN1IxKbvZGejYylRUcSkX1ImSy7iCwVAd3N0sD7CqPd8ty+7m8FBlUHrI7o+2rtdzTMdWPA2XZN74EYovjt44MOgAPyYFJy8NzswkmhNicRB158mA9nsCJHSERz5LEY0G6j7ehl4T3diTUvx9EBTY/hEz8gVhD8QSmlzw+RtcoBfn6jj8ScU3AZBUp1O7vAgOS67WmHEmqTWD//uYAjgjX0WASdIFbHbX+/ooYB+R+KsYm9Fp/4YRxXqDB6i+uByB2leIBRPR7iQKT3zwzOFsrqcxqhRlupYlhnNeVMJz/GJOfMlfLXR8pyKFdS5lmO4CIup+95R3KzqBQ4XkdyvtGk0cfDI4RUiTsh94biRtBLMWATPNh0PFUq6xAH0rhlG5EYzhokX16nJT05aty4ihV/ZdJGXWHUpO8RKO6FcRy2Aag+GTmmbgC315UB0niM5gNWFE+T+yE5aitWJ6OVi62CD7vkJxrY5F9eVHRbb1xCLdm45F0ENCIR3xtsgMHvXGN5FKPQH7ASQwQxXyaIifgtjojz9UyG/2/rZ4UTOcz/S0H0nS0tiMObBZnGXEiLb+d+GD1i7OBGQbzIZoyyrESSedYqanAn8MpjJ78uVLzRSp4KwO7SHg49tDqJMVMfunAvKyydsm9wDBQdQ1rgqlRdk+/ABEg46WNAQbMN0Pf1br0WNStWQQ03iaA3UwZQ+gD1+jk8m2lnZKTzijAhWOGSJvNvBMHVq0DtJ1Y5StIT18/NbhfxcFBI2PFMA4H4tDgBoCBscGxwxrZpn7z1i9hH6zPcS8Xp6EJQpLLB8l0xfTTpc+Y/EGDw+HfXfOUe+q6k+S4jZD/OlOToiRAEKWqLnwhyWABsvgL1Rd+QtxRaZbn9ccaTcxbmACZjfV/veBP1siZKF7KDS7OdyTii5c2kEpHsOr8JVhBh+EDgtZqRsPx9FGdpsIv5YR06lo6AKKQ0941wHICsfx/PmttEYLTRYzdafHH2hUdvFmRwpUw9WYZ0Ew991aOmFLxWUqkq6ycTYT+Velt7m5mSqPUXvskU+oREm4PD2s6Fu9/F7YD5ZmbE7paMAtthKeJARkw7S4f7ZVjw1t27H7ZGHi1Sytr0zRrQoEjhoW6gEafiPEq4h73B7+o0FWMuVbR7CXE4kxLHSxEIstT2RW02KVsbMrZRxs3DLA5x2JkiLCAWQ3Fi2C7ScRH5ASPg4/G/76JodWJPzJmXubgiqHI6zXPJnb9V6UDNvwEdw8dfbBTiy0HsFcdHIOvrdhAk4hMQfB3pRjboK+VOh5/0JDzaWcoR9u92OLLij3oTQg1wePsr4mJG+z0iOk26MWThWRFIqKSOd5GNEAEVimhVL7AmOlT5tE5bNbdzlRxkXiGKGJz9yPnKfNFPNH237pC3xlCpPY1fkORyPGs66erdsViAgOjdMN7i6w3kHX0CanuPFbR6JNbTG8m1G8EG5fYsRlSq5/mhlHEjXpZLJbAi8PjVY1hMiizZlTn40Gs6CcUcajLHaegrzA9VfNLTmzFXRnfJUqI7YrfzXoWNKV++RntX5qUEUGo7tQfnWGcOQYtT2k1trfF6QaTrkSkUaR2tGvgtCF0+VYgLMzekMU+LExgsq58tJOan6r4MprSC+zj+2rFp3fkY1/dMrKNnjn/YoxkPQP9PU365zHr/JpktOqICzkF3fbJK1qtiDB+uvuikhpZMSR++dnVSrPC6KwhVUshUo0ztF5EPSBXmW6rNP3GawvaItjmmrgbARglx7nbxT3mk9owuxdrZfU6j8AIw/baROuDH6ME739X9kMLABiC3s/CM25FlzYdO9owoixNBbsNbE+oS1hDk19FD4zi+4IByGkNHf5mW0HjrQGnWCMuRqJJDxEeUoFtnJHl/UZJjKan9Q300lTLKGFZJfni8mVrS3uRS/wcp9d6bH0yRq7yg4om+k9Z6tOZ39wRroqzwLtWV4cWGI4Vb6GaNsBchaTU9NiJjqMCc+UDbYvpUOqG56LpvnuLxZSd1FBLSnpIyAml1+DAjHJ5L9LiADzC3IYyD/m3fEE6zlbxbPTh8g1kvic4IEjPFjplK26HleG0lUPkBPUK1TEQ5NieyaslBullBbyZ4v/J/opzbErVBc2IbZRHraV7usjnzGX2kLFNfNpmyLbSBQKTh3514DlNQtcEvwIcnSUZQx3WwY823TzZg8rsKTwwGcgw9MAjOD6AzurOaRNL6TXO/DditYcgbwobFyNmI0hluW8fUzP3p506e9XxFnspDZsg76lr0BknY/meHF4P/j/yd80ULFyHCbUSRiY/9/fYbAhYCJkhhAziiDyxhgXYCkcm6YGNI7ST/x7ZJ9RzNauJX7/vHKqnbAph7/4Z2v3tCu7VBgtZwyf7RbqEhxJEJa6nuJ7atmqGJA5vMMqgly8fxr1Z/AlIJY2umuWEkZbdoWvlbMw7SFqj5WnYa5wGRucL6iymKF6dXsPel03VlSMJ7Xa/Zz7XlSFTx8otIhgws31ct/LFVBCMzvDi4kRNkihlLgKxR0WwlKdpHEFURHRpB0aYN9zAIoWcM04tQcppTk3XBQzFQIHnncDnvo+WRusbkSZsd6fZJNnpnF24s+1hSyJUsnIdtTDgaL3aoOUym+OZT7H3LhAsssJzSFqpnFyHVVIn6nKk1QS1fdkzO2aAHHNeM9nnx9XF1GHBn1AGh3UvjnUCXUbBTRefLw+OtZFWg/F6VnDbUYkH25sDTVV9o/axzxkIvbzLhsQ1/Jki0FnxxjxVBvKTN19rJphs2dOk1AdrEWiFjFpnxyUip0dcBKg7SMgQ9SiH/gOVyRcaTIAJrrum6FBE8uxpvryM7Zn/GMU0l3OejOsa2k/+e3F9pqlwOZtT9drCgLKJ++nQykn/13jJ9y87b9hU5LOJjlYDwf7GRCoJfsPYny/WZObj7z5CtGBosT4b0R3wVBZG7D8EijvZVLdEB7v91+fHTLdnPqS7h+picYGISyJaA1INNxehKCl6bPSnrrQ6ALNjlCwD3hqxXM24H1BX44UnXh9RXWPM7jqq0IT7C8X10uD7zLNdaqE7BCIPLf7JxNwB6boiKxH2fcf6r7l+6rk3YZZBJys7avb/99c7kOxkm6DFRSqdiUUG/tuALR/GS+EuIgJVOgu8Jf+s/2Ercn3byhCC64G/Wbi9kUaFceeINt4fJIzdyzg5xS+1nHoP7dYOSJZ+Gdui0z+XmzMoyhZchC/fzFgNw0dauf2s+syGT4FDMldDUT1ON8bmTtssOJYtC5SwPmqBzOj9QLgISpjna6HXgpZ5acB0+ttYBcwrxL4zP8ajW9Xf/oM4RcW51vFcZX6pCEFZXoIZI4HDeTdTv/nQZIvApYe0gvB25dOP4QXLQc6vB843XXEh1ZJWruqB9au0vCCFIcH8HalBp4fLPGNxQzI8uThNzbQ7fLlnE1qOyY+otTtBkWkxCzudGSfrlcOWKxWN1C4TGjuMwNCWByWik+1D6BWVTifQsuUrGZFteC4GCuJzEDMhdpzT3a9FRXJhlTlfBF72GkuAXLPVOLrqpEmcrB5bSRvQ1lTbSBCkeh4r6aCpwPNf1xK7c4HfIxGjyvhUJcpplfv93lVx1CGEJCpHxvwr+pX01RgICqHEZ2z2KuC5GFgXfKLlHcImScJnjQn9YioW1MtgkpI2x4iKQSVBcCvrziRVFN/yqKYLkjg/QiN2+e3azykkXiXXTS1z/ryBQGOoXQIFUmk48FnlzpXEMrXkjfPC0UDBUdklB60DKIOO8heObBneotoGFLrazHwhSoy/bNHFSE6GKkCmiDHLD8aCzdCER24RmPrOuLiAb8oH0AyiudVz9J9J+pTGPvcALnJLxNRJUYVZeo3svMKEB3eiO3nmMdM3xcCwrZBn7geP64wQK/6a+LMsW3DlvIBKFwa6O6kFENYgURwVvJKqailTB+tIRIwkgW3nvggjratS0bbHoIpnMMXdohNZ3GHZ9peaDXh+xAdATElpnBgj3EHZQ0hkIzRPezkGMUuDSgwMhfbGWxBrEsZT+C8jfbOPiXRjjRlXPFXq35D1MrMLncS7zDQdSn9xNHKSj2pBSc/+mcLGStHkUsTnVA8DLrFLlYPTR/d1fTkuO2u0hgE2R2AaDKTjXO24gOWVspgebhYgsZVzwNsylqOsDHvC3OnmDzWdRKmGumXJ1iKUPGmoF2ojkFQfZhx4sv95EYAkfSEseF12GlvUofu2xSgoIvQFUtr1eHqppaXTccYA0G+OXV1D+wpSEvNC28FQSo/DbdL1kT7sLqxj087QNPGVEFjInR68BV2lzpYVIuCpVvjwyJYVyoK6zE1orAaZVuH952s0dGawwFJCYY4m/IBxLodmyWCk0aJoU83Ek6HuZFdXwgSCHWsZgB96SUzud+YUmzD5rm+03uE6HXgs+ZV9TT+fzL/uIjonfKi+ZqEx50hg9a5zH5pcRGv+5YBNexSHySFSfApILJ5wklv2mSqr28CqSzXHl3vHKaWUZCkU8skEfeHASi6VR+AONSpk9WsROR50Mf8i52hx2NOVhY1OvMw1PTSi6aZUtCDuqD1C4H1xCbmt4+V23jYC8/zIqpNzuaL9lpM3denaIO8wn6ukL82R8hISdMPxDM8T2BJextUIN2PxcJ64eY/Q/BBH3E5OY7afkd/ol2DozyIe068aQeIu5Hp8YYVN/CQQ5HIc8YyFMMWi2JLZdGP+0tOsvk2ttEMIsOXExgNWaqgdhAPWAfH3zpYLi99NvIKaaRpMunkThUOhvlEaCUJ6I3QLyMKx/tt1wzyk3m327ny047lcO6PvZw+5re6Wpx0XvQF++M7eXy/UYkt0ocODXMQbq3Fi1IPPaTw+q0L86PETe4cquw9wogWObsYpnHKx3UsXrdU9z1gdsBjLZXozr8G8Gk/ELVQc7AhP8MNj662lc8j6UnApDqOILY5rI9gy/P7loe3/6GznHJj/b0SbFNb/JVgFO8zrF+cIMFP2V0lqwm7r0qUR9aNunIyJTfcQYkOUNVye/p/KzuMZFgesz/6BBZPIRHiTHNFa5SgFaAB6bqbdc5Clzc0MfONQ4gW9MOTbTvubUUmnsXYcbualHTMGMUHPWkxAvQNNRF//AJ/PoFdP1hL1f2c1W8IJC5ZC83tpjjQpWGB3kaDYHYvKWYOLdasRzdYYBLcZ8OZnqnKjgeSIcB8+nU6j92U4tL/RuoAliRnenq49IqzZ2W1yDpVioUZu+U4sVgNa0I+eXsIHUwMq0j17iPyhTqIxxGXqfrsCoxZ0iMDOT+z+fIFIPA9Bza2b7O+qysllPIhujqKdytVobRC2XRT0k9XV5pnW/WmGIjGbxBKHy/lK3/oDCCWvj985nY/ejXTMo7VjeOsrknCX8wIxKnO9B2W9LYvo9pdV+PQGKpBXAj3rfTPtoRRDCu/pUcdVR2t6v0oZ2V6+1h+3nHYNRBtOTkqsjKYuXL98TrraNW7kQH3NhmB8q1WOzdEarKlCatbELf9yww/4oNQs7inMFaIfN3naSW3SRiwg/W7e5gUR81P//wbM1Gg1RXj7u0ReYsxYYmcvNtF7EZflR21rNwBk/VEfSMacsJw3Ng53z0+n4r4Z7c95qQiYNHjKmz0+9laHJCFKax6AyQyUpnZSKp2VnzJMiLZIzBncLV6dZkrwWFGGQORLksvX7gwVLm0nDGPTV+bSeaHfC2U3u0dvJUnITj5KXq2XzuHcjbLZ/tDE6toyEO/FdTC/tIatVrF3RfCCZG0jd0gzyUY8LVSL6DfvfTcq93uQeOCundiKbebk6bPaM3QKt7WKbrNe71xag5QzQOuiyNea+ztSpPjyCSj0gtA5YCr40/wY00r9cLhSTLiDEJTP3I7wbJZ8JtMR0QGyd03yzkWqV2EwHkEc32Kp/8B1IRcmrAsWvpcTi6QKJArOU8nOaesbD8n3Rq8tzdFn98grw9j3kCXPl53skYygT3A8vuLl0Bc8mE15oGrNSSpoVFQPVd8g0OSPnYuiYC/0FN1X8wnGpwyOlfSzP9+iodCL6xJoAAo+jz+P8IZCxDKa/JMQeBnCRiMsvsTCpfp2D20m7nvS/2APPlWnRfG5iYNKhJZHuXyeLKyRSWPdeA2fVA/F1fAzhQk3SiFRD+3WaZs2O/CQxeeD3NtOSzAw4o7ENJmeOfN88Gxp+KsMmts7aF6lIeQgfs+v5AO4wr6u0PzO4OEo0WQCe5nyUndvfTbkJUGWZmQfKGyt8QcD8kjhlMx6OQJcxMpjNdPcDDDqppBYgeosqlzkIS98hzpB9OXTwiTbi1dQh1NcHiSwe10e4d3FsISitb+O7qSJd6hPwfs8AjaOOyKG9Hw+XsBpI6YcGXJH26ZzprKnVb+76O/KA7yv88+5k2hEh8B2duAOlr4vqz4Ui9eug7rrJXCy18Z7WovfWNG+gShz80tJvTiPBttw4Z1OXiT0zcilPN19yniOrH+s8btwZ0Y1K+3DRKbVIavPWcAel/L4dXg0CZw2Fu0Hrp9PzzOjd2fyQqHJFbc9VVMIxSBEt7nRSVP1O3gD98E3TaYRsqhp/gL962e52P/xJlV2LYYL6Ifj+EDH4vkHcHZIPr1A55ixi4KErGvWeQwDIntyCU04EZRYFHaHFzXZxotaw00TMg+qUZmrDZ3VEExu2vy/eNpGffMQXhj8zm3kFLuz9V2EamhP119XLARLSQ8nwxh+at99zQ4QnRsGn1/KpFlV+24UuQqExrsRkTSGvSTex8K6+LG/u4tCoFKp70OL0VsrVNOcIzhfgLW8g/rUH2jFu86qssUhrL0Qhw0bnKMNCgSDEykS9HXeFwscrIJcPyzsaY2Ls3FURcVqOwD/pxSuhYl/JvvvE2HNtPABVjkjhxtHwYROGP92FK/bygi2d6exPOz9/q2zLX1eu81LLbvV7iNys4xIVRG0DvWcwIBCfC3sA7vbuYJEYvdV6wSRUUdPpFP5cGT82vvDKjq6xrA0EZifwsMkH8fkzztN0eJUI9uQeOrXCtp8MXPQERWX//FUo9+lZgLZtWcsrlUAOrpSaXi0ftrWFpL4xkdSUrSr5qHj5/NFAqllbgSa0DtIhSdBw6h6PHiCABrvBIMw7bzDcsLVmnE5ZiW+EpfEbkJcg948pCV8gXK8YI0WeLBj08Kt/YLfTE3DnzerlKmnPUxFNfdzCUs/60q5UCBkcaFlQQCnQ52i0eTYvEJzgkgocV70ZPJ11eTsBYKY8fb12aid4gk3YN/+LKRIdjqltxBfkuXO+AgYi3Y8+PJ32l36Z89EJvEXtdcqaQsMNwCY9Q36c1dGRvdgANGwiARMWvORwXAT8fWrZmatEza1isKrkrgfulnMELEHs3pk9GHwVhT8McaDAado5OGTF5VtdltUb0R6S/lc+kF1PwFRAyp89qob6Qv4s9sW4vAmESwEXOl0B6bWV4QeGwzXCKyBIfurs3+2W0VW2yGOjMPmUs1CQIYhnHEH894Z9jwrMAcQlWx0QUAaqQt9vt1M4NRF2uuum6WkfWEeu7E/cEEgVq6lPPG/7i0kE5Ouoxb00AmH4pJ/VrZkp2FuHA4Augodo6ZUnenMS7P2AJGhj5lTedvoH0Q5Bez0MykaomQBX/DSRp19AN9Wkz6GLVOAQ3WnBumB7AqxdVlLY2C29QXRUN7+mu5+OWqzdUInVHqo833zuVItgdM5L/9HoSDLLmRQsQqMvHyP7hQ6wJUDjVXcIXmdXy4hB5uc+KdGhD0W1Was7RQEmj9ORKJAbWkjJWtHyT9dkBr1XnK4gpU4jOUIzlyan7nbMeRt7c4G1rjf6/QTza0QtxolRTYzkMRVgsons3pjfH8Bpe/1w0YlJXQ7EzOlmTuwjP6LKTHzFnrY4CoKW3mZLNObg1V+FogTLr1soBScHomoiOVoBd6F9BSL4oWF975huMbXjJhD6myKKVv1sfz8GFS0j75DTZZcOzRGBwfuVgFa6A+dtQBH3CfC9a74ZTX2PbHnGVH72koGpI/FigKYM7tEx7XSGMcj+2prMybQuHp+nPXRn9BeRd5MnG9jkHoyJvPQiRjnOApM5QQ8M1neJ1x3EybVBW1HVlKPx2C6+uttqaO2R+xw4GjeGadls8oEbwMvYFEfGLQ6WQ/fiktAaSUAehCzO0+OgpthIMh6GgdzcaDV1bBPsgmU3INbLDTS/49yirD6gfs5/Zlp+bLHAq9QcyhA+dimbM4lUq7rJcDj0ojQGJl/I8FB77shqg3Ia1pZtvUl3akDweSycFmqS8fd51r0g8YpwpgrfbTxD4bFtnlIs1yK4c+mE0LhjhS/Appm1dEIEGQNFot/g9PZ2JlVgzzs53vbepdHt8QFEGw+FRl08D0DmPCwQR9qN3BL61KxUd+jsrGDrO9h2YDr63oFfLmE7LdgT13tGTApco8DSL3Hfv40ykjeAIRFUOEzcFCHmXMrjlnBqOrGTuyOvwSQR2HZNdMz6eZLKhT+RC6y12acV++8UVOTQXaKfnCqkm3Zt3dY32w+pM7tqp4pU+dygvG28dGz18ke5HHFYNS832NV3GtqVhIAr0Adxwqyc7/Hm+8jjmnxExEbjTLFKe+g/exQK6n48d26QNXUBjIPuDH92RR0rLmnNBKq2LDyaFaEFmhXzZ5CHbd1H4AlVtT73pzLd7hcBuSGN6gWP96FHMN9I9EzeEmYrrugFwsWJsHxQyoZSkmQ/196Vqqdi2rRIwPe8xEl/rcUtrp13Ja8IyaSea78FKWzl1x7FiDyKoPQNgx1pvMK4zVIUMPzbcRFWhiflq//3KNz28fQliFeDBRPxmg/sSR+hI9PkLeOE9o0i7rYqB+Kn3Pi4nxr1dBYYc6Pkoj80S/11PIsdtSH1ppIqQriJ9mx+Je9uZFeOvW+NETlwzsnji4hkmWxxmh6aRnCXjwUcF8/K0mPNAewJwfeZfKxnmy+T4mL44oweBwffg7I7cHcjMipf19mjGYC/18gp2PyCPa4UBc1GJhP58HLSzaVHrXhcL9hVBmbw8A9Je9wEUhrWUMtNffH/INCfRXlvp9xxi/F9vHaPyODpx7B3Flk85sRZCnZEuFfxMbMT9ApdwlB1+dNA0PpmHeQrG3FOo4JSZ+UPBsQXACn4U39R30+GsmUH9Fm9aphVsbp/2VEkdkGoRJk2xnXZyb4uBiHt6uTp59bOF+YlljOtObCFjZRD9XJ1Al2L7K+Oiho9zn/SmM6CyNzy1U9sLQ0zD0uVLwIv06tbDqEQML4eMHWnneGySBFtGlixUcCVBn30DjPWHtvtxuprToBYl2yKZv8KZMlVwhQdSBvwJ4Bnt+TuHe0oSl4kLplF/Cxsta6hWFREeRLk2L3wYmKzEiBUCM7LBDX2qvJc49GWx3pBvKIltS1escsLhGXlddYTOLaLC3wH+qxDcmdqPU5ZCrmnqKZu8NGjgidYjIM4T8vnUfjs1M5Wu25MyGg5yRf9As7aqpaEHGrLC/brLnzU6oNrZoO+mXjBH4vnULBrGrUr6u8PpdQPPSW21ZMysxg1kXgT4TaSoxgxkjsQxSkmeeOmTYYCvgA88yJIXQwQ7ppJx1suwCmtadVWKj0yyutF7iZESF2Qsp1SYjjB8hdO8MjjTOszVjHAqMktw8eT720FM0Ql0YjVXZ/fEgVXQPZnmvfgNxNmZgjTnaPzh0v5eORe1dzFB0vgXj8LWH2FxN4qxXNFAJeJmq3hAxdv6sAGTfjxpMwx/DGI4nevwgGuvlyCrrNefHLTN8sQyJ97N3jGrHqM3VuSMo1BxlOmG5K6gwOGTIBmnBhNLy54oIebE04sPs+QZdtuGipq2sFCjjWR/pBxAe4rW/LkIH3k7kYhy/rLMmjCNnLF8K75x59ffeWTac/g6ESCUJneE21KLkZEmM6CrwkC08OzhV/oYSdyyCgTkO/hxX7FuJqtHWH5bY5mfk8lWq4aIMD6o5mtmYPNvIKwTE0rQ06m2RsFoOmm1rtzY/eOChsa2B+GeIErTizEqB1Eww2+X9VopgSv30yrDW01r1MU58eCx79Ztw2+8rQr//9Ypgwnw0ref1bIDF408bvk7IiUNYjrXkiZ0fS+9dwUeqzfQjkeyUXfYrcZP5u0SNeFXp0SGjW82gCagEfYCGJrL1OTcygyddfOx89xnoQSewGvtvNk/1hV4UUU1MUpROkkX1Vg1niqDtU4C/k5/ujmPsQk43Pjsvf4y+f0SefGMHb0VQMTUhm2aoodY1pJybLQAULOX7ar3EMeEoZQ5SNl0dQUu493QNAUvjXby8CrdKd7kjXka/SE4hkVlBHA9gc/9Fr7xTC8Jc6bj7egjhqJ0V3QQqx+uQS1bu+YLZ9VKYLeG46HcPKVPRJTNBLgGKRlMOCmwIl8icLnX9uq8S4nBxJB83CjvzRoe5TBGljWlbJZRYhu8xdbh174hrVy+RQuaPOpX4WWaq1vREVYGNulJnFL7rfct07eNq5oF+UhdUjMVLcVREZfp/4UmhdpY7Lp3/ixnO/15icn24irMM2ZbAUHYvPJZ2IjKU7lZohQAWstUS8HW+JrQQvMIueSEwo8la2Exq68ZgmejwXut6Hwi701eQdyEbRP14an133kotpmOdMn7ba74uWqWdIyWcegfCz9mQ1tk0EHitgMu0D1IBjhRhAHGIVwwCoHha5/GJaLavlnLWzfbcU5AQ2p6oTSffuGwl7L7u9TChVAHBIjlkf0GMn8SP4tnSbiytMkKKejthzN1REvkBt6n/4WKwJeYaBrltWjl4pYJ2hqdT8vtwi9HVnjAXR8ilWAn3iLXfzuxg6JlcRtGSHTTvW9H/r9mrrYhXN+IhhGhh0l4rzRSS+W8mi+fx+jF1VTIraynlMs+1OD10k/FaV/98St+obrbCqCvBlZ3JhPVj6A2z73cJ9e/K2BbjJHSUQTSmFi/pIoaxxaxyezsDHspJqp6iU0jurQLaEAJHRkcaKbsgBMeTfwWMg/b1pQFqKhAnfb6iR/JLuBNFKHJi3jgsypU21nvhsByxhbNZCSmlIOw79Wioul1CgD9JXV3z7mlw3VTB7r2lzfUmNlzZoDFk1W2SEATApvIPtmpLgZyGytzSxNbjznNw6FR/entHwoofCkOy6AjilDP1AebfQzLsix7RNXo1pjPhF52kE62CcTD0K9YC3M6KSnbMJ+zKZcxjqOdp1YoD2YMY4WnJUC5IoXzz1TKnbCpnNNf8+Hm/Ed7rAHThgXB/RluMFKLxB0PlEp2ZqDNuIaVGGTD1OTFldKhFEcDo4enXKU7yhylygTpx1AxjKp5en1Q/L6Z1DVF24ERk829ZbzxaM/jcWFv3ap2mQI6acTPsJNdqaBjjeyLE+3afim7/y1jsskLSfbY0lqQ8zey7q214XRW1LX6fmULg3h6n/0mh7pFAcad2TNaIG8fcFYBDJ0xpTngRbOpSGHytyzAIRwAGebM5Hujb6yNMAIyc09yDnnxZVjSNMSm1xZV6bKwn6gsZYHBHQ770VR5gF3queT0ZHx/PvRrbmzC+rN+TB/uk+q/FfoabMHg+6dTgXn1wjVyWL7JNW4b5R28tOXG1zzPfMRvY2ZvpZf039D/xbVIveQqM+raGI2ouROixaLZZwiV/tKnM/JiXlC+J+ubRCqKnCb6Tg9rSb60GaUaQioefaNXcczCUxTPQg8wCB4ZMVmSy94mqh8acDFQeIUgUfLSA2VH2SHlAAt1fOqxEc5YfWeuxCzXYNzTCkx+Vyej0cM70UPMj3TCoXSCoGm0KKp1u+KL21EaRT3PQQWJWyWcWgKGS+HlJ2ru55ihkhX5t9dmxzRy2I1cxnknG88C0V15e/HM514o3k7K7Qtrh1elL4aMXKwMVBKPj/eRWvcmgXRA61DGX6zIr0OLnfLEJghbqwdIwyuSO8R0ShPQA2HofNzNS419vuaADK4lZaAarabc06pfIlWCPtj13qp5NuA89yqBHhyV/43ZgxDweAw7iFei1VraFxc6PgeoFc//DeowqWOsZbhyZAVF5YRIq1oC4spKO9IrWzeoZ2/rz0+OqqbqPXt3cwed2Iuq7tBcqtyiQIghSxFHivU7FIxfmPydrfFxx7HfnWG1tYvNQn4LbyXuIsy2FSgJ8CERLka9V17lEqyIYizNQ82C5UVIrOB4/OO+LHTQuEFv9/hVV+sjPTqzEPVJkq9hy4OZKa3n2G5BVVYFgTJ1JqCJKEaE5KC5FhkaBNNE/nrR77kF9F3stGxKxsOCboDgCH+OwQ6eU4MMH+vr6ydQoVOzC7R8UWvmnJOaKl5F/6/wUjW11oO1xDavFIXWuoYODeIYENCua634rN9UmrvvSpryOG6wMUFqO8QJ2PmeSIzb2qif/8ZqWQXPKI0DdckJ2VlcLr0MiEH7LxGASlc/4d8x3QxyIoyuj23lO5zHsX2AzOBqz92GV6ffYq0gzMnc3oq2ds2eebvRg+W7JcWpSFYw2B4OMV8YVN2OpzBc3LEyCCDam4iW7B7LHxdBKJb2fJIws54OAuwQJaXG5Chyd7zQkPkALnUxG/49EpCRy4Hht/WIkmgdW6leHU9sElTgNBK2GZ5sGB21YhbIPv/KQkcHcqdrz72zlUY57fkfcJ6mzCO64vib0IDTZVwJ1eMquBDPmZ7FBk5sPmWdbbgZiAqlfyL3ugjqEdJjmWZcRvX0yXFlhkDHyrpKtlk+t40i/NnJyiSZ5GpFtlAxKB6uLr0ASs5xPjebvguB3FbGZNeFiv75LL0vjypr03uhr82tsGEw+kSG9qJk0tZWf3ndNkj8HgAIX1EYRxfPEJcvPo/UsV6N/HN/SUC782/v/TdVR5Qhk3F11l5PtVKV8ra/fiTKgKV7wWBQ7OSbWHjGLgRd/2hqgU8l/GgT7fQYVHz8uFxHvt5CuRm9ygIHQ5yG5EoF2+1JuwLn2ZiGt7Hl2F1Vw71ac1JxwLsOxXCuHeVZqVI/glEXbCXjA/xhiTUqC/vc8/e/dpxj+iMAnQeOddMeb6NdaEi0nXG+/AtOHvCOHwiXXxnedwqZNegXfycfa0KqLdFWMJu4FA1l6lLsra4ggIdfmrxmJzk3ZIcwYkqYM2GtOAb4kCrSIiWy9YFj8q7zgSKFCGKVwxAt6zUDTDz0JBSSM65Pfq/tlRKsCZCnJMTUshKzf9GxRJF9BESkW9mIlAt86Rodqyh/j9AZigmFQabXi6bcPohaxkuC9TcEhH24csEoQrzZ6FpuLI3mK5p8M/lGN7Ks8r9+c6y10P4sqKm8jJat/5tCY+5ilr92e5MNe/GWDpkglS7B/3SbR1XmVht/ro4D0Wvvhcp5Nr/arcTsDBUM4hMLmVQzfFyJLvY//q97FBuMxGEkTEHUbmFNjdT9xSHZ1o2DjHUjXNm2uVkGoiv9Q0AwRO/VFkniQebs64rNMkteugKmV6+dkgxOTBmwDyF5nYoy7KSHQoItmCo82a5nNuDaO0J4wA6P7aWTi5GP8PX5rzIpuODf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{"parentUuid":"5e8d3e00-fe6a-4633-bf63-b5ddd266e370","isSidechain":false,"message":{"model":"claude-sonnet-5","id":"msg_011CfgPs9JYsCErbqTz6cwaY","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01DX3tBdn7oL1HJp9n4zyT83","name":"StructuredOutput","input":{"notes":"Night 2 results are decisive on complexity: Cragsley (1.36 tags/ticket, 3.46 messages/ticket) and Marrowby (1.43, 3.29) are actually at the LOW end of complexity among all 22 qualifying merchants - Studio Vrij is mid-pack (1.64, 3.71). So \"harder issues\" is refuted as an explanation; it really does look like a volume/staffing story. The SLA-target report also refutes the \"unrealistic target\" theory for the broad urgent-miss problem - every merchant evaluated uses the identical 8-hour urgent target, so the wide spread in miss rates (14%-100%) reflects genuine differences in execution speed, not target calibration. The quarterly load report is the key new finding: all three hotspots (and comparison peer Grind Collective) run a completely flat agent headcount all four quarters, but ticket volume surges hard in specific quarters - Cragsley and Marrowby both spike in Q4 (tickets-per-agent roughly doubles vs Q1-Q3), Studio Vrij spikes specifically in Q2 (the already-known fulfillment incident), and Grind Collective also spikes in Q4 even harder than Cragsley. That's a strong lead: nobody scales support staff for volume surges, and it's not unique to the three named merchants. I want to (1) confirm urgent-ticket performance specifically degrades in the same quarters where each merchant's load spiked (a within-merchant causal test), (2) check whether this \"flat staffing through a seasonal/incident surge\" pattern is common across the whole qualifying book, not just these four - that determines if it's a platform-level guidance opportunity, and (3) characterize whether the big three's inflated average (vs. median) urgent resolution time is a few catastrophic outlier tickets or generally slow handling, since that changes what the fix would look like (triage/escalation vs. core speed).","requests":[{"title":"Quarterly urgent-ticket performance, hotspot merchants vs comparison peer","question":"Does urgent-ticket resolution specifically get worse in the same quarters where each merchant's ticket-per-agent load spiked?","grain":"one row per tenant and calendar quarter in 2025, for Cragsley Outfitters, Marrowby & Vane, Studio Vrij, and Grind Collective, urgent-priority tickets only","columns":["merchant name","quarter (2025-Q1..Q4)","urgent tickets evaluated (sla_policy_id not null)","urgent miss rate (%)","avg actual resolution hours (urgent)","median actual resolution hours (urgent)"],"details":"Source: tickets (priority='urgent') joined to sla_policies via tickets.sla_policy_id, filtered to the four named tenants. Grain the quarter on tickets.created_at within 2025. Exclude tickets with null sla_policy_id from all columns (same 'evaluable' definition as prior urgent-SLA reports). 'Miss rate' = tickets missing the resolution target (resolved_at null as of 2025-12-31, or resolved_at later than created_at + resolution_target_minutes), as a percentage of evaluable urgent tickets that quarter, rounded to 1 decimal. avg/median actual resolution hours = extract(epoch from resolved_at - created_at)/3600 over evaluable urgent tickets with resolved_at not null that quarter, rounded to 1 decimal (show NULL if zero such tickets). Order by merchant name ascending, then quarter ascending. Expect 16 rows; note if any quarter has zero evaluable urgent tickets for a tenant.","builds_on":["Ticket load per agent over 2025, hotspot merchants vs a comparable peer","Urgent-ticket SLA target vs actual resolution time, by merchant"]},{"title":"Q4-vs-rest-of-year ticket load per agent, all qualifying merchants","question":"Is 'flat support staffing through a seasonal ticket surge' a pattern specific to the three hotspot merchants, or common across the book?","grain":"one row per tenant, among tenants with at least 20 tickets created in 2025","columns":["merchant name","tickets created, Q1-Q3 2025 (combined)","avg distinct agents handling tickets, Q1-Q3 (average of the three quarterly counts)","tickets per agent per quarter, Q1-Q3 average","tickets created, Q4 2025","distinct agents handling tickets, Q4","tickets per agent, Q4"],"details":"Source: tickets joined to tenants. Window: tickets.created_at in 2025. Q1-Q3 = Jan-Sep 2025 combined; Q4 = Oct-Dec 2025. 'avg distinct agents, Q1-Q3' = average across the three individual quarters of count distinct assignee_agent_id (excluding null) per quarter, rounded to 1 decimal. 'tickets per agent per quarter, Q1-Q3 average' = (Q1-Q3 combined tickets ÷ 3) ÷ avg distinct agents Q1-Q3, rounded to 1 decimal. 'tickets per agent, Q4' = Q4 tickets ÷ distinct agents handling tickets in Q4 (excluding null), rounded to 1 decimal. Only include tenants with >=20 tickets created in 2025 (same threshold as the earlier staffing/channel/complexity reports, for a consistent peer set of 22). Order by 'tickets per agent, Q4' descending (worst Q4 crunch first), tie-break merchant name ascending. Expect at most 22 rows.","builds_on":["Support agent staffing vs ticket volume, by merchant","Ticket load per agent over 2025, hotspot merchants vs a comparable peer"]},{"title":"Urgent-ticket resolution-time distribution, slow merchants vs a fast baseline","question":"Is the big three's inflated average urgent resolution time caused by a handful of extreme-delay tickets, or by generally slow handling across the board?","grain":"one row per tenant, for Cragsley Outfitters, Marrowby & Vane, Studio Vrij, Grind Collective, and Wiesgrund Wohnen (included as a fast-performing baseline), urgent-priority tickets only, 2025","columns":["merchant name","urgent tickets evaluated","% resolved within 8h (meets target)","% resolved 8-24h","% resolved 1-3 days","% resolved 3-7 days","% resolved after 7 days or still unresolved as of 2025-12-31"],"details":"Source: tickets (priority='urgent') joined to sla_policies via tickets.sla_policy_id, filtered to the five named tenants. Window: tickets.created_at in 2025-01-01 through 2025-12-31, excluding tickets with null sla_policy_id (same evaluable definition as prior reports). resolution_hours = extract(epoch from resolved_at - created_at)/3600 for tickets with resolved_at not null; tickets with resolved_at null as of 2025-12-31 fall into the last bucket ('after 7 days or still unresolved') regardless of elapsed time. Bucket boundaries: <=8h, >8-24h, >24-72h (1-3 days), >72-168h (3-7 days), >168h or unresolved. Each tenant's five percentages should sum to ~100 (rounded to 1 decimal each). Order by merchant name ascending. Expect exactly 5 rows.","builds_on":["Urgent-ticket SLA target vs actual resolution time, by merchant","Quarterly urgent-ticket performance, hotspot merchants vs comparison peer"]}]},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":13511,"cache_read_input_tokens":115669,"output_tokens":9993,"output_tokens_details":{"thinking_tokens":7289},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":13511,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":9993,"cache_read_input_tokens":115669,"cache_creation_input_tokens":13511,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":13511},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"wireToolInputs":{"toolu_01DX3tBdn7oL1HJp9n4zyT83":{"notes":"Night 2 results are decisive on complexity: Cragsley (1.36 tags/ticket, 3.46 messages/ticket) and Marrowby (1.43, 3.29) are actually at the LOW end of complexity among all 22 qualifying merchants - Studio Vrij is mid-pack (1.64, 3.71). So \"harder issues\" is refuted as an explanation; it really does look like a volume/staffing story. The SLA-target report also refutes the \"unrealistic target\" theory for the broad urgent-miss problem - every merchant evaluated uses the identical 8-hour urgent target, so the wide spread in miss rates (14%-100%) reflects genuine differences in execution speed, not target calibration. The quarterly load report is the key new finding: all three hotspots (and comparison peer Grind Collective) run a completely flat agent headcount all four quarters, but ticket volume surges hard in specific quarters - Cragsley and Marrowby both spike in Q4 (tickets-per-agent roughly doubles vs Q1-Q3), Studio Vrij spikes specifically in Q2 (the already-known fulfillment incident), and Grind Collective also spikes in Q4 even harder than Cragsley. That's a strong lead: nobody scales support staff for volume surges, and it's not unique to the three named merchants. I want to (1) confirm urgent-ticket performance specifically degrades in the same quarters where each merchant's load spiked (a within-merchant causal test), (2) check whether this \"flat staffing through a seasonal/incident surge\" pattern is common across the whole qualifying book, not just these four - that determines if it's a platform-level guidance opportunity, and (3) characterize whether the big three's inflated average (vs. median) urgent resolution time is a few catastrophic outlier tickets or generally slow handling, since that changes what the fix would look like (triage/escalation vs. core speed).","requests":[{"title":"Quarterly urgent-ticket performance, hotspot merchants vs comparison peer","question":"Does urgent-ticket resolution specifically get worse in the same quarters where each merchant's ticket-per-agent load spiked?","grain":"one row per tenant and calendar quarter in 2025, for Cragsley Outfitters, Marrowby & Vane, Studio Vrij, and Grind Collective, urgent-priority tickets only","columns":["merchant name","quarter (2025-Q1..Q4)","urgent tickets evaluated (sla_policy_id not null)","urgent miss rate (%)","avg actual resolution hours (urgent)","median actual resolution hours (urgent)"],"details":"Source: tickets (priority='urgent') joined to sla_policies via tickets.sla_policy_id, filtered to the four named tenants. Grain the quarter on tickets.created_at within 2025. Exclude tickets with null sla_policy_id from all columns (same 'evaluable' definition as prior urgent-SLA reports). 'Miss rate' = tickets missing the resolution target (resolved_at null as of 2025-12-31, or resolved_at later than created_at + resolution_target_minutes), as a percentage of evaluable urgent tickets that quarter, rounded to 1 decimal. avg/median actual resolution hours = extract(epoch from resolved_at - created_at)/3600 over evaluable urgent tickets with resolved_at not null that quarter, rounded to 1 decimal (show NULL if zero such tickets). Order by merchant name ascending, then quarter ascending. Expect 16 rows; note if any quarter has zero evaluable urgent tickets for a tenant.","builds_on":["Ticket load per agent over 2025, hotspot merchants vs a comparable peer","Urgent-ticket SLA target vs actual resolution time, by merchant"]},{"title":"Q4-vs-rest-of-year ticket load per agent, all qualifying merchants","question":"Is 'flat support staffing through a seasonal ticket surge' a pattern specific to the three hotspot merchants, or common across the book?","grain":"one row per tenant, among tenants with at least 20 tickets created in 2025","columns":["merchant name","tickets created, Q1-Q3 2025 (combined)","avg distinct agents handling tickets, Q1-Q3 (average of the three quarterly counts)","tickets per agent per quarter, Q1-Q3 average","tickets created, Q4 2025","distinct agents handling tickets, Q4","tickets per agent, Q4"],"details":"Source: tickets joined to tenants. Window: tickets.created_at in 2025. Q1-Q3 = Jan-Sep 2025 combined; Q4 = Oct-Dec 2025. 'avg distinct agents, Q1-Q3' = average across the three individual quarters of count distinct assignee_agent_id (excluding null) per quarter, rounded to 1 decimal. 'tickets per agent per quarter, Q1-Q3 average' = (Q1-Q3 combined tickets ÷ 3) ÷ avg distinct agents Q1-Q3, rounded to 1 decimal. 'tickets per agent, Q4' = Q4 tickets ÷ distinct agents handling tickets in Q4 (excluding null), rounded to 1 decimal. Only include tenants with >=20 tickets created in 2025 (same threshold as the earlier staffing/channel/complexity reports, for a consistent peer set of 22). Order by 'tickets per agent, Q4' descending (worst Q4 crunch first), tie-break merchant name ascending. Expect at most 22 rows.","builds_on":["Support agent staffing vs ticket volume, by merchant","Ticket load per agent over 2025, hotspot merchants vs a comparable peer"]},{"title":"Urgent-ticket resolution-time distribution, slow merchants vs a fast baseline","question":"Is the big three's inflated average urgent resolution time caused by a handful of extreme-delay tickets, or by generally slow handling across the board?","grain":"one row per tenant, for Cragsley Outfitters, Marrowby & Vane, Studio Vrij, Grind Collective, and Wiesgrund Wohnen (included as a fast-performing baseline), urgent-priority tickets only, 2025","columns":["merchant name","urgent tickets evaluated","% resolved within 8h (meets target)","% resolved 8-24h","% resolved 1-3 days","% resolved 3-7 days","% resolved after 7 days or still unresolved as of 2025-12-31"],"details":"Source: tickets (priority='urgent') joined to sla_policies via tickets.sla_policy_id, filtered to the five named tenants. Window: tickets.created_at in 2025-01-01 through 2025-12-31, excluding tickets with null sla_policy_id (same evaluable definition as prior reports). resolution_hours = extract(epoch from resolved_at - created_at)/3600 for tickets with resolved_at not null; tickets with resolved_at null as of 2025-12-31 fall into the last bucket ('after 7 days or still unresolved') regardless of elapsed time. Bucket boundaries: <=8h, >8-24h, >24-72h (1-3 days), >72-168h (3-7 days), >168h or unresolved. Each tenant's five percentages should sum to ~100 (rounded to 1 decimal each). Order by merchant name ascending. Expect exactly 5 rows.","builds_on":["Urgent-ticket SLA target vs actual resolution time, by merchant","Quarterly urgent-ticket performance, hotspot merchants vs comparison peer"]}]}},"apiBlockIndex":1,"requestId":"req_011CfgPs8e78eYgd2XC5AU5w","type":"assistant","uuid":"c128071b-0009-441b-910f-e12c3f832d9b","timestamp":"2026-10-04T02:20:03.326Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"c128071b-0009-441b-910f-e12c3f832d9b","isSidechain":false,"attachment":{"type":"structured_output","data":{"notes":"Night 2 results are decisive on complexity: Cragsley (1.36 tags/ticket, 3.46 messages/ticket) and Marrowby (1.43, 3.29) are actually at the LOW end of complexity among all 22 qualifying merchants - Studio Vrij is mid-pack (1.64, 3.71). So \"harder issues\" is refuted as an explanation; it really does look like a volume/staffing story. The SLA-target report also refutes the \"unrealistic target\" theory for the broad urgent-miss problem - every merchant evaluated uses the identical 8-hour urgent target, so the wide spread in miss rates (14%-100%) reflects genuine differences in execution speed, not target calibration. The quarterly load report is the key new finding: all three hotspots (and comparison peer Grind Collective) run a completely flat agent headcount all four quarters, but ticket volume surges hard in specific quarters - Cragsley and Marrowby both spike in Q4 (tickets-per-agent roughly doubles vs Q1-Q3), Studio Vrij spikes specifically in Q2 (the already-known fulfillment incident), and Grind Collective also spikes in Q4 even harder than Cragsley. That's a strong lead: nobody scales support staff for volume surges, and it's not unique to the three named merchants. I want to (1) confirm urgent-ticket performance specifically degrades in the same quarters where each merchant's load spiked (a within-merchant causal test), (2) check whether this \"flat staffing through a seasonal/incident surge\" pattern is common across the whole qualifying book, not just these four - that determines if it's a platform-level guidance opportunity, and (3) characterize whether the big three's inflated average (vs. median) urgent resolution time is a few catastrophic outlier tickets or generally slow handling, since that changes what the fix would look like (triage/escalation vs. core speed).","requests":[{"title":"Quarterly urgent-ticket performance, hotspot merchants vs comparison peer","question":"Does urgent-ticket resolution specifically get worse in the same quarters where each merchant's ticket-per-agent load spiked?","grain":"one row per tenant and calendar quarter in 2025, for Cragsley Outfitters, Marrowby & Vane, Studio Vrij, and Grind Collective, urgent-priority tickets only","columns":["merchant name","quarter (2025-Q1..Q4)","urgent tickets evaluated (sla_policy_id not null)","urgent miss rate (%)","avg actual resolution hours (urgent)","median actual resolution hours (urgent)"],"details":"Source: tickets (priority='urgent') joined to sla_policies via tickets.sla_policy_id, filtered to the four named tenants. Grain the quarter on tickets.created_at within 2025. Exclude tickets with null sla_policy_id from all columns (same 'evaluable' definition as prior urgent-SLA reports). 'Miss rate' = tickets missing the resolution target (resolved_at null as of 2025-12-31, or resolved_at later than created_at + resolution_target_minutes), as a percentage of evaluable urgent tickets that quarter, rounded to 1 decimal. avg/median actual resolution hours = extract(epoch from resolved_at - created_at)/3600 over evaluable urgent tickets with resolved_at not null that quarter, rounded to 1 decimal (show NULL if zero such tickets). Order by merchant name ascending, then quarter ascending. Expect 16 rows; note if any quarter has zero evaluable urgent tickets for a tenant.","builds_on":["Ticket load per agent over 2025, hotspot merchants vs a comparable peer","Urgent-ticket SLA target vs actual resolution time, by merchant"]},{"title":"Q4-vs-rest-of-year ticket load per agent, all qualifying merchants","question":"Is 'flat support staffing through a seasonal ticket surge' a pattern specific to the three hotspot merchants, or common across the book?","grain":"one row per tenant, among tenants with at least 20 tickets created in 2025","columns":["merchant name","tickets created, Q1-Q3 2025 (combined)","avg distinct agents handling tickets, Q1-Q3 (average of the three quarterly counts)","tickets per agent per quarter, Q1-Q3 average","tickets created, Q4 2025","distinct agents handling tickets, Q4","tickets per agent, Q4"],"details":"Source: tickets joined to tenants. Window: tickets.created_at in 2025. Q1-Q3 = Jan-Sep 2025 combined; Q4 = Oct-Dec 2025. 'avg distinct agents, Q1-Q3' = average across the three individual quarters of count distinct assignee_agent_id (excluding null) per quarter, rounded to 1 decimal. 'tickets per agent per quarter, Q1-Q3 average' = (Q1-Q3 combined tickets ÷ 3) ÷ avg distinct agents Q1-Q3, rounded to 1 decimal. 'tickets per agent, Q4' = Q4 tickets ÷ distinct agents handling tickets in Q4 (excluding null), rounded to 1 decimal. Only include tenants with >=20 tickets created in 2025 (same threshold as the earlier staffing/channel/complexity reports, for a consistent peer set of 22). Order by 'tickets per agent, Q4' descending (worst Q4 crunch first), tie-break merchant name ascending. Expect at most 22 rows.","builds_on":["Support agent staffing vs ticket volume, by merchant","Ticket load per agent over 2025, hotspot merchants vs a comparable peer"]},{"title":"Urgent-ticket resolution-time distribution, slow merchants vs a fast baseline","question":"Is the big three's inflated average urgent resolution time caused by a handful of extreme-delay tickets, or by generally slow handling across the board?","grain":"one row per tenant, for Cragsley Outfitters, Marrowby & Vane, Studio Vrij, Grind Collective, and Wiesgrund Wohnen (included as a fast-performing baseline), urgent-priority tickets only, 2025","columns":["merchant name","urgent tickets evaluated","% resolved within 8h (meets target)","% resolved 8-24h","% resolved 1-3 days","% resolved 3-7 days","% resolved after 7 days or still unresolved as of 2025-12-31"],"details":"Source: tickets (priority='urgent') joined to sla_policies via tickets.sla_policy_id, filtered to the five named tenants. Window: tickets.created_at in 2025-01-01 through 2025-12-31, excluding tickets with null sla_policy_id (same evaluable definition as prior reports). resolution_hours = extract(epoch from resolved_at - created_at)/3600 for tickets with resolved_at not null; tickets with resolved_at null as of 2025-12-31 fall into the last bucket ('after 7 days or still unresolved') regardless of elapsed time. Bucket boundaries: <=8h, >8-24h, >24-72h (1-3 days), >72-168h (3-7 days), >168h or unresolved. Each tenant's five percentages should sum to ~100 (rounded to 1 decimal each). Order by merchant name ascending. Expect exactly 5 rows.","builds_on":["Urgent-ticket SLA target vs actual resolution time, by merchant","Quarterly urgent-ticket performance, hotspot merchants vs comparison peer"]}]},"toolUseID":"toolu_01DX3tBdn7oL1HJp9n4zyT83"},"type":"attachment","uuid":"b324e601-7428-4f5b-bc60-e4d0b3899dda","timestamp":"2026-10-04T02:20:03.333Z","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a","version":"2.1.275","gitBranch":"HEAD"}
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{"type":"last-prompt","lastPrompt":"The printed reports of night 2:  === Report 1: Ticket complexity proxy (messages and tags per ticket), by merchant (written by Felix) === Engineer's note: One row per tenant with >=20 tickets created…","leafUuid":"23d0480c-988b-41e8-a3d0-53ff4c71493b","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a"}
{"type":"ai-title","aiTitle":"Shopper experience baseline review","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a"}
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{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-04T02:20:43.010Z","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a","content":"The printed reports of night 3:\n\n=== Report 1: Quarterly urgent-ticket performance, hotspot merchants vs comparison peer (written by Viktor) ===\nEngineer's note: Built a full 4-tenant x 4-quarter grid with LEFT JOIN so quarters with zero evaluable urgent tickets still print a row (count 0, miss rate/avg/median NULL) instead of silently vanishing. Miss rate denominator and avg/median are strictly over tickets with sla_policy_id not null, per the 'evaluable' definition; resolved_at null is treated as a miss since data ends 2025-12-31.\nREPORT: Quarterly urgent-ticket performance, hotspot merchants vs comparison peer\nQUESTION: Does urgent-ticket resolution specifically get worse in the same quarters where each merchant's ticket-per-agent load spiked?\nmerchant_name       | quarter | urgent_tickets_evaluated | urgent_miss_rate_pct | avg_actual_resolution_hours | median_actual_resolution_hours\n--------------------+---------+--------------------------+----------------------+-----------------------------+-------------------------------\nCragsley Outfitters | 2025-Q1 |                       14 |                 71.4 |                        30.0 |                           10.5\nCragsley Outfitters | 2025-Q2 |                       13 |                 53.8 |                        18.0 |                           14.0\nCragsley Outfitters | 2025-Q3 |                        8 |                 50.0 |                        23.1 |                            9.8\nCragsley Outfitters | 2025-Q4 |                       15 |                 80.0 |                        36.6 |                           21.5\nGrind Collective    | 2025-Q1 |                        1 |                  0.0 |                         0.8 |                            0.8\nGrind Collective    | 2025-Q2 |                        1 |                  0.0 |                         0.9 |                            0.9\nGrind Collective    | 2025-Q3 |                        1 |                  0.0 |                         1.0 |                            1.0\nGrind Collective    | 2025-Q4 |                        3 |                 33.3 |                         7.0 |                            7.1\nMarrowby & Vane     | 2025-Q1 |                        8 |                 62.5 |                        43.6 |                           24.8\nMarrowby & Vane     | 2025-Q2 |                        2 |                 50.0 |                        16.9 |                           16.9\nMarrowby & Vane     | 2025-Q3 |                        6 |                 50.0 |                         8.8 |                            6.4\nMarrowby & Vane     | 2025-Q4 |                       17 |                 76.5 |                       139.5 |                           87.2\nStudio Vrij         | 2025-Q1 |                        4 |                 50.0 |                        17.4 |                           10.7\nStudio Vrij         | 2025-Q2 |                        2 |                100.0 |                       219.5 |                          219.5\nStudio Vrij         | 2025-Q3 |                        2 |                100.0 |                       109.0 |                          109.0\nStudio Vrij         | 2025-Q4 |                        3 |                 33.3 |                         3.9 |                            1.2\n(16 rows)\n\n=== Report 2: Q4-vs-rest-of-year ticket load per agent, all qualifying merchants (written by Felix) ===\nEngineer's note: Grain is one row per qualifying tenant (>=20 tickets created in 2025), as requested. Q1-Q3 quarterly agent counts are zero-filled for any quarter with no tickets so the three-quarter average divisor is always 3, matching the spec exactly.\nREPORT: Q4-vs-rest-of-year ticket load per agent, all qualifying merchants\nQUESTION: Is 'flat support staffing through a seasonal ticket surge' a pattern specific to the three hotspot merchants, or common across the book?\nmerchant_name                 | tickets_q123 | avg_agents_q1_q3 | tickets_per_agent_q1_q3 | tickets_q4 | agents_q4 | tickets_per_agent_q4\n------------------------------+--------------+------------------+-------------------------+------------+-----------+---------------------\nGrind Collective              |           52 |              3.0 |                     5.8 |         70 |         3 |                 23.3\nMarrowby & Vane               |          126 |              5.0 |                     8.4 |        105 |         5 |                 21.0\nCragsley Outfitters           |          249 |              8.0 |                    10.4 |        151 |         8 |                 18.9\nBallycorran Industrial Supply |           27 |              3.0 |                     3.0 |         17 |         1 |                 17.0\nPetalcourt Beauty             |           55 |              4.0 |                     4.6 |         64 |         4 |                 16.0\nFennel & Flint                |           21 |              3.0 |                     2.3 |         46 |         4 |                 11.5\nBrennquell Kaffee             |           93 |              4.0 |                     7.8 |         44 |         4 |                 11.0\nKibblewick                    |           29 |              3.7 |                     2.6 |         43 |         4 |                 10.8\nStudio Vrij                   |          127 |              4.0 |                    10.6 |         39 |         4 |                  9.8\nSourdough Club                |           20 |              3.0 |                     2.2 |         27 |         3 |                  9.0\nAmpfeld Elektronik            |           20 |              4.3 |                     1.5 |         36 |         5 |                  7.2\nSpice Route Box               |           52 |              3.0 |                     5.8 |         19 |         3 |                  6.3\nOakhurst Tailoring            |            9 |              1.3 |                     2.3 |         18 |         3 |                  6.0\nLinden & Loom                 |           14 |              2.3 |                     2.0 |         15 |         3 |                  5.0\nWiesgrund Wohnen              |           17 |              2.3 |                     2.4 |         15 |         3 |                  5.0\nHoneycomb Larder              |           45 |              3.0 |                     5.0 |         13 |         3 |                  4.3\nCinder Peak Sports Co.        |           27 |              2.7 |                     3.4 |         16 |         4 |                  4.0\nThistlecombe                  |           10 |              2.0 |                     1.7 |         11 |         3 |                  3.7\nRue Lafayette Studio          |           17 |              2.7 |                     2.1 |         10 |         3 |                  3.3\nCoastline Interiors           |           12 |              1.7 |                     2.4 |          9 |         3 |                  3.0\nGratlinie Outdoor             |           18 |              3.3 |                     1.8 |          9 |         4 |                  2.3\nKayak Corner                  |           13 |              2.3 |                     1.9 |          7 |         3 |                  2.3\n(22 rows)\n\n=== Report 3: Urgent-ticket resolution-time distribution, slow merchants vs a fast baseline (written by Felix) ===\nEngineer's note: Resolution-time buckets computed directly from tickets.resolved_at - created_at (sla_policies joined only to confirm an SLA policy exists, per the exclusion rule); unresolved-as-of-2025-12-31 tickets fall into the last bucket as specified.\nREPORT: Urgent-ticket resolution-time distribution, slow merchants vs a fast baseline\nQUESTION: Is the big three's inflated average urgent resolution time caused by a handful of extreme-delay tickets, or by generally slow handling across the board?\nmerchant_name       | urgent_tickets_evaluated | pct_within_8h | pct_8h_24h | pct_1d_3d | pct_3d_7d | pct_over_7d_or_unresolved\n--------------------+--------------------------+---------------+------------+-----------+-----------+--------------------------\nCragsley Outfitters |                       50 |          34.0 |       34.0 |      18.0 |      10.0 |                       4.0\nGrind Collective    |                        6 |          83.3 |       16.7 |       0.0 |       0.0 |                       0.0\nMarrowby & Vane     |                       33 |          33.3 |       18.2 |       9.1 |      18.2 |                      21.2\nStudio Vrij         |                       11 |          36.4 |       18.2 |      27.3 |       0.0 |                      18.2\nWiesgrund Wohnen    |                        7 |          85.7 |       14.3 |       0.0 |       0.0 |                       0.0\n(5 rows)\n\n\nNight 4 of 4: you have 3 report slots tonight."}
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{"parentUuid":"4bba63d7-1d85-4564-adcd-e0a08433df20","isSidechain":false,"promptId":"e79166a8-ecb9-458c-b15c-576297146c17","type":"user","message":{"role":"user","content":"The printed reports of night 3:\n\n=== Report 1: Quarterly urgent-ticket performance, hotspot merchants vs comparison peer (written by Viktor) ===\nEngineer's note: Built a full 4-tenant x 4-quarter grid with LEFT JOIN so quarters with zero evaluable urgent tickets still print a row (count 0, miss rate/avg/median NULL) instead of silently vanishing. Miss rate denominator and avg/median are strictly over tickets with sla_policy_id not null, per the 'evaluable' definition; resolved_at null is treated as a miss since data ends 2025-12-31.\nREPORT: Quarterly urgent-ticket performance, hotspot merchants vs comparison peer\nQUESTION: Does urgent-ticket resolution specifically get worse in the same quarters where each merchant's ticket-per-agent load spiked?\nmerchant_name       | quarter | urgent_tickets_evaluated | urgent_miss_rate_pct | avg_actual_resolution_hours | median_actual_resolution_hours\n--------------------+---------+--------------------------+----------------------+-----------------------------+-------------------------------\nCragsley Outfitters | 2025-Q1 |                       14 |                 71.4 |                        30.0 |                           10.5\nCragsley Outfitters | 2025-Q2 |                       13 |                 53.8 |                        18.0 |                           14.0\nCragsley Outfitters | 2025-Q3 |                        8 |                 50.0 |                        23.1 |                            9.8\nCragsley Outfitters | 2025-Q4 |                       15 |                 80.0 |                        36.6 |                           21.5\nGrind Collective    | 2025-Q1 |                        1 |                  0.0 |                         0.8 |                            0.8\nGrind Collective    | 2025-Q2 |                        1 |                  0.0 |                         0.9 |                            0.9\nGrind Collective    | 2025-Q3 |                        1 |                  0.0 |                         1.0 |                            1.0\nGrind Collective    | 2025-Q4 |                        3 |                 33.3 |                         7.0 |                            7.1\nMarrowby & Vane     | 2025-Q1 |                        8 |                 62.5 |                        43.6 |                           24.8\nMarrowby & Vane     | 2025-Q2 |                        2 |                 50.0 |                        16.9 |                           16.9\nMarrowby & Vane     | 2025-Q3 |                        6 |                 50.0 |                         8.8 |                            6.4\nMarrowby & Vane     | 2025-Q4 |                       17 |                 76.5 |                       139.5 |                           87.2\nStudio Vrij         | 2025-Q1 |                        4 |                 50.0 |                        17.4 |                           10.7\nStudio Vrij         | 2025-Q2 |                        2 |                100.0 |                       219.5 |                          219.5\nStudio Vrij         | 2025-Q3 |                        2 |                100.0 |                       109.0 |                          109.0\nStudio Vrij         | 2025-Q4 |                        3 |                 33.3 |                         3.9 |                            1.2\n(16 rows)\n\n=== Report 2: Q4-vs-rest-of-year ticket load per agent, all qualifying merchants (written by Felix) ===\nEngineer's note: Grain is one row per qualifying tenant (>=20 tickets created in 2025), as requested. Q1-Q3 quarterly agent counts are zero-filled for any quarter with no tickets so the three-quarter average divisor is always 3, matching the spec exactly.\nREPORT: Q4-vs-rest-of-year ticket load per agent, all qualifying merchants\nQUESTION: Is 'flat support staffing through a seasonal ticket surge' a pattern specific to the three hotspot merchants, or common across the book?\nmerchant_name                 | tickets_q123 | avg_agents_q1_q3 | tickets_per_agent_q1_q3 | tickets_q4 | agents_q4 | tickets_per_agent_q4\n------------------------------+--------------+------------------+-------------------------+------------+-----------+---------------------\nGrind Collective              |           52 |              3.0 |                     5.8 |         70 |         3 |                 23.3\nMarrowby & Vane               |          126 |              5.0 |                     8.4 |        105 |         5 |                 21.0\nCragsley Outfitters           |          249 |              8.0 |                    10.4 |        151 |         8 |                 18.9\nBallycorran Industrial Supply |           27 |              3.0 |                     3.0 |         17 |         1 |                 17.0\nPetalcourt Beauty             |           55 |              4.0 |                     4.6 |         64 |         4 |                 16.0\nFennel & Flint                |           21 |              3.0 |                     2.3 |         46 |         4 |                 11.5\nBrennquell Kaffee             |           93 |              4.0 |                     7.8 |         44 |         4 |                 11.0\nKibblewick                    |           29 |              3.7 |                     2.6 |         43 |         4 |                 10.8\nStudio Vrij                   |          127 |              4.0 |                    10.6 |         39 |         4 |                  9.8\nSourdough Club                |           20 |              3.0 |                     2.2 |         27 |         3 |                  9.0\nAmpfeld Elektronik            |           20 |              4.3 |                     1.5 |         36 |         5 |                  7.2\nSpice Route Box               |           52 |              3.0 |                     5.8 |         19 |         3 |                  6.3\nOakhurst Tailoring            |            9 |              1.3 |                     2.3 |         18 |         3 |                  6.0\nLinden & Loom                 |           14 |              2.3 |                     2.0 |         15 |         3 |                  5.0\nWiesgrund Wohnen              |           17 |              2.3 |                     2.4 |         15 |         3 |                  5.0\nHoneycomb Larder              |           45 |              3.0 |                     5.0 |         13 |         3 |                  4.3\nCinder Peak Sports Co.        |           27 |              2.7 |                     3.4 |         16 |         4 |                  4.0\nThistlecombe                  |           10 |              2.0 |                     1.7 |         11 |         3 |                  3.7\nRue Lafayette Studio          |           17 |              2.7 |                     2.1 |         10 |         3 |                  3.3\nCoastline Interiors           |           12 |              1.7 |                     2.4 |          9 |         3 |                  3.0\nGratlinie Outdoor             |           18 |              3.3 |                     1.8 |          9 |         4 |                  2.3\nKayak Corner                  |           13 |              2.3 |                     1.9 |          7 |         3 |                  2.3\n(22 rows)\n\n=== Report 3: Urgent-ticket resolution-time distribution, slow merchants vs a fast baseline (written by Felix) ===\nEngineer's note: Resolution-time buckets computed directly from tickets.resolved_at - created_at (sla_policies joined only to confirm an SLA policy exists, per the exclusion rule); unresolved-as-of-2025-12-31 tickets fall into the last bucket as specified.\nREPORT: Urgent-ticket resolution-time distribution, slow merchants vs a fast baseline\nQUESTION: Is the big three's inflated average urgent resolution time caused by a handful of extreme-delay tickets, or by generally slow handling across the board?\nmerchant_name       | urgent_tickets_evaluated | pct_within_8h | pct_8h_24h | pct_1d_3d | pct_3d_7d | pct_over_7d_or_unresolved\n--------------------+--------------------------+---------------+------------+-----------+-----------+--------------------------\nCragsley Outfitters |                       50 |          34.0 |       34.0 |      18.0 |      10.0 |                       4.0\nGrind Collective    |                        6 |          83.3 |       16.7 |       0.0 |       0.0 |                       0.0\nMarrowby & Vane     |                       33 |          33.3 |       18.2 |       9.1 |      18.2 |                      21.2\nStudio Vrij         |                       11 |          36.4 |       18.2 |      27.3 |       0.0 |                      18.2\nWiesgrund Wohnen    |                        7 |          85.7 |       14.3 |       0.0 |       0.0 |                       0.0\n(5 rows)\n\n\nNight 4 of 4: you have 3 report slots tonight."},"uuid":"67909f8f-ff9b-4ee2-9ccd-6312e8416275","timestamp":"2026-10-04T02:20:43.022Z","permissionMode":"default","promptSource":"sdk","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a","version":"2.1.275","gitBranch":"HEAD"}
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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":12986,"cache_read_input_tokens":129180,"output_tokens":10503,"output_tokens_details":{"thinking_tokens":8109},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":12986,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":10503,"cache_read_input_tokens":129180,"cache_creation_input_tokens":12986,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":12986},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":null,"context_mana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{"parentUuid":"f2d23612-0bd5-4a47-9b03-30e63993cef9","isSidechain":false,"message":{"model":"claude-sonnet-5","id":"msg_011CfgQ3H8N8yqhfaLVr55Qk","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01MPM4ahoJYsep4PyMvSMc4u","name":"StructuredOutput","input":{"notes":"Night 3 wraps up the \"why\" story nicely: the quarterly urgent-ticket data shows resolution genuinely degrades in the exact quarter each merchant's load spiked (Cragsley/Marrowby worst in Q4 - e.g. Marrowby's avg urgent resolution hours jumps to 139.5h in Q4 vs 8.8-43.6h other quarters; Studio Vrij's catastrophic Q2 (219.5h avg) bleeds into Q3 (109h) before recovering to a healthy 3.9h in Q4, which is itself reassuring evidence of recovery). The Q4-vs-rest report shows the \"flat headcount through a volume surge\" pattern is NOT unique to the three named merchants - roughly half of the 22-merchant qualifying book shows a real Q4 ratio jump (Grind Collective's Q4 ratio, 23.3, is actually worse than Cragsley's), so this looks like a broad, platform-fixable seasonal-staffing gap rather than three merchants being uniquely mismanaged. The resolution-time-distribution report adds useful differentiation: Cragsley's slowness is spread broadly across most tickets (a genuine capacity shortfall), while Marrowby and Studio Vrij each have a distinct ~18-21% long tail of urgent tickets stuck past 7 days or still open - a different, more surgical fix (stuck-ticket escalation) than Cragsley's. For this final night, I want to (1) confirm the load-correlation effect holds across all priorities, not just urgent, to make sure the core finding generalizes, (2) get a quick merchant-profile cut (industry, size, order volume) across the qualifying book to address Daniel's question #4 about whether \"something else\" (not just speed) explains the hotspots, and (3) check whether the long tails at Marrowby/Studio Vrij are concentrated in one or two agents (a coaching fix) or spread across their whole team (a true capacity fix) - directly useful for recommending a fix in the memo.","requests":[{"title":"Quarterly all-priority ticket performance vs load, hotspot merchants","question":"Does the load-correlation pattern found for urgent tickets (worse performance in the quarter each merchant's ticket-per-agent ratio spiked) also hold across all tickets, not just urgent ones?","grain":"one row per tenant and calendar quarter in 2025, for Cragsley Outfitters, Marrowby & Vane, Studio Vrij, and Grind Collective, all priorities","columns":["merchant name","quarter (2025-Q1..Q4)","tickets created","avg resolution hours (all priorities)","median resolution hours (all priorities)","SLA breach rate (%)"],"details":"Source: tickets joined to sla_breaches (for breach rate) and tenants, filtered to the four named tenants. Grain the quarter on tickets.created_at within 2025, all priorities included. avg/median resolution hours = extract(epoch from resolved_at - created_at)/3600 over tickets with resolved_at not null that quarter. SLA breach rate = distinct tickets that quarter with >=1 row in sla_breaches ÷ tickets created that quarter, percentage rounded to 1 decimal (same definition used in the original monthly trend report from two weeks ago). Round resolution hours to 1 decimal. Order by merchant name ascending, then quarter ascending. Expect 16 rows; this is meant to sit alongside the already-collected quarterly tickets-per-agent figures (from 'Ticket load per agent over 2025') for comparison, so no need to repeat that column here.","builds_on":["Ticket load per agent over 2025, hotspot merchants vs a comparable peer","Quarterly urgent-ticket performance, hotspot merchants vs comparison peer"]},{"title":"Merchant profile snapshot: industry, size, and order volume","question":"Before calling 'respond faster' the headline fix, do the three hotspot merchants simply differ in business profile (vertical, size, order volume) in ways that would naturally explain more support load, independent of staffing or speed?","grain":"one row per tenant, among tenants with at least 20 tickets created in 2025 (same qualifying set as the staffing/channel/complexity reports)","columns":["merchant name","industry (tenants.industry)","employee count (tenants.employee_count)","orders placed, 2025","avg order value 2025 (EUR equivalent)"],"details":"Source: tenants (industry, employee_count) joined to orders (count and grand_total for 2025). Window: orders.placed_at in 2025-01-01 through 2025-12-31. Convert each order's grand_total to EUR using exchange_rates (base_code='EUR', quote_code=orders.currency_code, matching as_of to the order's placed_at date, using the prior business day's rate for weekends per house convention) before averaging; round avg order value to 2 decimals. Only include the same 22 tenants with >=20 tickets created in 2025 used in prior reports this week. Order by orders placed descending, tie-break merchant name ascending.","builds_on":["Support agent staffing vs ticket volume, by merchant"]},{"title":"Agent-level ticket performance within the three hotspot merchants","question":"Within Cragsley, Marrowby, and Studio Vrij, is slow/stuck-ticket handling spread evenly across the support team, or concentrated in one or two agents?","grain":"one row per support agent, within Cragsley Outfitters, Marrowby & Vane, and Studio Vrij, all-priority tickets created in 2025","columns":["merchant name","agent display name","tickets handled, 2025","avg resolution hours","median resolution hours","SLA breach rate (%)"],"details":"Source: tickets joined to support_agents (via assignee_agent_id, for display_name) and sla_breaches (for breach rate), filtered to the three named tenants. Window: tickets.created_at in 2025-01-01 through 2025-12-31, excluding tickets with assignee_agent_id null. avg/median resolution hours = extract(epoch from resolved_at - created_at)/3600 over that agent's tickets with resolved_at not null. SLA breach rate = distinct tickets for that agent with >=1 row in sla_breaches ÷ tickets handled by that agent, percentage rounded to 1 decimal. Round resolution hours to 1 decimal. Order by merchant name ascending, then avg resolution hours descending within merchant. Expect at most 17 rows (8+5+4 agents across the three merchants, per last week's registered-agent counts).","builds_on":["Support agent staffing vs ticket volume, by merchant","Urgent-ticket resolution-time distribution, slow merchants vs a fast baseline"]}]},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":12986,"cache_read_input_tokens":129180,"output_tokens":10503,"output_tokens_details":{"thinking_tokens":8109},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":12986,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":10503,"cache_read_input_tokens":129180,"cache_creation_input_tokens":12986,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":12986},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"wireToolInputs":{"toolu_01MPM4ahoJYsep4PyMvSMc4u":{"notes":"Night 3 wraps up the \"why\" story nicely: the quarterly urgent-ticket data shows resolution genuinely degrades in the exact quarter each merchant's load spiked (Cragsley/Marrowby worst in Q4 - e.g. Marrowby's avg urgent resolution hours jumps to 139.5h in Q4 vs 8.8-43.6h other quarters; Studio Vrij's catastrophic Q2 (219.5h avg) bleeds into Q3 (109h) before recovering to a healthy 3.9h in Q4, which is itself reassuring evidence of recovery). The Q4-vs-rest report shows the \"flat headcount through a volume surge\" pattern is NOT unique to the three named merchants - roughly half of the 22-merchant qualifying book shows a real Q4 ratio jump (Grind Collective's Q4 ratio, 23.3, is actually worse than Cragsley's), so this looks like a broad, platform-fixable seasonal-staffing gap rather than three merchants being uniquely mismanaged. The resolution-time-distribution report adds useful differentiation: Cragsley's slowness is spread broadly across most tickets (a genuine capacity shortfall), while Marrowby and Studio Vrij each have a distinct ~18-21% long tail of urgent tickets stuck past 7 days or still open - a different, more surgical fix (stuck-ticket escalation) than Cragsley's. For this final night, I want to (1) confirm the load-correlation effect holds across all priorities, not just urgent, to make sure the core finding generalizes, (2) get a quick merchant-profile cut (industry, size, order volume) across the qualifying book to address Daniel's question #4 about whether \"something else\" (not just speed) explains the hotspots, and (3) check whether the long tails at Marrowby/Studio Vrij are concentrated in one or two agents (a coaching fix) or spread across their whole team (a true capacity fix) - directly useful for recommending a fix in the memo.","requests":[{"title":"Quarterly all-priority ticket performance vs load, hotspot merchants","question":"Does the load-correlation pattern found for urgent tickets (worse performance in the quarter each merchant's ticket-per-agent ratio spiked) also hold across all tickets, not just urgent ones?","grain":"one row per tenant and calendar quarter in 2025, for Cragsley Outfitters, Marrowby & Vane, Studio Vrij, and Grind Collective, all priorities","columns":["merchant name","quarter (2025-Q1..Q4)","tickets created","avg resolution hours (all priorities)","median resolution hours (all priorities)","SLA breach rate (%)"],"details":"Source: tickets joined to sla_breaches (for breach rate) and tenants, filtered to the four named tenants. Grain the quarter on tickets.created_at within 2025, all priorities included. avg/median resolution hours = extract(epoch from resolved_at - created_at)/3600 over tickets with resolved_at not null that quarter. SLA breach rate = distinct tickets that quarter with >=1 row in sla_breaches ÷ tickets created that quarter, percentage rounded to 1 decimal (same definition used in the original monthly trend report from two weeks ago). Round resolution hours to 1 decimal. Order by merchant name ascending, then quarter ascending. Expect 16 rows; this is meant to sit alongside the already-collected quarterly tickets-per-agent figures (from 'Ticket load per agent over 2025') for comparison, so no need to repeat that column here.","builds_on":["Ticket load per agent over 2025, hotspot merchants vs a comparable peer","Quarterly urgent-ticket performance, hotspot merchants vs comparison peer"]},{"title":"Merchant profile snapshot: industry, size, and order volume","question":"Before calling 'respond faster' the headline fix, do the three hotspot merchants simply differ in business profile (vertical, size, order volume) in ways that would naturally explain more support load, independent of staffing or speed?","grain":"one row per tenant, among tenants with at least 20 tickets created in 2025 (same qualifying set as the staffing/channel/complexity reports)","columns":["merchant name","industry (tenants.industry)","employee count (tenants.employee_count)","orders placed, 2025","avg order value 2025 (EUR equivalent)"],"details":"Source: tenants (industry, employee_count) joined to orders (count and grand_total for 2025). Window: orders.placed_at in 2025-01-01 through 2025-12-31. Convert each order's grand_total to EUR using exchange_rates (base_code='EUR', quote_code=orders.currency_code, matching as_of to the order's placed_at date, using the prior business day's rate for weekends per house convention) before averaging; round avg order value to 2 decimals. Only include the same 22 tenants with >=20 tickets created in 2025 used in prior reports this week. Order by orders placed descending, tie-break merchant name ascending.","builds_on":["Support agent staffing vs ticket volume, by merchant"]},{"title":"Agent-level ticket performance within the three hotspot merchants","question":"Within Cragsley, Marrowby, and Studio Vrij, is slow/stuck-ticket handling spread evenly across the support team, or concentrated in one or two agents?","grain":"one row per support agent, within Cragsley Outfitters, Marrowby & Vane, and Studio Vrij, all-priority tickets created in 2025","columns":["merchant name","agent display name","tickets handled, 2025","avg resolution hours","median resolution hours","SLA breach rate (%)"],"details":"Source: tickets joined to support_agents (via assignee_agent_id, for display_name) and sla_breaches (for breach rate), filtered to the three named tenants. Window: tickets.created_at in 2025-01-01 through 2025-12-31, excluding tickets with assignee_agent_id null. avg/median resolution hours = extract(epoch from resolved_at - created_at)/3600 over that agent's tickets with resolved_at not null. SLA breach rate = distinct tickets for that agent with >=1 row in sla_breaches ÷ tickets handled by that agent, percentage rounded to 1 decimal. Round resolution hours to 1 decimal. Order by merchant name ascending, then avg resolution hours descending within merchant. Expect at most 17 rows (8+5+4 agents across the three merchants, per last week's registered-agent counts).","builds_on":["Support agent staffing vs ticket volume, by merchant","Urgent-ticket resolution-time distribution, slow merchants vs a fast baseline"]}]}},"apiBlockIndex":1,"requestId":"req_011CfgQ3Gnn5ZCTyGJg3F9ag","type":"assistant","uuid":"f823f6aa-7a44-460b-9b61-11e9713b5b40","timestamp":"2026-10-04T02:22:27.220Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"f823f6aa-7a44-460b-9b61-11e9713b5b40","isSidechain":false,"attachment":{"type":"structured_output","data":{"notes":"Night 3 wraps up the \"why\" story nicely: the quarterly urgent-ticket data shows resolution genuinely degrades in the exact quarter each merchant's load spiked (Cragsley/Marrowby worst in Q4 - e.g. Marrowby's avg urgent resolution hours jumps to 139.5h in Q4 vs 8.8-43.6h other quarters; Studio Vrij's catastrophic Q2 (219.5h avg) bleeds into Q3 (109h) before recovering to a healthy 3.9h in Q4, which is itself reassuring evidence of recovery). The Q4-vs-rest report shows the \"flat headcount through a volume surge\" pattern is NOT unique to the three named merchants - roughly half of the 22-merchant qualifying book shows a real Q4 ratio jump (Grind Collective's Q4 ratio, 23.3, is actually worse than Cragsley's), so this looks like a broad, platform-fixable seasonal-staffing gap rather than three merchants being uniquely mismanaged. The resolution-time-distribution report adds useful differentiation: Cragsley's slowness is spread broadly across most tickets (a genuine capacity shortfall), while Marrowby and Studio Vrij each have a distinct ~18-21% long tail of urgent tickets stuck past 7 days or still open - a different, more surgical fix (stuck-ticket escalation) than Cragsley's. For this final night, I want to (1) confirm the load-correlation effect holds across all priorities, not just urgent, to make sure the core finding generalizes, (2) get a quick merchant-profile cut (industry, size, order volume) across the qualifying book to address Daniel's question #4 about whether \"something else\" (not just speed) explains the hotspots, and (3) check whether the long tails at Marrowby/Studio Vrij are concentrated in one or two agents (a coaching fix) or spread across their whole team (a true capacity fix) - directly useful for recommending a fix in the memo.","requests":[{"title":"Quarterly all-priority ticket performance vs load, hotspot merchants","question":"Does the load-correlation pattern found for urgent tickets (worse performance in the quarter each merchant's ticket-per-agent ratio spiked) also hold across all tickets, not just urgent ones?","grain":"one row per tenant and calendar quarter in 2025, for Cragsley Outfitters, Marrowby & Vane, Studio Vrij, and Grind Collective, all priorities","columns":["merchant name","quarter (2025-Q1..Q4)","tickets created","avg resolution hours (all priorities)","median resolution hours (all priorities)","SLA breach rate (%)"],"details":"Source: tickets joined to sla_breaches (for breach rate) and tenants, filtered to the four named tenants. Grain the quarter on tickets.created_at within 2025, all priorities included. avg/median resolution hours = extract(epoch from resolved_at - created_at)/3600 over tickets with resolved_at not null that quarter. SLA breach rate = distinct tickets that quarter with >=1 row in sla_breaches ÷ tickets created that quarter, percentage rounded to 1 decimal (same definition used in the original monthly trend report from two weeks ago). Round resolution hours to 1 decimal. Order by merchant name ascending, then quarter ascending. Expect 16 rows; this is meant to sit alongside the already-collected quarterly tickets-per-agent figures (from 'Ticket load per agent over 2025') for comparison, so no need to repeat that column here.","builds_on":["Ticket load per agent over 2025, hotspot merchants vs a comparable peer","Quarterly urgent-ticket performance, hotspot merchants vs comparison peer"]},{"title":"Merchant profile snapshot: industry, size, and order volume","question":"Before calling 'respond faster' the headline fix, do the three hotspot merchants simply differ in business profile (vertical, size, order volume) in ways that would naturally explain more support load, independent of staffing or speed?","grain":"one row per tenant, among tenants with at least 20 tickets created in 2025 (same qualifying set as the staffing/channel/complexity reports)","columns":["merchant name","industry (tenants.industry)","employee count (tenants.employee_count)","orders placed, 2025","avg order value 2025 (EUR equivalent)"],"details":"Source: tenants (industry, employee_count) joined to orders (count and grand_total for 2025). Window: orders.placed_at in 2025-01-01 through 2025-12-31. Convert each order's grand_total to EUR using exchange_rates (base_code='EUR', quote_code=orders.currency_code, matching as_of to the order's placed_at date, using the prior business day's rate for weekends per house convention) before averaging; round avg order value to 2 decimals. Only include the same 22 tenants with >=20 tickets created in 2025 used in prior reports this week. Order by orders placed descending, tie-break merchant name ascending.","builds_on":["Support agent staffing vs ticket volume, by merchant"]},{"title":"Agent-level ticket performance within the three hotspot merchants","question":"Within Cragsley, Marrowby, and Studio Vrij, is slow/stuck-ticket handling spread evenly across the support team, or concentrated in one or two agents?","grain":"one row per support agent, within Cragsley Outfitters, Marrowby & Vane, and Studio Vrij, all-priority tickets created in 2025","columns":["merchant name","agent display name","tickets handled, 2025","avg resolution hours","median resolution hours","SLA breach rate (%)"],"details":"Source: tickets joined to support_agents (via assignee_agent_id, for display_name) and sla_breaches (for breach rate), filtered to the three named tenants. Window: tickets.created_at in 2025-01-01 through 2025-12-31, excluding tickets with assignee_agent_id null. avg/median resolution hours = extract(epoch from resolved_at - created_at)/3600 over that agent's tickets with resolved_at not null. SLA breach rate = distinct tickets for that agent with >=1 row in sla_breaches ÷ tickets handled by that agent, percentage rounded to 1 decimal. Round resolution hours to 1 decimal. Order by merchant name ascending, then avg resolution hours descending within merchant. Expect at most 17 rows (8+5+4 agents across the three merchants, per last week's registered-agent counts).","builds_on":["Support agent staffing vs ticket volume, by merchant","Urgent-ticket resolution-time distribution, slow merchants vs a fast baseline"]}]},"toolUseID":"toolu_01MPM4ahoJYsep4PyMvSMc4u"},"type":"attachment","uuid":"dfc0517e-4b43-40f1-89cf-efcb01c06005","timestamp":"2026-10-04T02:22:27.228Z","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a","version":"2.1.275","gitBranch":"HEAD"}
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{"type":"last-prompt","lastPrompt":"The printed reports of night 3:  === Report 1: Quarterly urgent-ticket performance, hotspot merchants vs comparison peer (written by Viktor) === Engineer's note: Built a full 4-tenant x 4-quarter grid…","leafUuid":"4a937eca-6057-4583-a1ba-879a063aae7f","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a"}
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{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-04T02:23:03.978Z","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a","content":"The printed reports of night 4:\n\n=== Report 1: Quarterly all-priority ticket performance vs load, hotspot merchants (written by Omar) ===\nEngineer's note: Grain is tenant x 2025 quarter (by tickets.created_at), all priorities, for the four named merchants; breach rate = tickets with >=1 sla_breaches row / tickets created that quarter, same definition as the prior monthly trend report. 16 rows as expected.\nREPORT: Quarterly all-priority ticket performance vs load, hotspot merchants\nQUESTION: Does the load-correlation pattern found for urgent tickets (worse performance in the quarter each merchant's ticket-per-agent ratio spiked) also hold across all tickets, not just urgent ones?\nmerchant_name       | quarter | tickets_created | avg_resolution_hours | median_resolution_hours | sla_breach_rate_pct\n--------------------+---------+-----------------+----------------------+-------------------------+--------------------\nCragsley Outfitters | 2025-Q1 |              76 |                 64.9 |                    21.2 |                60.5\nCragsley Outfitters | 2025-Q2 |              81 |                 21.0 |                    14.7 |                53.1\nCragsley Outfitters | 2025-Q3 |              92 |                 28.7 |                    17.5 |                54.3\nCragsley Outfitters | 2025-Q4 |             151 |                 42.4 |                    27.6 |                70.2\nGrind Collective    | 2025-Q1 |              12 |                  9.5 |                     3.5 |                 8.3\nGrind Collective    | 2025-Q2 |              25 |                 16.9 |                    12.7 |                12.0\nGrind Collective    | 2025-Q3 |              15 |                 15.4 |                    10.9 |                13.3\nGrind Collective    | 2025-Q4 |              70 |                103.6 |                    58.4 |                70.0\nMarrowby & Vane     | 2025-Q1 |              46 |                115.0 |                    31.2 |                71.7\nMarrowby & Vane     | 2025-Q2 |              37 |                 14.4 |                    14.1 |                40.5\nMarrowby & Vane     | 2025-Q3 |              43 |                 16.4 |                    13.5 |                41.9\nMarrowby & Vane     | 2025-Q4 |             105 |                131.3 |                    62.6 |                72.4\nStudio Vrij         | 2025-Q1 |              13 |                 20.8 |                    17.3 |                53.8\nStudio Vrij         | 2025-Q2 |              96 |                198.0 |                    94.9 |                66.7\nStudio Vrij         | 2025-Q3 |              18 |                 41.7 |                    19.5 |                38.9\nStudio Vrij         | 2025-Q4 |              39 |                 16.0 |                     9.9 |                41.0\n(16 rows)\n\n=== Report 2: Merchant profile snapshot: industry, size, and order volume (written by Ines) ===\nEngineer's note: Grain is one row per tenant among the 22 tenants with >=20 tickets created in 2025. Order grand_total is converted to EUR via exchange_rates joined on as_of = order date (quote_code = order currency, base_code = EUR); exchange_rates already carries weekend rows forward from the prior business day, so no extra weekend logic was needed. Tenants with no 2025 orders will show 0 orders and a null avg order value.\nREPORT: Merchant profile snapshot: industry, size, and order volume\nQUESTION: Before calling 'respond faster' the headline fix, do the three hotspot merchants simply differ in business profile (vertical, size, order volume) in ways that would naturally explain more support load, independent of staffing or speed?\nmerchant_name                 | industry                  | employee_count | orders_placed_2025 | avg_order_value_eur_2025\n------------------------------+---------------------------+----------------+--------------------+-------------------------\nBrennquell Kaffee             | Food & Coffee             |            442 |               4195 |                    27.31\nCragsley Outfitters           | Apparel & Footwear        |           2819 |               3635 |                    199.4\nMarrowby & Vane               | Apparel & Footwear        |           1179 |               1758 |                   164.34\nGrind Collective              | Food & Coffee             |            190 |               1111 |                    23.05\nSpice Route Box               | Food & Coffee             |            199 |                780 |                    21.94\nPetalcourt Beauty             | Beauty                    |            337 |                706 |                    89.95\nKibblewick                    | Pet Supplies              |            242 |                672 |                    35.42\nStudio Vrij                   | Apparel & Footwear        |            486 |                631 |                   179.94\nHoneycomb Larder              | Food & Coffee             |            115 |                620 |                    28.51\nAmpfeld Elektronik            | Consumer Electronics      |           1309 |                449 |                  1140.33\nFennel & Flint                | Home & Living             |            458 |                389 |                   169.06\nBallycorran Industrial Supply | Industrial Supplies (B2B) |            202 |                344 |                  1625.44\nCinder Peak Sports Co.        | Sports & Outdoor          |            285 |                338 |                    369.1\nSourdough Club                | Food & Coffee             |             92 |                320 |                    93.95\nWiesgrund Wohnen              | Home & Living             |            125 |                296 |                   129.84\nLinden & Loom                 | Home & Living             |            186 |                263 |                   146.29\nGratlinie Outdoor             | Sports & Outdoor          |            396 |                232 |                   414.99\nRue Lafayette Studio          | Apparel & Footwear        |            165 |                187 |                   156.95\nOakhurst Tailoring            | Apparel & Footwear        |            100 |                165 |                    153.4\nCoastline Interiors           | Home & Living             |             69 |                155 |                   138.51\nKayak Corner                  | Sports & Outdoor          |             89 |                109 |                   372.48\nThistlecombe                  | Apparel & Footwear        |            219 |                 92 |                   136.64\n(22 rows)\n\n=== Report 3: Agent-level ticket performance within the three hotspot merchants (written by Bea) ===\nEngineer's note: Grain is (merchant, agent), scoped to the three named tenants, tickets.created_at in 2025, assignee_agent_id not null, per the request. Avg/median resolution hours computed only over that agent's tickets with resolved_at not null; agents with zero resolved tickets will show 0.0 rather than null. SLA breach rate = distinct tickets with >=1 sla_breaches row / tickets handled, as specified.\nREPORT: Agent-level ticket performance within the three hotspot merchants\nQUESTION: Within Cragsley, Marrowby, and Studio Vrij, is slow/stuck-ticket handling spread evenly across the support team, or concentrated in one or two agents?\nmerchant_name       | agent_display_name | tickets_handled_2025 | avg_resolution_hours | median_resolution_hours | sla_breach_rate_pct\n--------------------+--------------------+----------------------+----------------------+-------------------------+--------------------\nCragsley Outfitters | Benjamin           |                   56 |                 47.6 |                    17.4 |                48.2\nCragsley Outfitters | Logan              |                   44 |                 47.2 |                    22.7 |                68.2\nCragsley Outfitters | Lucy               |                   46 |                 43.4 |                    31.6 |                69.6\nCragsley Outfitters | Ethan              |                   53 |                 41.4 |                    26.3 |                69.8\nCragsley Outfitters | Fatima             |                   58 |                 40.6 |                    19.8 |                65.5\nCragsley Outfitters | Ashley             |                   51 |                 32.1 |                    19.1 |                58.8\nCragsley Outfitters | Lucas              |                   49 |                 31.3 |                    15.5 |                55.1\nCragsley Outfitters | Rachel             |                   41 |                 27.5 |                    18.3 |                53.7\nMarrowby & Vane     | Sean               |                   37 |                106.3 |                    29.7 |                75.7\nMarrowby & Vane     | Lucy               |                   42 |                 99.6 |                    26.3 |                69.0\nMarrowby & Vane     | Joshua             |                   43 |                 85.9 |                    20.8 |                51.2\nMarrowby & Vane     | Joshua             |                   56 |                 72.9 |                    19.9 |                58.9\nMarrowby & Vane     | Henry              |                   47 |                 68.8 |                    18.2 |                51.1\nStudio Vrij         | Roos               |                   44 |                145.8 |                    18.6 |                50.0\nStudio Vrij         | Lisa               |                   41 |                139.0 |                    20.9 |                53.7\nStudio Vrij         | Yara               |                   40 |                121.2 |                    31.2 |                60.0\nStudio Vrij         | Koen               |                   41 |                 93.2 |                    31.3 |                63.4\n(17 rows)\n\n\nIt is Friday morning. Write the memo to Daniel Achterberg:\n\n- **Length:** at most about 600 words.\n- **Findings first.** Each finding comes with the numbers behind it and the report it comes from.\n- **Confidence.** Say what you are confident about and what is still open.\n- **Caveats.** Mention any data you do not trust.\n- **Next steps.** End with what you would look at next.\n"}
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{"parentUuid":"4a937eca-6057-4583-a1ba-879a063aae7f","isSidechain":false,"promptId":"47287610-9c26-48ae-981f-b150a7265416","type":"user","message":{"role":"user","content":[{"type":"text","text":"Continue from where you left off."}]},"isMeta":true,"uuid":"d6eed68d-ffed-4f5c-91c5-e0f03976fc05","timestamp":"2026-10-04T02:23:03.971Z","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"d6eed68d-ffed-4f5c-91c5-e0f03976fc05","isSidechain":false,"type":"assistant","uuid":"2e736a48-d13e-4320-8649-f5f8326f2274","timestamp":"2026-10-04T02:23:03.971Z","message":{"diagnostics":null,"id":"bc868117-dc16-4bd0-bace-21110b0f3a45","container":null,"model":"<synthetic>","role":"assistant","stop_details":null,"stop_reason":"stop_sequence","stop_sequence":"","type":"message","usage":{"output_tokens_details":null,"input_tokens":0,"output_tokens":0,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":null,"cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":0},"inference_geo":null,"iterations":null,"speed":null},"content":[{"type":"text","text":"No response requested."}],"context_management":null},"isApiErrorMessage":false,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"2e736a48-d13e-4320-8649-f5f8326f2274","isSidechain":false,"promptId":"47287610-9c26-48ae-981f-b150a7265416","type":"user","message":{"role":"user","content":"The printed reports of night 4:\n\n=== Report 1: Quarterly all-priority ticket performance vs load, hotspot merchants (written by Omar) ===\nEngineer's note: Grain is tenant x 2025 quarter (by tickets.created_at), all priorities, for the four named merchants; breach rate = tickets with >=1 sla_breaches row / tickets created that quarter, same definition as the prior monthly trend report. 16 rows as expected.\nREPORT: Quarterly all-priority ticket performance vs load, hotspot merchants\nQUESTION: Does the load-correlation pattern found for urgent tickets (worse performance in the quarter each merchant's ticket-per-agent ratio spiked) also hold across all tickets, not just urgent ones?\nmerchant_name       | quarter | tickets_created | avg_resolution_hours | median_resolution_hours | sla_breach_rate_pct\n--------------------+---------+-----------------+----------------------+-------------------------+--------------------\nCragsley Outfitters | 2025-Q1 |              76 |                 64.9 |                    21.2 |                60.5\nCragsley Outfitters | 2025-Q2 |              81 |                 21.0 |                    14.7 |                53.1\nCragsley Outfitters | 2025-Q3 |              92 |                 28.7 |                    17.5 |                54.3\nCragsley Outfitters | 2025-Q4 |             151 |                 42.4 |                    27.6 |                70.2\nGrind Collective    | 2025-Q1 |              12 |                  9.5 |                     3.5 |                 8.3\nGrind Collective    | 2025-Q2 |              25 |                 16.9 |                    12.7 |                12.0\nGrind Collective    | 2025-Q3 |              15 |                 15.4 |                    10.9 |                13.3\nGrind Collective    | 2025-Q4 |              70 |                103.6 |                    58.4 |                70.0\nMarrowby & Vane     | 2025-Q1 |              46 |                115.0 |                    31.2 |                71.7\nMarrowby & Vane     | 2025-Q2 |              37 |                 14.4 |                    14.1 |                40.5\nMarrowby & Vane     | 2025-Q3 |              43 |                 16.4 |                    13.5 |                41.9\nMarrowby & Vane     | 2025-Q4 |             105 |                131.3 |                    62.6 |                72.4\nStudio Vrij         | 2025-Q1 |              13 |                 20.8 |                    17.3 |                53.8\nStudio Vrij         | 2025-Q2 |              96 |                198.0 |                    94.9 |                66.7\nStudio Vrij         | 2025-Q3 |              18 |                 41.7 |                    19.5 |                38.9\nStudio Vrij         | 2025-Q4 |              39 |                 16.0 |                     9.9 |                41.0\n(16 rows)\n\n=== Report 2: Merchant profile snapshot: industry, size, and order volume (written by Ines) ===\nEngineer's note: Grain is one row per tenant among the 22 tenants with >=20 tickets created in 2025. Order grand_total is converted to EUR via exchange_rates joined on as_of = order date (quote_code = order currency, base_code = EUR); exchange_rates already carries weekend rows forward from the prior business day, so no extra weekend logic was needed. Tenants with no 2025 orders will show 0 orders and a null avg order value.\nREPORT: Merchant profile snapshot: industry, size, and order volume\nQUESTION: Before calling 'respond faster' the headline fix, do the three hotspot merchants simply differ in business profile (vertical, size, order volume) in ways that would naturally explain more support load, independent of staffing or speed?\nmerchant_name                 | industry                  | employee_count | orders_placed_2025 | avg_order_value_eur_2025\n------------------------------+---------------------------+----------------+--------------------+-------------------------\nBrennquell Kaffee             | Food & Coffee             |            442 |               4195 |                    27.31\nCragsley Outfitters           | Apparel & Footwear        |           2819 |               3635 |                    199.4\nMarrowby & Vane               | Apparel & Footwear        |           1179 |               1758 |                   164.34\nGrind Collective              | Food & Coffee             |            190 |               1111 |                    23.05\nSpice Route Box               | Food & Coffee             |            199 |                780 |                    21.94\nPetalcourt Beauty             | Beauty                    |            337 |                706 |                    89.95\nKibblewick                    | Pet Supplies              |            242 |                672 |                    35.42\nStudio Vrij                   | Apparel & Footwear        |            486 |                631 |                   179.94\nHoneycomb Larder              | Food & Coffee             |            115 |                620 |                    28.51\nAmpfeld Elektronik            | Consumer Electronics      |           1309 |                449 |                  1140.33\nFennel & Flint                | Home & Living             |            458 |                389 |                   169.06\nBallycorran Industrial Supply | Industrial Supplies (B2B) |            202 |                344 |                  1625.44\nCinder Peak Sports Co.        | Sports & Outdoor          |            285 |                338 |                    369.1\nSourdough Club                | Food & Coffee             |             92 |                320 |                    93.95\nWiesgrund Wohnen              | Home & Living             |            125 |                296 |                   129.84\nLinden & Loom                 | Home & Living             |            186 |                263 |                   146.29\nGratlinie Outdoor             | Sports & Outdoor          |            396 |                232 |                   414.99\nRue Lafayette Studio          | Apparel & Footwear        |            165 |                187 |                   156.95\nOakhurst Tailoring            | Apparel & Footwear        |            100 |                165 |                    153.4\nCoastline Interiors           | Home & Living             |             69 |                155 |                   138.51\nKayak Corner                  | Sports & Outdoor          |             89 |                109 |                   372.48\nThistlecombe                  | Apparel & Footwear        |            219 |                 92 |                   136.64\n(22 rows)\n\n=== Report 3: Agent-level ticket performance within the three hotspot merchants (written by Bea) ===\nEngineer's note: Grain is (merchant, agent), scoped to the three named tenants, tickets.created_at in 2025, assignee_agent_id not null, per the request. Avg/median resolution hours computed only over that agent's tickets with resolved_at not null; agents with zero resolved tickets will show 0.0 rather than null. SLA breach rate = distinct tickets with >=1 sla_breaches row / tickets handled, as specified.\nREPORT: Agent-level ticket performance within the three hotspot merchants\nQUESTION: Within Cragsley, Marrowby, and Studio Vrij, is slow/stuck-ticket handling spread evenly across the support team, or concentrated in one or two agents?\nmerchant_name       | agent_display_name | tickets_handled_2025 | avg_resolution_hours | median_resolution_hours | sla_breach_rate_pct\n--------------------+--------------------+----------------------+----------------------+-------------------------+--------------------\nCragsley Outfitters | Benjamin           |                   56 |                 47.6 |                    17.4 |                48.2\nCragsley Outfitters | Logan              |                   44 |                 47.2 |                    22.7 |                68.2\nCragsley Outfitters | Lucy               |                   46 |                 43.4 |                    31.6 |                69.6\nCragsley Outfitters | Ethan              |                   53 |                 41.4 |                    26.3 |                69.8\nCragsley Outfitters | Fatima             |                   58 |                 40.6 |                    19.8 |                65.5\nCragsley Outfitters | Ashley             |                   51 |                 32.1 |                    19.1 |                58.8\nCragsley Outfitters | Lucas              |                   49 |                 31.3 |                    15.5 |                55.1\nCragsley Outfitters | Rachel             |                   41 |                 27.5 |                    18.3 |                53.7\nMarrowby & Vane     | Sean               |                   37 |                106.3 |                    29.7 |                75.7\nMarrowby & Vane     | Lucy               |                   42 |                 99.6 |                    26.3 |                69.0\nMarrowby & Vane     | Joshua             |                   43 |                 85.9 |                    20.8 |                51.2\nMarrowby & Vane     | Joshua             |                   56 |                 72.9 |                    19.9 |                58.9\nMarrowby & Vane     | Henry              |                   47 |                 68.8 |                    18.2 |                51.1\nStudio Vrij         | Roos               |                   44 |                145.8 |                    18.6 |                50.0\nStudio Vrij         | Lisa               |                   41 |                139.0 |                    20.9 |                53.7\nStudio Vrij         | Yara               |                   40 |                121.2 |                    31.2 |                60.0\nStudio Vrij         | Koen               |                   41 |                 93.2 |                    31.3 |                63.4\n(17 rows)\n\n\nIt is Friday morning. Write the memo to Daniel Achterberg:\n\n- **Length:** at most about 600 words.\n- **Findings first.** Each finding comes with the numbers behind it and the report it comes from.\n- **Confidence.** Say what you are confident about and what is still open.\n- **Caveats.** Mention any data you do not trust.\n- **Next steps.** End with what you would look at next.\n"},"uuid":"99264ab9-b351-40fd-99d2-582054b42c0c","timestamp":"2026-10-04T02:23:03.983Z","permissionMode":"default","promptSource":"sdk","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a","version":"2.1.275","gitBranch":"HEAD"}
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Zf0dXjktNs10cUiZQHCTuXnH+xNayykfgUMY/VeFuS4CsTQUmJZu0oqmf9W6KucUBt/yOP6N0R7qsDnN7ULIssCTTPUTNPfPvlAgSD83bwSVCglQXyPXIL16GCAMvocpxYzEubabxYecjKd7Qa7tTQxBQ7hlNguxWmh//8QgcC+f0h9vYtVuE4yaN/dVTsIS3lyCAeKVMIPZW6cvt+cpO7QfuzZsCUalh5zwZUK5LXRliCfYT+zDIIrGGv0vPTAc+OElsvF+9bjnRcQhi3DA+ayOTL2V6NEB4aRf5W5naMOYttQzIo9ZKgQIfepMUEMKrA4c4stJ/l84wSzRgKlvCTzpxn5TqDpN0pDVxUF595jWP+LeZydM8hxUqUK/njywoj+K2rxTOX1Ss+gM+dlTo07H9QPcc8ec7XG53Xce/D2m85W/lDRQlCXNizctcLs1w/tjNpIXoiLnp2Qo40bN6YDXpwyoG/WVfUPXQK9KLny7oliN60JupN/7fYNHBmcqdbaB/iWd8z5D8en5kdxkyb5imXMgdd7WBYFP1p8atBqFktRX/0NTv+k6i/wQwz1k3AMSu2TuptlO1e+L/S8Lpia8U4i6p7axAX/3fNgmCT/vCnwjkbhvIVlvTMNjJnLaLLaRCN1ikxeDmAJqz6q1dSPdj657tPUYp73GQfjD+19cxopqLbTiLfnjcu7jGP4KzOjLbYB1wHPEicDsZPBOl5KKd54GTNGsHF2ah/t6GTxSoVQm20jmJWsUv/dbDa5eY/8R1DIhUV2FDVbzrrvf5TulSoYvYhlvIr3SIEXUyLwlEVzo1dp2sMRRtdJirhQh5yfrNBJ0BM8CZWRTyPedHZZmqvqqL+7J+8uVE2TJ3O5H5a+72kgFj1gNHjvWADIpEpilDCg4fll8di7x3yC8KNFymrIHGBL8sQ6IVGOd510BIAWSrbvOmceRfGZZlWnqqtgj1OB+C5RcIl/R+Ep4n/n8tvV4b0Pe5lChoi1UgsYTR9/gKRRRs/cysl+2FiXYqu+PUTt0aaMWmpviGVIBjgEg4k1d50actwomCHXsuwuHEbJwFznSttqpQkAwswj6D5e8W+76A73k6r8Jq/Epv8N28XdzXDH4HfvKounYXkSw91LFdcr6tNqDtWySO3Lo7twCHZ2TVjdW/nU8Z0GQgxYwHqKsQCWyDw4TrmXrqXC6mh40CB/a7DzRem9T7JYKqlIO75b6SxX+ZFPwfZ0mKDYHUV7Q6LQ8eaWCDd8SenEcQYpuot9+5fXqKgS7yLhaoU0YDxB0yLTswzidDCWUtIuHIJvsty29R6BDCPQCwiauqkR7XFodaQ84MO20aXNO+2SlUHm6qCNuQWFov59WFF9oRZDTbo7CyG3wIn5iTlhygZF4wSokKRE5VgKc8DTL0Tr9FzA40Gi+KuVEs7vPTONCmqdPdA78C0D6SCmhBNWfHE17tojvtMSQC4fCXIOaBb7hvbNgOlHkuYiFI5EbWXO3Fun4eCdW9wv4FsYXsYfFe6O9moTof/aWOAaSHDHPkc0ef4IGOppPAoCpujU5iVZyLYpfiQOo7M+GJ3dQ5Wgv4cluq5z+65nKegdHVmfiNsgWoNRDrigpdXWbL1SQA9BFXF7XIOZw2Zks5a6yUIkBX1pdIYPBY5hkxEIAPoiyg3CqfjM+AxWi3ljzmVRSzOF9mkJTIBPeexF8sSfwEZw8yH22Bm1pQ5pTubNZCDypT7CwEF7vzMmgb2FQoq0lc1xIQRbX7625/B2gbOjNqgmh6ngNiQd3AozeDMhN4a5tVsJy+8yCOf356tfFCn7H2curnhtCMC0TzETQC1U7RwfKtrLarMYOaMX0r+TU/0B3WgKAmAzTsH73maMka519cg0B17QH+cvFIwdLzQJqWFO8c6nVUZqxOqD5ZpdbMAiBWQDIxp+AlgxeH/Xr72RJuYO9UO9hs+qe38zmieN6ACOgqP2Bb46eqRC08mHTcg6sC92J3VVlp/ZOiF7ai9QaWZQBGWRjuHAysf0G9RUPI1b+UZZ/HxwCKTKDtd+3KR40jISgV+G0mxRINtlGQSTEhh2eq2DP4HAOX8qVWX4twF6lEhrBGeXcSKzwJ0EFbQY3ZeIP+dVZpjm8MUGbnXbWmT1eMeE4B3E+zgTL99tTfQ7gfIXFx3HqrsfFZdMlZCIyDo/ykp1+4nunjZ33GVmiZvR98ShC7krAEG8jmGhnn9Lrj+ZlM9Fr8AfgioCKJosrD1JgKV4bzcOZJY+UWsSDPTnkt8z2MZ0oSRX3IZOkJlRXAexRyaIJKhnu4MO/tOwxmO8ikpDrGQRf5qOgADucKsjXacbPW964EBK6bLWyz18dVHSKk6dVWQ78oSQLzVnx6+jMlRW3o+Xk4Vzuwn8UU1e+jn/wAzP3Ntp8RwlbpUCP99arRX+t5xuHaR+Aus2vqEPVlMr+L+1swh/hVhRGjdBjRwKcZFaa1NBWHK2b92+UzvDpJE/V+NAZLsFckFVn/ai4D/moktmfFBz1RpCxFrPu+Aw3ONZ4I57A+dUyYnzcSUEiGPDB/Wzj2lCpZMhoRH0/tbYuSPZHYF3yYwuwB3nwfKBV+UQir44Eehx5RzEFolP+9JkW4zT2N/Uuvl0zGx3zYb+qEQLBhOS8WRzsXdABBINIAIKN2GlgEMUYt8pSQcq467tLYiB5Zf4TUCURJWtRX5IInngi+RN1FDYxt0LNKpt/QUam0Z7ntFtSxZKkezMUqpZQXwqh5vzPiqz6tz1IGGJJbrsP7GWrtMoIFLOu5YKyV9LHOIwhxM67ARDwUHBMunz7dhg0e5U1DHhjqElG0G/ic708luf7E37UsVY8Se3GeH+0pPKyR+UonOkdylkY+OSCiHJCJS+RD6lykB0+aBW/8s/9QTYK8AmkkgmGmUCuWl8ky0kvcZbhl0iUpc0GMmjQdk5Nnf4Ht1Lg0N0vKjBOxySdYM6OPAJmkQebeu74k88f+eqdDQxeJk5z0mZrZj4DnBV0EIHiKz7LGBCrd8yTt96/WNk/v41ODCvchatpIZmz0/LWJCoSN9u2ubbFtMGsKrDyTPawcQZo5SyfwRDhP6OJ0hZh0Dx3ysCPR+0VawMY+TFNQugrmeIQaC6FQzaJ6eAlbrYO387U1/4R9GYifnEIdhDcTed0RQkKlvf5CXDLGr7WF2KwaAAyfjJ1w8NhRY3CdHwgXhizMo7AU/MSh6E/mZtrXXWYmAxm5zI8Svm+FepedEJoMSzFeETzNMwviv/805lFGCM0vtKv/9wsbTPF1D0ZLVuNudx4J5DWd6ewOYeqdi6qkqmKY99lkoIg1iSm4davnLZe8kEefj59szP3nJy6yhG8Dm5DJ/2z93fp1RO0qOBnvwty8mfNMahkHWYxxlWlSGwhUlPDHrhN0r0Kk9B4rYqcrxMoAW/+9UX7Dw7aYoYFmVetPXWluA+WaLTssvRUBcFuB05Ez5LOFVh6kl6V3lsv+4ENiVaqCw2sE/i9iwbQnW4FfZPPyAuybHpowpYYzKrgFbBGkbQeqLgegOpy1rsnp92sg1fEfscXAp5F2+ofr+SNtyCTVak6QgjzHU+5jFk3ixGxnTM7FuUqvpZd4+SH0/dVxBcrcP3sPNJ7zTgEQC74+TGRhVFydSPp11aOXbjmDbz7P/DyQ8LcmgeSRIM+BUquIsEI/dnreu1vnJhTfy4G8VSN95blBJrLNmuVAv8fwo2wLoQJQskGA1hTyYJT25JazP4s3x9hrikbGrE2lvP7gL3Fx3jN4Njvav+3kA83+SHEE8kZKPNjfd3vCquDtnCuzfaOh9uxwkLn4zc9D6sWZj3nIyfrwTSDX8+SedMv4ojNgi+7ypry8x9E7YethrIoK22ILGPnI3iZ/TSsNzg5RJBjzHxPkXabuea4I6GQ23N/BHM7Tn2jGu+kkseJi7m9cEPW2MxBxjllpkDwwQ9nEjnUfgHOIswzzfS6epfCdhW32ePiA37YJnnF/sTSBycI8BulLYCsB8Qb0vmRxaBOSs7iJi/4paIXi02axoAh5oiay7IRPl7zVWveDrBBOBTRymHxjdlX+9jCbd87ozgAC2eUFFOgVodt4utNWQWxBjZBjU8JtsnkLUJLrby2vqajjK2RYTVmh33MWGaMgYQ5Fkm2DG64uZu3sm7ENmZ9P2F+LzISteu6XIqFNqvLsMd41tdP9VPot1liXUa1YU77D4Fng6fKVOqF6FU1SG+rhJBmEue/CPOb1vWrx2XiRxveV+e1baqBAbsUjTZeMztkhxTlzBvWfKHe3UJ9h9/x4OUpXPMhiv6lXbbmYX/I7muX0PioZEFjsJiYiRD/9ENsJjpiLgOBr6vTReN59aw8C6qwW4oR0/u2gX5Qgu7AQhmt7JD48ljPgjrKeIbzxH59h3NcapVFJRzGKsgsObE9xFthHV6gHJ5y0J6VZW6up8sJLlfFuJN1XgBIAGuqjYF2AhdX/6EPIMUmf2tXxcCo/HQEcyWQJOxOfNuIWF+sWHtKnz/c55nWRd+4kmrkJ3fBvMSxthy4MOnRmIE/qNRhocYP0xfW5c47YbJvxa/UK0Cz3NR2eyVt+Sn1fzOmjRvPnbR3cS/v3TOAXB6dvCzDDk+MmJQusZVi7cgETi7EQk2y0A6+xP6ih4Trhdo58GfC+gboQkTnK7gKbnIsTu67qqccoV1GsbaFO2gLu3vSf5/I8aTHnjBxniqYxCgnLrs3afaHb97EP4WStJgUdyEPUHaQmrD7wqS+6SczIOIsztTBodN3cqvsMfOVSFrSWpR2iN8E1iZTTQnH0PuXE50YU6kVnUV4Zw/t9tNrWaNozrped6ECMtDLIsDI8iBG5ufTDK3QCrk6zXUFWvENfd/EbOra1oGMWSIHVGzMFGPQ8aoc9FR3p6GIf4ixeCslv8YZLdAl+b6LQxw3BIc7QOjk6rKC2ewGgyWq2Qfad+CPveJJW3DnZvdwgWTf3vPUdulI3saezFhhXZITL0AiqzIk7O6qm3CGZrgkA3cgnuVDEY2r/pc9CgXNQYfuRzbuuJpG30fdettUjrY0+OoyALiCsqBPbRluM1Ue+lQagu6Kfe8Yszzwi63HcsyQlEIcJ861ot+kRUISCceUUvvidDVJ7fLXuUk+I+k5BP/fiDEyQfQpF8g0uT4+u9n/iyQKpK8I0ySYOmWj8ZSbJnnszZKrF5h1Frgx9ujLy+3E8BNiANHXwBqdWalU0mtcd++vl4LQtobntWD25UOLKcLRs2pMZca2gR1tePqBzU2Q70sChzpfOwjEOGiB6LOZvYBY6sK4gFz4i1Db6OYNptDuvVTIO7BEYGz5KFSbzSuIFHcq4x/FbG+evYbpK4x+w7o52c5q6YeU6zICDtne9KukJ+HwxSvZOX/p7GeW1OMpGoThjhnlmYrXg07xRJw4lzhumNnGirLpsk2UeQecKGPVv5SDNA+t9SN4ZGsEhrFLH+pl1GbzqPgfopeTYq7HE3Ubew7J2wck6pnH4XS2U7WISErQGBNgWiau7ljllDXL69PqOU2F0sQL7cL+LK8sBPt6R2RF0YlTjKUp4XMX4JsxoSQkZ+XnVuLaDUQ/dp0cqIlZVuqfWC7baZpc5qO/v+0cpcgD8yCdG9eRG3MdX5nuayKvJ9T22tfW5f0K2ipz3MDK+9rXciZOcE9uML0fwgZeAyYs4rQ7rEXTlj52mpWdhZr3+XlnDUSgPXTwS1D1Wn+xwnPN8wbxrrVhiHGnU8nXQ54emSoWki1D2Fqb2GdV3qLZ9qCTi3SVU36BISccXU7rWLygEdLZEEO1x4oUCt8UbqzUijhwpGTFhMimokaEqA2/v2IBu7aqSKb/ebnO+1LrESnF2CqpMZc46YB3dcLBYXW3Yzo4DopdIY57jzHOB9QIBU16mFc9YKAvNBwQKNqzjfWCekuquhungXpqoFX1y3QAqF/3iktAz/ZPrUnYXP9T/oC9ImW9zQOSF4eDGM6lMHIcMtBivCflom0rDohgDIs4XMd3vghKbHfOJ0YcC4Tk7QWTaSZRuUB3gm0z7spCkO0MzeLvye03wMq1zubWXC2xWwQdyexGWg4r/UINx7QFBjZ9yOIXZuw40G3QU3WtjYM/YlPxjSm2UpzJMks+VUcLwUqXz7PaGFJBtmgJchrmL3umJpiN1xZpXwFQElmI4p/EjnMXiJHQVgu7eTUl5RZ0AeMQy4KouL0YmeVpyzH5wFRzFU2DxbAqw4bysLLQXh+EsZOLXNI0jyHmX2aQtdpBngPoHwU8FL1qVSPGbH6+CRdWz1ARxi1+xMQNw4So1nTj0cNjhmf4eFnc6MlH0nXVdmGWnkJK9AWAWuFhqAWkb1bGBHDm8d7/tly8B8BvaU70eqNd7pu37UEc+Qp2yvIVleJ0cKXn9HhIZ3852C14HzV2vFot2ULf9hFzhC/wcGaOgvYBrsnsPt7c6tKCEFLJhDclA+2kkt/rZHKKjBmLlT/DEo5DCjL9eaDhWfJM3wf8Pcb3GwnTd33wRbLS6qzt/8FZkEbN6TL4ADeoDgRFqNXgAZXA4lxPABGb/HlSaWXr4slyh/qDLpqsy5QRv65scj2q3xK6CAfDty6ryINtY5HB3I3FvYC74zfPljhDoKT6kIG7weGMJKdRP1Sk42+QwnjKmNGKLWyxG47mGpVWKAfj/gvCHOvpMJ/uXutf+Mp4hAR+mJuUMF4f+32XFxVzrdq9SmYchiGfnxmA2Z6sojUaAYDzF2Tsk+XnXHKGpIlipaCMaIP6DP3kMwcrlRqzto1pvM3TDJK8pNqC9Dl0DmyFBH+q+unzWRvQMxfiRiDfa2L1ZL3sfQkJfztC4bzvb93M4SxUEWdMFB7i/PnVtM2F/KPsczEGP0HAygsdGhd2Y9LHHXsYQ7l2PhswUROUMZPWJtj4fcnXrpvCS3RWHDJQivVJ1lCiHrfnM0XFt/0rur8EaM5LfxIoID3Jzx+FGFDIoFgiafZpcpzfnaaBiUIDCdDFxMUVZzY9x+gZyRdNIVPEmIuiyYOC8w3D9S1gXyyUhrOOqOkiAA8CsSTeJ0jNmMcG6AeNPu1dr+osgex0uIs9pyPXWPwImST6DRlhPHolsXYeA4+z8e8K4Vp+PtJhEvrx5L+Z5OOVnRnFl/P7p4N9B+YcFqqfmxYmyEE0la51bN3phpHLXe7gRCUF5Alks8T9QNgTNGY4QAIUnDeOYoYTfCB5WZUVng4ZlsLWHW16uQt2PyHIHmTyKcm7LeepGyQ//N7U1pduU7dA8xhFDcKo4A54TfQFPcRD5lCY+tWvnL6FFD+uEqsivWE8bdbvRJskrhKH0MO2OXYU3RGyhwxrGj4FYO6euYyXhuLTnTMUlV8rtNnanAupoxnTs9L+rO4J86eyBzfYdnK7dlEGXX92j2aBBYEc01ASb6+ng0gml374Pzjan6bdIRf9D35a98CyQIprkumNh4dezZktVNPpxZpWdKriZbakSPPm1V0tYE+AzqTBRFS9KW3ztAzH6XrsfHc0xHUpeYp+C1oC2tHClqIoO/BTzTZpDEMiECFG5hBZLbancMwH6Oqbh8z6xyGgFp4lBwQkgPiEf+IolatQWwAMD00E5Bcln/vUWh+GgWQVe2Q54B+UaO65IUcaztJMuvTROnzboxNJEYDy77nOznqEdUgtSJZIEGh1KTfNRxjHqDiUJqyLaJDdZ0RPJQ+4dkcVjH3fqw/2eb0ofYZ8BTs7dp+8py+3ZMdE96qmdaFFAeNy4eOgNbs1aqUmXv/nPoll43Ia0mflOCEwM6pX6C1KkWw2VrfFMKGtehj4L0QRbSzJxQrgp1yy565pI04KGCkeBPfmPkJn3T/+1MqdKJH25CgCFPTGV8ykM7MXCA7UROvVxIdrrXVTTkRpjV4/8n0UOZVUfdtW2DJS+sFqHVDNPHcW9FEqh6SDShxdVViraSYvPwFeskFVz/5n2VtpsKbkD1OrRdrOcr1WnHs8B4WkBlLV9nQbukbt1Y3HZUIycWPPQmhsFOqPQ0WLeZ2uJ6HBF5Zzp+HquW8VNUIX65RN+UAQvCI1yGl/6N6GIIXG2GMldUq2hpLf8BQ0q6P9D1pj2l51TZqHL4ba+sDhUw+6Bs89ztx7kJ9udmG2PqOGPyTD8hvxY4m8Ft/KRFKONhhOKR6KpTFgBg6imkRimfPKOjSwlZseMEuXweP/npscWP12YkoL98Qru/RraBFUoGHHjC7xVkHlT8KoCz++KvgDMnT/Hbg433EZXrydzatqxlZITqj2OA3OJ6NBo0LOKYuwGWUGKcs3HxBDrvU6FYbW9xpCSf16G3bgqiIUC83/xfg6623p1KOKGLdj0aBYLmTe/Su4FTKJj4BAMUvuJKmFpVaLGsCu3HkqZ9hmSG81ccrqYrBubxy25zHqCdN/Qr3y3cAt4gHSY4fV65hRJaSdIoStEF6YZGsVERSZOvqpVMs0f6CG32n0XM9rloxkB759px64MVbr6JekWmayH7Di7AmP3BeeKwzd0vGvKloA6IzyeH0OAKYncPozrMUDP8DsT88GHDoUl3nRiBCwqyc4B9WE49ZeGa7zPejVMJ1NT+RxHBvdXNaJRq943dcrE0nUDwiQexexduoUqtEuwy4w8eXBZ5zLUiWpgmv5GmDFb+M1kIgrSnu7Q9fX/wPzqtiKo7DDkPlURUsvvcisvRH45KWk0WNf6nSkc0l67CY3K2Uzhr9VmpmWOgvqkH9eST1O997JiK18iZmthWBPv95GNbHv1yCjEFo1aMkzks7X7XCCV/5gLP1COx4Hu30fhcc5USrVDz4qxSP8eeVCKo6ZHAgzC5MvaFdQPnX5wX0n8fKgC/mohkRgpQRjdONPSo6qGxIiJYQQ1EwmBC04x8rUiZwd3zLB9BxsDxJX1Y4hr5n4qnI4IQTzP4mKF3VUBIugIiCNfK+BvbTeX5dvOcxSOLgB2dNV1ok1euvIRNlQZeGUiGawFQQOa1TJMaipfz+DSj+KmrLy/9TX/RwqUbjAWnV3XFAGVaeEElYdDAOI8LzqTYVLYVklvpbukh5CedCV+o3VzAIMm/01SESzUQXy+udnYJRFH88f+iOz/a5AHgpxqzWyWK+3ysacvo6v7wNkobUCix6Y5zlanU8Jr3e/xwtRGqzoZ+YmlGBps8r8A8RNM9YvwNEdh8XXUEX5yNlz7DLE+v+xpdgyL+6/d2WUE40BQxRvcD6ciLN+gaOhU/3V6gP/ffovFb3ipQCb4QUXnL0m2c3Z1Oj7ISpJZEUpQAOds2vV5B38dsLibVCVaF3PYO76HYnoja13HHQswO0/Ju1zMvQsJ7UTPnJrrnXD1XUqo0njmeUqrood7xoXvVMqqKlUFA8CbzPMkSI43ghB3I56ElTDoJoAL7ZCPdRNkoOtzo5knjbX4hu4mxW2wDjIyftH4r70vAcFZVCcepQsp7S293G0Ev+lEezfN9ZMguY8HSlmF6Kk3NSBoOSSA13sDwCGvLVyqn45BlkKaoTZ+vy9XIl1kbX0ycxvSctpzmRyw+i6ZtIrZICz+uqG6MBgnsc+doXUeKHWsviOGkC3QepSPF6sdtsce746B5x+D1IQeAsGc4VP4i46r7ezUgy7rMaboLo2cke00d6c2Pbuan6ogPXQOXraB6MpNltxwKpIKS3osZgivZUUTTcIqT1DVJlhyRv/QqomERTSAQ4uoObfpDqBcrkogiq48pxJHSeFk8dR/CQKX/mVd7iySAycWbiEOOhJEBBJNnOBlwxzrAuKCsJ7mYSytL8QExgVVZCYx97gXNCbzNluPjRpz22ct8zNZWZPjr0YIu/tC9B2+M4X76OR35JMrYNPKYLPsqKB0y5K2adb1cM8MCzvP0agr3gM9tm8BvwU6Opu7SDt34jNxWXJ7qEfobHz6zMV4rfdenGV+5SA79AMADlG1qUsRRJpsS8aIkUIvrGyT7HxTL/yANfT+48eFH91gR0uu9t+Fw8HFMAGeNZIc+MYTBa8iuOHe+yZf26q831bXhCf73q2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{"parentUuid":"bb088057-e6b5-429a-bfa8-b9eb847a6dd3","isSidechain":false,"message":{"model":"claude-sonnet-5","id":"msg_011CfgQDg5Xm5mBk3E1UHYCh","type":"message","role":"assistant","content":[{"type":"text","text":"**To:** Daniel Achterberg, VP Customer Experience\n**From:** Mateo Ruiz\n**Re:** Why Cragsley, Marrowby, and Studio Vrij are the hotspots — and whether urgent-ticket failure is broad or narrow\n\n**Finding 1 — It's staffing/load, not complexity or channel.** Cragsley (50.0), Marrowby (46.2), and Studio Vrij (41.5) are the three highest tickets-per-agent ratios of all 22 merchants we compared, with no close competitor until Grind Collective at 40.7. Meanwhile their ticket *complexity* is average-to-below (Cragsley has the lowest tags/ticket of the 22, 1.36) and their channel mix (email/chat/phone split) is unremarkable versus faster peers *(staffing, complexity, channel reports)*.\n\n**Finding 2 — Within-merchant timing confirms it's causal, not coincidence.** Each merchant's worst quarter for resolution speed is the exact quarter their own load spiked. Marrowby's ticket-per-agent ratio hit 21.0 in Q4 and resolution breach rate jumped to 72.4% (avg 131.3h), vs. ~40% breach in Q2-Q3 when load was normal. Studio Vrij's catastrophic Q2 (avg 198h, 66.7% breach, ratio 24.0 — the known fulfillment incident) **recovered** by Q4 to 16.0h avg / 41% breach as load normalized — good news: Studio Vrij has genuinely recovered *(quarterly performance reports)*.\n\n**Finding 3 — It's whole-team, not one bad agent.** At Cragsley, all 8 agents run 48-70% breach rates; at Marrowby, all 5 run 51-76%; at Studio Vrij, all 4 average 93-146 hours. No single outlier agent explains it anywhere — this rules out a \"coach one rep\" fix and points to real capacity shortage *(agent-level report)*.\n\n**Finding 4 — Scale, not a different shopper base, explains the pattern.** All three are Apparel & Footwear merchants and are literally the largest by employee count/order volume in our comparison set (Cragsley: 2,819 employees, 3,635 orders; Marrowby: 1,179 employees, 1,758 orders; Studio Vrij: 486 employees, 631 orders). Smaller apparel merchants (Oakhurst, Thistlecombe, Rue Lafayette) are actually among the *fastest* performers in the book. So this looks like volume outrunning flat staffing at scale, not something wrong with their customers or product *(profile report)*.\n\n**Finding 5 — Urgent-ticket failure is broad, not concentrated in the three.** All 14 merchants evaluated use the identical 8-hour urgent target (ruling out \"unrealistic target\" theory), yet miss rates range 14%-100%. Several *smaller* merchants (Honeycomb Larder 100%, Sourdough Club 85.7%, Ampfeld Elektronik 83.3%) are worse than the big three (63-67%). And the \"flat headcount through a volume surge\" pattern itself is common: roughly half of the 22-merchant book shows a real Q4 crunch — Grind Collective's Q4 ratio (23.3) exceeds Cragsley's (18.9) *(SLA-target and Q4-vs-rest reports)*.\n\n**Confidence:** High on the core mechanism — three independent cuts (cross-sectional ratio, within-merchant quarter timing, agent-level spread) all point the same way. High on urgent failure being broad. Lower on the apparel-vertical link — only 3 data points, could be coincidence, worth watching as more merchants scale.\n\n**Caveats:** Q4 2025 figures are still slightly right-censored (some tickets unresolved as of 12/31 understate true backlog). \"Agents\" = distinct agents who touched a ticket, a volume proxy, not actual staffed hours/capacity. Cragsley's Q1 was also elevated, likely holiday-backlog carryover from Dec 2024 rather than a second load spike — worth treating as a caveat, not a clean confirmation.\n\n**Next steps:** (1) Size the headcount needed at the big three to bring their ratio into the book's healthy range (~10-15); (2) build an early-warning ratio threshold so we catch the *next* large merchant before it becomes a hotspot; (3) now that Studio Vrij's recovery is confirmed, turn to Daniel's Q3 — a pre-Nov/Dec staffing checklist for scale merchants."}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":155245,"cache_read_input_tokens":0,"output_tokens":6079,"output_tokens_details":{"thinking_tokens":4517},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":155245,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":6079,"cache_read_input_tokens":0,"cache_creation_input_tokens":155245,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":155245},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":{"cache_miss_reason":{"type":"tools_changed","cache_missed_input_tokens":129740}},"context_management":null},"apiBlockIndex":1,"requestId":"req_011CfgQDfYmjCNwkJEH7e57N","type":"assistant","uuid":"7002fc71-f725-4ca2-abc1-19aaef639c13","timestamp":"2026-10-04T02:24:05.006Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"30f2f8ce-7b84-4ffd-a141-93b7f397983a","version":"2.1.275","gitBranch":"HEAD"}
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