{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-03T23:00:00.762Z","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120","content":"Monday 2026-01-05. Note from Priya Raman (Chief Operating Officer):\n\nSubject: Getting my bearings on fulfillment — questions for this week\n\nHi Lena,\n\nThanks for taking these on. I'm new here and coming from the logistics side, so my first priority is understanding whether the delivery promise we make to shoppers actually holds up — that's the lens for most of what's below. Given the slot budget, here's what matters most, roughly in priority order:\n\n1. **How long does it actually take us to get an order into a customer's hands, and how much does that vary?** I want to understand the real end-to-end time from order placed to delivered, and whether that's gotten better, worse, or stayed flat over the past year or so. I'm especially interested in how consistent we are — are there merchants or categories where delivery is reliably fast, and others where it's a coin toss?\n\n2. **How did we do during the last peak/busy season?** I'd like to know whether fulfillment speed and reliability held up when volume spiked, or whether we saw things slip — delays, backlogs, stockouts — and how quickly we recovered afterward. This matters because peak is the real stress test of the whole operation.\n\n3. **Where are the weak links — specific carriers, warehouses, or regions?** I want to know if there are particular carriers or fulfillment sites that are consistently underperforming on delivery time or dependability, so we know where to focus attention or have a harder conversation with a partner.\n\n4. **How well do we manage stock so we're not promising things we can't deliver?** I'm curious about how often we run into stockouts or have to delay orders for lack of inventory, and whether our replenishment from suppliers (purchase orders) is keeping pace — are we chronically waiting too long on restocks?\n\n5. **(If there's room) What does the returns process look like from a logistics standpoint?** Specifically, how long it takes for returned goods to get back into our system and become sellable again — that's a cost and customer-experience lever I want to keep an eye on.\n\n**What I'll use this for:** I'm forming my first impressions of operational health in this area and want to walk into conversations with merchants and carrier partners armed with facts, not assumptions. I'd rather have a smaller number of really clear answers than a lot of surface-level ones — so if you need to cut scope to fit your slots this week, prioritize #1 and #2, and we can pick up the rest next week.\n\nThanks — looking forward to Friday's memo.\n\nPriya\n\nNight 1 of 4: you have 3 report slots tonight."}
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{"parentUuid":null,"isSidechain":false,"promptId":"b40c3ea1-b35b-4089-b6de-ca1713a08690","type":"user","message":{"role":"user","content":"Monday 2026-01-05. Note from Priya Raman (Chief Operating Officer):\n\nSubject: Getting my bearings on fulfillment — questions for this week\n\nHi Lena,\n\nThanks for taking these on. I'm new here and coming from the logistics side, so my first priority is understanding whether the delivery promise we make to shoppers actually holds up — that's the lens for most of what's below. Given the slot budget, here's what matters most, roughly in priority order:\n\n1. **How long does it actually take us to get an order into a customer's hands, and how much does that vary?** I want to understand the real end-to-end time from order placed to delivered, and whether that's gotten better, worse, or stayed flat over the past year or so. I'm especially interested in how consistent we are — are there merchants or categories where delivery is reliably fast, and others where it's a coin toss?\n\n2. **How did we do during the last peak/busy season?** I'd like to know whether fulfillment speed and reliability held up when volume spiked, or whether we saw things slip — delays, backlogs, stockouts — and how quickly we recovered afterward. This matters because peak is the real stress test of the whole operation.\n\n3. **Where are the weak links — specific carriers, warehouses, or regions?** I want to know if there are particular carriers or fulfillment sites that are consistently underperforming on delivery time or dependability, so we know where to focus attention or have a harder conversation with a partner.\n\n4. **How well do we manage stock so we're not promising things we can't deliver?** I'm curious about how often we run into stockouts or have to delay orders for lack of inventory, and whether our replenishment from suppliers (purchase orders) is keeping pace — are we chronically waiting too long on restocks?\n\n5. **(If there's room) What does the returns process look like from a logistics standpoint?** Specifically, how long it takes for returned goods to get back into our system and become sellable again — that's a cost and customer-experience lever I want to keep an eye on.\n\n**What I'll use this for:** I'm forming my first impressions of operational health in this area and want to walk into conversations with merchants and carrier partners armed with facts, not assumptions. I'd rather have a smaller number of really clear answers than a lot of surface-level ones — so if you need to cut scope to fit your slots this week, prioritize #1 and #2, and we can pick up the rest next week.\n\nThanks — looking forward to Friday's memo.\n\nPriya\n\nNight 1 of 4: you have 3 report slots tonight."},"uuid":"47546901-401d-446e-9291-7cc151a67611","timestamp":"2026-10-03T23:00:00.773Z","permissionMode":"default","promptSource":"sdk","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120","version":"2.1.275","gitBranch":"HEAD"}
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{"parentUuid":"2270d484-9158-4831-9609-2097fbda6031","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 Lena Hartmann, a data analyst at Tidewick Commerce. You work for Priya Raman, Chief Operating Officer. Your working style is skeptical: checks definitions and sanity totals before trusting a trend.\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":"ff2d2ad2-a9ba-49d6-9ec4-809569a01b0c","timestamp":"2026-10-03T23:00:00.779Z","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120","version":"2.1.275","gitBranch":"HEAD"}
{"type":"last-prompt","lastPrompt":"Monday 2026-01-05. Note from Priya Raman (Chief Operating Officer): Subject: Getting my bearings on fulfillment — questions for this week Hi Lena, Thanks for taking these on. I'm new here and comin…","leafUuid":"ff2d2ad2-a9ba-49d6-9ec4-809569a01b0c","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120"}
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{"type":"ai-title","aiTitle":"Fulfillment delivery performance analysis","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120"}
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per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"0db2352c-bb77-46d0-91e1-8b446081d2c9","isSidechain":false,"message":{"model":"claude-sonnet-5","id":"msg_011Cfg8jbShDSRCCnJ4GhfJj","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01JwcFk3bYhUg54o4irZiFgG","name":"StructuredOutput","input":{"notes":"Priya's top two priorities are (1) end-to-end delivery speed and its consistency/trend, and (2) how the last peak season held up. I'm spending all 3 slots tonight on those, leaving carriers/warehouses, inventory and returns for later nights once I've seen where the baseline numbers point. I'm defining \"delivered\" as order status = fulfilled with all linked shipments having a non-null delivered_at, and delivery time as placed_at -> last shipment delivered_at. Report 1 gives the 24-month trend + variability (median/p90/SLA-hit-rate) so I can see direction of travel and spread in one page. Report 2 cuts the same metrics by merchant for 2025 to see who is reliably fast vs. a coin toss. Report 3 looks at the most recent peak window (Oct-Dec 2025) weekly to see if speed degraded under volume and whether a backlog built up and cleared by year-end. I'll sanity-check all three against each other (e.g. does the peak-season weekly average line up with the Nov-Dec rows in the monthly trend) before going further.","requests":[{"title":"Delivery time trend, monthly (2024-2025)","question":"Over the last two years, how long does it take from order placed to delivered, and how much does that vary month to month?","grain":"one row per calendar month of order placement","columns":["month (YYYY-MM)","orders_delivered (count of orders fully delivered)","avg_delivery_days (mean, placed_at to final delivered_at)","median_delivery_days (p50)","p90_delivery_days","pct_within_5_days (share of delivered orders where delivery_days <= 5)"],"details":"Grain: one row per month_label (from calendar_days) of orders.placed_at. Scope: orders with placed_at between 2024-01-01 and 2025-12-31 inclusive, status = 'fulfilled'. For each such order, compute delivery_days = DATEDIFF(day, orders.placed_at, max(shipments.delivered_at) across all shipments reachable via fulfillments.order_id = orders.id and shipments.fulfillment_id = fulfillments.id), only including orders where every shipment linked to that order has a non-null delivered_at (i.e. fully delivered, no partial). Exclude orders with no linked shipments. Round avg/median/p90 to 1 decimal place; pct_within_5_days as a percentage rounded to 1 decimal. Order by month ascending. Expect 24 rows.","builds_on":[]},{"title":"Delivery time by merchant, 2025","question":"Which merchants deliver reliably fast, and which are a coin toss, over the past year?","grain":"one row per tenant (merchant)","columns":["tenant_name","industry","orders_delivered (count fully delivered in 2025)","avg_delivery_days","median_delivery_days (p50)","p90_delivery_days","pct_within_5_days (share of delivered orders where delivery_days <= 5)"],"details":"Grain: one row per tenants.id. Scope: orders with placed_at in 2025-01-01 through 2025-12-31, status = 'fulfilled'. Same delivery_days definition as the monthly trend report (placed_at to max linked shipment.delivered_at via fulfillments, only orders where all linked shipments are delivered). Join tenants for tenant_name and industry. Include only tenants with at least 50 delivered orders in the window (to exclude noise from tiny merchants). Order by orders_delivered descending, tie-break by tenant_name ascending. Cap at 50 rows (should be well under given ~120 tenants total).","builds_on":[]},{"title":"Peak season weekly fulfillment performance (Oct-Dec 2025)","question":"Did delivery speed and reliability hold up during the most recent peak season, and did any backlog build up or clear by year-end?","grain":"one row per ISO week (week_start), based on order placement week","columns":["week_start","orders_placed (all orders placed that week, any status)","orders_delivered (of those, count fully delivered by 2025-12-31)","avg_delivery_days (for the fully-delivered subset)","pct_within_5_days (share of fully-delivered orders in that week where delivery_days <= 5)","orders_still_undelivered (placed that week, status not 'fulfilled' or missing a delivered_at as of 2025-12-31 cutoff)"],"details":"Grain: one row per week_start (Monday, from calendar_days.week_start) for orders.placed_at between 2025-10-01 and 2025-12-31 inclusive. orders_placed = count of all orders placed in that week regardless of status. delivery_days defined exactly as in the monthly trend report (placed_at to max linked shipment.delivered_at via fulfillments, only counted when all shipments for that order are delivered). orders_still_undelivered = orders placed in that week whose status is not 'fulfilled', or status is 'fulfilled' but no delivered_at could be resolved by the 2025-12-31 data cutoff -- this flags backlog/slippage, understanding the most recent weeks will naturally show more undelivered due to right-censoring (transit time), which is expected and should just be noted in the report, not treated as a defect. Order by week_start ascending. Expect about 13 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":49413,"cache_read_input_tokens":0,"output_tokens":4695,"output_tokens_details":{"thinking_tokens":2724},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":49413,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":4695,"cache_read_input_tokens":0,"cache_creation_input_tokens":49413,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":49413},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"wireToolInputs":{"toolu_01JwcFk3bYhUg54o4irZiFgG":{"notes":"Priya's top two priorities are (1) end-to-end delivery speed and its consistency/trend, and (2) how the last peak season held up. I'm spending all 3 slots tonight on those, leaving carriers/warehouses, inventory and returns for later nights once I've seen where the baseline numbers point. I'm defining \"delivered\" as order status = fulfilled with all linked shipments having a non-null delivered_at, and delivery time as placed_at -> last shipment delivered_at. Report 1 gives the 24-month trend + variability (median/p90/SLA-hit-rate) so I can see direction of travel and spread in one page. Report 2 cuts the same metrics by merchant for 2025 to see who is reliably fast vs. a coin toss. Report 3 looks at the most recent peak window (Oct-Dec 2025) weekly to see if speed degraded under volume and whether a backlog built up and cleared by year-end. I'll sanity-check all three against each other (e.g. does the peak-season weekly average line up with the Nov-Dec rows in the monthly trend) before going further.","requests":[{"title":"Delivery time trend, monthly (2024-2025)","question":"Over the last two years, how long does it take from order placed to delivered, and how much does that vary month to month?","grain":"one row per calendar month of order placement","columns":["month (YYYY-MM)","orders_delivered (count of orders fully delivered)","avg_delivery_days (mean, placed_at to final delivered_at)","median_delivery_days (p50)","p90_delivery_days","pct_within_5_days (share of delivered orders where delivery_days <= 5)"],"details":"Grain: one row per month_label (from calendar_days) of orders.placed_at. Scope: orders with placed_at between 2024-01-01 and 2025-12-31 inclusive, status = 'fulfilled'. For each such order, compute delivery_days = DATEDIFF(day, orders.placed_at, max(shipments.delivered_at) across all shipments reachable via fulfillments.order_id = orders.id and shipments.fulfillment_id = fulfillments.id), only including orders where every shipment linked to that order has a non-null delivered_at (i.e. fully delivered, no partial). Exclude orders with no linked shipments. Round avg/median/p90 to 1 decimal place; pct_within_5_days as a percentage rounded to 1 decimal. Order by month ascending. Expect 24 rows.","builds_on":[]},{"title":"Delivery time by merchant, 2025","question":"Which merchants deliver reliably fast, and which are a coin toss, over the past year?","grain":"one row per tenant (merchant)","columns":["tenant_name","industry","orders_delivered (count fully delivered in 2025)","avg_delivery_days","median_delivery_days (p50)","p90_delivery_days","pct_within_5_days (share of delivered orders where delivery_days <= 5)"],"details":"Grain: one row per tenants.id. Scope: orders with placed_at in 2025-01-01 through 2025-12-31, status = 'fulfilled'. Same delivery_days definition as the monthly trend report (placed_at to max linked shipment.delivered_at via fulfillments, only orders where all linked shipments are delivered). Join tenants for tenant_name and industry. Include only tenants with at least 50 delivered orders in the window (to exclude noise from tiny merchants). Order by orders_delivered descending, tie-break by tenant_name ascending. Cap at 50 rows (should be well under given ~120 tenants total).","builds_on":[]},{"title":"Peak season weekly fulfillment performance (Oct-Dec 2025)","question":"Did delivery speed and reliability hold up during the most recent peak season, and did any backlog build up or clear by year-end?","grain":"one row per ISO week (week_start), based on order placement week","columns":["week_start","orders_placed (all orders placed that week, any status)","orders_delivered (of those, count fully delivered by 2025-12-31)","avg_delivery_days (for the fully-delivered subset)","pct_within_5_days (share of fully-delivered orders in that week where delivery_days <= 5)","orders_still_undelivered (placed that week, status not 'fulfilled' or missing a delivered_at as of 2025-12-31 cutoff)"],"details":"Grain: one row per week_start (Monday, from calendar_days.week_start) for orders.placed_at between 2025-10-01 and 2025-12-31 inclusive. orders_placed = count of all orders placed in that week regardless of status. delivery_days defined exactly as in the monthly trend report (placed_at to max linked shipment.delivered_at via fulfillments, only counted when all shipments for that order are delivered). orders_still_undelivered = orders placed in that week whose status is not 'fulfilled', or status is 'fulfilled' but no delivered_at could be resolved by the 2025-12-31 data cutoff -- this flags backlog/slippage, understanding the most recent weeks will naturally show more undelivered due to right-censoring (transit time), which is expected and should just be noted in the report, not treated as a defect. Order by week_start ascending. Expect about 13 rows.","builds_on":[]}]}},"apiBlockIndex":1,"requestId":"req_011Cfg8jb9LipPXpLEEgC5X6","type":"assistant","uuid":"23b39697-123e-4358-9f84-1746a14b7000","timestamp":"2026-10-03T23:00:48.170Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"23b39697-123e-4358-9f84-1746a14b7000","isSidechain":false,"attachment":{"type":"structured_output","data":{"notes":"Priya's top two priorities are (1) end-to-end delivery speed and its consistency/trend, and (2) how the last peak season held up. I'm spending all 3 slots tonight on those, leaving carriers/warehouses, inventory and returns for later nights once I've seen where the baseline numbers point. I'm defining \"delivered\" as order status = fulfilled with all linked shipments having a non-null delivered_at, and delivery time as placed_at -> last shipment delivered_at. Report 1 gives the 24-month trend + variability (median/p90/SLA-hit-rate) so I can see direction of travel and spread in one page. Report 2 cuts the same metrics by merchant for 2025 to see who is reliably fast vs. a coin toss. Report 3 looks at the most recent peak window (Oct-Dec 2025) weekly to see if speed degraded under volume and whether a backlog built up and cleared by year-end. I'll sanity-check all three against each other (e.g. does the peak-season weekly average line up with the Nov-Dec rows in the monthly trend) before going further.","requests":[{"title":"Delivery time trend, monthly (2024-2025)","question":"Over the last two years, how long does it take from order placed to delivered, and how much does that vary month to month?","grain":"one row per calendar month of order placement","columns":["month (YYYY-MM)","orders_delivered (count of orders fully delivered)","avg_delivery_days (mean, placed_at to final delivered_at)","median_delivery_days (p50)","p90_delivery_days","pct_within_5_days (share of delivered orders where delivery_days <= 5)"],"details":"Grain: one row per month_label (from calendar_days) of orders.placed_at. Scope: orders with placed_at between 2024-01-01 and 2025-12-31 inclusive, status = 'fulfilled'. For each such order, compute delivery_days = DATEDIFF(day, orders.placed_at, max(shipments.delivered_at) across all shipments reachable via fulfillments.order_id = orders.id and shipments.fulfillment_id = fulfillments.id), only including orders where every shipment linked to that order has a non-null delivered_at (i.e. fully delivered, no partial). Exclude orders with no linked shipments. Round avg/median/p90 to 1 decimal place; pct_within_5_days as a percentage rounded to 1 decimal. Order by month ascending. Expect 24 rows.","builds_on":[]},{"title":"Delivery time by merchant, 2025","question":"Which merchants deliver reliably fast, and which are a coin toss, over the past year?","grain":"one row per tenant (merchant)","columns":["tenant_name","industry","orders_delivered (count fully delivered in 2025)","avg_delivery_days","median_delivery_days (p50)","p90_delivery_days","pct_within_5_days (share of delivered orders where delivery_days <= 5)"],"details":"Grain: one row per tenants.id. Scope: orders with placed_at in 2025-01-01 through 2025-12-31, status = 'fulfilled'. Same delivery_days definition as the monthly trend report (placed_at to max linked shipment.delivered_at via fulfillments, only orders where all linked shipments are delivered). Join tenants for tenant_name and industry. Include only tenants with at least 50 delivered orders in the window (to exclude noise from tiny merchants). Order by orders_delivered descending, tie-break by tenant_name ascending. Cap at 50 rows (should be well under given ~120 tenants total).","builds_on":[]},{"title":"Peak season weekly fulfillment performance (Oct-Dec 2025)","question":"Did delivery speed and reliability hold up during the most recent peak season, and did any backlog build up or clear by year-end?","grain":"one row per ISO week (week_start), based on order placement week","columns":["week_start","orders_placed (all orders placed that week, any status)","orders_delivered (of those, count fully delivered by 2025-12-31)","avg_delivery_days (for the fully-delivered subset)","pct_within_5_days (share of fully-delivered orders in that week where delivery_days <= 5)","orders_still_undelivered (placed that week, status not 'fulfilled' or missing a delivered_at as of 2025-12-31 cutoff)"],"details":"Grain: one row per week_start (Monday, from calendar_days.week_start) for orders.placed_at between 2025-10-01 and 2025-12-31 inclusive. orders_placed = count of all orders placed in that week regardless of status. delivery_days defined exactly as in the monthly trend report (placed_at to max linked shipment.delivered_at via fulfillments, only counted when all shipments for that order are delivered). orders_still_undelivered = orders placed in that week whose status is not 'fulfilled', or status is 'fulfilled' but no delivered_at could be resolved by the 2025-12-31 data cutoff -- this flags backlog/slippage, understanding the most recent weeks will naturally show more undelivered due to right-censoring (transit time), which is expected and should just be noted in the report, not treated as a defect. Order by week_start ascending. Expect about 13 rows.","builds_on":[]}]},"toolUseID":"toolu_01JwcFk3bYhUg54o4irZiFgG"},"type":"attachment","uuid":"7f421294-0286-4ff9-a80c-79a08d554829","timestamp":"2026-10-03T23:00:48.175Z","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"23b39697-123e-4358-9f84-1746a14b7000","isSidechain":false,"promptId":"b40c3ea1-b35b-4089-b6de-ca1713a08690","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01JwcFk3bYhUg54o4irZiFgG","type":"tool_result","content":"Structured output provided successfully"}]},"uuid":"ae4ec7d9-df0c-4f1e-acd0-945c1070096d","timestamp":"2026-10-03T23:00:48.178Z","toolUseResult":"Structured output provided successfully","toolEndsTurn":true,"sourceToolAssistantUUID":"23b39697-123e-4358-9f84-1746a14b7000","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"ae4ec7d9-df0c-4f1e-acd0-945c1070096d","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 Lena Hartmann, a data analyst at Tidewick Commerce. You work for Priya Raman, Chief Operating Officer. Your working style is skeptical: checks definitions and sanity totals before trusting a trend.\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":"dd9e0664-883f-4b8c-ab7a-98e8b772c967","timestamp":"2026-10-03T23:00:48.188Z","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120","version":"2.1.275","gitBranch":"HEAD"}
{"type":"last-prompt","lastPrompt":"Monday 2026-01-05. Note from Priya Raman (Chief Operating Officer): Subject: Getting my bearings on fulfillment — questions for this week Hi Lena, Thanks for taking these on. I'm new here and comin…","leafUuid":"dd9e0664-883f-4b8c-ab7a-98e8b772c967","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120"}
{"type":"ai-title","aiTitle":"Fulfillment delivery performance analysis","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120"}
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{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-03T23:01:35.603Z","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120","content":"The printed reports of night 1:\n\n=== Report 1: Delivery time trend, monthly (2024-2025) (written by Felix) ===\nEngineer's note: Delivery days are computed as calendar-day difference between placed_at and the latest shipment delivered_at across all shipments for the order (requiring all linked shipments to be delivered); orders with no shipments are excluded as specified. Month grain uses calendar_days.month_label on the order's placed_at date.\nREPORT: Delivery time trend, monthly (2024-2025)\nQUESTION: Over the last two years, how long does it take from order placed to delivered, and how much does that vary month to month?\nmonth | orders_delivered | avg_delivery_days | median_delivery_days | p90_delivery_days | pct_within_5_days\n--------+------------------+-------------------+----------------------+-------------------+------------------\n2024-01 | 1234 | 3.5 | 3.0 | 6.0 | 89.6\n2024-02 | 1061 | 3.8 | 3.0 | 6.0 | 86.1\n2024-03 | 1139 | 3.8 | 3.0 | 6.0 | 86.9\n2024-04 | 1154 | 4.0 | 4.0 | 6.0 | 83.4\n2024-05 | 1275 | 4.2 | 4.0 | 6.0 | 79.1\n2024-06 | 1223 | 4.1 | 4.0 | 6.0 | 83.7\n2024-07 | 1292 | 3.9 | 4.0 | 6.0 | 84.9\n2024-08 | 1233 | 4.0 | 4.0 | 6.0 | 83.4\n2024-09 | 1281 | 3.9 | 3.0 | 6.0 | 85.0\n2024-10 | 1422 | 4.0 | 4.0 | 6.0 | 82.6\n2024-11 | 1961 | 3.9 | 4.0 | 6.0 | 84.8\n2024-12 | 2432 | 4.3 | 4.0 | 7.0 | 77.9\n2025-01 | 1717 | 3.6 | 3.0 | 5.0 | 90.3\n2025-02 | 1329 | 3.7 | 3.0 | 6.0 | 89.4\n2025-03 | 1545 | 3.8 | 4.0 | 6.0 | 87.1\n2025-04 | 1477 | 3.7 | 3.0 | 6.0 | 88.8\n2025-05 | 1574 | 4.3 | 4.0 | 7.0 | 80.6\n2025-06 | 1419 | 3.8 | 3.0 | 6.0 | 86.3\n2025-07 | 1565 | 3.9 | 4.0 | 6.0 | 85.2\n2025-08 | 1457 | 3.9 | 4.0 | 6.0 | 87.2\n2025-09 | 1611 | 4.1 | 4.0 | 6.0 | 83.2\n2025-10 | 1767 | 4.1 | 4.0 | 6.0 | 83.1\n2025-11 | 2419 | 4.7 | 4.0 | 8.0 | 78.7\n2025-12 | 2653 | 4.2 | 3.0 | 7.0 | 81.1\n(24 rows)\n\n=== Report 2: Delivery time by merchant, 2025 (written by Felix) ===\nEngineer's note: Delivery days computed as placed_at to the latest linked shipment.delivered_at (via fulfillments), restricted to orders where every linked shipment has delivered_at set, matching the monthly trend report's definition. Tenants with fewer than 50 such delivered orders in 2025 are excluded.\nREPORT: Delivery time by merchant, 2025\nQUESTION: Which merchants deliver reliably fast, and which are a coin toss, over the past year?\ntenant_name | industry | orders_delivered | avg_delivery_days | median_delivery_days | p90_delivery_days | pct_within_5_days\n------------------------------+---------------------------+------------------+-------------------+----------------------+-------------------+------------------\nBrennquell Kaffee | Food & Coffee | 4071 | 3.48 | 3.22 | 5.15 | 0.8828\nCragsley Outfitters | Apparel & Footwear | 3219 | 3.39 | 3.13 | 4.96 | 0.9031\nMarrowby & Vane | Apparel & Footwear | 1529 | 4.09 | 3.34 | 7.28 | 0.7933\nGrind Collective | Food & Coffee | 1082 | 5.1 | 3.98 | 9.12 | 0.6636\nSpice Route Box | Food & Coffee | 755 | 4.31 | 4.18 | 6.55 | 0.6742\nPetalcourt Beauty | Beauty | 646 | 4.98 | 3.82 | 7.92 | 0.7183\nKibblewick | Pet Supplies | 644 | 4.72 | 3.88 | 7.98 | 0.7298\nHoneycomb Larder | Food & Coffee | 592 | 3.75 | 3.56 | 5.78 | 0.8226\nStudio Vrij | Apparel & Footwear | 532 | 4.92 | 4.02 | 8.96 | 0.6259\nAmpfeld Elektronik | Consumer Electronics | 418 | 3.88 | 3.61 | 5.97 | 0.7919\nFennel & Flint | Home & Living | 360 | 5.36 | 4.0 | 10.09 | 0.6583\nBallycorran Industrial Supply | Industrial Supplies (B2B) | 338 | 4.74 | 4.14 | 6.94 | 0.6509\nCinder Peak Sports Co. | Sports & Outdoor | 303 | 3.91 | 3.63 | 6.24 | 0.7954\nSourdough Club | Food & Coffee | 298 | 5.03 | 4.24 | 8.77 | 0.6342\nWiesgrund Wohnen | Home & Living | 270 | 3.94 | 3.77 | 6.14 | 0.7704\nKontor24 Bürobedarf | Office Supplies (B2B) | 258 | 3.6 | 3.28 | 5.29 | 0.8295\nLinden & Loom | Home & Living | 237 | 4.12 | 3.86 | 6.12 | 0.7679\nGratlinie Outdoor | Sports & Outdoor | 212 | 3.91 | 3.43 | 6.06 | 0.7925\nFeather & Fin | Pet Supplies | 207 | 3.63 | 3.56 | 5.01 | 0.8937\nWhisker Works | Pet Supplies | 172 | 3.89 | 3.72 | 6.18 | 0.7733\nOfficeHub Nordic | Office Supplies (B2B) | 163 | 4.16 | 3.86 | 6.32 | 0.7301\nCastell Fasteners | Industrial Supplies (B2B) | 155 | 3.47 | 3.33 | 5.11 | 0.8839\nRue Lafayette Studio | Apparel & Footwear | 155 | 4.13 | 3.94 | 6.69 | 0.7226\nJuniper Apothecary | Beauty | 146 | 4.23 | 3.92 | 6.69 | 0.7055\nCoastline Interiors | Home & Living | 143 | 4.2 | 4.1 | 6.14 | 0.7203\nMidland Safety Wear | Industrial Supplies (B2B) | 139 | 4.0 | 3.86 | 6.27 | 0.7482\nOakhurst Tailoring | Apparel & Footwear | 139 | 4.32 | 4.1 | 6.35 | 0.7194\nWohnglück | Home & Living | 138 | 3.65 | 3.65 | 5.16 | 0.8478\nNordic Bloom Cosmetics | Beauty | 132 | 4.19 | 3.96 | 6.29 | 0.75\nVelvet Ash | Apparel & Footwear | 114 | 4.26 | 3.96 | 6.91 | 0.693\nSaffron & Sage | Beauty | 113 | 3.86 | 3.73 | 5.91 | 0.7876\nPfotenglück | Pet Supplies | 111 | 3.37 | 3.16 | 4.91 | 0.9189\nOstwind Mode | Apparel & Footwear | 108 | 3.85 | 3.49 | 6.01 | 0.75\nRidgeback Gear | Sports & Outdoor | 102 | 3.76 | 3.76 | 5.36 | 0.8627\nThe Balm Room | Beauty | 100 | 5.0 | 4.22 | 7.37 | 0.7\nChocolaterie Mireille | Food & Coffee | 99 | 4.9 | 4.78 | 7.2 | 0.5657\nKayak Corner | Sports & Outdoor | 96 | 5.22 | 4.06 | 7.3 | 0.6875\nSorella Skin | Beauty | 93 | 3.94 | 3.84 | 5.87 | 0.7957\nHautnah | Beauty | 90 | 4.0 | 3.78 | 6.17 | 0.7222\nWillow & Wren | Home & Living | 90 | 4.66 | 3.68 | 6.81 | 0.7889\nTail Wag Supply | Pet Supplies | 87 | 3.6 | 3.55 | 5.43 | 0.8736\nThistlecombe | Apparel & Footwear | 84 | 6.0 | 4.75 | 11.51 | 0.5476\nDeskline Supplies | Office Supplies (B2B) | 75 | 4.55 | 4.14 | 6.91 | 0.7067\nFell & Fjell | Sports & Outdoor | 72 | 4.05 | 3.98 | 6.02 | 0.7083\nPurrfect Pantry | Pet Supplies | 72 | 5.92 | 4.37 | 10.92 | 0.5417\nHanse Industriebedarf | Industrial Supplies (B2B) | 71 | 4.01 | 3.99 | 6.05 | 0.7465\nMirelle | Beauty | 60 | 3.82 | 3.8 | 5.54 | 0.85\nAmpere & Co. | Consumer Electronics | 56 | 4.22 | 4.0 | 6.2 | 0.6964\nKettle & Key | Home & Living | 55 | 4.48 | 4.16 | 6.52 | 0.6182\nPolder Packaging | Industrial Supplies (B2B) | 55 | 4.56 | 4.25 | 6.79 | 0.6\n(50 rows)\n\n=== Report 3: Peak season weekly fulfillment performance (Oct-Dec 2025) (written by Ines) ===\nEngineer's note: Report is platform-wide (all tenants pooled) since the question concerns overall fulfillment ops, not specific merchants; no currency conversion was needed as no money columns are reported. orders_still_undelivered follows the spec literally (status != 'fulfilled' OR delivery unresolved), so it can include orders that are physically delivered but not yet marked 'fulfilled' in status, and will naturally rise in the most recent weeks due to transit-time right-censoring as noted in the request.\nREPORT: Peak season weekly fulfillment performance (Oct-Dec 2025)\nQUESTION: Did delivery speed and reliability hold up during the most recent peak season, and did any backlog build up or clear by year-end?\nweek_start | orders_placed | orders_delivered | avg_delivery_days | pct_within_5_days | orders_still_undelivered\n-----------+---------------+------------------+-------------------+-------------------+-------------------------\n2025-09-29 | 279 | 271 | 4.5 | 72.7 | 19\n2025-10-06 | 397 | 383 | 3.94 | 79.4 | 25\n2025-10-13 | 423 | 414 | 3.92 | 80.7 | 25\n2025-10-20 | 448 | 440 | 4.31 | 73.9 | 21\n2025-10-27 | 441 | 427 | 3.91 | 82.4 | 27\n2025-11-03 | 480 | 466 | 3.92 | 80.5 | 41\n2025-11-10 | 470 | 452 | 4.73 | 64.8 | 35\n2025-11-17 | 576 | 559 | 4.29 | 73.3 | 41\n2025-11-24 | 1010 | 966 | 5.26 | 73.5 | 104\n2025-12-01 | 969 | 928 | 5.73 | 56.1 | 87\n2025-12-08 | 635 | 611 | 3.96 | 80.7 | 34\n2025-12-15 | 660 | 628 | 3.43 | 87.9 | 38\n2025-12-22 | 577 | 510 | 3.1 | 92.5 | 67\n2025-12-29 | 271 | 38 | 1.87 | 100.0 | 233\n(14 rows)\n\n\nNight 2 of 4: you have 3 report slots tonight."}
{"type":"queue-operation","operation":"dequeue","timestamp":"2026-10-03T23:01:35.604Z","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120"}
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{"parentUuid":"8206a1f5-c261-4c3f-8cdd-47955a8005e9","isSidechain":false,"type":"assistant","uuid":"f472acde-a6d6-47cc-9ea3-84aac2360788","timestamp":"2026-10-03T23:01:35.597Z","message":{"diagnostics":null,"id":"289aef8f-faa3-482f-bb87-7b65f223be07","container":null,"model":"","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":"e0b165aa-32dc-460c-8e74-dc2db9d40120","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"f472acde-a6d6-47cc-9ea3-84aac2360788","isSidechain":false,"promptId":"ca1c6362-500d-40af-b838-ea1cbf7c33d0","type":"user","message":{"role":"user","content":"The printed reports of night 1:\n\n=== Report 1: Delivery time trend, monthly (2024-2025) (written by Felix) ===\nEngineer's note: Delivery days are computed as calendar-day difference between placed_at and the latest shipment delivered_at across all shipments for the order (requiring all linked shipments to be delivered); orders with no shipments are excluded as specified. Month grain uses calendar_days.month_label on the order's placed_at date.\nREPORT: Delivery time trend, monthly (2024-2025)\nQUESTION: Over the last two years, how long does it take from order placed to delivered, and how much does that vary month to month?\nmonth | orders_delivered | avg_delivery_days | median_delivery_days | p90_delivery_days | pct_within_5_days\n--------+------------------+-------------------+----------------------+-------------------+------------------\n2024-01 | 1234 | 3.5 | 3.0 | 6.0 | 89.6\n2024-02 | 1061 | 3.8 | 3.0 | 6.0 | 86.1\n2024-03 | 1139 | 3.8 | 3.0 | 6.0 | 86.9\n2024-04 | 1154 | 4.0 | 4.0 | 6.0 | 83.4\n2024-05 | 1275 | 4.2 | 4.0 | 6.0 | 79.1\n2024-06 | 1223 | 4.1 | 4.0 | 6.0 | 83.7\n2024-07 | 1292 | 3.9 | 4.0 | 6.0 | 84.9\n2024-08 | 1233 | 4.0 | 4.0 | 6.0 | 83.4\n2024-09 | 1281 | 3.9 | 3.0 | 6.0 | 85.0\n2024-10 | 1422 | 4.0 | 4.0 | 6.0 | 82.6\n2024-11 | 1961 | 3.9 | 4.0 | 6.0 | 84.8\n2024-12 | 2432 | 4.3 | 4.0 | 7.0 | 77.9\n2025-01 | 1717 | 3.6 | 3.0 | 5.0 | 90.3\n2025-02 | 1329 | 3.7 | 3.0 | 6.0 | 89.4\n2025-03 | 1545 | 3.8 | 4.0 | 6.0 | 87.1\n2025-04 | 1477 | 3.7 | 3.0 | 6.0 | 88.8\n2025-05 | 1574 | 4.3 | 4.0 | 7.0 | 80.6\n2025-06 | 1419 | 3.8 | 3.0 | 6.0 | 86.3\n2025-07 | 1565 | 3.9 | 4.0 | 6.0 | 85.2\n2025-08 | 1457 | 3.9 | 4.0 | 6.0 | 87.2\n2025-09 | 1611 | 4.1 | 4.0 | 6.0 | 83.2\n2025-10 | 1767 | 4.1 | 4.0 | 6.0 | 83.1\n2025-11 | 2419 | 4.7 | 4.0 | 8.0 | 78.7\n2025-12 | 2653 | 4.2 | 3.0 | 7.0 | 81.1\n(24 rows)\n\n=== Report 2: Delivery time by merchant, 2025 (written by Felix) ===\nEngineer's note: Delivery days computed as placed_at to the latest linked shipment.delivered_at (via fulfillments), restricted to orders where every linked shipment has delivered_at set, matching the monthly trend report's definition. Tenants with fewer than 50 such delivered orders in 2025 are excluded.\nREPORT: Delivery time by merchant, 2025\nQUESTION: Which merchants deliver reliably fast, and which are a coin toss, over the past year?\ntenant_name | industry | orders_delivered | avg_delivery_days | median_delivery_days | p90_delivery_days | pct_within_5_days\n------------------------------+---------------------------+------------------+-------------------+----------------------+-------------------+------------------\nBrennquell Kaffee | Food & Coffee | 4071 | 3.48 | 3.22 | 5.15 | 0.8828\nCragsley Outfitters | Apparel & Footwear | 3219 | 3.39 | 3.13 | 4.96 | 0.9031\nMarrowby & Vane | Apparel & Footwear | 1529 | 4.09 | 3.34 | 7.28 | 0.7933\nGrind Collective | Food & Coffee | 1082 | 5.1 | 3.98 | 9.12 | 0.6636\nSpice Route Box | Food & Coffee | 755 | 4.31 | 4.18 | 6.55 | 0.6742\nPetalcourt Beauty | Beauty | 646 | 4.98 | 3.82 | 7.92 | 0.7183\nKibblewick | Pet Supplies | 644 | 4.72 | 3.88 | 7.98 | 0.7298\nHoneycomb Larder | Food & Coffee | 592 | 3.75 | 3.56 | 5.78 | 0.8226\nStudio Vrij | Apparel & Footwear | 532 | 4.92 | 4.02 | 8.96 | 0.6259\nAmpfeld Elektronik | Consumer Electronics | 418 | 3.88 | 3.61 | 5.97 | 0.7919\nFennel & Flint | Home & Living | 360 | 5.36 | 4.0 | 10.09 | 0.6583\nBallycorran Industrial Supply | Industrial Supplies (B2B) | 338 | 4.74 | 4.14 | 6.94 | 0.6509\nCinder Peak Sports Co. | Sports & Outdoor | 303 | 3.91 | 3.63 | 6.24 | 0.7954\nSourdough Club | Food & Coffee | 298 | 5.03 | 4.24 | 8.77 | 0.6342\nWiesgrund Wohnen | Home & Living | 270 | 3.94 | 3.77 | 6.14 | 0.7704\nKontor24 Bürobedarf | Office Supplies (B2B) | 258 | 3.6 | 3.28 | 5.29 | 0.8295\nLinden & Loom | Home & Living | 237 | 4.12 | 3.86 | 6.12 | 0.7679\nGratlinie Outdoor | Sports & Outdoor | 212 | 3.91 | 3.43 | 6.06 | 0.7925\nFeather & Fin | Pet Supplies | 207 | 3.63 | 3.56 | 5.01 | 0.8937\nWhisker Works | Pet Supplies | 172 | 3.89 | 3.72 | 6.18 | 0.7733\nOfficeHub Nordic | Office Supplies (B2B) | 163 | 4.16 | 3.86 | 6.32 | 0.7301\nCastell Fasteners | Industrial Supplies (B2B) | 155 | 3.47 | 3.33 | 5.11 | 0.8839\nRue Lafayette Studio | Apparel & Footwear | 155 | 4.13 | 3.94 | 6.69 | 0.7226\nJuniper Apothecary | Beauty | 146 | 4.23 | 3.92 | 6.69 | 0.7055\nCoastline Interiors | Home & Living | 143 | 4.2 | 4.1 | 6.14 | 0.7203\nMidland Safety Wear | Industrial Supplies (B2B) | 139 | 4.0 | 3.86 | 6.27 | 0.7482\nOakhurst Tailoring | Apparel & Footwear | 139 | 4.32 | 4.1 | 6.35 | 0.7194\nWohnglück | Home & Living | 138 | 3.65 | 3.65 | 5.16 | 0.8478\nNordic Bloom Cosmetics | Beauty | 132 | 4.19 | 3.96 | 6.29 | 0.75\nVelvet Ash | Apparel & Footwear | 114 | 4.26 | 3.96 | 6.91 | 0.693\nSaffron & Sage | Beauty | 113 | 3.86 | 3.73 | 5.91 | 0.7876\nPfotenglück | Pet Supplies | 111 | 3.37 | 3.16 | 4.91 | 0.9189\nOstwind Mode | Apparel & Footwear | 108 | 3.85 | 3.49 | 6.01 | 0.75\nRidgeback Gear | Sports & Outdoor | 102 | 3.76 | 3.76 | 5.36 | 0.8627\nThe Balm Room | Beauty | 100 | 5.0 | 4.22 | 7.37 | 0.7\nChocolaterie Mireille | Food & Coffee | 99 | 4.9 | 4.78 | 7.2 | 0.5657\nKayak Corner | Sports & Outdoor | 96 | 5.22 | 4.06 | 7.3 | 0.6875\nSorella Skin | Beauty | 93 | 3.94 | 3.84 | 5.87 | 0.7957\nHautnah | Beauty | 90 | 4.0 | 3.78 | 6.17 | 0.7222\nWillow & Wren | Home & Living | 90 | 4.66 | 3.68 | 6.81 | 0.7889\nTail Wag Supply | Pet Supplies | 87 | 3.6 | 3.55 | 5.43 | 0.8736\nThistlecombe | Apparel & Footwear | 84 | 6.0 | 4.75 | 11.51 | 0.5476\nDeskline Supplies | Office Supplies (B2B) | 75 | 4.55 | 4.14 | 6.91 | 0.7067\nFell & Fjell | Sports & Outdoor | 72 | 4.05 | 3.98 | 6.02 | 0.7083\nPurrfect Pantry | Pet Supplies | 72 | 5.92 | 4.37 | 10.92 | 0.5417\nHanse Industriebedarf | Industrial Supplies (B2B) | 71 | 4.01 | 3.99 | 6.05 | 0.7465\nMirelle | Beauty | 60 | 3.82 | 3.8 | 5.54 | 0.85\nAmpere & Co. | Consumer Electronics | 56 | 4.22 | 4.0 | 6.2 | 0.6964\nKettle & Key | Home & Living | 55 | 4.48 | 4.16 | 6.52 | 0.6182\nPolder Packaging | Industrial Supplies (B2B) | 55 | 4.56 | 4.25 | 6.79 | 0.6\n(50 rows)\n\n=== Report 3: Peak season weekly fulfillment performance (Oct-Dec 2025) (written by Ines) ===\nEngineer's note: Report is platform-wide (all tenants pooled) since the question concerns overall fulfillment ops, not specific merchants; no currency conversion was needed as no money columns are reported. orders_still_undelivered follows the spec literally (status != 'fulfilled' OR delivery unresolved), so it can include orders that are physically delivered but not yet marked 'fulfilled' in status, and will naturally rise in the most recent weeks due to transit-time right-censoring as noted in the request.\nREPORT: Peak season weekly fulfillment performance (Oct-Dec 2025)\nQUESTION: Did delivery speed and reliability hold up during the most recent peak season, and did any backlog build up or clear by year-end?\nweek_start | orders_placed | orders_delivered | avg_delivery_days | pct_within_5_days | orders_still_undelivered\n-----------+---------------+------------------+-------------------+-------------------+-------------------------\n2025-09-29 | 279 | 271 | 4.5 | 72.7 | 19\n2025-10-06 | 397 | 383 | 3.94 | 79.4 | 25\n2025-10-13 | 423 | 414 | 3.92 | 80.7 | 25\n2025-10-20 | 448 | 440 | 4.31 | 73.9 | 21\n2025-10-27 | 441 | 427 | 3.91 | 82.4 | 27\n2025-11-03 | 480 | 466 | 3.92 | 80.5 | 41\n2025-11-10 | 470 | 452 | 4.73 | 64.8 | 35\n2025-11-17 | 576 | 559 | 4.29 | 73.3 | 41\n2025-11-24 | 1010 | 966 | 5.26 | 73.5 | 104\n2025-12-01 | 969 | 928 | 5.73 | 56.1 | 87\n2025-12-08 | 635 | 611 | 3.96 | 80.7 | 34\n2025-12-15 | 660 | 628 | 3.43 | 87.9 | 38\n2025-12-22 | 577 | 510 | 3.1 | 92.5 | 67\n2025-12-29 | 271 | 38 | 1.87 | 100.0 | 233\n(14 rows)\n\n\nNight 2 of 4: you have 3 report slots tonight."},"uuid":"f0fd7647-17c3-4817-9d24-af597cc54c58","timestamp":"2026-10-03T23:01:35.609Z","permissionMode":"default","promptSource":"sdk","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120","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":9371,"cache_read_input_tokens":49413,"output_tokens":8913,"output_tokens_details":{"thinking_tokens":6556},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":9371,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":8913,"cache_read_input_tokens":49413,"cache_creation_input_tokens":9371,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":9371},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011Cfg8raeGLkBAF8ckxM1hN","type":"assistant","uuid":"260709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{"parentUuid":"260709f1-3b43-4229-8113-2111007c5a91","isSidechain":false,"message":{"model":"claude-sonnet-5","id":"msg_011Cfg8raycCPqAAv34pv36Y","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01UvoqvDt31PGzntHBN27gd7","name":"StructuredOutput","input":{"notes":"Night 1 take-aways: (a) delivery times are broadly stable most of the year (avg ~3.6-4.1 days, median 3-4, ~83-90% within 5 days), but both Nov 2025 and Dec 2024 show clear peak-season degradation (Nov 2025 worst: avg 4.7, only 78.7% within 5 days) -- confirms Priya's stress-test hypothesis; (b) the Oct-Dec 2025 weekly report pinpoints the worst weeks as 2025-11-24 and 2025-12-01 (Black Friday/Cyber Monday through early Dec), where pct-within-5-days fell to 73.5% and 56.1% and backlog (undelivered) roughly tripled, before recovering to 80-93% by mid-to-late December -- so the operation did wobble under peak load but recovered within 1-2 weeks; (c) the merchant-level report shows huge spread in reliability (pct-within-5-days from ~0.92 down to ~0.54) that does NOT line up cleanly with vertical (Food & Coffee has both the best and some of the worst performers), which points to carrier or warehouse-specific causes rather than product-category causes -- this is the natural next question (Priya's priority #3) and also helps explain the peak-season wobble. Tonight I'm moving into carriers and warehouses, and decomposing total delivery time into warehouse processing time vs. carrier transit time so we know which stage is actually the bottleneck, especially during peak.","requests":[{"title":"Carrier transit performance by quarter, 2025","question":"Which carriers are consistently fast and dependable, and did any carrier's transit time blow out specifically during peak (Q4)?","grain":"one row per carrier and calendar quarter","columns":["carrier","quarter (e.g. 2025-Q1)","shipments_shipped (count of shipments with shipped_at in that quarter)","shipments_delivered (of those, count with a non-null delivered_at as of the 2025-12-31 cutoff)","avg_transit_days (mean, shipped_at to delivered_at, delivered subset only)","median_transit_days","pct_within_3_days (share of delivered shipments with transit_days <= 3)","pct_delivered (shipments_delivered / shipments_shipped)"],"details":"Table: shipments (carrier, shipped_at, delivered_at). Scope: shipments.shipped_at between 2025-01-01 and 2025-12-31. Quarter = calendar quarter of shipped_at (use calendar_days.quarter_num/quarter_start). transit_days = DATEDIFF(day, shipped_at, delivered_at), computed only where delivered_at is not null; round avg/median to 2 decimals, pct columns to 1 decimal (as %). Note for the report: shipments shipped very late in Q4 (especially December) may show lower pct_delivered simply because delivery hadn't completed by the 2025-12-31 cutoff -- flag this caveat in the printed notes rather than treating it as a carrier failure. Group by carrier, quarter_start. Order by carrier ascending, quarter ascending.","builds_on":["Delivery time trend, monthly (2024-2025)","Peak season weekly fulfillment performance (Oct-Dec 2025)"]},{"title":"Fulfillment performance by warehouse, 2025","question":"Are there specific fulfillment sites that are consistently slower to process or less reliable on final delivery time?","grain":"one row per warehouse","columns":["warehouse_name","tenant_name","orders_shipped (count in 2025)","avg_processing_days (placed_at to fulfillment.shipped_at)","avg_delivery_days (placed_at to fulfillment.delivered_at)","pct_within_5_days (share with delivery_days <= 5)"],"details":"Scope: orders with placed_at in 2025-01-01 through 2025-12-31, status = 'fulfilled', restricted to orders that have exactly one row in fulfillments (so the order can be cleanly attributed to a single warehouse) with both shipped_at and delivered_at not null. processing_days = DATEDIFF(day, orders.placed_at, fulfillments.shipped_at); delivery_days = DATEDIFF(day, orders.placed_at, fulfillments.delivered_at). Join fulfillments.warehouse_id -> warehouses, and warehouses.tenant_id -> tenants for tenant_name. Round avg_processing_days and avg_delivery_days to 2 decimals, pct_within_5_days to 1 decimal (as %). Include only warehouses with at least 30 qualifying orders. Order by orders_shipped descending, tie-break by warehouse_name ascending. Cap at 50 rows.","builds_on":["Delivery time by merchant, 2025"]},{"title":"Order fulfillment stage breakdown, monthly 2025","question":"When total delivery time lengthens (e.g. during peak), is the slowdown in warehouse processing (pick/pack/ship) or in carrier transit?","grain":"one row per calendar month of order placement, 2025 only","columns":["month (YYYY-MM)","orders_qualifying (count used for this report)","avg_processing_days (placed_at to fulfillment.shipped_at)","avg_transit_days (fulfillment.shipped_at to fulfillment.delivered_at)","avg_total_days (placed_at to fulfillment.delivered_at)","pct_within_5_days_total"],"details":"Scope: orders with placed_at in 2025-01-01 through 2025-12-31, status='fulfilled', restricted to orders with exactly one row in fulfillments (same simplifying rule as the warehouse report, for clean stage attribution) where that fulfillment has shipped_at and delivered_at both not null. processing_days = DATEDIFF(day, orders.placed_at, fulfillments.shipped_at); transit_days = DATEDIFF(day, fulfillments.shipped_at, fulfillments.delivered_at); total_days = DATEDIFF(day, orders.placed_at, fulfillments.delivered_at) (should roughly equal processing_days + transit_days -- use this as an internal sanity check, note any material mismatch in the report). Round all day averages to 2 decimals, pct to 1 decimal. Group by month_label of placed_at. Order by month ascending. Expect 12 rows. Also report orders_qualifying so I can compare this subset's size against the 'orders_delivered' counts in the original monthly trend report (single-fulfillment orders will be a subset of all delivered orders).","builds_on":["Delivery time trend, monthly (2024-2025)"]}]},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":9371,"cache_read_input_tokens":49413,"output_tokens":8913,"output_tokens_details":{"thinking_tokens":6556},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":9371,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":8913,"cache_read_input_tokens":49413,"cache_creation_input_tokens":9371,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":9371},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"wireToolInputs":{"toolu_01UvoqvDt31PGzntHBN27gd7":{"notes":"Night 1 take-aways: (a) delivery times are broadly stable most of the year (avg ~3.6-4.1 days, median 3-4, ~83-90% within 5 days), but both Nov 2025 and Dec 2024 show clear peak-season degradation (Nov 2025 worst: avg 4.7, only 78.7% within 5 days) -- confirms Priya's stress-test hypothesis; (b) the Oct-Dec 2025 weekly report pinpoints the worst weeks as 2025-11-24 and 2025-12-01 (Black Friday/Cyber Monday through early Dec), where pct-within-5-days fell to 73.5% and 56.1% and backlog (undelivered) roughly tripled, before recovering to 80-93% by mid-to-late December -- so the operation did wobble under peak load but recovered within 1-2 weeks; (c) the merchant-level report shows huge spread in reliability (pct-within-5-days from ~0.92 down to ~0.54) that does NOT line up cleanly with vertical (Food & Coffee has both the best and some of the worst performers), which points to carrier or warehouse-specific causes rather than product-category causes -- this is the natural next question (Priya's priority #3) and also helps explain the peak-season wobble. Tonight I'm moving into carriers and warehouses, and decomposing total delivery time into warehouse processing time vs. carrier transit time so we know which stage is actually the bottleneck, especially during peak.","requests":[{"title":"Carrier transit performance by quarter, 2025","question":"Which carriers are consistently fast and dependable, and did any carrier's transit time blow out specifically during peak (Q4)?","grain":"one row per carrier and calendar quarter","columns":["carrier","quarter (e.g. 2025-Q1)","shipments_shipped (count of shipments with shipped_at in that quarter)","shipments_delivered (of those, count with a non-null delivered_at as of the 2025-12-31 cutoff)","avg_transit_days (mean, shipped_at to delivered_at, delivered subset only)","median_transit_days","pct_within_3_days (share of delivered shipments with transit_days <= 3)","pct_delivered (shipments_delivered / shipments_shipped)"],"details":"Table: shipments (carrier, shipped_at, delivered_at). Scope: shipments.shipped_at between 2025-01-01 and 2025-12-31. Quarter = calendar quarter of shipped_at (use calendar_days.quarter_num/quarter_start). transit_days = DATEDIFF(day, shipped_at, delivered_at), computed only where delivered_at is not null; round avg/median to 2 decimals, pct columns to 1 decimal (as %). Note for the report: shipments shipped very late in Q4 (especially December) may show lower pct_delivered simply because delivery hadn't completed by the 2025-12-31 cutoff -- flag this caveat in the printed notes rather than treating it as a carrier failure. Group by carrier, quarter_start. Order by carrier ascending, quarter ascending.","builds_on":["Delivery time trend, monthly (2024-2025)","Peak season weekly fulfillment performance (Oct-Dec 2025)"]},{"title":"Fulfillment performance by warehouse, 2025","question":"Are there specific fulfillment sites that are consistently slower to process or less reliable on final delivery time?","grain":"one row per warehouse","columns":["warehouse_name","tenant_name","orders_shipped (count in 2025)","avg_processing_days (placed_at to fulfillment.shipped_at)","avg_delivery_days (placed_at to fulfillment.delivered_at)","pct_within_5_days (share with delivery_days <= 5)"],"details":"Scope: orders with placed_at in 2025-01-01 through 2025-12-31, status = 'fulfilled', restricted to orders that have exactly one row in fulfillments (so the order can be cleanly attributed to a single warehouse) with both shipped_at and delivered_at not null. processing_days = DATEDIFF(day, orders.placed_at, fulfillments.shipped_at); delivery_days = DATEDIFF(day, orders.placed_at, fulfillments.delivered_at). Join fulfillments.warehouse_id -> warehouses, and warehouses.tenant_id -> tenants for tenant_name. Round avg_processing_days and avg_delivery_days to 2 decimals, pct_within_5_days to 1 decimal (as %). Include only warehouses with at least 30 qualifying orders. Order by orders_shipped descending, tie-break by warehouse_name ascending. Cap at 50 rows.","builds_on":["Delivery time by merchant, 2025"]},{"title":"Order fulfillment stage breakdown, monthly 2025","question":"When total delivery time lengthens (e.g. during peak), is the slowdown in warehouse processing (pick/pack/ship) or in carrier transit?","grain":"one row per calendar month of order placement, 2025 only","columns":["month (YYYY-MM)","orders_qualifying (count used for this report)","avg_processing_days (placed_at to fulfillment.shipped_at)","avg_transit_days (fulfillment.shipped_at to fulfillment.delivered_at)","avg_total_days (placed_at to fulfillment.delivered_at)","pct_within_5_days_total"],"details":"Scope: orders with placed_at in 2025-01-01 through 2025-12-31, status='fulfilled', restricted to orders with exactly one row in fulfillments (same simplifying rule as the warehouse report, for clean stage attribution) where that fulfillment has shipped_at and delivered_at both not null. processing_days = DATEDIFF(day, orders.placed_at, fulfillments.shipped_at); transit_days = DATEDIFF(day, fulfillments.shipped_at, fulfillments.delivered_at); total_days = DATEDIFF(day, orders.placed_at, fulfillments.delivered_at) (should roughly equal processing_days + transit_days -- use this as an internal sanity check, note any material mismatch in the report). Round all day averages to 2 decimals, pct to 1 decimal. Group by month_label of placed_at. Order by month ascending. Expect 12 rows. Also report orders_qualifying so I can compare this subset's size against the 'orders_delivered' counts in the original monthly trend report (single-fulfillment orders will be a subset of all delivered orders).","builds_on":["Delivery time trend, monthly (2024-2025)"]}]}},"apiBlockIndex":1,"requestId":"req_011Cfg8raeGLkBAF8ckxM1hN","type":"assistant","uuid":"0195700d-175e-4f00-8811-ef14e78dd9dd","timestamp":"2026-10-03T23:02:59.722Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"0195700d-175e-4f00-8811-ef14e78dd9dd","isSidechain":false,"attachment":{"type":"structured_output","data":{"notes":"Night 1 take-aways: (a) delivery times are broadly stable most of the year (avg ~3.6-4.1 days, median 3-4, ~83-90% within 5 days), but both Nov 2025 and Dec 2024 show clear peak-season degradation (Nov 2025 worst: avg 4.7, only 78.7% within 5 days) -- confirms Priya's stress-test hypothesis; (b) the Oct-Dec 2025 weekly report pinpoints the worst weeks as 2025-11-24 and 2025-12-01 (Black Friday/Cyber Monday through early Dec), where pct-within-5-days fell to 73.5% and 56.1% and backlog (undelivered) roughly tripled, before recovering to 80-93% by mid-to-late December -- so the operation did wobble under peak load but recovered within 1-2 weeks; (c) the merchant-level report shows huge spread in reliability (pct-within-5-days from ~0.92 down to ~0.54) that does NOT line up cleanly with vertical (Food & Coffee has both the best and some of the worst performers), which points to carrier or warehouse-specific causes rather than product-category causes -- this is the natural next question (Priya's priority #3) and also helps explain the peak-season wobble. Tonight I'm moving into carriers and warehouses, and decomposing total delivery time into warehouse processing time vs. carrier transit time so we know which stage is actually the bottleneck, especially during peak.","requests":[{"title":"Carrier transit performance by quarter, 2025","question":"Which carriers are consistently fast and dependable, and did any carrier's transit time blow out specifically during peak (Q4)?","grain":"one row per carrier and calendar quarter","columns":["carrier","quarter (e.g. 2025-Q1)","shipments_shipped (count of shipments with shipped_at in that quarter)","shipments_delivered (of those, count with a non-null delivered_at as of the 2025-12-31 cutoff)","avg_transit_days (mean, shipped_at to delivered_at, delivered subset only)","median_transit_days","pct_within_3_days (share of delivered shipments with transit_days <= 3)","pct_delivered (shipments_delivered / shipments_shipped)"],"details":"Table: shipments (carrier, shipped_at, delivered_at). Scope: shipments.shipped_at between 2025-01-01 and 2025-12-31. Quarter = calendar quarter of shipped_at (use calendar_days.quarter_num/quarter_start). transit_days = DATEDIFF(day, shipped_at, delivered_at), computed only where delivered_at is not null; round avg/median to 2 decimals, pct columns to 1 decimal (as %). Note for the report: shipments shipped very late in Q4 (especially December) may show lower pct_delivered simply because delivery hadn't completed by the 2025-12-31 cutoff -- flag this caveat in the printed notes rather than treating it as a carrier failure. Group by carrier, quarter_start. Order by carrier ascending, quarter ascending.","builds_on":["Delivery time trend, monthly (2024-2025)","Peak season weekly fulfillment performance (Oct-Dec 2025)"]},{"title":"Fulfillment performance by warehouse, 2025","question":"Are there specific fulfillment sites that are consistently slower to process or less reliable on final delivery time?","grain":"one row per warehouse","columns":["warehouse_name","tenant_name","orders_shipped (count in 2025)","avg_processing_days (placed_at to fulfillment.shipped_at)","avg_delivery_days (placed_at to fulfillment.delivered_at)","pct_within_5_days (share with delivery_days <= 5)"],"details":"Scope: orders with placed_at in 2025-01-01 through 2025-12-31, status = 'fulfilled', restricted to orders that have exactly one row in fulfillments (so the order can be cleanly attributed to a single warehouse) with both shipped_at and delivered_at not null. processing_days = DATEDIFF(day, orders.placed_at, fulfillments.shipped_at); delivery_days = DATEDIFF(day, orders.placed_at, fulfillments.delivered_at). Join fulfillments.warehouse_id -> warehouses, and warehouses.tenant_id -> tenants for tenant_name. Round avg_processing_days and avg_delivery_days to 2 decimals, pct_within_5_days to 1 decimal (as %). Include only warehouses with at least 30 qualifying orders. Order by orders_shipped descending, tie-break by warehouse_name ascending. Cap at 50 rows.","builds_on":["Delivery time by merchant, 2025"]},{"title":"Order fulfillment stage breakdown, monthly 2025","question":"When total delivery time lengthens (e.g. during peak), is the slowdown in warehouse processing (pick/pack/ship) or in carrier transit?","grain":"one row per calendar month of order placement, 2025 only","columns":["month (YYYY-MM)","orders_qualifying (count used for this report)","avg_processing_days (placed_at to fulfillment.shipped_at)","avg_transit_days (fulfillment.shipped_at to fulfillment.delivered_at)","avg_total_days (placed_at to fulfillment.delivered_at)","pct_within_5_days_total"],"details":"Scope: orders with placed_at in 2025-01-01 through 2025-12-31, status='fulfilled', restricted to orders with exactly one row in fulfillments (same simplifying rule as the warehouse report, for clean stage attribution) where that fulfillment has shipped_at and delivered_at both not null. processing_days = DATEDIFF(day, orders.placed_at, fulfillments.shipped_at); transit_days = DATEDIFF(day, fulfillments.shipped_at, fulfillments.delivered_at); total_days = DATEDIFF(day, orders.placed_at, fulfillments.delivered_at) (should roughly equal processing_days + transit_days -- use this as an internal sanity check, note any material mismatch in the report). Round all day averages to 2 decimals, pct to 1 decimal. Group by month_label of placed_at. Order by month ascending. Expect 12 rows. Also report orders_qualifying so I can compare this subset's size against the 'orders_delivered' counts in the original monthly trend report (single-fulfillment orders will be a subset of all delivered orders).","builds_on":["Delivery time trend, monthly (2024-2025)"]}]},"toolUseID":"toolu_01UvoqvDt31PGzntHBN27gd7"},"type":"attachment","uuid":"197ab2a6-a1ff-4cbb-a2c0-6e521879c15d","timestamp":"2026-10-03T23:02:59.730Z","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120","version":"2.1.275","gitBranch":"HEAD"}
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{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-03T23:03:51.403Z","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120","content":"The printed reports of night 2:\n\n=== Report 1: Carrier transit performance by quarter, 2025 (written by Bea) ===\nEngineer's note: Grain is carrier x calendar quarter of shipped_at, scoped to 2025 shipments; pct_delivered for shipments shipped late in Q4 (esp. December) will understate carrier reliability since delivery may simply not have completed by the 2025-12-31 cutoff -- please flag this caveat next to the Q4 pct_delivered/pct_within_3_days figures rather than reading it as a carrier regression.\nREPORT: Carrier transit performance by quarter, 2025\nQUESTION: Which carriers are consistently fast and dependable, and did any carrier's transit time blow out specifically during peak (Q4)?\ncarrier | quarter | shipments_shipped | shipments_delivered | avg_transit_days | median_transit_days | pct_within_3_days | pct_delivered\n---------------------+---------+-------------------+---------------------+------------------+---------------------+-------------------+--------------\nAlbion Parcel | 2025-Q1 | 640 | 640 | 2.03 | 2.0 | 97.2 | 100.0\nAlbion Parcel | 2025-Q2 | 610 | 610 | 2.35 | 2.0 | 88.2 | 100.0\nAlbion Parcel | 2025-Q3 | 632 | 632 | 2.17 | 2.0 | 93.7 | 100.0\nAlbion Parcel | 2025-Q4 | 794 | 779 | 6.54 | 3.0 | 50.7 | 98.1\nAlpenpaket | 2025-Q1 | 507 | 507 | 2.24 | 2.0 | 89.2 | 100.0\nAlpenpaket | 2025-Q2 | 444 | 444 | 2.14 | 2.0 | 94.1 | 100.0\nAlpenpaket | 2025-Q3 | 443 | 443 | 2.15 | 2.0 | 94.8 | 100.0\nAlpenpaket | 2025-Q4 | 628 | 620 | 2.16 | 2.0 | 92.3 | 98.7\nGallia Colis | 2025-Q1 | 40 | 40 | 2.2 | 2.0 | 92.5 | 100.0\nGallia Colis | 2025-Q2 | 53 | 53 | 2.42 | 2.0 | 83.0 | 100.0\nGallia Colis | 2025-Q3 | 82 | 82 | 3.4 | 3.0 | 57.3 | 100.0\nGallia Colis | 2025-Q4 | 109 | 105 | 2.8 | 2.0 | 77.1 | 96.3\nKennet Freight | 2025-Q1 | 138 | 138 | 2.46 | 2.0 | 82.6 | 100.0\nKennet Freight | 2025-Q2 | 148 | 148 | 2.59 | 2.0 | 79.1 | 100.0\nKennet Freight | 2025-Q3 | 170 | 170 | 2.69 | 3.0 | 79.4 | 100.0\nKennet Freight | 2025-Q4 | 453 | 442 | 2.02 | 2.0 | 96.2 | 97.6\nLandfall Freight | 2025-Q1 | 433 | 433 | 4.69 | 4.0 | 27.0 | 100.0\nLandfall Freight | 2025-Q2 | 382 | 382 | 4.66 | 4.0 | 30.6 | 100.0\nLandfall Freight | 2025-Q3 | 415 | 415 | 4.96 | 5.0 | 24.3 | 100.0\nLandfall Freight | 2025-Q4 | 676 | 648 | 5.13 | 5.0 | 22.7 | 95.9\nLiberty Ground | 2025-Q1 | 459 | 459 | 2.19 | 2.0 | 90.8 | 100.0\nLiberty Ground | 2025-Q2 | 514 | 514 | 2.25 | 2.0 | 90.5 | 100.0\nLiberty Ground | 2025-Q3 | 516 | 516 | 2.14 | 2.0 | 92.8 | 100.0\nLiberty Ground | 2025-Q4 | 854 | 830 | 2.3 | 2.0 | 87.7 | 97.2\nLiffey Couriers | 2025-Q1 | 86 | 86 | 2.3 | 2.0 | 83.7 | 100.0\nLiffey Couriers | 2025-Q2 | 77 | 77 | 2.9 | 3.0 | 70.1 | 100.0\nLiffey Couriers | 2025-Q3 | 70 | 70 | 3.31 | 3.0 | 65.7 | 100.0\nLiffey Couriers | 2025-Q4 | 90 | 89 | 2.45 | 2.0 | 82.0 | 98.9\nLowlands Express | 2025-Q1 | 579 | 579 | 3.46 | 3.0 | 61.0 | 100.0\nLowlands Express | 2025-Q2 | 578 | 578 | 3.49 | 3.0 | 57.3 | 100.0\nLowlands Express | 2025-Q3 | 551 | 551 | 3.54 | 3.0 | 59.3 | 100.0\nLowlands Express | 2025-Q4 | 853 | 816 | 3.68 | 3.0 | 55.3 | 95.7\nPolder Post | 2025-Q1 | 188 | 188 | 2.63 | 2.0 | 77.7 | 100.0\nPolder Post | 2025-Q2 | 185 | 185 | 2.44 | 2.0 | 81.1 | 100.0\nPolder Post | 2025-Q3 | 209 | 209 | 2.74 | 3.0 | 77.5 | 100.0\nPolder Post | 2025-Q4 | 320 | 310 | 2.49 | 2.0 | 79.7 | 96.9\nPrairie Line Express | 2025-Q1 | 146 | 146 | 2.26 | 2.0 | 89.7 | 100.0\nPrairie Line Express | 2025-Q2 | 157 | 157 | 2.29 | 2.0 | 89.8 | 100.0\nPrairie Line Express | 2025-Q3 | 154 | 154 | 2.97 | 3.0 | 66.9 | 100.0\nPrairie Line Express | 2025-Q4 | 291 | 284 | 2.59 | 2.5 | 80.6 | 97.6\nRegent Parcels | 2025-Q1 | 174 | 174 | 2.17 | 2.0 | 92.5 | 100.0\nRegent Parcels | 2025-Q2 | 160 | 160 | 2.32 | 2.0 | 87.5 | 100.0\nRegent Parcels | 2025-Q3 | 187 | 187 | 2.63 | 2.0 | 82.4 | 100.0\nRegent Parcels | 2025-Q4 | 277 | 268 | 2.15 | 2.0 | 91.4 | 96.8\nSkerrylane Logistics | 2025-Q1 | 239 | 239 | 2.35 | 2.0 | 84.5 | 100.0\nSkerrylane Logistics | 2025-Q2 | 238 | 238 | 2.46 | 2.0 | 81.1 | 100.0\nSkerrylane Logistics | 2025-Q3 | 246 | 246 | 2.45 | 2.0 | 86.2 | 100.0\nSkerrylane Logistics | 2025-Q4 | 401 | 393 | 2.64 | 2.0 | 75.6 | 98.0\nStrombogen Kurier | 2025-Q1 | 722 | 722 | 2.15 | 2.0 | 93.8 | 100.0\nStrombogen Kurier | 2025-Q2 | 676 | 676 | 2.08 | 2.0 | 94.8 | 100.0\n(56 rows, first 50 printed)\n\n=== Report 2: Fulfillment performance by warehouse, 2025 (written by Grace) ===\nEngineer's note: Warehouses have a single tenant (no cross-tenant sharing in the schema), so warehouse_name alone would be unique, but tenant_name is included as requested for readability. Days are computed as plain calendar-date differences (not fractional/time-of-day), matching DATEDIFF(day, ...) semantics.\nREPORT: Fulfillment performance by warehouse, 2025\nQUESTION: Are there specific fulfillment sites that are consistently slower to process or less reliable on final delivery time?\nwarehouse_name | tenant_name | orders_shipped | avg_processing_days | avg_delivery_days | pct_within_5_days\n--------------------------------------+-------------------------------+----------------+---------------------+-------------------+------------------\nKöln Fulfilment Centre | Brennquell Kaffee | 4071 | 1.03 | 3.55 | 91.7\nAtlanta Fulfilment Centre | Cragsley Outfitters | 3219 | 0.79 | 3.2 | 95.2\nReading Fulfilment Centre | Marrowby & Vane | 1529 | 1.02 | 4.13 | 82.5\nManchester Fulfilment Centre | Grind Collective | 1082 | 1.5 | 5.12 | 71.6\nLiège Fulfilment Centre | Spice Route Box | 755 | 1.58 | 4.38 | 76.2\nBromley Fulfilment Centre | Petalcourt Beauty | 646 | 1.14 | 5.0 | 77.4\nBromley Fulfilment Centre | Kibblewick | 644 | 1.26 | 4.75 | 77.6\nSan Francisco Fulfilment Centre | Honeycomb Larder | 592 | 1.22 | 3.59 | 88.5\nDen Haag Fulfilment Centre | Studio Vrij | 532 | 2.26 | 4.97 | 69.0\nHamburg Fulfilment Centre | Ampfeld Elektronik | 418 | 1.51 | 3.92 | 84.9\nLiverpool Fulfilment Centre | Fennel & Flint | 360 | 1.4 | 5.41 | 72.2\nGalway Fulfilment Centre | Ballycorran Industrial Supply | 338 | 1.11 | 4.69 | 73.1\nSan Francisco Fulfilment Centre | Cinder Peak Sports Co. | 303 | 1.25 | 3.74 | 84.5\nDublin Fulfilment Centre | Sourdough Club | 298 | 1.53 | 5.07 | 69.8\nBerlin Fulfilment Centre | Wiesgrund Wohnen | 270 | 1.4 | 4.01 | 84.4\nBerlin Fulfilment Centre | Kontor24 Bürobedarf | 258 | 1.09 | 3.55 | 91.1\nAmsterdam Fulfilment Centre | Linden & Loom | 237 | 1.58 | 4.21 | 82.7\nDortmund Fulfilment Centre | Gratlinie Outdoor | 212 | 1.44 | 3.93 | 84.0\nHouston Fulfilment Centre | Feather & Fin | 207 | 0.98 | 3.4 | 95.2\nAmsterdam Fulfilment Centre | Whisker Works | 172 | 1.24 | 3.97 | 82.0\nOslo Fulfilment Centre | OfficeHub Nordic | 163 | 1.12 | 4.1 | 82.8\nParis Fulfilment Centre | Rue Lafayette Studio | 155 | 1.3 | 4.19 | 78.7\nSan Antonio Fulfilment Centre | Castell Fasteners | 155 | 1.06 | 3.45 | 96.8\nUppsala Fulfilment Centre | Juniper Apothecary | 146 | 1.49 | 4.29 | 74.7\nGöteborg Fulfilment Centre | Coastline Interiors | 143 | 1.44 | 4.27 | 79.7\nKøbenhavn Fulfilment Centre | Oakhurst Tailoring | 139 | 1.65 | 4.32 | 79.1\nRotterdam Fulfilment Centre | Midland Safety Wear | 139 | 1.19 | 3.96 | 80.6\nNürnberg Fulfilment Centre | Wohnglück | 138 | 1.41 | 3.72 | 93.5\nOdense Fulfilment Centre | Nordic Bloom Cosmetics | 132 | 1.2 | 4.28 | 81.1\nUppsala Fulfilment Centre | Velvet Ash | 114 | 1.29 | 4.3 | 76.3\nLund Fulfilment Centre | Saffron & Sage | 113 | 1.23 | 3.93 | 83.2\nKöln Fulfilment Centre | Pfotenglück | 111 | 1.3 | 3.5 | 95.5\nMünchen Fulfilment Centre | Ostwind Mode | 108 | 1.3 | 3.94 | 79.6\nRotterdam Fulfilment Centre | Ridgeback Gear | 102 | 1.38 | 3.84 | 92.2\nLeeds Fulfilment Centre | The Balm Room | 100 | 1.4 | 5.02 | 78.0\nBordeaux Fulfilment Centre | Chocolaterie Mireille | 99 | 1.31 | 4.93 | 64.6\nExeter Fulfilment Centre | Kayak Corner | 96 | 1.42 | 5.25 | 75.0\nZaandam Fulfilment Centre | Sorella Skin | 93 | 1.32 | 4.03 | 82.8\nAugsburg Fulfilment Centre | Hautnah | 90 | 1.46 | 4.06 | 82.2\nManchester Fulfilment Centre | Willow & Wren | 90 | 1.4 | 4.72 | 83.3\nHouston Fulfilment Centre | Tail Wag Supply | 87 | 1.05 | 3.39 | 93.1\nCambridge Fulfilment Centre | Thistlecombe | 84 | 1.25 | 6.07 | 58.3\nNewcastle upon Tyne Fulfilment Centre | Deskline Supplies | 75 | 1.13 | 4.52 | 76.0\nOdense Fulfilment Centre | Fell & Fjell | 72 | 1.25 | 4.08 | 80.6\nOslo Fulfilment Centre | Purrfect Pantry | 72 | 1.14 | 6.0 | 58.3\nHannover Fulfilment Centre | Hanse Industriebedarf | 71 | 1.2 | 3.96 | 85.9\nParis Fulfilment Centre | Mirelle | 60 | 1.3 | 3.83 | 88.3\nAmsterdam Fulfilment Centre | Ampere & Co. | 56 | 1.29 | 4.29 | 75.0\nAmersfoort Fulfilment Centre | Kettle & Key | 55 | 1.29 | 4.55 | 65.5\nLiège Fulfilment Centre | Polder Packaging | 55 | 1.13 | 4.49 | 72.7\n(50 rows)\n\n=== Report 3: Order fulfillment stage breakdown, monthly 2025 (written by Felix) ===\nEngineer's note: Scoped to fulfilled orders placed in 2025 with exactly one fulfillment row that has both shipped_at and delivered_at set, per the single-fulfillment simplifying rule. Day diffs use date-level subtraction (whole days); I could not verify in the empty dry-run schema whether avg_total_days materially deviates from avg_processing_days + avg_transit_days, so please sanity-check that once real data runs.\nREPORT: Order fulfillment stage breakdown, monthly 2025\nQUESTION: When total delivery time lengthens (e.g. during peak), is the slowdown in warehouse processing (pick/pack/ship) or in carrier transit?\nmonth | orders_qualifying | avg_processing_days | avg_transit_days | avg_total_days | pct_within_5_days_total\n--------+-------------------+---------------------+------------------+----------------+------------------------\n2025-01 | 1717 | 1.08 | 2.51 | 3.59 | 90.3\n2025-02 | 1329 | 1.1 | 2.58 | 3.68 | 89.4\n2025-03 | 1545 | 1.13 | 2.69 | 3.82 | 87.1\n2025-04 | 1477 | 1.14 | 2.56 | 3.7 | 88.8\n2025-05 | 1574 | 1.51 | 2.76 | 4.27 | 80.6\n2025-06 | 1419 | 1.21 | 2.63 | 3.84 | 86.3\n2025-07 | 1565 | 1.14 | 2.79 | 3.93 | 85.2\n2025-08 | 1457 | 1.22 | 2.64 | 3.86 | 87.2\n2025-09 | 1611 | 1.18 | 2.87 | 4.05 | 83.2\n2025-10 | 1767 | 1.14 | 2.94 | 4.08 | 83.1\n2025-11 | 2419 | 1.2 | 3.46 | 4.66 | 78.7\n2025-12 | 2653 | 1.16 | 3.07 | 4.24 | 81.1\n(12 rows)\n\n\nNight 3 of 4: you have 3 report slots tonight."}
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{"parentUuid":"f11717d8-2ff3-47ec-8104-ffb0eed4d25f","isSidechain":false,"promptId":"4be6440c-9f00-49e3-a121-28217e4e8ff5","type":"user","message":{"role":"user","content":"The printed reports of night 2:\n\n=== Report 1: Carrier transit performance by quarter, 2025 (written by Bea) ===\nEngineer's note: Grain is carrier x calendar quarter of shipped_at, scoped to 2025 shipments; pct_delivered for shipments shipped late in Q4 (esp. December) will understate carrier reliability since delivery may simply not have completed by the 2025-12-31 cutoff -- please flag this caveat next to the Q4 pct_delivered/pct_within_3_days figures rather than reading it as a carrier regression.\nREPORT: Carrier transit performance by quarter, 2025\nQUESTION: Which carriers are consistently fast and dependable, and did any carrier's transit time blow out specifically during peak (Q4)?\ncarrier | quarter | shipments_shipped | shipments_delivered | avg_transit_days | median_transit_days | pct_within_3_days | pct_delivered\n---------------------+---------+-------------------+---------------------+------------------+---------------------+-------------------+--------------\nAlbion Parcel | 2025-Q1 | 640 | 640 | 2.03 | 2.0 | 97.2 | 100.0\nAlbion Parcel | 2025-Q2 | 610 | 610 | 2.35 | 2.0 | 88.2 | 100.0\nAlbion Parcel | 2025-Q3 | 632 | 632 | 2.17 | 2.0 | 93.7 | 100.0\nAlbion Parcel | 2025-Q4 | 794 | 779 | 6.54 | 3.0 | 50.7 | 98.1\nAlpenpaket | 2025-Q1 | 507 | 507 | 2.24 | 2.0 | 89.2 | 100.0\nAlpenpaket | 2025-Q2 | 444 | 444 | 2.14 | 2.0 | 94.1 | 100.0\nAlpenpaket | 2025-Q3 | 443 | 443 | 2.15 | 2.0 | 94.8 | 100.0\nAlpenpaket | 2025-Q4 | 628 | 620 | 2.16 | 2.0 | 92.3 | 98.7\nGallia Colis | 2025-Q1 | 40 | 40 | 2.2 | 2.0 | 92.5 | 100.0\nGallia Colis | 2025-Q2 | 53 | 53 | 2.42 | 2.0 | 83.0 | 100.0\nGallia Colis | 2025-Q3 | 82 | 82 | 3.4 | 3.0 | 57.3 | 100.0\nGallia Colis | 2025-Q4 | 109 | 105 | 2.8 | 2.0 | 77.1 | 96.3\nKennet Freight | 2025-Q1 | 138 | 138 | 2.46 | 2.0 | 82.6 | 100.0\nKennet Freight | 2025-Q2 | 148 | 148 | 2.59 | 2.0 | 79.1 | 100.0\nKennet Freight | 2025-Q3 | 170 | 170 | 2.69 | 3.0 | 79.4 | 100.0\nKennet Freight | 2025-Q4 | 453 | 442 | 2.02 | 2.0 | 96.2 | 97.6\nLandfall Freight | 2025-Q1 | 433 | 433 | 4.69 | 4.0 | 27.0 | 100.0\nLandfall Freight | 2025-Q2 | 382 | 382 | 4.66 | 4.0 | 30.6 | 100.0\nLandfall Freight | 2025-Q3 | 415 | 415 | 4.96 | 5.0 | 24.3 | 100.0\nLandfall Freight | 2025-Q4 | 676 | 648 | 5.13 | 5.0 | 22.7 | 95.9\nLiberty Ground | 2025-Q1 | 459 | 459 | 2.19 | 2.0 | 90.8 | 100.0\nLiberty Ground | 2025-Q2 | 514 | 514 | 2.25 | 2.0 | 90.5 | 100.0\nLiberty Ground | 2025-Q3 | 516 | 516 | 2.14 | 2.0 | 92.8 | 100.0\nLiberty Ground | 2025-Q4 | 854 | 830 | 2.3 | 2.0 | 87.7 | 97.2\nLiffey Couriers | 2025-Q1 | 86 | 86 | 2.3 | 2.0 | 83.7 | 100.0\nLiffey Couriers | 2025-Q2 | 77 | 77 | 2.9 | 3.0 | 70.1 | 100.0\nLiffey Couriers | 2025-Q3 | 70 | 70 | 3.31 | 3.0 | 65.7 | 100.0\nLiffey Couriers | 2025-Q4 | 90 | 89 | 2.45 | 2.0 | 82.0 | 98.9\nLowlands Express | 2025-Q1 | 579 | 579 | 3.46 | 3.0 | 61.0 | 100.0\nLowlands Express | 2025-Q2 | 578 | 578 | 3.49 | 3.0 | 57.3 | 100.0\nLowlands Express | 2025-Q3 | 551 | 551 | 3.54 | 3.0 | 59.3 | 100.0\nLowlands Express | 2025-Q4 | 853 | 816 | 3.68 | 3.0 | 55.3 | 95.7\nPolder Post | 2025-Q1 | 188 | 188 | 2.63 | 2.0 | 77.7 | 100.0\nPolder Post | 2025-Q2 | 185 | 185 | 2.44 | 2.0 | 81.1 | 100.0\nPolder Post | 2025-Q3 | 209 | 209 | 2.74 | 3.0 | 77.5 | 100.0\nPolder Post | 2025-Q4 | 320 | 310 | 2.49 | 2.0 | 79.7 | 96.9\nPrairie Line Express | 2025-Q1 | 146 | 146 | 2.26 | 2.0 | 89.7 | 100.0\nPrairie Line Express | 2025-Q2 | 157 | 157 | 2.29 | 2.0 | 89.8 | 100.0\nPrairie Line Express | 2025-Q3 | 154 | 154 | 2.97 | 3.0 | 66.9 | 100.0\nPrairie Line Express | 2025-Q4 | 291 | 284 | 2.59 | 2.5 | 80.6 | 97.6\nRegent Parcels | 2025-Q1 | 174 | 174 | 2.17 | 2.0 | 92.5 | 100.0\nRegent Parcels | 2025-Q2 | 160 | 160 | 2.32 | 2.0 | 87.5 | 100.0\nRegent Parcels | 2025-Q3 | 187 | 187 | 2.63 | 2.0 | 82.4 | 100.0\nRegent Parcels | 2025-Q4 | 277 | 268 | 2.15 | 2.0 | 91.4 | 96.8\nSkerrylane Logistics | 2025-Q1 | 239 | 239 | 2.35 | 2.0 | 84.5 | 100.0\nSkerrylane Logistics | 2025-Q2 | 238 | 238 | 2.46 | 2.0 | 81.1 | 100.0\nSkerrylane Logistics | 2025-Q3 | 246 | 246 | 2.45 | 2.0 | 86.2 | 100.0\nSkerrylane Logistics | 2025-Q4 | 401 | 393 | 2.64 | 2.0 | 75.6 | 98.0\nStrombogen Kurier | 2025-Q1 | 722 | 722 | 2.15 | 2.0 | 93.8 | 100.0\nStrombogen Kurier | 2025-Q2 | 676 | 676 | 2.08 | 2.0 | 94.8 | 100.0\n(56 rows, first 50 printed)\n\n=== Report 2: Fulfillment performance by warehouse, 2025 (written by Grace) ===\nEngineer's note: Warehouses have a single tenant (no cross-tenant sharing in the schema), so warehouse_name alone would be unique, but tenant_name is included as requested for readability. Days are computed as plain calendar-date differences (not fractional/time-of-day), matching DATEDIFF(day, ...) semantics.\nREPORT: Fulfillment performance by warehouse, 2025\nQUESTION: Are there specific fulfillment sites that are consistently slower to process or less reliable on final delivery time?\nwarehouse_name | tenant_name | orders_shipped | avg_processing_days | avg_delivery_days | pct_within_5_days\n--------------------------------------+-------------------------------+----------------+---------------------+-------------------+------------------\nKöln Fulfilment Centre | Brennquell Kaffee | 4071 | 1.03 | 3.55 | 91.7\nAtlanta Fulfilment Centre | Cragsley Outfitters | 3219 | 0.79 | 3.2 | 95.2\nReading Fulfilment Centre | Marrowby & Vane | 1529 | 1.02 | 4.13 | 82.5\nManchester Fulfilment Centre | Grind Collective | 1082 | 1.5 | 5.12 | 71.6\nLiège Fulfilment Centre | Spice Route Box | 755 | 1.58 | 4.38 | 76.2\nBromley Fulfilment Centre | Petalcourt Beauty | 646 | 1.14 | 5.0 | 77.4\nBromley Fulfilment Centre | Kibblewick | 644 | 1.26 | 4.75 | 77.6\nSan Francisco Fulfilment Centre | Honeycomb Larder | 592 | 1.22 | 3.59 | 88.5\nDen Haag Fulfilment Centre | Studio Vrij | 532 | 2.26 | 4.97 | 69.0\nHamburg Fulfilment Centre | Ampfeld Elektronik | 418 | 1.51 | 3.92 | 84.9\nLiverpool Fulfilment Centre | Fennel & Flint | 360 | 1.4 | 5.41 | 72.2\nGalway Fulfilment Centre | Ballycorran Industrial Supply | 338 | 1.11 | 4.69 | 73.1\nSan Francisco Fulfilment Centre | Cinder Peak Sports Co. | 303 | 1.25 | 3.74 | 84.5\nDublin Fulfilment Centre | Sourdough Club | 298 | 1.53 | 5.07 | 69.8\nBerlin Fulfilment Centre | Wiesgrund Wohnen | 270 | 1.4 | 4.01 | 84.4\nBerlin Fulfilment Centre | Kontor24 Bürobedarf | 258 | 1.09 | 3.55 | 91.1\nAmsterdam Fulfilment Centre | Linden & Loom | 237 | 1.58 | 4.21 | 82.7\nDortmund Fulfilment Centre | Gratlinie Outdoor | 212 | 1.44 | 3.93 | 84.0\nHouston Fulfilment Centre | Feather & Fin | 207 | 0.98 | 3.4 | 95.2\nAmsterdam Fulfilment Centre | Whisker Works | 172 | 1.24 | 3.97 | 82.0\nOslo Fulfilment Centre | OfficeHub Nordic | 163 | 1.12 | 4.1 | 82.8\nParis Fulfilment Centre | Rue Lafayette Studio | 155 | 1.3 | 4.19 | 78.7\nSan Antonio Fulfilment Centre | Castell Fasteners | 155 | 1.06 | 3.45 | 96.8\nUppsala Fulfilment Centre | Juniper Apothecary | 146 | 1.49 | 4.29 | 74.7\nGöteborg Fulfilment Centre | Coastline Interiors | 143 | 1.44 | 4.27 | 79.7\nKøbenhavn Fulfilment Centre | Oakhurst Tailoring | 139 | 1.65 | 4.32 | 79.1\nRotterdam Fulfilment Centre | Midland Safety Wear | 139 | 1.19 | 3.96 | 80.6\nNürnberg Fulfilment Centre | Wohnglück | 138 | 1.41 | 3.72 | 93.5\nOdense Fulfilment Centre | Nordic Bloom Cosmetics | 132 | 1.2 | 4.28 | 81.1\nUppsala Fulfilment Centre | Velvet Ash | 114 | 1.29 | 4.3 | 76.3\nLund Fulfilment Centre | Saffron & Sage | 113 | 1.23 | 3.93 | 83.2\nKöln Fulfilment Centre | Pfotenglück | 111 | 1.3 | 3.5 | 95.5\nMünchen Fulfilment Centre | Ostwind Mode | 108 | 1.3 | 3.94 | 79.6\nRotterdam Fulfilment Centre | Ridgeback Gear | 102 | 1.38 | 3.84 | 92.2\nLeeds Fulfilment Centre | The Balm Room | 100 | 1.4 | 5.02 | 78.0\nBordeaux Fulfilment Centre | Chocolaterie Mireille | 99 | 1.31 | 4.93 | 64.6\nExeter Fulfilment Centre | Kayak Corner | 96 | 1.42 | 5.25 | 75.0\nZaandam Fulfilment Centre | Sorella Skin | 93 | 1.32 | 4.03 | 82.8\nAugsburg Fulfilment Centre | Hautnah | 90 | 1.46 | 4.06 | 82.2\nManchester Fulfilment Centre | Willow & Wren | 90 | 1.4 | 4.72 | 83.3\nHouston Fulfilment Centre | Tail Wag Supply | 87 | 1.05 | 3.39 | 93.1\nCambridge Fulfilment Centre | Thistlecombe | 84 | 1.25 | 6.07 | 58.3\nNewcastle upon Tyne Fulfilment Centre | Deskline Supplies | 75 | 1.13 | 4.52 | 76.0\nOdense Fulfilment Centre | Fell & Fjell | 72 | 1.25 | 4.08 | 80.6\nOslo Fulfilment Centre | Purrfect Pantry | 72 | 1.14 | 6.0 | 58.3\nHannover Fulfilment Centre | Hanse Industriebedarf | 71 | 1.2 | 3.96 | 85.9\nParis Fulfilment Centre | Mirelle | 60 | 1.3 | 3.83 | 88.3\nAmsterdam Fulfilment Centre | Ampere & Co. | 56 | 1.29 | 4.29 | 75.0\nAmersfoort Fulfilment Centre | Kettle & Key | 55 | 1.29 | 4.55 | 65.5\nLiège Fulfilment Centre | Polder Packaging | 55 | 1.13 | 4.49 | 72.7\n(50 rows)\n\n=== Report 3: Order fulfillment stage breakdown, monthly 2025 (written by Felix) ===\nEngineer's note: Scoped to fulfilled orders placed in 2025 with exactly one fulfillment row that has both shipped_at and delivered_at set, per the single-fulfillment simplifying rule. Day diffs use date-level subtraction (whole days); I could not verify in the empty dry-run schema whether avg_total_days materially deviates from avg_processing_days + avg_transit_days, so please sanity-check that once real data runs.\nREPORT: Order fulfillment stage breakdown, monthly 2025\nQUESTION: When total delivery time lengthens (e.g. during peak), is the slowdown in warehouse processing (pick/pack/ship) or in carrier transit?\nmonth | orders_qualifying | avg_processing_days | avg_transit_days | avg_total_days | pct_within_5_days_total\n--------+-------------------+---------------------+------------------+----------------+------------------------\n2025-01 | 1717 | 1.08 | 2.51 | 3.59 | 90.3\n2025-02 | 1329 | 1.1 | 2.58 | 3.68 | 89.4\n2025-03 | 1545 | 1.13 | 2.69 | 3.82 | 87.1\n2025-04 | 1477 | 1.14 | 2.56 | 3.7 | 88.8\n2025-05 | 1574 | 1.51 | 2.76 | 4.27 | 80.6\n2025-06 | 1419 | 1.21 | 2.63 | 3.84 | 86.3\n2025-07 | 1565 | 1.14 | 2.79 | 3.93 | 85.2\n2025-08 | 1457 | 1.22 | 2.64 | 3.86 | 87.2\n2025-09 | 1611 | 1.18 | 2.87 | 4.05 | 83.2\n2025-10 | 1767 | 1.14 | 2.94 | 4.08 | 83.1\n2025-11 | 2419 | 1.2 | 3.46 | 4.66 | 78.7\n2025-12 | 2653 | 1.16 | 3.07 | 4.24 | 81.1\n(12 rows)\n\n\nNight 3 of 4: you have 3 report slots tonight."},"uuid":"3cff9995-416b-4910-814d-9bc8f49d7e75","timestamp":"2026-10-03T23:03:51.409Z","permissionMode":"default","promptSource":"sdk","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120","version":"2.1.275","gitBranch":"HEAD"}
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{"parentUuid":"cbe0e130-722e-48fa-8678-6564bb38def8","isSidechain":false,"message":{"model":"claude-sonnet-5","id":"msg_011Cfg92e5ugEPxX43JtZ4MD","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_017MHxJWQPbj7yeLKbkbSKEV","name":"StructuredOutput","input":{"notes":"Night 2 findings, read critically: (1) Report 1 only printed 50 of 56 carrier-quarter rows -- it's missing the last few carriers' Q3/Q4 figures, so I can't yet see the full carrier picture for peak; I'm fixing that tonight by re-running Q4 only (one quarter -> all ~14 carriers fit on one page) and adding a volume-share column so I can judge materiality, not just rates. (2) Within what I could see, two carriers (Landfall Freight, Lowlands Express) are chronically mediocre all year -- not a peak story, a structural one. But Albion Parcel is the standout: rock-solid in Q1-Q3 (88-97% within 3 days) then collapses in Q4 (avg transit 6.54 days, only 50.7% within 3 days) -- a sharp, carrier-specific peak failure that deserves its own drill-down by month to see exactly when it broke and whether it recovered by year-end. (3) Report 3 (stage breakdown) is a clean, important result: warehouse processing time stayed flat (~1.1-1.2 days) straight through peak even as volume nearly doubled, while carrier transit time is what stretched (2.5-2.9 days baseline -> 3.46 in Nov) -- so the peak slowdown from Night 1 is a carrier/transit problem, not a warehouse-capacity problem. That reframes priority #3: warehouses look fine; carriers are the lever. (4) The warehouse report's worst performers (Thistlecombe, Purrfect Pantry, Chocolaterie Mireille, Kayak Corner, etc.) all have unremarkable processing times, so their poor delivery reliability must come from somewhere in the transit leg -- I want to test whether they're simply the merchants who happen to ship with the weak carriers. Tonight: complete the Q4 carrier picture, isolate Albion Parcel's break, and test the carrier-mix hypothesis for the best/worst merchants.","requests":[{"title":"Carrier transit performance, Q4 2025 (complete)","question":"Across every carrier, how did transit time and reliability hold up in the peak quarter, and how much volume does each carrier actually carry?","grain":"one row per carrier, for shipments shipped in Q4 2025","columns":["carrier","shipments_shipped (shipped_at between 2025-10-01 and 2025-12-31)","shipments_delivered (of those, delivered_at not null as of the 2025-12-31 cutoff)","avg_transit_days","median_transit_days","p90_transit_days","pct_within_3_days","pct_delivered","share_of_q4_shipments_pct (this carrier's shipments_shipped / total Q4 shipments across all carriers, x100)"],"details":"Table: shipments (carrier, shipped_at, delivered_at). Scope: shipped_at >= 2025-10-01 and <= 2025-12-31, all carriers (no row cap needed -- expect ~14 distinct carriers based on last night's report, so please return every carrier, do not silently truncate). transit_days = DATEDIFF(day, shipped_at, delivered_at), computed only where delivered_at is not null. Round day metrics to 2 decimals, pct columns to 1 decimal (as %). As before, flag that pct_delivered/pct_within_3_days for shipments shipped very late in December may be understated due to the 2025-12-31 cutoff (right-censoring), not necessarily a carrier failure. Order by shipments_shipped descending, tie-break by carrier ascending.","builds_on":["Carrier transit performance by quarter, 2025"]},{"title":"Albion Parcel transit performance by month, 2025","question":"Exactly when did Albion Parcel's transit time break down, how bad did it get, and had it recovered by year-end?","grain":"one row per calendar month of shipped_at, for Albion Parcel shipments only","columns":["month (YYYY-MM)","shipments_shipped","shipments_delivered","avg_transit_days","median_transit_days","p90_transit_days","pct_within_3_days","pct_delivered"],"details":"Table: shipments, filtered to carrier = 'Albion Parcel', shipped_at between 2025-01-01 and 2025-12-31. Same transit_days definition as the Q4 carrier report (shipped_at to delivered_at, delivered subset only). Round day metrics to 2 decimals, pct to 1 decimal. Note in the report that the last 1-2 weeks of December will show artificially low pct_delivered due to the 2025-12-31 data cutoff. Order by month ascending. Expect 12 rows.","builds_on":["Carrier transit performance by quarter, 2025"]},{"title":"Carrier mix for best vs worst performing merchants, 2025","question":"Do the merchants with the worst delivery reliability simply ship disproportionately with the weaker carriers?","grain":"one row per tenant and carrier combination, for a fixed list of 20 named tenants","columns":["tenant_name","performance_group ('best' or 'worst', per the fixed list below)","carrier","shipments_count (2025)","share_of_tenant_shipments_pct (this carrier's shipments / tenant's total shipments in 2025, x100)","avg_transit_days (this tenant+carrier combination, 2025)","pct_within_3_days"],"details":"Scope: shipments joined via fulfillments -> orders -> tenants, shipped_at in 2025-01-01 through 2025-12-31, restricted to these 20 tenants only (matched by tenants.name): WORST group = 'Thistlecombe', 'Purrfect Pantry', 'Chocolaterie Mireille', 'Kayak Corner', 'Studio Vrij', 'Sourdough Club', 'Grind Collective', 'Spice Route Box', 'Kettle & Key', 'Polder Packaging'. BEST group = 'Pfotenglück', 'Cragsley Outfitters', 'Brennquell Kaffee', 'Feather & Fin', 'Ridgeback Gear', 'Wohnglück', 'Mirelle', 'Honeycomb Larder', 'Kontor24 Bürobedarf', 'Tail Wag Supply'. transit_days = DATEDIFF(day, shipped_at, delivered_at), delivered subset only. Round avg_transit_days to 2 decimals, pct columns to 1 decimal. Order by performance_group ('worst' first, then 'best'), then tenant_name ascending, then shipments_count descending within each tenant.","builds_on":["Fulfillment performance by warehouse, 2025","Delivery time by merchant, 2025"]}]},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":15340,"cache_read_input_tokens":58784,"output_tokens":8416,"output_tokens_details":{"thinking_tokens":6041},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":15340,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":8416,"cache_read_input_tokens":58784,"cache_creation_input_tokens":15340,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":15340},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"wireToolInputs":{"toolu_017MHxJWQPbj7yeLKbkbSKEV":{"notes":"Night 2 findings, read critically: (1) Report 1 only printed 50 of 56 carrier-quarter rows -- it's missing the last few carriers' Q3/Q4 figures, so I can't yet see the full carrier picture for peak; I'm fixing that tonight by re-running Q4 only (one quarter -> all ~14 carriers fit on one page) and adding a volume-share column so I can judge materiality, not just rates. (2) Within what I could see, two carriers (Landfall Freight, Lowlands Express) are chronically mediocre all year -- not a peak story, a structural one. But Albion Parcel is the standout: rock-solid in Q1-Q3 (88-97% within 3 days) then collapses in Q4 (avg transit 6.54 days, only 50.7% within 3 days) -- a sharp, carrier-specific peak failure that deserves its own drill-down by month to see exactly when it broke and whether it recovered by year-end. (3) Report 3 (stage breakdown) is a clean, important result: warehouse processing time stayed flat (~1.1-1.2 days) straight through peak even as volume nearly doubled, while carrier transit time is what stretched (2.5-2.9 days baseline -> 3.46 in Nov) -- so the peak slowdown from Night 1 is a carrier/transit problem, not a warehouse-capacity problem. That reframes priority #3: warehouses look fine; carriers are the lever. (4) The warehouse report's worst performers (Thistlecombe, Purrfect Pantry, Chocolaterie Mireille, Kayak Corner, etc.) all have unremarkable processing times, so their poor delivery reliability must come from somewhere in the transit leg -- I want to test whether they're simply the merchants who happen to ship with the weak carriers. Tonight: complete the Q4 carrier picture, isolate Albion Parcel's break, and test the carrier-mix hypothesis for the best/worst merchants.","requests":[{"title":"Carrier transit performance, Q4 2025 (complete)","question":"Across every carrier, how did transit time and reliability hold up in the peak quarter, and how much volume does each carrier actually carry?","grain":"one row per carrier, for shipments shipped in Q4 2025","columns":["carrier","shipments_shipped (shipped_at between 2025-10-01 and 2025-12-31)","shipments_delivered (of those, delivered_at not null as of the 2025-12-31 cutoff)","avg_transit_days","median_transit_days","p90_transit_days","pct_within_3_days","pct_delivered","share_of_q4_shipments_pct (this carrier's shipments_shipped / total Q4 shipments across all carriers, x100)"],"details":"Table: shipments (carrier, shipped_at, delivered_at). Scope: shipped_at >= 2025-10-01 and <= 2025-12-31, all carriers (no row cap needed -- expect ~14 distinct carriers based on last night's report, so please return every carrier, do not silently truncate). transit_days = DATEDIFF(day, shipped_at, delivered_at), computed only where delivered_at is not null. Round day metrics to 2 decimals, pct columns to 1 decimal (as %). As before, flag that pct_delivered/pct_within_3_days for shipments shipped very late in December may be understated due to the 2025-12-31 cutoff (right-censoring), not necessarily a carrier failure. Order by shipments_shipped descending, tie-break by carrier ascending.","builds_on":["Carrier transit performance by quarter, 2025"]},{"title":"Albion Parcel transit performance by month, 2025","question":"Exactly when did Albion Parcel's transit time break down, how bad did it get, and had it recovered by year-end?","grain":"one row per calendar month of shipped_at, for Albion Parcel shipments only","columns":["month (YYYY-MM)","shipments_shipped","shipments_delivered","avg_transit_days","median_transit_days","p90_transit_days","pct_within_3_days","pct_delivered"],"details":"Table: shipments, filtered to carrier = 'Albion Parcel', shipped_at between 2025-01-01 and 2025-12-31. Same transit_days definition as the Q4 carrier report (shipped_at to delivered_at, delivered subset only). Round day metrics to 2 decimals, pct to 1 decimal. Note in the report that the last 1-2 weeks of December will show artificially low pct_delivered due to the 2025-12-31 data cutoff. Order by month ascending. Expect 12 rows.","builds_on":["Carrier transit performance by quarter, 2025"]},{"title":"Carrier mix for best vs worst performing merchants, 2025","question":"Do the merchants with the worst delivery reliability simply ship disproportionately with the weaker carriers?","grain":"one row per tenant and carrier combination, for a fixed list of 20 named tenants","columns":["tenant_name","performance_group ('best' or 'worst', per the fixed list below)","carrier","shipments_count (2025)","share_of_tenant_shipments_pct (this carrier's shipments / tenant's total shipments in 2025, x100)","avg_transit_days (this tenant+carrier combination, 2025)","pct_within_3_days"],"details":"Scope: shipments joined via fulfillments -> orders -> tenants, shipped_at in 2025-01-01 through 2025-12-31, restricted to these 20 tenants only (matched by tenants.name): WORST group = 'Thistlecombe', 'Purrfect Pantry', 'Chocolaterie Mireille', 'Kayak Corner', 'Studio Vrij', 'Sourdough Club', 'Grind Collective', 'Spice Route Box', 'Kettle & Key', 'Polder Packaging'. BEST group = 'Pfotenglück', 'Cragsley Outfitters', 'Brennquell Kaffee', 'Feather & Fin', 'Ridgeback Gear', 'Wohnglück', 'Mirelle', 'Honeycomb Larder', 'Kontor24 Bürobedarf', 'Tail Wag Supply'. transit_days = DATEDIFF(day, shipped_at, delivered_at), delivered subset only. Round avg_transit_days to 2 decimals, pct columns to 1 decimal. Order by performance_group ('worst' first, then 'best'), then tenant_name ascending, then shipments_count descending within each tenant.","builds_on":["Fulfillment performance by warehouse, 2025","Delivery time by merchant, 2025"]}]}},"apiBlockIndex":1,"requestId":"req_011Cfg92dQV5ETDzMZiXD8RG","type":"assistant","uuid":"f80a36a4-0ef3-44e0-b122-ec24f3eaeaed","timestamp":"2026-10-03T23:05:08.168Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"f80a36a4-0ef3-44e0-b122-ec24f3eaeaed","isSidechain":false,"attachment":{"type":"structured_output","data":{"notes":"Night 2 findings, read critically: (1) Report 1 only printed 50 of 56 carrier-quarter rows -- it's missing the last few carriers' Q3/Q4 figures, so I can't yet see the full carrier picture for peak; I'm fixing that tonight by re-running Q4 only (one quarter -> all ~14 carriers fit on one page) and adding a volume-share column so I can judge materiality, not just rates. (2) Within what I could see, two carriers (Landfall Freight, Lowlands Express) are chronically mediocre all year -- not a peak story, a structural one. But Albion Parcel is the standout: rock-solid in Q1-Q3 (88-97% within 3 days) then collapses in Q4 (avg transit 6.54 days, only 50.7% within 3 days) -- a sharp, carrier-specific peak failure that deserves its own drill-down by month to see exactly when it broke and whether it recovered by year-end. (3) Report 3 (stage breakdown) is a clean, important result: warehouse processing time stayed flat (~1.1-1.2 days) straight through peak even as volume nearly doubled, while carrier transit time is what stretched (2.5-2.9 days baseline -> 3.46 in Nov) -- so the peak slowdown from Night 1 is a carrier/transit problem, not a warehouse-capacity problem. That reframes priority #3: warehouses look fine; carriers are the lever. (4) The warehouse report's worst performers (Thistlecombe, Purrfect Pantry, Chocolaterie Mireille, Kayak Corner, etc.) all have unremarkable processing times, so their poor delivery reliability must come from somewhere in the transit leg -- I want to test whether they're simply the merchants who happen to ship with the weak carriers. Tonight: complete the Q4 carrier picture, isolate Albion Parcel's break, and test the carrier-mix hypothesis for the best/worst merchants.","requests":[{"title":"Carrier transit performance, Q4 2025 (complete)","question":"Across every carrier, how did transit time and reliability hold up in the peak quarter, and how much volume does each carrier actually carry?","grain":"one row per carrier, for shipments shipped in Q4 2025","columns":["carrier","shipments_shipped (shipped_at between 2025-10-01 and 2025-12-31)","shipments_delivered (of those, delivered_at not null as of the 2025-12-31 cutoff)","avg_transit_days","median_transit_days","p90_transit_days","pct_within_3_days","pct_delivered","share_of_q4_shipments_pct (this carrier's shipments_shipped / total Q4 shipments across all carriers, x100)"],"details":"Table: shipments (carrier, shipped_at, delivered_at). Scope: shipped_at >= 2025-10-01 and <= 2025-12-31, all carriers (no row cap needed -- expect ~14 distinct carriers based on last night's report, so please return every carrier, do not silently truncate). transit_days = DATEDIFF(day, shipped_at, delivered_at), computed only where delivered_at is not null. Round day metrics to 2 decimals, pct columns to 1 decimal (as %). As before, flag that pct_delivered/pct_within_3_days for shipments shipped very late in December may be understated due to the 2025-12-31 cutoff (right-censoring), not necessarily a carrier failure. Order by shipments_shipped descending, tie-break by carrier ascending.","builds_on":["Carrier transit performance by quarter, 2025"]},{"title":"Albion Parcel transit performance by month, 2025","question":"Exactly when did Albion Parcel's transit time break down, how bad did it get, and had it recovered by year-end?","grain":"one row per calendar month of shipped_at, for Albion Parcel shipments only","columns":["month (YYYY-MM)","shipments_shipped","shipments_delivered","avg_transit_days","median_transit_days","p90_transit_days","pct_within_3_days","pct_delivered"],"details":"Table: shipments, filtered to carrier = 'Albion Parcel', shipped_at between 2025-01-01 and 2025-12-31. Same transit_days definition as the Q4 carrier report (shipped_at to delivered_at, delivered subset only). Round day metrics to 2 decimals, pct to 1 decimal. Note in the report that the last 1-2 weeks of December will show artificially low pct_delivered due to the 2025-12-31 data cutoff. Order by month ascending. Expect 12 rows.","builds_on":["Carrier transit performance by quarter, 2025"]},{"title":"Carrier mix for best vs worst performing merchants, 2025","question":"Do the merchants with the worst delivery reliability simply ship disproportionately with the weaker carriers?","grain":"one row per tenant and carrier combination, for a fixed list of 20 named tenants","columns":["tenant_name","performance_group ('best' or 'worst', per the fixed list below)","carrier","shipments_count (2025)","share_of_tenant_shipments_pct (this carrier's shipments / tenant's total shipments in 2025, x100)","avg_transit_days (this tenant+carrier combination, 2025)","pct_within_3_days"],"details":"Scope: shipments joined via fulfillments -> orders -> tenants, shipped_at in 2025-01-01 through 2025-12-31, restricted to these 20 tenants only (matched by tenants.name): WORST group = 'Thistlecombe', 'Purrfect Pantry', 'Chocolaterie Mireille', 'Kayak Corner', 'Studio Vrij', 'Sourdough Club', 'Grind Collective', 'Spice Route Box', 'Kettle & Key', 'Polder Packaging'. BEST group = 'Pfotenglück', 'Cragsley Outfitters', 'Brennquell Kaffee', 'Feather & Fin', 'Ridgeback Gear', 'Wohnglück', 'Mirelle', 'Honeycomb Larder', 'Kontor24 Bürobedarf', 'Tail Wag Supply'. transit_days = DATEDIFF(day, shipped_at, delivered_at), delivered subset only. Round avg_transit_days to 2 decimals, pct columns to 1 decimal. Order by performance_group ('worst' first, then 'best'), then tenant_name ascending, then shipments_count descending within each tenant.","builds_on":["Fulfillment performance by warehouse, 2025","Delivery time by merchant, 2025"]}]},"toolUseID":"toolu_017MHxJWQPbj7yeLKbkbSKEV"},"type":"attachment","uuid":"ade212d5-b763-47cf-b118-954b27b808f1","timestamp":"2026-10-03T23:05:08.172Z","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120","version":"2.1.275","gitBranch":"HEAD"}
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{"type":"last-prompt","lastPrompt":"The printed reports of night 2: === Report 1: Carrier transit performance by quarter, 2025 (written by Bea) === Engineer's note: Grain is carrier x calendar quarter of shipped_at, scoped to 2025 ship…","leafUuid":"d499c1ec-efda-473a-9788-e693e6be25fa","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120"}
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{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-03T23:05:57.321Z","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120","content":"The printed reports of night 3:\n\n=== Report 1: Carrier transit performance, Q4 2025 (complete) (written by Grace) ===\nEngineer's note: Grouped by carrier name across all tenants/merchants (the request was carrier-level, not merchant-level); transit_days computed in fractional days then rounded, so avg/median/p90 reflect exact hour-level transit not just whole days. As requested, pct_delivered and pct_within_3_days for carriers with many late-December shipments may be understated due to the 2025-12-31 data cutoff (right-censoring), not necessarily worse carrier performance.\nREPORT: Carrier transit performance, Q4 2025 (complete)\nQUESTION: Across every carrier, how did transit time and reliability hold up in the peak quarter, and how much volume does each carrier actually carry?\ncarrier | shipments_shipped | shipments_delivered | avg_transit_days | median_transit_days | p90_transit_days | pct_within_3_days | pct_delivered | share_of_q4_shipments_pct\n---------------------+-------------------+---------------------+------------------+---------------------+------------------+-------------------+---------------+--------------------------\nStrombogen Kurier | 905 | 877 | 2.14 | 2.04 | 3.23 | 80.7 | 96.9 | 12.3\nLiberty Ground | 854 | 830 | 2.29 | 2.08 | 3.76 | 78.2 | 97.2 | 11.6\nLowlands Express | 853 | 816 | 3.70 | 3.16 | 6.18 | 44.6 | 95.7 | 11.6\nAlbion Parcel | 794 | 779 | 6.57 | 3.30 | 17.10 | 43.4 | 98.1 | 10.8\nTrailhead Express | 706 | 687 | 2.41 | 2.09 | 3.92 | 73.2 | 97.3 | 9.6\nLandfall Freight | 676 | 648 | 5.15 | 4.90 | 8.01 | 13.6 | 95.9 | 9.2\nAlpenpaket | 628 | 620 | 2.19 | 2.09 | 3.25 | 79.4 | 98.7 | 8.5\nKennet Freight | 453 | 442 | 2.04 | 2.01 | 3.10 | 86.9 | 97.6 | 6.2\nSkerrylane Logistics | 401 | 393 | 2.67 | 2.22 | 4.34 | 66.7 | 98.0 | 5.5\nPolder Post | 320 | 310 | 2.51 | 2.17 | 4.21 | 71.6 | 96.9 | 4.3\nPrairie Line Express | 291 | 284 | 2.57 | 2.28 | 3.99 | 67.6 | 97.6 | 4.0\nRegent Parcels | 277 | 268 | 2.20 | 2.08 | 3.29 | 81.0 | 96.8 | 3.8\nGallia Colis | 109 | 105 | 2.80 | 2.09 | 5.12 | 68.6 | 96.3 | 1.5\nLiffey Couriers | 90 | 89 | 2.48 | 2.11 | 4.10 | 66.3 | 98.9 | 1.2\n(14 rows)\n\n=== Report 2: Albion Parcel transit performance by month, 2025 (written by Felix) ===\nEngineer's note: transit_days = delivered_at - shipped_at in days, computed only over the delivered subset per month (shipments_shipped counts all Albion Parcel shipments shipped that month regardless of delivery). As flagged in the request, pct_delivered for the last 1-2 weeks of December will read artificially low because of the 2025-12-31 data cutoff (deliveries still in transit at cutoff aren't yet recorded).\nREPORT: Albion Parcel transit performance by month, 2025\nQUESTION: Exactly when did Albion Parcel's transit time break down, how bad did it get, and had it recovered by year-end?\nmonth | shipments_shipped | shipments_delivered | avg_transit_days | median_transit_days | p90_transit_days | pct_within_3_days | pct_delivered\n--------+-------------------+---------------------+------------------+---------------------+------------------+-------------------+--------------\n2025-01 | 259 | 259 | 2.00 | 1.98 | 3.05 | 86.9 | 100.0\n2025-02 | 177 | 177 | 2.06 | 2.09 | 3.12 | 86.4 | 100.0\n2025-03 | 204 | 204 | 2.15 | 2.07 | 3.22 | 81.4 | 100.0\n2025-04 | 203 | 203 | 2.36 | 2.12 | 3.76 | 76.8 | 100.0\n2025-05 | 197 | 197 | 2.41 | 2.23 | 3.79 | 70.6 | 100.0\n2025-06 | 210 | 210 | 2.35 | 2.17 | 3.92 | 74.8 | 100.0\n2025-07 | 218 | 218 | 2.23 | 2.07 | 3.29 | 80.7 | 100.0\n2025-08 | 194 | 194 | 2.15 | 2.08 | 3.12 | 83.5 | 100.0\n2025-09 | 220 | 220 | 2.20 | 2.11 | 3.18 | 79.1 | 100.0\n2025-10 | 247 | 247 | 2.39 | 2.19 | 3.31 | 75.7 | 100.0\n2025-11 | 287 | 287 | 7.51 | 6.91 | 16.83 | 28.2 | 100.0\n2025-12 | 260 | 245 | 9.69 | 7.14 | 20.69 | 28.6 | 94.2\n(12 rows)\n\n=== Report 3: Carrier mix for best vs worst performing merchants, 2025 (written by Bea) ===\nEngineer's note: avg_transit_days and pct_within_3_days are computed over the delivered subset only (shipments with a non-null delivered_at), as specified; tenants/carriers with zero delivered shipments show 0 for both rather than null. share_of_tenant_shipments_pct uses each tenant's total 2025 shipment count (all carriers) as the denominator.\nREPORT: Carrier mix for best vs worst performing merchants, 2025\nQUESTION: Do the merchants with the worst delivery reliability simply ship disproportionately with the weaker carriers?\ntenant_name | performance_group | carrier | shipments_count | share_of_tenant_shipments_pct | avg_transit_days | pct_within_3_days\n----------------------+-------------------+----------------------+-----------------+-------------------------------+------------------+------------------\nChocolaterie Mireille | worst | Landfall Freight | 38 | 38.0 | 3.86 | 43.2\nChocolaterie Mireille | worst | Lowlands Express | 32 | 32.0 | 3.0 | 59.4\nChocolaterie Mireille | worst | Gallia Colis | 30 | 30.0 | 2.63 | 76.7\nGrind Collective | worst | Albion Parcel | 515 | 47.1 | 3.12 | 80.7\nGrind Collective | worst | Kennet Freight | 222 | 20.3 | 1.97 | 88.7\nGrind Collective | worst | Regent Parcels | 187 | 17.1 | 1.91 | 94.0\nGrind Collective | worst | Landfall Freight | 109 | 10.0 | 6.27 | 19.4\nGrind Collective | worst | Lowlands Express | 61 | 5.6 | 6.98 | 11.7\nKayak Corner | worst | Albion Parcel | 38 | 37.6 | 4.45 | 84.2\nKayak Corner | worst | Kennet Freight | 21 | 20.8 | 2.05 | 81.0\nKayak Corner | worst | Landfall Freight | 19 | 18.8 | 3.42 | 42.1\nKayak Corner | worst | Regent Parcels | 13 | 12.9 | 1.38 | 100.0\nKayak Corner | worst | Lowlands Express | 10 | 9.9 | 3.9 | 50.0\nKettle & Key | worst | Polder Post | 26 | 44.1 | 2.04 | 92.0\nKettle & Key | worst | Landfall Freight | 18 | 30.5 | 4.22 | 27.8\nKettle & Key | worst | Lowlands Express | 15 | 25.4 | 2.4 | 80.0\nPolder Packaging | worst | Lowlands Express | 27 | 49.1 | 2.85 | 74.1\nPolder Packaging | worst | Polder Post | 18 | 32.7 | 2.39 | 72.2\nPolder Packaging | worst | Landfall Freight | 10 | 18.2 | 4.0 | 50.0\nPurrfect Pantry | worst | Skerrylane Logistics | 46 | 62.2 | 2.07 | 87.0\nPurrfect Pantry | worst | Lowlands Express | 17 | 23.0 | 8.12 | 0.0\nPurrfect Pantry | worst | Landfall Freight | 11 | 14.9 | 9.27 | 0.0\nSourdough Club | worst | Liffey Couriers | 142 | 46.4 | 2.2 | 87.3\nSourdough Club | worst | Albion Parcel | 76 | 24.8 | 3.0 | 85.1\nSourdough Club | worst | Landfall Freight | 52 | 17.0 | 4.84 | 35.3\nSourdough Club | worst | Lowlands Express | 36 | 11.8 | 4.91 | 28.6\nSpice Route Box | worst | Lowlands Express | 360 | 47.2 | 2.11 | 83.2\nSpice Route Box | worst | Polder Post | 311 | 40.8 | 2.21 | 83.5\nSpice Route Box | worst | Landfall Freight | 92 | 12.1 | 3.85 | 42.4\nStudio Vrij | worst | Lowlands Express | 347 | 58.1 | 2.04 | 87.8\nStudio Vrij | worst | Polder Post | 188 | 31.5 | 2.21 | 83.2\nStudio Vrij | worst | Landfall Freight | 62 | 10.4 | 3.82 | 33.3\nThistlecombe | worst | Albion Parcel | 29 | 33.3 | 5.0 | 65.5\nThistlecombe | worst | Kennet Freight | 22 | 25.3 | 2.48 | 76.2\nThistlecombe | worst | Landfall Freight | 20 | 23.0 | 6.0 | 15.0\nThistlecombe | worst | Regent Parcels | 10 | 11.5 | 2.2 | 80.0\nThistlecombe | worst | Lowlands Express | 6 | 6.9 | 6.83 | 33.3\nBrennquell Kaffee | best | Strombogen Kurier | 2050 | 49.7 | 1.73 | 96.7\nBrennquell Kaffee | best | Alpenpaket | 1368 | 33.1 | 1.72 | 96.3\nBrennquell Kaffee | best | Landfall Freight | 409 | 9.9 | 3.76 | 42.7\nBrennquell Kaffee | best | Lowlands Express | 300 | 7.3 | 3.89 | 40.1\nCragsley Outfitters | best | Liberty Ground | 1634 | 46.9 | 1.71 | 95.4\nCragsley Outfitters | best | Trailhead Express | 1253 | 36.0 | 1.92 | 89.7\nCragsley Outfitters | best | Prairie Line Express | 511 | 14.7 | 2.0 | 88.8\nCragsley Outfitters | best | Landfall Freight | 85 | 2.4 | 3.71 | 44.6\nFeather & Fin | best | Liberty Ground | 108 | 50.0 | 1.81 | 94.4\nFeather & Fin | best | Trailhead Express | 79 | 36.6 | 1.64 | 94.9\nFeather & Fin | best | Prairie Line Express | 29 | 13.4 | 2.14 | 89.3\nHoneycomb Larder | best | Liberty Ground | 248 | 41.1 | 1.69 | 95.9\nHoneycomb Larder | best | Trailhead Express | 246 | 40.7 | 2.0 | 88.0\n(73 rows, first 50 printed)\n\n\nNight 4 of 4: you have 3 report slots tonight."}
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{"parentUuid":"64e5f60c-30c4-4020-b90f-324a2a2d8f66","isSidechain":false,"promptId":"e9b4e623-358b-42df-a942-0ced798eb5b0","type":"user","message":{"role":"user","content":"The printed reports of night 3:\n\n=== Report 1: Carrier transit performance, Q4 2025 (complete) (written by Grace) ===\nEngineer's note: Grouped by carrier name across all tenants/merchants (the request was carrier-level, not merchant-level); transit_days computed in fractional days then rounded, so avg/median/p90 reflect exact hour-level transit not just whole days. As requested, pct_delivered and pct_within_3_days for carriers with many late-December shipments may be understated due to the 2025-12-31 data cutoff (right-censoring), not necessarily worse carrier performance.\nREPORT: Carrier transit performance, Q4 2025 (complete)\nQUESTION: Across every carrier, how did transit time and reliability hold up in the peak quarter, and how much volume does each carrier actually carry?\ncarrier | shipments_shipped | shipments_delivered | avg_transit_days | median_transit_days | p90_transit_days | pct_within_3_days | pct_delivered | share_of_q4_shipments_pct\n---------------------+-------------------+---------------------+------------------+---------------------+------------------+-------------------+---------------+--------------------------\nStrombogen Kurier | 905 | 877 | 2.14 | 2.04 | 3.23 | 80.7 | 96.9 | 12.3\nLiberty Ground | 854 | 830 | 2.29 | 2.08 | 3.76 | 78.2 | 97.2 | 11.6\nLowlands Express | 853 | 816 | 3.70 | 3.16 | 6.18 | 44.6 | 95.7 | 11.6\nAlbion Parcel | 794 | 779 | 6.57 | 3.30 | 17.10 | 43.4 | 98.1 | 10.8\nTrailhead Express | 706 | 687 | 2.41 | 2.09 | 3.92 | 73.2 | 97.3 | 9.6\nLandfall Freight | 676 | 648 | 5.15 | 4.90 | 8.01 | 13.6 | 95.9 | 9.2\nAlpenpaket | 628 | 620 | 2.19 | 2.09 | 3.25 | 79.4 | 98.7 | 8.5\nKennet Freight | 453 | 442 | 2.04 | 2.01 | 3.10 | 86.9 | 97.6 | 6.2\nSkerrylane Logistics | 401 | 393 | 2.67 | 2.22 | 4.34 | 66.7 | 98.0 | 5.5\nPolder Post | 320 | 310 | 2.51 | 2.17 | 4.21 | 71.6 | 96.9 | 4.3\nPrairie Line Express | 291 | 284 | 2.57 | 2.28 | 3.99 | 67.6 | 97.6 | 4.0\nRegent Parcels | 277 | 268 | 2.20 | 2.08 | 3.29 | 81.0 | 96.8 | 3.8\nGallia Colis | 109 | 105 | 2.80 | 2.09 | 5.12 | 68.6 | 96.3 | 1.5\nLiffey Couriers | 90 | 89 | 2.48 | 2.11 | 4.10 | 66.3 | 98.9 | 1.2\n(14 rows)\n\n=== Report 2: Albion Parcel transit performance by month, 2025 (written by Felix) ===\nEngineer's note: transit_days = delivered_at - shipped_at in days, computed only over the delivered subset per month (shipments_shipped counts all Albion Parcel shipments shipped that month regardless of delivery). As flagged in the request, pct_delivered for the last 1-2 weeks of December will read artificially low because of the 2025-12-31 data cutoff (deliveries still in transit at cutoff aren't yet recorded).\nREPORT: Albion Parcel transit performance by month, 2025\nQUESTION: Exactly when did Albion Parcel's transit time break down, how bad did it get, and had it recovered by year-end?\nmonth | shipments_shipped | shipments_delivered | avg_transit_days | median_transit_days | p90_transit_days | pct_within_3_days | pct_delivered\n--------+-------------------+---------------------+------------------+---------------------+------------------+-------------------+--------------\n2025-01 | 259 | 259 | 2.00 | 1.98 | 3.05 | 86.9 | 100.0\n2025-02 | 177 | 177 | 2.06 | 2.09 | 3.12 | 86.4 | 100.0\n2025-03 | 204 | 204 | 2.15 | 2.07 | 3.22 | 81.4 | 100.0\n2025-04 | 203 | 203 | 2.36 | 2.12 | 3.76 | 76.8 | 100.0\n2025-05 | 197 | 197 | 2.41 | 2.23 | 3.79 | 70.6 | 100.0\n2025-06 | 210 | 210 | 2.35 | 2.17 | 3.92 | 74.8 | 100.0\n2025-07 | 218 | 218 | 2.23 | 2.07 | 3.29 | 80.7 | 100.0\n2025-08 | 194 | 194 | 2.15 | 2.08 | 3.12 | 83.5 | 100.0\n2025-09 | 220 | 220 | 2.20 | 2.11 | 3.18 | 79.1 | 100.0\n2025-10 | 247 | 247 | 2.39 | 2.19 | 3.31 | 75.7 | 100.0\n2025-11 | 287 | 287 | 7.51 | 6.91 | 16.83 | 28.2 | 100.0\n2025-12 | 260 | 245 | 9.69 | 7.14 | 20.69 | 28.6 | 94.2\n(12 rows)\n\n=== Report 3: Carrier mix for best vs worst performing merchants, 2025 (written by Bea) ===\nEngineer's note: avg_transit_days and pct_within_3_days are computed over the delivered subset only (shipments with a non-null delivered_at), as specified; tenants/carriers with zero delivered shipments show 0 for both rather than null. share_of_tenant_shipments_pct uses each tenant's total 2025 shipment count (all carriers) as the denominator.\nREPORT: Carrier mix for best vs worst performing merchants, 2025\nQUESTION: Do the merchants with the worst delivery reliability simply ship disproportionately with the weaker carriers?\ntenant_name | performance_group | carrier | shipments_count | share_of_tenant_shipments_pct | avg_transit_days | pct_within_3_days\n----------------------+-------------------+----------------------+-----------------+-------------------------------+------------------+------------------\nChocolaterie Mireille | worst | Landfall Freight | 38 | 38.0 | 3.86 | 43.2\nChocolaterie Mireille | worst | Lowlands Express | 32 | 32.0 | 3.0 | 59.4\nChocolaterie Mireille | worst | Gallia Colis | 30 | 30.0 | 2.63 | 76.7\nGrind Collective | worst | Albion Parcel | 515 | 47.1 | 3.12 | 80.7\nGrind Collective | worst | Kennet Freight | 222 | 20.3 | 1.97 | 88.7\nGrind Collective | worst | Regent Parcels | 187 | 17.1 | 1.91 | 94.0\nGrind Collective | worst | Landfall Freight | 109 | 10.0 | 6.27 | 19.4\nGrind Collective | worst | Lowlands Express | 61 | 5.6 | 6.98 | 11.7\nKayak Corner | worst | Albion Parcel | 38 | 37.6 | 4.45 | 84.2\nKayak Corner | worst | Kennet Freight | 21 | 20.8 | 2.05 | 81.0\nKayak Corner | worst | Landfall Freight | 19 | 18.8 | 3.42 | 42.1\nKayak Corner | worst | Regent Parcels | 13 | 12.9 | 1.38 | 100.0\nKayak Corner | worst | Lowlands Express | 10 | 9.9 | 3.9 | 50.0\nKettle & Key | worst | Polder Post | 26 | 44.1 | 2.04 | 92.0\nKettle & Key | worst | Landfall Freight | 18 | 30.5 | 4.22 | 27.8\nKettle & Key | worst | Lowlands Express | 15 | 25.4 | 2.4 | 80.0\nPolder Packaging | worst | Lowlands Express | 27 | 49.1 | 2.85 | 74.1\nPolder Packaging | worst | Polder Post | 18 | 32.7 | 2.39 | 72.2\nPolder Packaging | worst | Landfall Freight | 10 | 18.2 | 4.0 | 50.0\nPurrfect Pantry | worst | Skerrylane Logistics | 46 | 62.2 | 2.07 | 87.0\nPurrfect Pantry | worst | Lowlands Express | 17 | 23.0 | 8.12 | 0.0\nPurrfect Pantry | worst | Landfall Freight | 11 | 14.9 | 9.27 | 0.0\nSourdough Club | worst | Liffey Couriers | 142 | 46.4 | 2.2 | 87.3\nSourdough Club | worst | Albion Parcel | 76 | 24.8 | 3.0 | 85.1\nSourdough Club | worst | Landfall Freight | 52 | 17.0 | 4.84 | 35.3\nSourdough Club | worst | Lowlands Express | 36 | 11.8 | 4.91 | 28.6\nSpice Route Box | worst | Lowlands Express | 360 | 47.2 | 2.11 | 83.2\nSpice Route Box | worst | Polder Post | 311 | 40.8 | 2.21 | 83.5\nSpice Route Box | worst | Landfall Freight | 92 | 12.1 | 3.85 | 42.4\nStudio Vrij | worst | Lowlands Express | 347 | 58.1 | 2.04 | 87.8\nStudio Vrij | worst | Polder Post | 188 | 31.5 | 2.21 | 83.2\nStudio Vrij | worst | Landfall Freight | 62 | 10.4 | 3.82 | 33.3\nThistlecombe | worst | Albion Parcel | 29 | 33.3 | 5.0 | 65.5\nThistlecombe | worst | Kennet Freight | 22 | 25.3 | 2.48 | 76.2\nThistlecombe | worst | Landfall Freight | 20 | 23.0 | 6.0 | 15.0\nThistlecombe | worst | Regent Parcels | 10 | 11.5 | 2.2 | 80.0\nThistlecombe | worst | Lowlands Express | 6 | 6.9 | 6.83 | 33.3\nBrennquell Kaffee | best | Strombogen Kurier | 2050 | 49.7 | 1.73 | 96.7\nBrennquell Kaffee | best | Alpenpaket | 1368 | 33.1 | 1.72 | 96.3\nBrennquell Kaffee | best | Landfall Freight | 409 | 9.9 | 3.76 | 42.7\nBrennquell Kaffee | best | Lowlands Express | 300 | 7.3 | 3.89 | 40.1\nCragsley Outfitters | best | Liberty Ground | 1634 | 46.9 | 1.71 | 95.4\nCragsley Outfitters | best | Trailhead Express | 1253 | 36.0 | 1.92 | 89.7\nCragsley Outfitters | best | Prairie Line Express | 511 | 14.7 | 2.0 | 88.8\nCragsley Outfitters | best | Landfall Freight | 85 | 2.4 | 3.71 | 44.6\nFeather & Fin | best | Liberty Ground | 108 | 50.0 | 1.81 | 94.4\nFeather & Fin | best | Trailhead Express | 79 | 36.6 | 1.64 | 94.9\nFeather & Fin | best | Prairie Line Express | 29 | 13.4 | 2.14 | 89.3\nHoneycomb Larder | best | Liberty Ground | 248 | 41.1 | 1.69 | 95.9\nHoneycomb Larder | best | Trailhead Express | 246 | 40.7 | 2.0 | 88.0\n(73 rows, first 50 printed)\n\n\nNight 4 of 4: you have 3 report slots tonight."},"uuid":"c6a28ed2-8018-42f9-84c9-d4a3f55e90be","timestamp":"2026-10-03T23:05:57.327Z","permissionMode":"default","promptSource":"sdk","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120","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":13083,"cache_read_input_tokens":74124,"output_tokens":7135,"output_tokens_details":{"thinking_tokens":4846},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":13083,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":7135,"cache_read_input_tokens":74124,"cache_creation_input_tokens":13083,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":13083},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011Cfg9BseyyhRYdt9qWQNCC","type":"assistant","uuid":"942814f2-273c-4c61-8f79-877b82af0e4a","timestamp":"2026-10-03T23:06:47.442Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: 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{"parentUuid":"942814f2-273c-4c61-8f79-877b82af0e4a","isSidechain":false,"message":{"model":"claude-sonnet-5","id":"msg_011Cfg9Bt15KFBcEd2m7ynMS","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01GaUbnoXBkJ6suTu4nZKRbV","name":"StructuredOutput","input":{"notes":"Night 3 nailed down priority #3 cleanly, so I'm done there and pivoting all of tonight's slots to priority #4 (inventory/replenishment), which I haven't touched yet. Key night-3 findings for the memo: (a) the complete Q4 carrier table confirms three chronically-weak carriers -- Landfall Freight (13.6% within 3 days, worst all year), Lowlands Express (44.6%), and now Albion Parcel -- together carrying ~32% of Q4 volume; (b) Albion Parcel's monthly breakdown shows it was perfectly fine through October (75.7% within 3 days) then catastrophically broke in November (28.2%) and got *worse*, not better, in December (p90 blew out to 20.7 days, pct_delivered slipped to 94.2%) -- this is an acute, ongoing carrier failure, not a peak blip that self-resolved, and is the single most actionable finding for a carrier conversation; (c) the carrier-mix report confirms the merchant-level variance from Night 1 is almost entirely explained by carrier assignment, not the merchant or warehouse itself -- e.g. Grind Collective's Kennet Freight leg hits 88.7% within 3 days while its Landfall Freight leg on the very same business hits 19.4%; the \"best\" merchants simply route nearly all volume through the strong carriers (Strombogen Kurier, Liberty Ground, Alpenpaket, Trailhead Express), while \"worst\" merchants have meaningful share stuck on the weak ones. That resolves priority #3 well enough to write up without further queries. Tonight (last night) I'm spending all 3 slots on priority #4: whether replenishment from suppliers keeps pace (purchase order lead times, by supplier and by quarter to check for a peak-season backlog) and a current snapshot of stockout exposure by warehouse. I'm leaving returns (#5) for a future week per Priya's explicit prioritization.","requests":[{"title":"Purchase order replenishment performance by supplier, 2025","question":"Which suppliers are chronically slow to deliver against purchase orders, relative to what they've promised?","grain":"one row per supplier","columns":["supplier_name","lead_time_days_contracted (suppliers.lead_time_days)","pos_ordered (count of purchase_orders with ordered_at in 2025)","pos_received (of those, count with received_at not null)","avg_actual_lead_days (DATEDIFF(day, ordered_at, received_at), received subset only)","pct_received_late (share of POs with both expected_at and received_at set, where received_at > expected_at)","avg_days_late (average of DATEDIFF(day, expected_at, received_at) for the late subset only)"],"details":"Tables: purchase_orders (supplier_id, ordered_at, expected_at, received_at, status) joined to suppliers (name, lead_time_days). Scope: purchase_orders.ordered_at between 2025-01-01 and 2025-12-31. Round avg_actual_lead_days and avg_days_late to 1 decimal, pct_received_late to 1 decimal (as %). Include only suppliers with at least 10 POs ordered in the window. Order by pos_ordered descending, tie-break by supplier_name ascending. Cap at 50 rows.","builds_on":[]},{"title":"Purchase order replenishment performance by quarter, 2025","question":"Did supplier replenishment slow down or back up around peak season, and did it clear by year-end?","grain":"one row per calendar quarter of ordered_at, 2025, aggregated across all suppliers","columns":["quarter (e.g. 2025-Q1)","pos_ordered","pos_received (received_at not null)","avg_actual_lead_days (ordered_at to received_at, received subset)","pct_received_late (received_at > expected_at, among POs with both dates set)","avg_days_late (for the late subset only)","pos_still_open_at_cutoff (status in ('submitted','partially_received') and received_at is null, as measured against the 2025-12-31 data cutoff)"],"details":"Table: purchase_orders. Scope: ordered_at between 2025-01-01 and 2025-12-31, grouped by calendar quarter of ordered_at (use calendar_days.quarter_num/quarter_start). Round day metrics to 1 decimal, pct to 1 decimal. Please flag in the report that pos_still_open_at_cutoff will naturally be higher for Q4 (especially POs ordered in December) simply because there hasn't been time to receive them before 2025-12-31 -- this is expected right-censoring, not necessarily a sign of backlog, and should be read alongside Q1-Q3 as a baseline. Order by quarter ascending. Expect 4 rows.","builds_on":[]},{"title":"Current stockout exposure by warehouse, as of 2025-12-31","question":"As of now, how exposed are our fulfillment sites to stockouts, and how many SKUs are already at or below their reorder point?","grain":"one row per warehouse","columns":["warehouse_name","tenant_name","variants_tracked (distinct variant_id with a stock_items row at a location in this warehouse)","variants_out_of_stock (quantity - reserved_quantity <= 0, summed across the warehouse's locations per variant, i.e. no sellable units left)","pct_out_of_stock","variants_at_or_below_reorder_point (reorder_point is not null and on-hand quantity <= reorder_point)","pct_at_or_below_reorder"],"details":"Tables: stock_items (variant_id, stock_location_id, quantity, reserved_quantity, reorder_point) joined to stock_locations (warehouse_id) joined to warehouses (name, tenant_id) joined to tenants (name). This reflects the current live snapshot as replicated into the warehouse (effectively as of 2025-12-31) -- note in the report that this is a point-in-time view, not a trend over the year, since we don't have a reliable historical stockout event log. For variants present at multiple locations within the same warehouse, aggregate (sum quantity and reserved_quantity) to one on-hand figure per variant per warehouse before applying the out-of-stock and reorder-point tests. Round pct columns to 1 decimal (as %). Include only warehouses with at least 20 tracked variants. Order by pct_out_of_stock descending, tie-break by warehouse_name ascending. Cap at 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":13083,"cache_read_input_tokens":74124,"output_tokens":7135,"output_tokens_details":{"thinking_tokens":4846},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":13083,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":7135,"cache_read_input_tokens":74124,"cache_creation_input_tokens":13083,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":13083},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"wireToolInputs":{"toolu_01GaUbnoXBkJ6suTu4nZKRbV":{"notes":"Night 3 nailed down priority #3 cleanly, so I'm done there and pivoting all of tonight's slots to priority #4 (inventory/replenishment), which I haven't touched yet. Key night-3 findings for the memo: (a) the complete Q4 carrier table confirms three chronically-weak carriers -- Landfall Freight (13.6% within 3 days, worst all year), Lowlands Express (44.6%), and now Albion Parcel -- together carrying ~32% of Q4 volume; (b) Albion Parcel's monthly breakdown shows it was perfectly fine through October (75.7% within 3 days) then catastrophically broke in November (28.2%) and got *worse*, not better, in December (p90 blew out to 20.7 days, pct_delivered slipped to 94.2%) -- this is an acute, ongoing carrier failure, not a peak blip that self-resolved, and is the single most actionable finding for a carrier conversation; (c) the carrier-mix report confirms the merchant-level variance from Night 1 is almost entirely explained by carrier assignment, not the merchant or warehouse itself -- e.g. Grind Collective's Kennet Freight leg hits 88.7% within 3 days while its Landfall Freight leg on the very same business hits 19.4%; the \"best\" merchants simply route nearly all volume through the strong carriers (Strombogen Kurier, Liberty Ground, Alpenpaket, Trailhead Express), while \"worst\" merchants have meaningful share stuck on the weak ones. That resolves priority #3 well enough to write up without further queries. Tonight (last night) I'm spending all 3 slots on priority #4: whether replenishment from suppliers keeps pace (purchase order lead times, by supplier and by quarter to check for a peak-season backlog) and a current snapshot of stockout exposure by warehouse. I'm leaving returns (#5) for a future week per Priya's explicit prioritization.","requests":[{"title":"Purchase order replenishment performance by supplier, 2025","question":"Which suppliers are chronically slow to deliver against purchase orders, relative to what they've promised?","grain":"one row per supplier","columns":["supplier_name","lead_time_days_contracted (suppliers.lead_time_days)","pos_ordered (count of purchase_orders with ordered_at in 2025)","pos_received (of those, count with received_at not null)","avg_actual_lead_days (DATEDIFF(day, ordered_at, received_at), received subset only)","pct_received_late (share of POs with both expected_at and received_at set, where received_at > expected_at)","avg_days_late (average of DATEDIFF(day, expected_at, received_at) for the late subset only)"],"details":"Tables: purchase_orders (supplier_id, ordered_at, expected_at, received_at, status) joined to suppliers (name, lead_time_days). Scope: purchase_orders.ordered_at between 2025-01-01 and 2025-12-31. Round avg_actual_lead_days and avg_days_late to 1 decimal, pct_received_late to 1 decimal (as %). Include only suppliers with at least 10 POs ordered in the window. Order by pos_ordered descending, tie-break by supplier_name ascending. Cap at 50 rows.","builds_on":[]},{"title":"Purchase order replenishment performance by quarter, 2025","question":"Did supplier replenishment slow down or back up around peak season, and did it clear by year-end?","grain":"one row per calendar quarter of ordered_at, 2025, aggregated across all suppliers","columns":["quarter (e.g. 2025-Q1)","pos_ordered","pos_received (received_at not null)","avg_actual_lead_days (ordered_at to received_at, received subset)","pct_received_late (received_at > expected_at, among POs with both dates set)","avg_days_late (for the late subset only)","pos_still_open_at_cutoff (status in ('submitted','partially_received') and received_at is null, as measured against the 2025-12-31 data cutoff)"],"details":"Table: purchase_orders. Scope: ordered_at between 2025-01-01 and 2025-12-31, grouped by calendar quarter of ordered_at (use calendar_days.quarter_num/quarter_start). Round day metrics to 1 decimal, pct to 1 decimal. Please flag in the report that pos_still_open_at_cutoff will naturally be higher for Q4 (especially POs ordered in December) simply because there hasn't been time to receive them before 2025-12-31 -- this is expected right-censoring, not necessarily a sign of backlog, and should be read alongside Q1-Q3 as a baseline. Order by quarter ascending. Expect 4 rows.","builds_on":[]},{"title":"Current stockout exposure by warehouse, as of 2025-12-31","question":"As of now, how exposed are our fulfillment sites to stockouts, and how many SKUs are already at or below their reorder point?","grain":"one row per warehouse","columns":["warehouse_name","tenant_name","variants_tracked (distinct variant_id with a stock_items row at a location in this warehouse)","variants_out_of_stock (quantity - reserved_quantity <= 0, summed across the warehouse's locations per variant, i.e. no sellable units left)","pct_out_of_stock","variants_at_or_below_reorder_point (reorder_point is not null and on-hand quantity <= reorder_point)","pct_at_or_below_reorder"],"details":"Tables: stock_items (variant_id, stock_location_id, quantity, reserved_quantity, reorder_point) joined to stock_locations (warehouse_id) joined to warehouses (name, tenant_id) joined to tenants (name). This reflects the current live snapshot as replicated into the warehouse (effectively as of 2025-12-31) -- note in the report that this is a point-in-time view, not a trend over the year, since we don't have a reliable historical stockout event log. For variants present at multiple locations within the same warehouse, aggregate (sum quantity and reserved_quantity) to one on-hand figure per variant per warehouse before applying the out-of-stock and reorder-point tests. Round pct columns to 1 decimal (as %). Include only warehouses with at least 20 tracked variants. Order by pct_out_of_stock descending, tie-break by warehouse_name ascending. Cap at 50 rows.","builds_on":[]}]}},"apiBlockIndex":1,"requestId":"req_011Cfg9BseyyhRYdt9qWQNCC","type":"assistant","uuid":"23b8d463-5a5f-417a-92a1-be910827958b","timestamp":"2026-10-03T23:07:05.788Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"23b8d463-5a5f-417a-92a1-be910827958b","isSidechain":false,"attachment":{"type":"structured_output","data":{"notes":"Night 3 nailed down priority #3 cleanly, so I'm done there and pivoting all of tonight's slots to priority #4 (inventory/replenishment), which I haven't touched yet. Key night-3 findings for the memo: (a) the complete Q4 carrier table confirms three chronically-weak carriers -- Landfall Freight (13.6% within 3 days, worst all year), Lowlands Express (44.6%), and now Albion Parcel -- together carrying ~32% of Q4 volume; (b) Albion Parcel's monthly breakdown shows it was perfectly fine through October (75.7% within 3 days) then catastrophically broke in November (28.2%) and got *worse*, not better, in December (p90 blew out to 20.7 days, pct_delivered slipped to 94.2%) -- this is an acute, ongoing carrier failure, not a peak blip that self-resolved, and is the single most actionable finding for a carrier conversation; (c) the carrier-mix report confirms the merchant-level variance from Night 1 is almost entirely explained by carrier assignment, not the merchant or warehouse itself -- e.g. Grind Collective's Kennet Freight leg hits 88.7% within 3 days while its Landfall Freight leg on the very same business hits 19.4%; the \"best\" merchants simply route nearly all volume through the strong carriers (Strombogen Kurier, Liberty Ground, Alpenpaket, Trailhead Express), while \"worst\" merchants have meaningful share stuck on the weak ones. That resolves priority #3 well enough to write up without further queries. Tonight (last night) I'm spending all 3 slots on priority #4: whether replenishment from suppliers keeps pace (purchase order lead times, by supplier and by quarter to check for a peak-season backlog) and a current snapshot of stockout exposure by warehouse. I'm leaving returns (#5) for a future week per Priya's explicit prioritization.","requests":[{"title":"Purchase order replenishment performance by supplier, 2025","question":"Which suppliers are chronically slow to deliver against purchase orders, relative to what they've promised?","grain":"one row per supplier","columns":["supplier_name","lead_time_days_contracted (suppliers.lead_time_days)","pos_ordered (count of purchase_orders with ordered_at in 2025)","pos_received (of those, count with received_at not null)","avg_actual_lead_days (DATEDIFF(day, ordered_at, received_at), received subset only)","pct_received_late (share of POs with both expected_at and received_at set, where received_at > expected_at)","avg_days_late (average of DATEDIFF(day, expected_at, received_at) for the late subset only)"],"details":"Tables: purchase_orders (supplier_id, ordered_at, expected_at, received_at, status) joined to suppliers (name, lead_time_days). Scope: purchase_orders.ordered_at between 2025-01-01 and 2025-12-31. Round avg_actual_lead_days and avg_days_late to 1 decimal, pct_received_late to 1 decimal (as %). Include only suppliers with at least 10 POs ordered in the window. Order by pos_ordered descending, tie-break by supplier_name ascending. Cap at 50 rows.","builds_on":[]},{"title":"Purchase order replenishment performance by quarter, 2025","question":"Did supplier replenishment slow down or back up around peak season, and did it clear by year-end?","grain":"one row per calendar quarter of ordered_at, 2025, aggregated across all suppliers","columns":["quarter (e.g. 2025-Q1)","pos_ordered","pos_received (received_at not null)","avg_actual_lead_days (ordered_at to received_at, received subset)","pct_received_late (received_at > expected_at, among POs with both dates set)","avg_days_late (for the late subset only)","pos_still_open_at_cutoff (status in ('submitted','partially_received') and received_at is null, as measured against the 2025-12-31 data cutoff)"],"details":"Table: purchase_orders. Scope: ordered_at between 2025-01-01 and 2025-12-31, grouped by calendar quarter of ordered_at (use calendar_days.quarter_num/quarter_start). Round day metrics to 1 decimal, pct to 1 decimal. Please flag in the report that pos_still_open_at_cutoff will naturally be higher for Q4 (especially POs ordered in December) simply because there hasn't been time to receive them before 2025-12-31 -- this is expected right-censoring, not necessarily a sign of backlog, and should be read alongside Q1-Q3 as a baseline. Order by quarter ascending. Expect 4 rows.","builds_on":[]},{"title":"Current stockout exposure by warehouse, as of 2025-12-31","question":"As of now, how exposed are our fulfillment sites to stockouts, and how many SKUs are already at or below their reorder point?","grain":"one row per warehouse","columns":["warehouse_name","tenant_name","variants_tracked (distinct variant_id with a stock_items row at a location in this warehouse)","variants_out_of_stock (quantity - reserved_quantity <= 0, summed across the warehouse's locations per variant, i.e. no sellable units left)","pct_out_of_stock","variants_at_or_below_reorder_point (reorder_point is not null and on-hand quantity <= reorder_point)","pct_at_or_below_reorder"],"details":"Tables: stock_items (variant_id, stock_location_id, quantity, reserved_quantity, reorder_point) joined to stock_locations (warehouse_id) joined to warehouses (name, tenant_id) joined to tenants (name). This reflects the current live snapshot as replicated into the warehouse (effectively as of 2025-12-31) -- note in the report that this is a point-in-time view, not a trend over the year, since we don't have a reliable historical stockout event log. For variants present at multiple locations within the same warehouse, aggregate (sum quantity and reserved_quantity) to one on-hand figure per variant per warehouse before applying the out-of-stock and reorder-point tests. Round pct columns to 1 decimal (as %). Include only warehouses with at least 20 tracked variants. Order by pct_out_of_stock descending, tie-break by warehouse_name ascending. Cap at 50 rows.","builds_on":[]}]},"toolUseID":"toolu_01GaUbnoXBkJ6suTu4nZKRbV"},"type":"attachment","uuid":"ae8468cb-0b4c-44b6-ae69-2f8fedbceb98","timestamp":"2026-10-03T23:07:05.794Z","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120","version":"2.1.275","gitBranch":"HEAD"}
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{"type":"last-prompt","lastPrompt":"The printed reports of night 3: === Report 1: Carrier transit performance, Q4 2025 (complete) (written by Grace) === Engineer's note: Grouped by carrier name across all tenants/merchants (the request…","leafUuid":"eafbdc0e-3a59-4756-a10d-e762526b502e","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120"}
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{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-03T23:07:48.807Z","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120","content":"The printed reports of night 4:\n\n=== Report 1: Purchase order replenishment performance by supplier, 2025 (written by Felix) ===\nEngineer's note: pct_received_late and avg_days_late are computed only over POs that have both expected_at and received_at populated (per the request's definition); POs missing either timestamp are excluded from those two measures but still count toward pos_ordered/pos_received.\nREPORT: Purchase order replenishment performance by supplier, 2025\nQUESTION: Which suppliers are chronically slow to deliver against purchase orders, relative to what they've promised?\nsupplier_name | lead_time_days_contracted | pos_ordered | pos_received | avg_actual_lead_days | pct_received_late | avg_days_late\n---------------------------------+---------------------------+-------------+--------------+----------------------+-------------------+--------------\nDalsgaard Roasters GmbH | 8 | 52 | 51 | 7.5 | 52.9 | 0.9\nHanbury Textiles Inc. | 7 | 52 | 51 | 7.5 | 52.9 | 1.2\nKuntai Textiles Co., Ltd. | 34 | 52 | 47 | 37.0 | 55.3 | 9.2\nNorvik Roasters Ltd | 6 | 52 | 51 | 6.2 | 43.1 | 0.5\nTessaro Roasters GmbH | 8 | 52 | 51 | 7.7 | 47.1 | 0.8\nTessaro Textiles GmbH | 15 | 52 | 50 | 16.9 | 64.0 | 2.8\nWeiyang Roasters Co., Ltd. | 37 | 52 | 45 | 40.6 | 46.7 | 12.0\nCastelo Roasters GmbH | 14 | 51 | 50 | 16.4 | 56.0 | 3.6\nKuntai Roasters Sp. z o.o. | 12 | 51 | 49 | 14.3 | 46.9 | 3.0\nKuntai Textiles S.p.A. | 15 | 51 | 49 | 18.2 | 55.1 | 2.2\nWeiyang Textiles Inc. | 4 | 51 | 50 | 4.8 | 74.0 | 0.8\nCastelo Roasters B.V. | 8 | 50 | 48 | 8.6 | 60.4 | 0.8\nWeiyang Textiles Co., Ltd. | 37 | 50 | 43 | 42.5 | 46.5 | 5.9\nOakmere Roasters Inc. | 7 | 48 | 47 | 6.9 | 55.3 | 0.5\nDalsgaard Textiles S.p.A. | 8 | 47 | 46 | 8.7 | 43.5 | 2.7\nTessaro Textiles Ltd | 9 | 47 | 46 | 8.6 | 50.0 | 0.8\nVireo Textiles Co., Ltd. | 28 | 47 | 40 | 39.9 | 80.0 | 16.0\nOakmere Cosmetics Ltd | 5 | 46 | 45 | 5.3 | 53.3 | 0.5\nTessaro Roasters Co., Ltd. | 43 | 46 | 38 | 52.8 | 39.5 | 11.2\nVireo Cosmetics Co., Ltd. | 42 | 46 | 39 | 50.0 | 41.0 | 8.1\nVireo Textiles Sp. z o.o. | 15 | 46 | 43 | 16.8 | 46.5 | 3.0\nDalsgaard Cosmetics GmbH | 14 | 45 | 44 | 14.7 | 50.0 | 3.5\nDalsgaard Textiles Ltd | 8 | 45 | 44 | 8.1 | 59.1 | 0.3\nMontval Pet Foods Co., Ltd. | 31 | 45 | 40 | 35.5 | 42.5 | 7.6\nHanbury Roasters Co., Ltd. | 42 | 44 | 39 | 43.1 | 41.0 | 7.7\nOakmere Textiles B.V. | 4 | 44 | 44 | 4.4 | 40.9 | 0.8\nWeiyang Cosmetics Ltd | 6 | 43 | 42 | 6.4 | 35.7 | 0.9\nHaizhou Pet Foods GmbH | 9 | 42 | 42 | 11.0 | 52.4 | 1.6\nNorvik Industrial Co., Ltd. | 32 | 42 | 38 | 37.7 | 52.6 | 5.4\nHaizhou Roasters Co., Ltd. | 28 | 40 | 34 | 37.9 | 76.5 | 13.7\nHanbury Roasters B.V. | 15 | 40 | 37 | 16.6 | 48.6 | 2.1\nLucerna Pet Foods Ltd | 8 | 40 | 39 | 6.8 | 51.3 | 0.5\nLucerna Roasters Co., Ltd. | 35 | 40 | 35 | 41.7 | 51.4 | 6.8\nSelwyn Homeware Co., Ltd. | 35 | 40 | 33 | 43.6 | 84.8 | 11.1\nTanaka-Ri Industrial S.p.A. | 16 | 39 | 37 | 18.4 | 45.9 | 2.8\nLucerna Textiles Co., Ltd. | 34 | 38 | 32 | 43.6 | 40.6 | 13.3\nOakmere Office Products B.V. | 11 | 38 | 36 | 14.1 | 77.8 | 4.7\nOakmere Homeware Co., Ltd. | 31 | 37 | 31 | 34.2 | 51.6 | 6.9\nTessaro Electronics GmbH | 6 | 37 | 37 | 7.0 | 70.3 | 1.0\nVireo Office Products Sp. z o.o. | 14 | 37 | 34 | 19.1 | 55.9 | 3.3\nWeiyang Outdoor Gear Sp. z o.o. | 8 | 37 | 36 | 8.4 | 38.9 | 1.9\nDalsgaard Industrial Sp. z o.o. | 12 | 36 | 35 | 12.7 | 42.9 | 2.6\nSelwyn Industrial Ltd | 5 | 36 | 35 | 4.5 | 42.9 | 0.9\nBrenholt Textiles GmbH | 8 | 35 | 35 | 9.3 | 40.0 | 3.9\nVireo Homeware GmbH | 12 | 35 | 33 | 12.3 | 57.6 | 1.4\nDalsgaard Outdoor Gear Co., Ltd. | 28 | 34 | 31 | 34.1 | 51.6 | 4.4\nHaizhou Textiles B.V. | 4 | 34 | 34 | 4.4 | 44.1 | 0.4\nHanbury Electronics Co., Ltd. | 43 | 34 | 28 | 48.1 | 67.9 | 11.2\nLucerna Roasters B.V. | 13 | 34 | 32 | 16.8 | 75.0 | 5.6\nVireo Office Products Ltd | 4 | 34 | 34 | 4.3 | 38.2 | 0.7\n(50 rows)\n\n=== Report 2: Purchase order replenishment performance by quarter, 2025 (written by Grace) ===\nEngineer's note: pos_still_open_at_cutoff for Q4 (and especially POs placed in December) will be inflated by right-censoring: there simply hasn't been enough elapsed time before the 2025-12-31 cutoff for those POs to be received, independent of any real backlog. Read Q4 alongside Q1-Q3 as a baseline rather than as a standalone signal of slowdown.\nREPORT: Purchase order replenishment performance by quarter, 2025\nQUESTION: Did supplier replenishment slow down or back up around peak season, and did it clear by year-end?\nquarter | pos_ordered | pos_received | avg_actual_lead_days | pct_received_late | avg_days_late | pos_still_open_at_cutoff\n--------+-------------+--------------+----------------------+-------------------+---------------+-------------------------\n2025-Q1 | 1183 | 1183 | 22.4 | 52.9 | 6.1 | 0\n2025-Q2 | 1209 | 1209 | 20.0 | 47.4 | 3.8 | 0\n2025-Q3 | 1238 | 1238 | 20.7 | 54.0 | 4.8 | 0\n2025-Q4 | 1588 | 1170 | 15.8 | 57.7 | 3.1 | 418\n(4 rows)\n\n=== Report 3: Current stockout exposure by warehouse, as of 2025-12-31 (written by Bea) ===\nEngineer's note: This is a point-in-time snapshot of current stock_items (as replicated, effectively as of 2025-12-31), not a trend over the year, since there is no reliable historical stockout event log. For variants held at multiple locations within a warehouse, quantity, reserved_quantity and reorder_point were all summed to one on-hand figure and one reorder threshold per variant-warehouse before applying the out-of-stock and reorder-point tests.\nREPORT: Current stockout exposure by warehouse, as of 2025-12-31\nQUESTION: As of now, how exposed are our fulfillment sites to stockouts, and how many SKUs are already at or below their reorder point?\nwarehouse_name | tenant_name | variants_tracked | variants_out_of_stock | pct_out_of_stock | variants_at_or_below_reorder_point | pct_at_or_below_reorder\n--------------------------------------+-------------------------------+------------------+-----------------------+------------------+------------------------------------+------------------------\nKöln Fulfilment Centre | Brennquell Kaffee | 854 | 127 | 14.9 | 244 | 28.6\nSan Francisco Fulfilment Centre | Honeycomb Larder | 255 | 28 | 11.0 | 59 | 23.1\nNewcastle upon Tyne Fulfilment Centre | Deskline Supplies | 191 | 18 | 9.4 | 21 | 11.0\nSan Antonio Fulfilment Centre | Castell Fasteners | 314 | 29 | 9.2 | 32 | 10.2\nOslo Fulfilment Centre | OfficeHub Nordic | 352 | 31 | 8.8 | 37 | 10.5\nLiège Fulfilment Centre | Spice Route Box | 397 | 32 | 8.1 | 61 | 15.4\nManchester Fulfilment Centre | Grind Collective | 411 | 33 | 8.0 | 68 | 16.5\nGalway Fulfilment Centre | Ballycorran Industrial Supply | 806 | 59 | 7.3 | 66 | 8.2\nHannover Fulfilment Centre | Hanse Industriebedarf | 286 | 21 | 7.3 | 26 | 9.1\nBerlin Fulfilment Centre | Kontor24 Bürobedarf | 373 | 27 | 7.2 | 29 | 7.8\nDublin Fulfilment Centre | Sourdough Club | 287 | 18 | 6.3 | 57 | 19.9\nBerlin Fulfilment Centre | Wiesgrund Wohnen | 681 | 36 | 5.3 | 102 | 15.0\nUppsala Fulfilment Centre | Juniper Apothecary | 416 | 22 | 5.3 | 54 | 13.0\nAmsterdam Fulfilment Centre | Linden & Loom | 750 | 39 | 5.2 | 118 | 15.7\nLund Fulfilment Centre | Saffron & Sage | 257 | 13 | 5.1 | 46 | 17.9\nLiverpool Fulfilment Centre | Fennel & Flint | 904 | 42 | 4.6 | 121 | 13.4\nLeeds Fulfilment Centre | The Balm Room | 308 | 14 | 4.5 | 37 | 12.0\nOslo Fulfilment Centre | Purrfect Pantry | 155 | 7 | 4.5 | 20 | 12.9\nHouston Fulfilment Centre | Tail Wag Supply | 196 | 8 | 4.1 | 20 | 10.2\nParis Fulfilment Centre | Mirelle | 294 | 12 | 4.1 | 27 | 9.2\nRotterdam Fulfilment Centre | Midland Safety Wear | 460 | 19 | 4.1 | 21 | 4.6\nManchester Fulfilment Centre | Willow & Wren | 460 | 18 | 3.9 | 56 | 12.2\nBromley Fulfilment Centre | Petalcourt Beauty | 920 | 35 | 3.8 | 118 | 12.8\nInnsbruck Fulfilment Centre | Buchstabe | 260 | 10 | 3.8 | 19 | 7.3\nLiège Fulfilment Centre | Polder Packaging | 234 | 9 | 3.8 | 11 | 4.7\nOdense Fulfilment Centre | Nordic Bloom Cosmetics | 468 | 18 | 3.8 | 62 | 13.2\nAmsterdam Fulfilment Centre | Whisker Works | 272 | 10 | 3.7 | 35 | 12.9\nNürnberg Fulfilment Centre | Wohnglück | 557 | 20 | 3.6 | 56 | 10.1\nGöteborg Fulfilment Centre | Coastline Interiors | 550 | 19 | 3.5 | 65 | 11.8\nBromley Fulfilment Centre | Kibblewick | 522 | 18 | 3.4 | 101 | 19.3\nCharlotte Fulfilment Centre | Roast Republic | 212 | 7 | 3.3 | 31 | 14.6\nBordeaux Fulfilment Centre | Chocolaterie Mireille | 157 | 5 | 3.2 | 18 | 11.5\nKöln Fulfilment Centre | Pfotenglück | 242 | 7 | 2.9 | 33 | 13.6\nHamburg Fulfilment Centre | Ampfeld Elektronik | 1515 | 39 | 2.6 | 171 | 11.3\nBerlin Fulfilment Centre | Kaffeekontor Nord | 83 | 2 | 2.4 | 2 | 2.4\nAmsterdam Fulfilment Centre | Ampere & Co. | 441 | 10 | 2.3 | 38 | 8.6\nAugsburg Fulfilment Centre | Hautnah | 345 | 8 | 2.3 | 35 | 10.1\nHouston Fulfilment Centre | Feather & Fin | 386 | 9 | 2.3 | 66 | 17.1\nZaandam Fulfilment Centre | Sorella Skin | 260 | 6 | 2.3 | 33 | 12.7\nBergen Fulfilment Centre | Hus & Hem | 274 | 6 | 2.2 | 30 | 10.9\nPreston Fulfilment Centre | Rosehip Lab | 193 | 4 | 2.1 | 8 | 4.1\nDublin Fulfilment Centre | Basecamp Nord | 658 | 11 | 1.7 | 27 | 4.1\nAmersfoort Fulfilment Centre | Kettle & Key | 310 | 5 | 1.6 | 17 | 5.5\nOrlando Fulfilment Centre | Stoneware Studio | 378 | 6 | 1.6 | 16 | 4.2\nAtlanta Fulfilment Centre | Cragsley Outfitters | 16301 | 251 | 1.5 | 1045 | 6.4\nEindhoven Fulfilment Centre | Hound Hamper | 200 | 3 | 1.5 | 19 | 9.5\nGöteborg Fulfilment Centre | Paperplane Press | 265 | 4 | 1.5 | 28 | 10.6\nBirmingham Fulfilment Centre | Folio & Fern | 309 | 4 | 1.3 | 30 | 9.7\nExeter Fulfilment Centre | Kayak Corner | 1598 | 21 | 1.3 | 65 | 4.1\nStockholm Fulfilment Centre | Barkley & Co. | 149 | 2 | 1.3 | 15 | 10.1\n(50 rows)\n\n\nIt is Friday morning. Write the memo to Priya Raman:\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":"df408a37-947f-4af7-b762-aa302a9aad75","isSidechain":false,"promptId":"384aa17a-1156-440b-b0e0-12563cfa873b","type":"user","message":{"role":"user","content":"The printed reports of night 4:\n\n=== Report 1: Purchase order replenishment performance by supplier, 2025 (written by Felix) ===\nEngineer's note: pct_received_late and avg_days_late are computed only over POs that have both expected_at and received_at populated (per the request's definition); POs missing either timestamp are excluded from those two measures but still count toward pos_ordered/pos_received.\nREPORT: Purchase order replenishment performance by supplier, 2025\nQUESTION: Which suppliers are chronically slow to deliver against purchase orders, relative to what they've promised?\nsupplier_name | lead_time_days_contracted | pos_ordered | pos_received | avg_actual_lead_days | pct_received_late | avg_days_late\n---------------------------------+---------------------------+-------------+--------------+----------------------+-------------------+--------------\nDalsgaard Roasters GmbH | 8 | 52 | 51 | 7.5 | 52.9 | 0.9\nHanbury Textiles Inc. | 7 | 52 | 51 | 7.5 | 52.9 | 1.2\nKuntai Textiles Co., Ltd. | 34 | 52 | 47 | 37.0 | 55.3 | 9.2\nNorvik Roasters Ltd | 6 | 52 | 51 | 6.2 | 43.1 | 0.5\nTessaro Roasters GmbH | 8 | 52 | 51 | 7.7 | 47.1 | 0.8\nTessaro Textiles GmbH | 15 | 52 | 50 | 16.9 | 64.0 | 2.8\nWeiyang Roasters Co., Ltd. | 37 | 52 | 45 | 40.6 | 46.7 | 12.0\nCastelo Roasters GmbH | 14 | 51 | 50 | 16.4 | 56.0 | 3.6\nKuntai Roasters Sp. z o.o. | 12 | 51 | 49 | 14.3 | 46.9 | 3.0\nKuntai Textiles S.p.A. | 15 | 51 | 49 | 18.2 | 55.1 | 2.2\nWeiyang Textiles Inc. | 4 | 51 | 50 | 4.8 | 74.0 | 0.8\nCastelo Roasters B.V. | 8 | 50 | 48 | 8.6 | 60.4 | 0.8\nWeiyang Textiles Co., Ltd. | 37 | 50 | 43 | 42.5 | 46.5 | 5.9\nOakmere Roasters Inc. | 7 | 48 | 47 | 6.9 | 55.3 | 0.5\nDalsgaard Textiles S.p.A. | 8 | 47 | 46 | 8.7 | 43.5 | 2.7\nTessaro Textiles Ltd | 9 | 47 | 46 | 8.6 | 50.0 | 0.8\nVireo Textiles Co., Ltd. | 28 | 47 | 40 | 39.9 | 80.0 | 16.0\nOakmere Cosmetics Ltd | 5 | 46 | 45 | 5.3 | 53.3 | 0.5\nTessaro Roasters Co., Ltd. | 43 | 46 | 38 | 52.8 | 39.5 | 11.2\nVireo Cosmetics Co., Ltd. | 42 | 46 | 39 | 50.0 | 41.0 | 8.1\nVireo Textiles Sp. z o.o. | 15 | 46 | 43 | 16.8 | 46.5 | 3.0\nDalsgaard Cosmetics GmbH | 14 | 45 | 44 | 14.7 | 50.0 | 3.5\nDalsgaard Textiles Ltd | 8 | 45 | 44 | 8.1 | 59.1 | 0.3\nMontval Pet Foods Co., Ltd. | 31 | 45 | 40 | 35.5 | 42.5 | 7.6\nHanbury Roasters Co., Ltd. | 42 | 44 | 39 | 43.1 | 41.0 | 7.7\nOakmere Textiles B.V. | 4 | 44 | 44 | 4.4 | 40.9 | 0.8\nWeiyang Cosmetics Ltd | 6 | 43 | 42 | 6.4 | 35.7 | 0.9\nHaizhou Pet Foods GmbH | 9 | 42 | 42 | 11.0 | 52.4 | 1.6\nNorvik Industrial Co., Ltd. | 32 | 42 | 38 | 37.7 | 52.6 | 5.4\nHaizhou Roasters Co., Ltd. | 28 | 40 | 34 | 37.9 | 76.5 | 13.7\nHanbury Roasters B.V. | 15 | 40 | 37 | 16.6 | 48.6 | 2.1\nLucerna Pet Foods Ltd | 8 | 40 | 39 | 6.8 | 51.3 | 0.5\nLucerna Roasters Co., Ltd. | 35 | 40 | 35 | 41.7 | 51.4 | 6.8\nSelwyn Homeware Co., Ltd. | 35 | 40 | 33 | 43.6 | 84.8 | 11.1\nTanaka-Ri Industrial S.p.A. | 16 | 39 | 37 | 18.4 | 45.9 | 2.8\nLucerna Textiles Co., Ltd. | 34 | 38 | 32 | 43.6 | 40.6 | 13.3\nOakmere Office Products B.V. | 11 | 38 | 36 | 14.1 | 77.8 | 4.7\nOakmere Homeware Co., Ltd. | 31 | 37 | 31 | 34.2 | 51.6 | 6.9\nTessaro Electronics GmbH | 6 | 37 | 37 | 7.0 | 70.3 | 1.0\nVireo Office Products Sp. z o.o. | 14 | 37 | 34 | 19.1 | 55.9 | 3.3\nWeiyang Outdoor Gear Sp. z o.o. | 8 | 37 | 36 | 8.4 | 38.9 | 1.9\nDalsgaard Industrial Sp. z o.o. | 12 | 36 | 35 | 12.7 | 42.9 | 2.6\nSelwyn Industrial Ltd | 5 | 36 | 35 | 4.5 | 42.9 | 0.9\nBrenholt Textiles GmbH | 8 | 35 | 35 | 9.3 | 40.0 | 3.9\nVireo Homeware GmbH | 12 | 35 | 33 | 12.3 | 57.6 | 1.4\nDalsgaard Outdoor Gear Co., Ltd. | 28 | 34 | 31 | 34.1 | 51.6 | 4.4\nHaizhou Textiles B.V. | 4 | 34 | 34 | 4.4 | 44.1 | 0.4\nHanbury Electronics Co., Ltd. | 43 | 34 | 28 | 48.1 | 67.9 | 11.2\nLucerna Roasters B.V. | 13 | 34 | 32 | 16.8 | 75.0 | 5.6\nVireo Office Products Ltd | 4 | 34 | 34 | 4.3 | 38.2 | 0.7\n(50 rows)\n\n=== Report 2: Purchase order replenishment performance by quarter, 2025 (written by Grace) ===\nEngineer's note: pos_still_open_at_cutoff for Q4 (and especially POs placed in December) will be inflated by right-censoring: there simply hasn't been enough elapsed time before the 2025-12-31 cutoff for those POs to be received, independent of any real backlog. Read Q4 alongside Q1-Q3 as a baseline rather than as a standalone signal of slowdown.\nREPORT: Purchase order replenishment performance by quarter, 2025\nQUESTION: Did supplier replenishment slow down or back up around peak season, and did it clear by year-end?\nquarter | pos_ordered | pos_received | avg_actual_lead_days | pct_received_late | avg_days_late | pos_still_open_at_cutoff\n--------+-------------+--------------+----------------------+-------------------+---------------+-------------------------\n2025-Q1 | 1183 | 1183 | 22.4 | 52.9 | 6.1 | 0\n2025-Q2 | 1209 | 1209 | 20.0 | 47.4 | 3.8 | 0\n2025-Q3 | 1238 | 1238 | 20.7 | 54.0 | 4.8 | 0\n2025-Q4 | 1588 | 1170 | 15.8 | 57.7 | 3.1 | 418\n(4 rows)\n\n=== Report 3: Current stockout exposure by warehouse, as of 2025-12-31 (written by Bea) ===\nEngineer's note: This is a point-in-time snapshot of current stock_items (as replicated, effectively as of 2025-12-31), not a trend over the year, since there is no reliable historical stockout event log. For variants held at multiple locations within a warehouse, quantity, reserved_quantity and reorder_point were all summed to one on-hand figure and one reorder threshold per variant-warehouse before applying the out-of-stock and reorder-point tests.\nREPORT: Current stockout exposure by warehouse, as of 2025-12-31\nQUESTION: As of now, how exposed are our fulfillment sites to stockouts, and how many SKUs are already at or below their reorder point?\nwarehouse_name | tenant_name | variants_tracked | variants_out_of_stock | pct_out_of_stock | variants_at_or_below_reorder_point | pct_at_or_below_reorder\n--------------------------------------+-------------------------------+------------------+-----------------------+------------------+------------------------------------+------------------------\nKöln Fulfilment Centre | Brennquell Kaffee | 854 | 127 | 14.9 | 244 | 28.6\nSan Francisco Fulfilment Centre | Honeycomb Larder | 255 | 28 | 11.0 | 59 | 23.1\nNewcastle upon Tyne Fulfilment Centre | Deskline Supplies | 191 | 18 | 9.4 | 21 | 11.0\nSan Antonio Fulfilment Centre | Castell Fasteners | 314 | 29 | 9.2 | 32 | 10.2\nOslo Fulfilment Centre | OfficeHub Nordic | 352 | 31 | 8.8 | 37 | 10.5\nLiège Fulfilment Centre | Spice Route Box | 397 | 32 | 8.1 | 61 | 15.4\nManchester Fulfilment Centre | Grind Collective | 411 | 33 | 8.0 | 68 | 16.5\nGalway Fulfilment Centre | Ballycorran Industrial Supply | 806 | 59 | 7.3 | 66 | 8.2\nHannover Fulfilment Centre | Hanse Industriebedarf | 286 | 21 | 7.3 | 26 | 9.1\nBerlin Fulfilment Centre | Kontor24 Bürobedarf | 373 | 27 | 7.2 | 29 | 7.8\nDublin Fulfilment Centre | Sourdough Club | 287 | 18 | 6.3 | 57 | 19.9\nBerlin Fulfilment Centre | Wiesgrund Wohnen | 681 | 36 | 5.3 | 102 | 15.0\nUppsala Fulfilment Centre | Juniper Apothecary | 416 | 22 | 5.3 | 54 | 13.0\nAmsterdam Fulfilment Centre | Linden & Loom | 750 | 39 | 5.2 | 118 | 15.7\nLund Fulfilment Centre | Saffron & Sage | 257 | 13 | 5.1 | 46 | 17.9\nLiverpool Fulfilment Centre | Fennel & Flint | 904 | 42 | 4.6 | 121 | 13.4\nLeeds Fulfilment Centre | The Balm Room | 308 | 14 | 4.5 | 37 | 12.0\nOslo Fulfilment Centre | Purrfect Pantry | 155 | 7 | 4.5 | 20 | 12.9\nHouston Fulfilment Centre | Tail Wag Supply | 196 | 8 | 4.1 | 20 | 10.2\nParis Fulfilment Centre | Mirelle | 294 | 12 | 4.1 | 27 | 9.2\nRotterdam Fulfilment Centre | Midland Safety Wear | 460 | 19 | 4.1 | 21 | 4.6\nManchester Fulfilment Centre | Willow & Wren | 460 | 18 | 3.9 | 56 | 12.2\nBromley Fulfilment Centre | Petalcourt Beauty | 920 | 35 | 3.8 | 118 | 12.8\nInnsbruck Fulfilment Centre | Buchstabe | 260 | 10 | 3.8 | 19 | 7.3\nLiège Fulfilment Centre | Polder Packaging | 234 | 9 | 3.8 | 11 | 4.7\nOdense Fulfilment Centre | Nordic Bloom Cosmetics | 468 | 18 | 3.8 | 62 | 13.2\nAmsterdam Fulfilment Centre | Whisker Works | 272 | 10 | 3.7 | 35 | 12.9\nNürnberg Fulfilment Centre | Wohnglück | 557 | 20 | 3.6 | 56 | 10.1\nGöteborg Fulfilment Centre | Coastline Interiors | 550 | 19 | 3.5 | 65 | 11.8\nBromley Fulfilment Centre | Kibblewick | 522 | 18 | 3.4 | 101 | 19.3\nCharlotte Fulfilment Centre | Roast Republic | 212 | 7 | 3.3 | 31 | 14.6\nBordeaux Fulfilment Centre | Chocolaterie Mireille | 157 | 5 | 3.2 | 18 | 11.5\nKöln Fulfilment Centre | Pfotenglück | 242 | 7 | 2.9 | 33 | 13.6\nHamburg Fulfilment Centre | Ampfeld Elektronik | 1515 | 39 | 2.6 | 171 | 11.3\nBerlin Fulfilment Centre | Kaffeekontor Nord | 83 | 2 | 2.4 | 2 | 2.4\nAmsterdam Fulfilment Centre | Ampere & Co. | 441 | 10 | 2.3 | 38 | 8.6\nAugsburg Fulfilment Centre | Hautnah | 345 | 8 | 2.3 | 35 | 10.1\nHouston Fulfilment Centre | Feather & Fin | 386 | 9 | 2.3 | 66 | 17.1\nZaandam Fulfilment Centre | Sorella Skin | 260 | 6 | 2.3 | 33 | 12.7\nBergen Fulfilment Centre | Hus & Hem | 274 | 6 | 2.2 | 30 | 10.9\nPreston Fulfilment Centre | Rosehip Lab | 193 | 4 | 2.1 | 8 | 4.1\nDublin Fulfilment Centre | Basecamp Nord | 658 | 11 | 1.7 | 27 | 4.1\nAmersfoort Fulfilment Centre | Kettle & Key | 310 | 5 | 1.6 | 17 | 5.5\nOrlando Fulfilment Centre | Stoneware Studio | 378 | 6 | 1.6 | 16 | 4.2\nAtlanta Fulfilment Centre | Cragsley Outfitters | 16301 | 251 | 1.5 | 1045 | 6.4\nEindhoven Fulfilment Centre | Hound Hamper | 200 | 3 | 1.5 | 19 | 9.5\nGöteborg Fulfilment Centre | Paperplane Press | 265 | 4 | 1.5 | 28 | 10.6\nBirmingham Fulfilment Centre | Folio & Fern | 309 | 4 | 1.3 | 30 | 9.7\nExeter Fulfilment Centre | Kayak Corner | 1598 | 21 | 1.3 | 65 | 4.1\nStockholm Fulfilment Centre | Barkley & Co. | 149 | 2 | 1.3 | 15 | 10.1\n(50 rows)\n\n\nIt is Friday morning. Write the memo to Priya Raman:\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":"ac8ae354-2957-4a21-8a09-8e6ed08fab29","timestamp":"2026-10-03T23:07:48.813Z","permissionMode":"default","promptSource":"sdk","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120","version":"2.1.275","gitBranch":"HEAD"}
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{"parentUuid":"7c07c555-8871-44ec-a407-acd4021db34c","isSidechain":false,"message":{"model":"claude-sonnet-5","id":"msg_011Cfg9L6bq8gnLQBDfTZJhX","type":"message","role":"assistant","content":[{"type":"text","text":"**To:** Priya Raman\n**From:** Lena Hartmann\n**Re:** Fulfillment operational health — first-week findings\n\n**1. End-to-end delivery time (priority #1).** Typical delivery (order placed → delivered) runs 3.6–4.3 days on average, median 3–4 days, with 83–90% of orders inside 5 days in a normal month (monthly trend report, 2024–2025). That's held roughly flat over the past two years — no structural drift, good or bad. The real variability is between merchants, not over time: 2025 pct-within-5-days ranges from 92% (Pfotenglück, Cragsley Outfitters) down to 54–58% (Thistlecombe, Purrfect Pantry) — genuinely a coin toss for some. Critically, this doesn't track vertical (Food & Coffee has both the best and some of the worst performers), so it isn't a category problem.\n\n**2. Peak season (priority #2).** Nov–Dec 2025 is the stress test: weekly data shows pct-within-5-days falling to 73.5% (week of Nov 24) and 56.1% (week of Dec 1) as volume roughly doubled, with backlog (undelivered orders) tripling. It recovered to 80–93% by mid-to-late December. Decomposing the time (processing vs. transit, monthly) shows warehouses held up fine — processing time stayed flat at ~1.1–1.2 days straight through peak — so the slowdown was entirely in carrier transit (2.5–2.9 days baseline → 3.46 in November). **The warehouses are not the bottleneck; carriers are.**\n\n**3. Weak links (priority #3) — high confidence.** Two carriers are chronically weak all year, not just at peak: Landfall Freight (13.6% within 3 days in Q4, similarly bad all year) and Lowlands Express (44.6%). A third, Albion Parcel, was solid through October (75.7% within 3 days) then collapsed in November (28.2%, avg 7.5 days) and got *worse* in December (28.6%, avg 9.7 days, p90 ~21 days) — no recovery by year-end. Together these three carried ~32% of Q4 volume. Most importantly, carrier-mix data directly explains the merchant variance from #1: the same merchant shows wildly different reliability by carrier (e.g., Grind Collective: 88.7% within 3 days on Kennet Freight vs. 19.4% on Landfall Freight). The \"bad\" merchants are simply the ones with meaningful volume stuck on weak carriers — this is a carrier issue, not a merchant or warehouse issue.\n\n**4. Replenishment (priority #4) — partial, lower confidence.** Across all suppliers, 40–85% of purchase orders arrive after their expected date, and this rate (47–58%) is stable across quarters — it's a chronic baseline issue, not peak-specific. A few supplier families (\"Roasters,\" \"Textiles\" from certain vendors) show the longest promised lead times (28–43 days) and the worst lateness. The Q4 \"open PO\" count (418, vs. 0 in Q1–Q3) looks alarming but I don't trust it as a standalone signal: POs placed late in December haven't had time to be received before our 2025-12-31 cutoff, so this is partly a right-censoring artifact rather than proof of a real backlog — needs a cleaner test (e.g., restrict to POs ordered before Nov 15). Current stockout exposure (point-in-time snapshot only, not a trend) ranges from <2% to ~15% of tracked SKUs out of stock by warehouse, with Köln (Brennquell Kaffee) highest; I'd treat this as a rough baseline, not a reliable historical pattern, since we have no stockout event history to check if it's worse than usual.\n\n**Caveats:** delivery/transit definitions require all shipments on an order to be fully delivered, so partially-shipped orders are excluded; very recent weeks/months understate reliability simply because deliveries are still in transit at the data cutoff (flagged in each affected report).\n\n**Next steps:** (1) a clean, non-censored read on the Q4 PO backlog; (2) confirm whether Albion Parcel and Landfall Freight are concentrated on specific lanes/regions before the carrier conversation; (3) returns/restock cycle time, deferred this week as agreed."}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":99959,"cache_read_input_tokens":0,"output_tokens":2602,"output_tokens_details":{"thinking_tokens":1140},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":99959,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":2602,"cache_read_input_tokens":0,"cache_creation_input_tokens":99959,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":99959},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":{"cache_miss_reason":{"type":"tools_changed","cache_missed_input_tokens":77018}},"context_management":null},"apiBlockIndex":1,"requestId":"req_011Cfg9L6GF3ctwvRqn8UyDQ","type":"assistant","uuid":"fc2764c8-a0f0-46ca-8cec-7d880abe7ad0","timestamp":"2026-10-03T23:08:15.455Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120","version":"2.1.275","gitBranch":"HEAD"}
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{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-04T02:01:54.257Z","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120","content":"Monday, week 2. Priya Raman replied to your memo:\n\nSubject: Carrier picture confirms it — now I want to know what we can actually fix\n\nLena,\n\nThis is exactly the clarity I was hoping for — \"it's the carriers, not the warehouses\" is a much more actionable story than \"fulfillment is inconsistent.\" A few things jumped out, including from what Daniel's and Tomasz's teams posted this week, and I want to chase them down before we draft anything for merchants or carrier partners.\n\nWhat surprised me: Albion Parcel going from solid to broken in November and *staying* broken through December is different from Landfall and Lowlands, which sound like long-term weak performers we've just tolerated. One is a relationship we may need to end; the other looks like it could be an acute problem (capacity, a route, a depot) that might actually be fixable if we understood the trigger. I don't want to lump all three into one \"bad carriers\" conversation when they may need completely different responses.\n\nI also noticed Daniel's team's finding that \"where's my order\" tickets spike in lockstep with late deliveries each November/December — that's a direct line from the carrier problem to support cost and customer satisfaction, and I'd like to be able to say that explicitly in my own numbers rather than borrowing his.\n\nQuestions for this week:\n\n1. **Of the three weak carriers, which ones are tied to specific regions, lanes, or merchants, versus broadly weak everywhere?** I want to know if Albion's November collapse was localized (a single hub, country, or set of merchants) or general — that tells us whether this is a \"fix one bad link\" problem or a \"this carrier can't be trusted\" problem.\n\n2. **If we could bring our weak carriers up to the standard of our good ones, how much of our delivery unreliability would actually go away?** I want a sense of the ceiling — is this 80% of our problem, or just the long tail?\n\n3. **Can you give me a clean, non-censored view of whether we're genuinely behind on restocking from suppliers, or if that was a year-end timing artifact?** You flagged this yourself — I'd like it resolved before I mention supplier reliability to anyone.\n\n4. **Is there any sign that the picking discrepancies Tomasz's team flagged (concentrated in apparel/footwear) are contributing to delivery delays or failed promises** — e.g., orders stalling or being corrected after the fact — or is that purely a shrinkage/accounting issue with no customer-facing effect? I want to know if it belongs in the same story as delivery reliability or is a separate track entirely.\n\nIf it's a tight week, prioritize #1 and #2 — I want the carrier narrative airtight before I take it anywhere. Returns/restock cycle time can wait one more week if needed.\n\nThanks,\nPriya\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":"d29ad734-f125-485f-bce3-42464f755748","isSidechain":false,"promptId":"b0c1c50a-890c-4a64-b916-06d28027bd74","type":"user","message":{"role":"user","content":"Monday, week 2. Priya Raman replied to your memo:\n\nSubject: Carrier picture confirms it — now I want to know what we can actually fix\n\nLena,\n\nThis is exactly the clarity I was hoping for — \"it's the carriers, not the warehouses\" is a much more actionable story than \"fulfillment is inconsistent.\" A few things jumped out, including from what Daniel's and Tomasz's teams posted this week, and I want to chase them down before we draft anything for merchants or carrier partners.\n\nWhat surprised me: Albion Parcel going from solid to broken in November and *staying* broken through December is different from Landfall and Lowlands, which sound like long-term weak performers we've just tolerated. One is a relationship we may need to end; the other looks like it could be an acute problem (capacity, a route, a depot) that might actually be fixable if we understood the trigger. I don't want to lump all three into one \"bad carriers\" conversation when they may need completely different responses.\n\nI also noticed Daniel's team's finding that \"where's my order\" tickets spike in lockstep with late deliveries each November/December — that's a direct line from the carrier problem to support cost and customer satisfaction, and I'd like to be able to say that explicitly in my own numbers rather than borrowing his.\n\nQuestions for this week:\n\n1. **Of the three weak carriers, which ones are tied to specific regions, lanes, or merchants, versus broadly weak everywhere?** I want to know if Albion's November collapse was localized (a single hub, country, or set of merchants) or general — that tells us whether this is a \"fix one bad link\" problem or a \"this carrier can't be trusted\" problem.\n\n2. **If we could bring our weak carriers up to the standard of our good ones, how much of our delivery unreliability would actually go away?** I want a sense of the ceiling — is this 80% of our problem, or just the long tail?\n\n3. **Can you give me a clean, non-censored view of whether we're genuinely behind on restocking from suppliers, or if that was a year-end timing artifact?** You flagged this yourself — I'd like it resolved before I mention supplier reliability to anyone.\n\n4. **Is there any sign that the picking discrepancies Tomasz's team flagged (concentrated in apparel/footwear) are contributing to delivery delays or failed promises** — e.g., orders stalling or being corrected after the fact — or is that purely a shrinkage/accounting issue with no customer-facing effect? I want to know if it belongs in the same story as delivery reliability or is a separate track entirely.\n\nIf it's a tight week, prioritize #1 and #2 — I want the carrier narrative airtight before I take it anywhere. Returns/restock cycle time can wait one more week if needed.\n\nThanks,\nPriya\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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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":110093,"cache_read_input_tokens":0,"output_tokens":6556,"output_tokens_details":{"thinking_tokens":4691},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":110093,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":6556,"cache_read_input_tokens":0,"cache_creation_input_tokens":110093,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":110093},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":{"cache_miss_reason":{"type":"previous_message_not_found"}},"context_management":null},"apiBlockIndex":0,"requestId":"req_011CfgNc4hwMkQvoR3a1LXQV","type":"assistant","uuid":"eadaf8d9-e217-4e72-86be-3a2b706b7a36","timestamp":"2026-10-04T02:02:44.273Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: 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{"parentUuid":"eadaf8d9-e217-4e72-86be-3a2b706b7a36","isSidechain":false,"message":{"model":"claude-sonnet-5","id":"msg_011CfgNc58zLQwbn2QZ7gGRd","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_0194Sbptn7rp7p2rBZQ1fE4U","name":"StructuredOutput","input":{"notes":"Priya wants the carrier narrative airtight before it goes anywhere, and specifically wants to know whether Albion Parcel's November collapse is a localized, fixable problem (one hub/country/merchant) versus Landfall Freight and Lowlands Express being broadly, chronically weak (a relationship-ending problem). That's priority #1 and it needs its own evidence on both the destination side (country/region) and the origin side (warehouse/hub), split into the pre-collapse baseline (Jan-Oct) vs. the collapse window (Nov-Dec) for Albion specifically. I'm spending all 3 slots tonight on that, since getting this distinction right shapes two very different conversations with carrier partners. I'll tackle priority #2 (the reliability \"ceiling\" if weak carriers matched good ones) first thing tomorrow night, using a full-year, non-truncated carrier table I don't yet have cleanly. Priority #3 (clean PO backlog view) and #4 (picking discrepancies vs. delivery) will follow later this week as time allows, per Priya's own prioritization.","requests":[{"title":"Albion Parcel performance by destination country, baseline vs collapse","question":"Is Albion Parcel's November-December breakdown concentrated in specific destination countries, or did it hit all regions roughly equally?","grain":"one row per destination country and period, for Albion Parcel shipments only","columns":["destination_country","period ('baseline' = shipped_at 2025-01-01 to 2025-10-31; 'collapse' = shipped_at 2025-11-01 to 2025-12-31)","shipments_shipped","avg_transit_days (shipped_at to delivered_at, delivered subset only)","pct_within_3_days","share_of_period_shipments_pct (this country's shipments / all Albion shipments in that period, x100)"],"details":"Tables: shipments (carrier='Albion Parcel', shipped_at, delivered_at) joined via fulfillments (fulfillment_id) -> orders (order_id) -> addresses (orders.shipping_address_id) -> countries (destination country name). transit_days = DATEDIFF(day, shipped_at, delivered_at), delivered subset only. Round avg_transit_days to 2 decimals, pct columns to 1 decimal. Include only countries with at least 15 shipments in at least one of the two periods (to avoid noise from one-off destinations). Order by destination_country ascending, then period (baseline before collapse).","builds_on":["Albion Parcel transit performance by month, 2025"]},{"title":"Albion Parcel performance by origin warehouse, baseline vs collapse","question":"Is Albion Parcel's November-December breakdown concentrated in a specific warehouse/hub (or the merchants it serves), or did it hit all origins roughly equally?","grain":"one row per origin warehouse and period, for Albion Parcel shipments only","columns":["warehouse_name","tenant_name","period ('baseline' = shipped_at 2025-01-01 to 2025-10-31; 'collapse' = shipped_at 2025-11-01 to 2025-12-31)","shipments_shipped","avg_transit_days","pct_within_3_days","share_of_period_shipments_pct (this warehouse's shipments / all Albion shipments in that period, x100)"],"details":"Tables: shipments (carrier='Albion Parcel', shipped_at, delivered_at) joined via fulfillments (fulfillment_id, warehouse_id) -> warehouses (name, tenant_id) -> tenants (name). Same transit_days definition as the destination-country report. Round avg_transit_days to 2 decimals, pct columns to 1 decimal. Include only warehouses with at least 10 shipments in at least one of the two periods. Order by warehouse_name ascending, then period (baseline before collapse).","builds_on":["Albion Parcel transit performance by month, 2025"]},{"title":"Landfall Freight & Lowlands Express performance by destination country, full year 2025","question":"Are our two chronically weak carriers bad everywhere, or concentrated on specific lanes/countries?","grain":"one row per carrier and destination country (Landfall Freight and Lowlands Express only), full year 2025","columns":["carrier","destination_country","shipments_shipped","avg_transit_days","pct_within_3_days","share_of_carrier_shipments_pct (this country's shipments / that carrier's total 2025 shipments, x100)"],"details":"Tables: shipments (carrier in ('Landfall Freight','Lowlands Express'), shipped_at between 2025-01-01 and 2025-12-31, delivered_at) joined via fulfillments -> orders -> addresses (shipping_address_id) -> countries. transit_days = DATEDIFF(day, shipped_at, delivered_at), delivered subset only. Round avg_transit_days to 2 decimals, pct columns to 1 decimal. Include only countries with at least 15 shipments for that carrier in the year. Order by carrier ascending, then shipments_shipped descending.","builds_on":["Carrier transit performance by quarter, 2025","Carrier transit performance, Q4 2025 (complete)"]}]},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":110093,"cache_read_input_tokens":0,"output_tokens":6556,"output_tokens_details":{"thinking_tokens":4691},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":110093,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":6556,"cache_read_input_tokens":0,"cache_creation_input_tokens":110093,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":110093},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":{"cache_miss_reason":{"type":"previous_message_not_found"}},"context_management":null},"wireToolInputs":{"toolu_0194Sbptn7rp7p2rBZQ1fE4U":{"notes":"Priya wants the carrier narrative airtight before it goes anywhere, and specifically wants to know whether Albion Parcel's November collapse is a localized, fixable problem (one hub/country/merchant) versus Landfall Freight and Lowlands Express being broadly, chronically weak (a relationship-ending problem). That's priority #1 and it needs its own evidence on both the destination side (country/region) and the origin side (warehouse/hub), split into the pre-collapse baseline (Jan-Oct) vs. the collapse window (Nov-Dec) for Albion specifically. I'm spending all 3 slots tonight on that, since getting this distinction right shapes two very different conversations with carrier partners. I'll tackle priority #2 (the reliability \"ceiling\" if weak carriers matched good ones) first thing tomorrow night, using a full-year, non-truncated carrier table I don't yet have cleanly. Priority #3 (clean PO backlog view) and #4 (picking discrepancies vs. delivery) will follow later this week as time allows, per Priya's own prioritization.","requests":[{"title":"Albion Parcel performance by destination country, baseline vs collapse","question":"Is Albion Parcel's November-December breakdown concentrated in specific destination countries, or did it hit all regions roughly equally?","grain":"one row per destination country and period, for Albion Parcel shipments only","columns":["destination_country","period ('baseline' = shipped_at 2025-01-01 to 2025-10-31; 'collapse' = shipped_at 2025-11-01 to 2025-12-31)","shipments_shipped","avg_transit_days (shipped_at to delivered_at, delivered subset only)","pct_within_3_days","share_of_period_shipments_pct (this country's shipments / all Albion shipments in that period, x100)"],"details":"Tables: shipments (carrier='Albion Parcel', shipped_at, delivered_at) joined via fulfillments (fulfillment_id) -> orders (order_id) -> addresses (orders.shipping_address_id) -> countries (destination country name). transit_days = DATEDIFF(day, shipped_at, delivered_at), delivered subset only. Round avg_transit_days to 2 decimals, pct columns to 1 decimal. Include only countries with at least 15 shipments in at least one of the two periods (to avoid noise from one-off destinations). Order by destination_country ascending, then period (baseline before collapse).","builds_on":["Albion Parcel transit performance by month, 2025"]},{"title":"Albion Parcel performance by origin warehouse, baseline vs collapse","question":"Is Albion Parcel's November-December breakdown concentrated in a specific warehouse/hub (or the merchants it serves), or did it hit all origins roughly equally?","grain":"one row per origin warehouse and period, for Albion Parcel shipments only","columns":["warehouse_name","tenant_name","period ('baseline' = shipped_at 2025-01-01 to 2025-10-31; 'collapse' = shipped_at 2025-11-01 to 2025-12-31)","shipments_shipped","avg_transit_days","pct_within_3_days","share_of_period_shipments_pct (this warehouse's shipments / all Albion shipments in that period, x100)"],"details":"Tables: shipments (carrier='Albion Parcel', shipped_at, delivered_at) joined via fulfillments (fulfillment_id, warehouse_id) -> warehouses (name, tenant_id) -> tenants (name). Same transit_days definition as the destination-country report. Round avg_transit_days to 2 decimals, pct columns to 1 decimal. Include only warehouses with at least 10 shipments in at least one of the two periods. Order by warehouse_name ascending, then period (baseline before collapse).","builds_on":["Albion Parcel transit performance by month, 2025"]},{"title":"Landfall Freight & Lowlands Express performance by destination country, full year 2025","question":"Are our two chronically weak carriers bad everywhere, or concentrated on specific lanes/countries?","grain":"one row per carrier and destination country (Landfall Freight and Lowlands Express only), full year 2025","columns":["carrier","destination_country","shipments_shipped","avg_transit_days","pct_within_3_days","share_of_carrier_shipments_pct (this country's shipments / that carrier's total 2025 shipments, x100)"],"details":"Tables: shipments (carrier in ('Landfall Freight','Lowlands Express'), shipped_at between 2025-01-01 and 2025-12-31, delivered_at) joined via fulfillments -> orders -> addresses (shipping_address_id) -> countries. transit_days = DATEDIFF(day, shipped_at, delivered_at), delivered subset only. Round avg_transit_days to 2 decimals, pct columns to 1 decimal. Include only countries with at least 15 shipments for that carrier in the year. Order by carrier ascending, then shipments_shipped descending.","builds_on":["Carrier transit performance by quarter, 2025","Carrier transit performance, Q4 2025 (complete)"]}]}},"apiBlockIndex":1,"requestId":"req_011CfgNc4hwMkQvoR3a1LXQV","type":"assistant","uuid":"bf1ade3a-81af-47db-a580-ca6cb324194e","timestamp":"2026-10-04T02:02:57.905Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"bf1ade3a-81af-47db-a580-ca6cb324194e","isSidechain":false,"attachment":{"type":"structured_output","data":{"notes":"Priya wants the carrier narrative airtight before it goes anywhere, and specifically wants to know whether Albion Parcel's November collapse is a localized, fixable problem (one hub/country/merchant) versus Landfall Freight and Lowlands Express being broadly, chronically weak (a relationship-ending problem). That's priority #1 and it needs its own evidence on both the destination side (country/region) and the origin side (warehouse/hub), split into the pre-collapse baseline (Jan-Oct) vs. the collapse window (Nov-Dec) for Albion specifically. I'm spending all 3 slots tonight on that, since getting this distinction right shapes two very different conversations with carrier partners. I'll tackle priority #2 (the reliability \"ceiling\" if weak carriers matched good ones) first thing tomorrow night, using a full-year, non-truncated carrier table I don't yet have cleanly. Priority #3 (clean PO backlog view) and #4 (picking discrepancies vs. delivery) will follow later this week as time allows, per Priya's own prioritization.","requests":[{"title":"Albion Parcel performance by destination country, baseline vs collapse","question":"Is Albion Parcel's November-December breakdown concentrated in specific destination countries, or did it hit all regions roughly equally?","grain":"one row per destination country and period, for Albion Parcel shipments only","columns":["destination_country","period ('baseline' = shipped_at 2025-01-01 to 2025-10-31; 'collapse' = shipped_at 2025-11-01 to 2025-12-31)","shipments_shipped","avg_transit_days (shipped_at to delivered_at, delivered subset only)","pct_within_3_days","share_of_period_shipments_pct (this country's shipments / all Albion shipments in that period, x100)"],"details":"Tables: shipments (carrier='Albion Parcel', shipped_at, delivered_at) joined via fulfillments (fulfillment_id) -> orders (order_id) -> addresses (orders.shipping_address_id) -> countries (destination country name). transit_days = DATEDIFF(day, shipped_at, delivered_at), delivered subset only. Round avg_transit_days to 2 decimals, pct columns to 1 decimal. Include only countries with at least 15 shipments in at least one of the two periods (to avoid noise from one-off destinations). Order by destination_country ascending, then period (baseline before collapse).","builds_on":["Albion Parcel transit performance by month, 2025"]},{"title":"Albion Parcel performance by origin warehouse, baseline vs collapse","question":"Is Albion Parcel's November-December breakdown concentrated in a specific warehouse/hub (or the merchants it serves), or did it hit all origins roughly equally?","grain":"one row per origin warehouse and period, for Albion Parcel shipments only","columns":["warehouse_name","tenant_name","period ('baseline' = shipped_at 2025-01-01 to 2025-10-31; 'collapse' = shipped_at 2025-11-01 to 2025-12-31)","shipments_shipped","avg_transit_days","pct_within_3_days","share_of_period_shipments_pct (this warehouse's shipments / all Albion shipments in that period, x100)"],"details":"Tables: shipments (carrier='Albion Parcel', shipped_at, delivered_at) joined via fulfillments (fulfillment_id, warehouse_id) -> warehouses (name, tenant_id) -> tenants (name). Same transit_days definition as the destination-country report. Round avg_transit_days to 2 decimals, pct columns to 1 decimal. Include only warehouses with at least 10 shipments in at least one of the two periods. Order by warehouse_name ascending, then period (baseline before collapse).","builds_on":["Albion Parcel transit performance by month, 2025"]},{"title":"Landfall Freight & Lowlands Express performance by destination country, full year 2025","question":"Are our two chronically weak carriers bad everywhere, or concentrated on specific lanes/countries?","grain":"one row per carrier and destination country (Landfall Freight and Lowlands Express only), full year 2025","columns":["carrier","destination_country","shipments_shipped","avg_transit_days","pct_within_3_days","share_of_carrier_shipments_pct (this country's shipments / that carrier's total 2025 shipments, x100)"],"details":"Tables: shipments (carrier in ('Landfall Freight','Lowlands Express'), shipped_at between 2025-01-01 and 2025-12-31, delivered_at) joined via fulfillments -> orders -> addresses (shipping_address_id) -> countries. transit_days = DATEDIFF(day, shipped_at, delivered_at), delivered subset only. Round avg_transit_days to 2 decimals, pct columns to 1 decimal. Include only countries with at least 15 shipments for that carrier in the year. Order by carrier ascending, then shipments_shipped descending.","builds_on":["Carrier transit performance by quarter, 2025","Carrier transit performance, Q4 2025 (complete)"]}]},"toolUseID":"toolu_0194Sbptn7rp7p2rBZQ1fE4U"},"type":"attachment","uuid":"dcd72689-8b99-40a3-8c68-e6173f38fe5e","timestamp":"2026-10-04T02:02:57.911Z","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120","version":"2.1.275","gitBranch":"HEAD"}
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{"type":"last-prompt","lastPrompt":"Monday, week 2. Priya Raman replied to your memo: Subject: Carrier picture confirms it — now I want to know what we can actually fix Lena, This is exactly the clarity I was hoping for — \"it's the c…","leafUuid":"24fb8904-3a42-4626-9033-c2952f43474a","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120"}
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{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-04T02:03:36.694Z","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120","content":"The printed reports of night 1:\n\n=== Report 1: Albion Parcel performance by destination country, baseline vs collapse (written by Omar) ===\nEngineer's note: Countries qualify if they had >=15 Albion Parcel shipments in at least one of the two periods (baseline or collapse), per the request; both periods are then shown for every qualifying country. pct_within_3_days and avg_transit_days are computed over the delivered subset only, as specified.\nREPORT: Albion Parcel performance by destination country, baseline vs collapse\nQUESTION: Is Albion Parcel's November-December breakdown concentrated in specific destination countries, or did it hit all regions roughly equally?\ndestination_country | period | shipments_shipped | avg_transit_days | pct_within_3_days | share_of_period_shipments_pct\n--------------------+----------+-------------------+------------------+-------------------+------------------------------\nIreland | baseline | 147 | 2.30 | 91.8 | 6.9\nIreland | collapse | 44 | 8.81 | 35.7 | 8.0\nUnited Kingdom | baseline | 1982 | 2.19 | 93.0 | 93.1\nUnited Kingdom | collapse | 503 | 8.46 | 31.4 | 92.0\n(4 rows)\n\n=== Report 2: Albion Parcel performance by origin warehouse, baseline vs collapse (written by Omar) ===\nEngineer's note: avg_transit_days and pct_within_3_days are computed only over shipments with a non-null delivered_at (in-transit/undelivered shipments are excluded from those two measures but still count toward shipments_shipped and the share%); transit_days = delivered_at - shipped_at in days, matching the destination-country report's definition.\nREPORT: Albion Parcel performance by origin warehouse, baseline vs collapse\nQUESTION: Is Albion Parcel's November-December breakdown concentrated in a specific warehouse/hub (or the merchants it serves), or did it hit all origins roughly equally?\nwarehouse_name | tenant_name | period | shipments_shipped | avg_transit_days | pct_within_3_days | share_of_period_shipments_pct\n--------------------------------------+-------------------------------+----------+-------------------+------------------+-------------------+------------------------------\nBirmingham Fulfilment Centre | Folio & Fern | baseline | 21 | 2.75 | 66.7 | 1.0\nBirmingham Fulfilment Centre | Folio & Fern | collapse | 7 | 7.63 | 16.7 | 1.3\nBromley Fulfilment Centre | Kibblewick | baseline | 240 | 2.28 | 77.1 | 11.3\nBromley Fulfilment Centre | Petalcourt Beauty | baseline | 226 | 2.26 | 79.6 | 10.6\nBromley Fulfilment Centre | Kibblewick | collapse | 74 | 8.05 | 33.8 | 13.5\nBromley Fulfilment Centre | Petalcourt Beauty | collapse | 88 | 10.44 | 20.5 | 16.1\nCambridge Fulfilment Centre | Thistlecombe | baseline | 18 | 2.15 | 83.3 | 0.8\nCambridge Fulfilment Centre | Thistlecombe | collapse | 11 | 10.86 | 18.2 | 2.0\nDublin Fulfilment Centre | Sourdough Club | baseline | 59 | 2.26 | 76.3 | 2.8\nDublin Fulfilment Centre | Sourdough Club | collapse | 17 | 8.08 | 40.0 | 3.1\nExeter Fulfilment Centre | Kayak Corner | baseline | 31 | 1.96 | 87.1 | 1.5\nExeter Fulfilment Centre | Kayak Corner | collapse | 7 | 17.48 | 14.3 | 1.3\nGalway Fulfilment Centre | Ballycorran Industrial Supply | baseline | 73 | 2.39 | 72.6 | 3.4\nGalway Fulfilment Centre | Ballycorran Industrial Supply | collapse | 19 | 9.04 | 26.3 | 3.5\nLeeds Fulfilment Centre | The Balm Room | baseline | 44 | 2.45 | 68.2 | 2.1\nLeeds Fulfilment Centre | The Balm Room | collapse | 14 | 8.95 | 21.4 | 2.6\nLiverpool Fulfilment Centre | Fennel & Flint | baseline | 112 | 2.18 | 79.5 | 5.3\nLiverpool Fulfilment Centre | Fennel & Flint | collapse | 56 | 9.16 | 30.9 | 10.2\nManchester Fulfilment Centre | Willow & Wren | baseline | 30 | 2.0 | 83.3 | 1.4\nManchester Fulfilment Centre | Haven Linen | baseline | 10 | 1.95 | 90.0 | 0.5\nManchester Fulfilment Centre | Grind Collective | baseline | 399 | 2.22 | 81.0 | 18.7\nManchester Fulfilment Centre | Grind Collective | collapse | 116 | 8.31 | 26.1 | 21.2\nManchester Fulfilment Centre | Willow & Wren | collapse | 11 | 10.8 | 27.3 | 2.0\nManchester Fulfilment Centre | Haven Linen | collapse | 2 | 10.49 | 0.0 | 0.4\nNewcastle upon Tyne Fulfilment Centre | Deskline Supplies | baseline | 25 | 2.47 | 72.0 | 1.2\nNewcastle upon Tyne Fulfilment Centre | Deskline Supplies | collapse | 5 | 8.32 | 0.0 | 0.9\nOxford Fulfilment Centre | Tiny Atelier | baseline | 10 | 2.58 | 90.0 | 0.5\nOxford Fulfilment Centre | Tiny Atelier | collapse | 2 | 11.9 | 50.0 | 0.4\nPreston Fulfilment Centre | Rosehip Lab | baseline | 10 | 2.56 | 70.0 | 0.5\nPreston Fulfilment Centre | Rosehip Lab | collapse | 10 | 9.44 | 33.3 | 1.8\nReading Fulfilment Centre | Marrowby & Vane | baseline | 778 | 2.19 | 81.2 | 36.5\nReading Fulfilment Centre | Marrowby & Vane | collapse | 85 | 5.2 | 36.5 | 15.5\n(32 rows)\n\n=== Report 3: Landfall Freight & Lowlands Express performance by destination country, full year 2025 (written by Omar) ===\nEngineer's note: pct_within_3_days is computed as (delivered shipments with transit_days<=3) / all shipments_shipped for that carrier+country (undelivered shipments count as not-within-3-days); avg_transit_days is averaged over delivered shipments only, per the given transit_days definition. share_of_carrier_shipments_pct uses the carrier's full-year total across all countries (not just those meeting the 15-shipment threshold) as the denominator.\nREPORT: Landfall Freight & Lowlands Express performance by destination country, full year 2025\nQUESTION: Are our two chronically weak carriers bad everywhere, or concentrated on specific lanes/countries?\ncarrier | destination_country | shipments_shipped | avg_transit_days | pct_within_3_days | share_of_carrier_shipments_pct\n-----------------+---------------------+-------------------+------------------+-------------------+-------------------------------\nLandfall Freight | Ireland | 233 | 4.28 | 29.2 | 12.2\nLandfall Freight | Belgium | 187 | 4.34 | 26.7 | 9.8\nLandfall Freight | Netherlands | 182 | 5.94 | 19.8 | 9.5\nLandfall Freight | Germany | 177 | 6.15 | 14.1 | 9.3\nLandfall Freight | Austria | 161 | 4.3 | 29.2 | 8.4\nLandfall Freight | United Kingdom | 147 | 4.32 | 32.7 | 7.7\nLandfall Freight | United States | 141 | 4.79 | 24.8 | 7.4\nLandfall Freight | Switzerland | 139 | 4.42 | 25.9 | 7.3\nLandfall Freight | France | 120 | 6.37 | 17.5 | 6.3\nLandfall Freight | Canada | 85 | 4.2 | 37.6 | 4.5\nLandfall Freight | Poland | 62 | 4.18 | 35.5 | 3.3\nLandfall Freight | Finland | 56 | 6.04 | 19.6 | 2.9\nLandfall Freight | Australia | 45 | 4.73 | 20.0 | 2.4\nLandfall Freight | Luxembourg | 42 | 3.95 | 31.0 | 2.2\nLandfall Freight | Norway | 36 | 4.19 | 25.0 | 1.9\nLandfall Freight | Sweden | 29 | 5.54 | 6.9 | 1.5\nLandfall Freight | Denmark | 28 | 6.07 | 25.0 | 1.5\nLandfall Freight | Italy | 18 | 4.39 | 27.8 | 0.9\nLowlands Express | Netherlands | 1002 | 2.73 | 79.9 | 39.1\nLowlands Express | Belgium | 440 | 2.81 | 69.3 | 17.2\nLowlands Express | France | 211 | 3.69 | 63.0 | 8.2\nLowlands Express | Ireland | 154 | 4.42 | 27.3 | 6.0\nLowlands Express | Austria | 130 | 4.29 | 26.2 | 5.1\nLowlands Express | Germany | 124 | 6.15 | 16.9 | 4.8\nLowlands Express | Switzerland | 108 | 4.4 | 25.0 | 4.2\nLowlands Express | United States | 91 | 4.52 | 27.5 | 3.6\nLowlands Express | United Kingdom | 84 | 4.39 | 32.1 | 3.3\nLowlands Express | Poland | 42 | 4.56 | 23.8 | 1.6\nLowlands Express | Finland | 36 | 6.31 | 16.7 | 1.4\nLowlands Express | Luxembourg | 32 | 4.58 | 15.6 | 1.2\nLowlands Express | Denmark | 25 | 5.54 | 20.0 | 1.0\nLowlands Express | Norway | 22 | 4.32 | 36.4 | 0.9\nLowlands Express | Sweden | 21 | 6.71 | 14.3 | 0.8\nLowlands Express | Australia | 20 | 4.95 | 20.0 | 0.8\n(34 rows)\n\n\nNight 2 of 4: you have 3 report slots tonight."}
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{"parentUuid":"6872b83f-3172-4a51-b265-61ce355a7320","isSidechain":false,"type":"assistant","uuid":"c235ec16-d381-4b61-a930-6b4c4321b8cb","timestamp":"2026-10-04T02:03:36.687Z","message":{"diagnostics":null,"id":"e087ffb5-1129-417c-b4a3-afa465664788","container":null,"model":"","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":"e0b165aa-32dc-460c-8e74-dc2db9d40120","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"c235ec16-d381-4b61-a930-6b4c4321b8cb","isSidechain":false,"promptId":"850bc152-518c-4509-b9f7-912c49df5693","type":"user","message":{"role":"user","content":"The printed reports of night 1:\n\n=== Report 1: Albion Parcel performance by destination country, baseline vs collapse (written by Omar) ===\nEngineer's note: Countries qualify if they had >=15 Albion Parcel shipments in at least one of the two periods (baseline or collapse), per the request; both periods are then shown for every qualifying country. pct_within_3_days and avg_transit_days are computed over the delivered subset only, as specified.\nREPORT: Albion Parcel performance by destination country, baseline vs collapse\nQUESTION: Is Albion Parcel's November-December breakdown concentrated in specific destination countries, or did it hit all regions roughly equally?\ndestination_country | period | shipments_shipped | avg_transit_days | pct_within_3_days | share_of_period_shipments_pct\n--------------------+----------+-------------------+------------------+-------------------+------------------------------\nIreland | baseline | 147 | 2.30 | 91.8 | 6.9\nIreland | collapse | 44 | 8.81 | 35.7 | 8.0\nUnited Kingdom | baseline | 1982 | 2.19 | 93.0 | 93.1\nUnited Kingdom | collapse | 503 | 8.46 | 31.4 | 92.0\n(4 rows)\n\n=== Report 2: Albion Parcel performance by origin warehouse, baseline vs collapse (written by Omar) ===\nEngineer's note: avg_transit_days and pct_within_3_days are computed only over shipments with a non-null delivered_at (in-transit/undelivered shipments are excluded from those two measures but still count toward shipments_shipped and the share%); transit_days = delivered_at - shipped_at in days, matching the destination-country report's definition.\nREPORT: Albion Parcel performance by origin warehouse, baseline vs collapse\nQUESTION: Is Albion Parcel's November-December breakdown concentrated in a specific warehouse/hub (or the merchants it serves), or did it hit all origins roughly equally?\nwarehouse_name | tenant_name | period | shipments_shipped | avg_transit_days | pct_within_3_days | share_of_period_shipments_pct\n--------------------------------------+-------------------------------+----------+-------------------+------------------+-------------------+------------------------------\nBirmingham Fulfilment Centre | Folio & Fern | baseline | 21 | 2.75 | 66.7 | 1.0\nBirmingham Fulfilment Centre | Folio & Fern | collapse | 7 | 7.63 | 16.7 | 1.3\nBromley Fulfilment Centre | Kibblewick | baseline | 240 | 2.28 | 77.1 | 11.3\nBromley Fulfilment Centre | Petalcourt Beauty | baseline | 226 | 2.26 | 79.6 | 10.6\nBromley Fulfilment Centre | Kibblewick | collapse | 74 | 8.05 | 33.8 | 13.5\nBromley Fulfilment Centre | Petalcourt Beauty | collapse | 88 | 10.44 | 20.5 | 16.1\nCambridge Fulfilment Centre | Thistlecombe | baseline | 18 | 2.15 | 83.3 | 0.8\nCambridge Fulfilment Centre | Thistlecombe | collapse | 11 | 10.86 | 18.2 | 2.0\nDublin Fulfilment Centre | Sourdough Club | baseline | 59 | 2.26 | 76.3 | 2.8\nDublin Fulfilment Centre | Sourdough Club | collapse | 17 | 8.08 | 40.0 | 3.1\nExeter Fulfilment Centre | Kayak Corner | baseline | 31 | 1.96 | 87.1 | 1.5\nExeter Fulfilment Centre | Kayak Corner | collapse | 7 | 17.48 | 14.3 | 1.3\nGalway Fulfilment Centre | Ballycorran Industrial Supply | baseline | 73 | 2.39 | 72.6 | 3.4\nGalway Fulfilment Centre | Ballycorran Industrial Supply | collapse | 19 | 9.04 | 26.3 | 3.5\nLeeds Fulfilment Centre | The Balm Room | baseline | 44 | 2.45 | 68.2 | 2.1\nLeeds Fulfilment Centre | The Balm Room | collapse | 14 | 8.95 | 21.4 | 2.6\nLiverpool Fulfilment Centre | Fennel & Flint | baseline | 112 | 2.18 | 79.5 | 5.3\nLiverpool Fulfilment Centre | Fennel & Flint | collapse | 56 | 9.16 | 30.9 | 10.2\nManchester Fulfilment Centre | Willow & Wren | baseline | 30 | 2.0 | 83.3 | 1.4\nManchester Fulfilment Centre | Haven Linen | baseline | 10 | 1.95 | 90.0 | 0.5\nManchester Fulfilment Centre | Grind Collective | baseline | 399 | 2.22 | 81.0 | 18.7\nManchester Fulfilment Centre | Grind Collective | collapse | 116 | 8.31 | 26.1 | 21.2\nManchester Fulfilment Centre | Willow & Wren | collapse | 11 | 10.8 | 27.3 | 2.0\nManchester Fulfilment Centre | Haven Linen | collapse | 2 | 10.49 | 0.0 | 0.4\nNewcastle upon Tyne Fulfilment Centre | Deskline Supplies | baseline | 25 | 2.47 | 72.0 | 1.2\nNewcastle upon Tyne Fulfilment Centre | Deskline Supplies | collapse | 5 | 8.32 | 0.0 | 0.9\nOxford Fulfilment Centre | Tiny Atelier | baseline | 10 | 2.58 | 90.0 | 0.5\nOxford Fulfilment Centre | Tiny Atelier | collapse | 2 | 11.9 | 50.0 | 0.4\nPreston Fulfilment Centre | Rosehip Lab | baseline | 10 | 2.56 | 70.0 | 0.5\nPreston Fulfilment Centre | Rosehip Lab | collapse | 10 | 9.44 | 33.3 | 1.8\nReading Fulfilment Centre | Marrowby & Vane | baseline | 778 | 2.19 | 81.2 | 36.5\nReading Fulfilment Centre | Marrowby & Vane | collapse | 85 | 5.2 | 36.5 | 15.5\n(32 rows)\n\n=== Report 3: Landfall Freight & Lowlands Express performance by destination country, full year 2025 (written by Omar) ===\nEngineer's note: pct_within_3_days is computed as (delivered shipments with transit_days<=3) / all shipments_shipped for that carrier+country (undelivered shipments count as not-within-3-days); avg_transit_days is averaged over delivered shipments only, per the given transit_days definition. share_of_carrier_shipments_pct uses the carrier's full-year total across all countries (not just those meeting the 15-shipment threshold) as the denominator.\nREPORT: Landfall Freight & Lowlands Express performance by destination country, full year 2025\nQUESTION: Are our two chronically weak carriers bad everywhere, or concentrated on specific lanes/countries?\ncarrier | destination_country | shipments_shipped | avg_transit_days | pct_within_3_days | share_of_carrier_shipments_pct\n-----------------+---------------------+-------------------+------------------+-------------------+-------------------------------\nLandfall Freight | Ireland | 233 | 4.28 | 29.2 | 12.2\nLandfall Freight | Belgium | 187 | 4.34 | 26.7 | 9.8\nLandfall Freight | Netherlands | 182 | 5.94 | 19.8 | 9.5\nLandfall Freight | Germany | 177 | 6.15 | 14.1 | 9.3\nLandfall Freight | Austria | 161 | 4.3 | 29.2 | 8.4\nLandfall Freight | United Kingdom | 147 | 4.32 | 32.7 | 7.7\nLandfall Freight | United States | 141 | 4.79 | 24.8 | 7.4\nLandfall Freight | Switzerland | 139 | 4.42 | 25.9 | 7.3\nLandfall Freight | France | 120 | 6.37 | 17.5 | 6.3\nLandfall Freight | Canada | 85 | 4.2 | 37.6 | 4.5\nLandfall Freight | Poland | 62 | 4.18 | 35.5 | 3.3\nLandfall Freight | Finland | 56 | 6.04 | 19.6 | 2.9\nLandfall Freight | Australia | 45 | 4.73 | 20.0 | 2.4\nLandfall Freight | Luxembourg | 42 | 3.95 | 31.0 | 2.2\nLandfall Freight | Norway | 36 | 4.19 | 25.0 | 1.9\nLandfall Freight | Sweden | 29 | 5.54 | 6.9 | 1.5\nLandfall Freight | Denmark | 28 | 6.07 | 25.0 | 1.5\nLandfall Freight | Italy | 18 | 4.39 | 27.8 | 0.9\nLowlands Express | Netherlands | 1002 | 2.73 | 79.9 | 39.1\nLowlands Express | Belgium | 440 | 2.81 | 69.3 | 17.2\nLowlands Express | France | 211 | 3.69 | 63.0 | 8.2\nLowlands Express | Ireland | 154 | 4.42 | 27.3 | 6.0\nLowlands Express | Austria | 130 | 4.29 | 26.2 | 5.1\nLowlands Express | Germany | 124 | 6.15 | 16.9 | 4.8\nLowlands Express | Switzerland | 108 | 4.4 | 25.0 | 4.2\nLowlands Express | United States | 91 | 4.52 | 27.5 | 3.6\nLowlands Express | United Kingdom | 84 | 4.39 | 32.1 | 3.3\nLowlands Express | Poland | 42 | 4.56 | 23.8 | 1.6\nLowlands Express | Finland | 36 | 6.31 | 16.7 | 1.4\nLowlands Express | Luxembourg | 32 | 4.58 | 15.6 | 1.2\nLowlands Express | Denmark | 25 | 5.54 | 20.0 | 1.0\nLowlands Express | Norway | 22 | 4.32 | 36.4 | 0.9\nLowlands Express | Sweden | 21 | 6.71 | 14.3 | 0.8\nLowlands Express | Australia | 20 | 4.95 | 20.0 | 0.8\n(34 rows)\n\n\nNight 2 of 4: you have 3 report slots tonight."},"uuid":"839c015f-72b9-4f21-865a-1f7399d0e9be","timestamp":"2026-10-04T02:03:36.699Z","permissionMode":"default","promptSource":"sdk","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120","version":"2.1.275","gitBranch":"HEAD"}
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{"parentUuid":"3fd21cef-0eba-41bb-bf24-03210dfe5730","isSidechain":false,"message":{"model":"claude-sonnet-5","id":"msg_011CfgNjdEEX6xwaAELbeJTC","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01UvUf9TFg9oeAaf5BEzsDwD","name":"StructuredOutput","input":{"notes":"Night 1 resolved priority #1 cleanly, and the answer is more nuanced than \"weak carrier = weak carrier\": (a) Albion Parcel's collapse is NOT localized -- it hit both countries it serves (UK and Ireland) and literally every origin warehouse/merchant it ships for, uniformly, the moment November started (pct-within-3-days fell from ~70-93% to ~0-40% everywhere at once, transit days jumping from ~2-2.5 to 8-17 days across the board). That rules out \"one bad depot\" or \"one bad merchant\" and points to a systemic capacity failure at Albion itself -- useful to know before the carrier conversation, but it's a harder story (a carrier that broke under load everywhere), not an easy single-fix. (b) Landfall Freight is uniformly bad across all 18 countries it serves (14-38% within 3 days everywhere, no bright spots) -- genuinely a \"this carrier can't be trusted\" case. (c) Lowlands Express is the interesting exception: it's actually solid on its home turf (Netherlands 79.9%, Belgium 69.3%, France 63.0% within 3 days -- 64.5% of its volume) but poor everywhere else (15-36% within 3 days on the remaining 35.5%). That's a lane-reassignment fix, not a carrier breakup. Tonight I'm covering priority #2 (the reliability ceiling, using a clean full-year carrier table so I can compute the weighted improvement by hand) and starting on #3 (a non-censored PO view, restricting to purchase orders old enough to have fully played out) and #4 (whether the apparel/footwear picking discrepancies Fatima's team flagged show up in delivery shortfalls, not just inventory accounting).","requests":[{"title":"Carrier performance, full year 2025 (complete)","question":"What is each carrier's full-year transit reliability and volume share, so I can estimate how much overall unreliability would disappear if the weak carriers matched the good ones?","grain":"one row per carrier, full year 2025","columns":["carrier","shipments_shipped (shipped_at in 2025)","shipments_delivered","avg_transit_days","median_transit_days","pct_within_3_days","share_of_total_shipments_pct (this carrier's shipments / all carriers' shipments in 2025, x100)"],"details":"Table: shipments. Scope: shipped_at between 2025-01-01 and 2025-12-31, all carriers, grouped by carrier (no row cap needed -- return every carrier, expect ~14 rows). transit_days = DATEDIFF(day, shipped_at, delivered_at), delivered subset only. Round day metrics to 2 decimals, pct columns to 1 decimal. Order by shipments_shipped descending, tie-break by carrier ascending.","builds_on":["Carrier transit performance, Q4 2025 (complete)","Carrier transit performance by quarter, 2025"]},{"title":"Purchase order replenishment performance by quarter, 2025 (mature POs only, non-censored)","question":"Once we only look at purchase orders old enough to have fully played out, are we genuinely behind on restocking, or was the Q4 backlog just a year-end timing artifact?","grain":"one row per calendar quarter of ordered_at, 2025","columns":["quarter (e.g. 2025-Q1)","pos_ordered_total (all POs with ordered_at in that quarter, for reference)","pos_matured (subset of pos_ordered_total with ordered_at <= 2025-11-01, i.e. at least 60 days elapsed before the 2025-12-31 cutoff)","pos_matured_received (matured subset with received_at not null)","pct_matured_still_open (matured subset with received_at null and status in ('submitted','partially_received') -- a true backlog signal, not a recency artifact)","avg_actual_lead_days (DATEDIFF(day, ordered_at, received_at), matured+received subset only)","pct_received_late (matured subset with both expected_at and received_at set, where received_at > expected_at)","avg_days_late (matured, late subset only)"],"details":"Table: purchase_orders. Scope: ordered_at between 2025-01-01 and 2025-12-31, grouped by calendar quarter of ordered_at. The 60-day maturity threshold (ordered_at <= 2025-11-01) is chosen because the longest contracted supplier lead time we've seen is ~43 days, so 60 days gives real buffer for a PO to have resolved one way or the other by the data cutoff; note in the report that Q4's matured subset will only reflect POs ordered in October (since November/December POs aren't yet mature), which is expected and intentional. Round day metrics to 1 decimal, pct columns to 1 decimal. Order by quarter ascending. Expect 4 rows.","builds_on":["Purchase order replenishment performance by quarter, 2025"]},{"title":"Fulfillment shortfall and delivery speed by merchant vertical, 2025","question":"Do apparel/footwear merchants, where picking discrepancies are concentrated, actually see more orders go unfulfilled or delivered slowly -- or is that purely an inventory/accounting issue with no customer-facing effect?","grain":"one row per merchant industry/vertical (tenants.industry), 2025","columns":["industry","orders_placed_2025 (orders with placed_at in 2025 and a terminal status: fulfilled, cancelled, or refunded)","pct_orders_with_fulfillment_shortfall (share of those orders where SUM(order_items.quantity_fulfilled) < SUM(order_items.quantity) for the order)","orders_fully_delivered (subset of those orders where status='fulfilled' and every linked shipment, via fulfillments, has a non-null delivered_at)","avg_delivery_days (placed_at to max linked shipment delivered_at, fully-delivered subset only)","pct_within_5_days (fully-delivered subset only)"],"details":"Tables: orders, order_items, fulfillments, shipments, tenants (industry). Scope: orders.placed_at between 2025-01-01 and 2025-12-31, status in ('fulfilled','cancelled','refunded') for the shortfall calculation (terminal orders only, so the shortfall flag is a final-state fact, not an in-progress one). Group by tenants.industry. Round pct columns to 1 decimal, avg_delivery_days to 2 decimals. Order by pct_orders_with_fulfillment_shortfall descending, tie-break by industry ascending. Expect under 12 rows (one per distinct industry value).","builds_on":["Fulfillment performance by warehouse, 2025"]}]},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":10632,"cache_read_input_tokens":110093,"output_tokens":9628,"output_tokens_details":{"thinking_tokens":7256},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":10632,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":9628,"cache_read_input_tokens":110093,"cache_creation_input_tokens":10632,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":10632},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"wireToolInputs":{"toolu_01UvUf9TFg9oeAaf5BEzsDwD":{"notes":"Night 1 resolved priority #1 cleanly, and the answer is more nuanced than \"weak carrier = weak carrier\": (a) Albion Parcel's collapse is NOT localized -- it hit both countries it serves (UK and Ireland) and literally every origin warehouse/merchant it ships for, uniformly, the moment November started (pct-within-3-days fell from ~70-93% to ~0-40% everywhere at once, transit days jumping from ~2-2.5 to 8-17 days across the board). That rules out \"one bad depot\" or \"one bad merchant\" and points to a systemic capacity failure at Albion itself -- useful to know before the carrier conversation, but it's a harder story (a carrier that broke under load everywhere), not an easy single-fix. (b) Landfall Freight is uniformly bad across all 18 countries it serves (14-38% within 3 days everywhere, no bright spots) -- genuinely a \"this carrier can't be trusted\" case. (c) Lowlands Express is the interesting exception: it's actually solid on its home turf (Netherlands 79.9%, Belgium 69.3%, France 63.0% within 3 days -- 64.5% of its volume) but poor everywhere else (15-36% within 3 days on the remaining 35.5%). That's a lane-reassignment fix, not a carrier breakup. Tonight I'm covering priority #2 (the reliability ceiling, using a clean full-year carrier table so I can compute the weighted improvement by hand) and starting on #3 (a non-censored PO view, restricting to purchase orders old enough to have fully played out) and #4 (whether the apparel/footwear picking discrepancies Fatima's team flagged show up in delivery shortfalls, not just inventory accounting).","requests":[{"title":"Carrier performance, full year 2025 (complete)","question":"What is each carrier's full-year transit reliability and volume share, so I can estimate how much overall unreliability would disappear if the weak carriers matched the good ones?","grain":"one row per carrier, full year 2025","columns":["carrier","shipments_shipped (shipped_at in 2025)","shipments_delivered","avg_transit_days","median_transit_days","pct_within_3_days","share_of_total_shipments_pct (this carrier's shipments / all carriers' shipments in 2025, x100)"],"details":"Table: shipments. Scope: shipped_at between 2025-01-01 and 2025-12-31, all carriers, grouped by carrier (no row cap needed -- return every carrier, expect ~14 rows). transit_days = DATEDIFF(day, shipped_at, delivered_at), delivered subset only. Round day metrics to 2 decimals, pct columns to 1 decimal. Order by shipments_shipped descending, tie-break by carrier ascending.","builds_on":["Carrier transit performance, Q4 2025 (complete)","Carrier transit performance by quarter, 2025"]},{"title":"Purchase order replenishment performance by quarter, 2025 (mature POs only, non-censored)","question":"Once we only look at purchase orders old enough to have fully played out, are we genuinely behind on restocking, or was the Q4 backlog just a year-end timing artifact?","grain":"one row per calendar quarter of ordered_at, 2025","columns":["quarter (e.g. 2025-Q1)","pos_ordered_total (all POs with ordered_at in that quarter, for reference)","pos_matured (subset of pos_ordered_total with ordered_at <= 2025-11-01, i.e. at least 60 days elapsed before the 2025-12-31 cutoff)","pos_matured_received (matured subset with received_at not null)","pct_matured_still_open (matured subset with received_at null and status in ('submitted','partially_received') -- a true backlog signal, not a recency artifact)","avg_actual_lead_days (DATEDIFF(day, ordered_at, received_at), matured+received subset only)","pct_received_late (matured subset with both expected_at and received_at set, where received_at > expected_at)","avg_days_late (matured, late subset only)"],"details":"Table: purchase_orders. Scope: ordered_at between 2025-01-01 and 2025-12-31, grouped by calendar quarter of ordered_at. The 60-day maturity threshold (ordered_at <= 2025-11-01) is chosen because the longest contracted supplier lead time we've seen is ~43 days, so 60 days gives real buffer for a PO to have resolved one way or the other by the data cutoff; note in the report that Q4's matured subset will only reflect POs ordered in October (since November/December POs aren't yet mature), which is expected and intentional. Round day metrics to 1 decimal, pct columns to 1 decimal. Order by quarter ascending. Expect 4 rows.","builds_on":["Purchase order replenishment performance by quarter, 2025"]},{"title":"Fulfillment shortfall and delivery speed by merchant vertical, 2025","question":"Do apparel/footwear merchants, where picking discrepancies are concentrated, actually see more orders go unfulfilled or delivered slowly -- or is that purely an inventory/accounting issue with no customer-facing effect?","grain":"one row per merchant industry/vertical (tenants.industry), 2025","columns":["industry","orders_placed_2025 (orders with placed_at in 2025 and a terminal status: fulfilled, cancelled, or refunded)","pct_orders_with_fulfillment_shortfall (share of those orders where SUM(order_items.quantity_fulfilled) < SUM(order_items.quantity) for the order)","orders_fully_delivered (subset of those orders where status='fulfilled' and every linked shipment, via fulfillments, has a non-null delivered_at)","avg_delivery_days (placed_at to max linked shipment delivered_at, fully-delivered subset only)","pct_within_5_days (fully-delivered subset only)"],"details":"Tables: orders, order_items, fulfillments, shipments, tenants (industry). Scope: orders.placed_at between 2025-01-01 and 2025-12-31, status in ('fulfilled','cancelled','refunded') for the shortfall calculation (terminal orders only, so the shortfall flag is a final-state fact, not an in-progress one). Group by tenants.industry. Round pct columns to 1 decimal, avg_delivery_days to 2 decimals. Order by pct_orders_with_fulfillment_shortfall descending, tie-break by industry ascending. Expect under 12 rows (one per distinct industry value).","builds_on":["Fulfillment performance by warehouse, 2025"]}]}},"apiBlockIndex":1,"requestId":"req_011CfgNjcuNnYfHnbPqTX1os","type":"assistant","uuid":"db529965-b890-472b-800e-1f54a8544bc6","timestamp":"2026-10-04T02:05:12.156Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"db529965-b890-472b-800e-1f54a8544bc6","isSidechain":false,"attachment":{"type":"structured_output","data":{"notes":"Night 1 resolved priority #1 cleanly, and the answer is more nuanced than \"weak carrier = weak carrier\": (a) Albion Parcel's collapse is NOT localized -- it hit both countries it serves (UK and Ireland) and literally every origin warehouse/merchant it ships for, uniformly, the moment November started (pct-within-3-days fell from ~70-93% to ~0-40% everywhere at once, transit days jumping from ~2-2.5 to 8-17 days across the board). That rules out \"one bad depot\" or \"one bad merchant\" and points to a systemic capacity failure at Albion itself -- useful to know before the carrier conversation, but it's a harder story (a carrier that broke under load everywhere), not an easy single-fix. (b) Landfall Freight is uniformly bad across all 18 countries it serves (14-38% within 3 days everywhere, no bright spots) -- genuinely a \"this carrier can't be trusted\" case. (c) Lowlands Express is the interesting exception: it's actually solid on its home turf (Netherlands 79.9%, Belgium 69.3%, France 63.0% within 3 days -- 64.5% of its volume) but poor everywhere else (15-36% within 3 days on the remaining 35.5%). That's a lane-reassignment fix, not a carrier breakup. Tonight I'm covering priority #2 (the reliability ceiling, using a clean full-year carrier table so I can compute the weighted improvement by hand) and starting on #3 (a non-censored PO view, restricting to purchase orders old enough to have fully played out) and #4 (whether the apparel/footwear picking discrepancies Fatima's team flagged show up in delivery shortfalls, not just inventory accounting).","requests":[{"title":"Carrier performance, full year 2025 (complete)","question":"What is each carrier's full-year transit reliability and volume share, so I can estimate how much overall unreliability would disappear if the weak carriers matched the good ones?","grain":"one row per carrier, full year 2025","columns":["carrier","shipments_shipped (shipped_at in 2025)","shipments_delivered","avg_transit_days","median_transit_days","pct_within_3_days","share_of_total_shipments_pct (this carrier's shipments / all carriers' shipments in 2025, x100)"],"details":"Table: shipments. Scope: shipped_at between 2025-01-01 and 2025-12-31, all carriers, grouped by carrier (no row cap needed -- return every carrier, expect ~14 rows). transit_days = DATEDIFF(day, shipped_at, delivered_at), delivered subset only. Round day metrics to 2 decimals, pct columns to 1 decimal. Order by shipments_shipped descending, tie-break by carrier ascending.","builds_on":["Carrier transit performance, Q4 2025 (complete)","Carrier transit performance by quarter, 2025"]},{"title":"Purchase order replenishment performance by quarter, 2025 (mature POs only, non-censored)","question":"Once we only look at purchase orders old enough to have fully played out, are we genuinely behind on restocking, or was the Q4 backlog just a year-end timing artifact?","grain":"one row per calendar quarter of ordered_at, 2025","columns":["quarter (e.g. 2025-Q1)","pos_ordered_total (all POs with ordered_at in that quarter, for reference)","pos_matured (subset of pos_ordered_total with ordered_at <= 2025-11-01, i.e. at least 60 days elapsed before the 2025-12-31 cutoff)","pos_matured_received (matured subset with received_at not null)","pct_matured_still_open (matured subset with received_at null and status in ('submitted','partially_received') -- a true backlog signal, not a recency artifact)","avg_actual_lead_days (DATEDIFF(day, ordered_at, received_at), matured+received subset only)","pct_received_late (matured subset with both expected_at and received_at set, where received_at > expected_at)","avg_days_late (matured, late subset only)"],"details":"Table: purchase_orders. Scope: ordered_at between 2025-01-01 and 2025-12-31, grouped by calendar quarter of ordered_at. The 60-day maturity threshold (ordered_at <= 2025-11-01) is chosen because the longest contracted supplier lead time we've seen is ~43 days, so 60 days gives real buffer for a PO to have resolved one way or the other by the data cutoff; note in the report that Q4's matured subset will only reflect POs ordered in October (since November/December POs aren't yet mature), which is expected and intentional. Round day metrics to 1 decimal, pct columns to 1 decimal. Order by quarter ascending. Expect 4 rows.","builds_on":["Purchase order replenishment performance by quarter, 2025"]},{"title":"Fulfillment shortfall and delivery speed by merchant vertical, 2025","question":"Do apparel/footwear merchants, where picking discrepancies are concentrated, actually see more orders go unfulfilled or delivered slowly -- or is that purely an inventory/accounting issue with no customer-facing effect?","grain":"one row per merchant industry/vertical (tenants.industry), 2025","columns":["industry","orders_placed_2025 (orders with placed_at in 2025 and a terminal status: fulfilled, cancelled, or refunded)","pct_orders_with_fulfillment_shortfall (share of those orders where SUM(order_items.quantity_fulfilled) < SUM(order_items.quantity) for the order)","orders_fully_delivered (subset of those orders where status='fulfilled' and every linked shipment, via fulfillments, has a non-null delivered_at)","avg_delivery_days (placed_at to max linked shipment delivered_at, fully-delivered subset only)","pct_within_5_days (fully-delivered subset only)"],"details":"Tables: orders, order_items, fulfillments, shipments, tenants (industry). Scope: orders.placed_at between 2025-01-01 and 2025-12-31, status in ('fulfilled','cancelled','refunded') for the shortfall calculation (terminal orders only, so the shortfall flag is a final-state fact, not an in-progress one). Group by tenants.industry. Round pct columns to 1 decimal, avg_delivery_days to 2 decimals. Order by pct_orders_with_fulfillment_shortfall descending, tie-break by industry ascending. Expect under 12 rows (one per distinct industry value).","builds_on":["Fulfillment performance by warehouse, 2025"]}]},"toolUseID":"toolu_01UvUf9TFg9oeAaf5BEzsDwD"},"type":"attachment","uuid":"20075487-dd06-4c56-ad07-32b2c2df017d","timestamp":"2026-10-04T02:05:12.163Z","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120","version":"2.1.275","gitBranch":"HEAD"}
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{"type":"last-prompt","lastPrompt":"The printed reports of night 1: === Report 1: Albion Parcel performance by destination country, baseline vs collapse (written by Omar) === Engineer's note: Countries qualify if they had >=15 Albion P…","leafUuid":"3a491980-8644-4368-8880-6f3549599bad","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120"}
{"type":"ai-title","aiTitle":"Fulfillment delivery performance analysis","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120"}
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{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-04T02:05:54.076Z","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120","content":"The printed reports of night 2:\n\n=== Report 1: Carrier performance, full year 2025 (complete) (written by Ines) ===\nEngineer's note: transit_days computed as delivered_at::date - shipped_at::date (whole calendar days); avg/median/pct_within_3_days are based only on the delivered subset per carrier, while shipments_shipped and share_of_total_shipments_pct cover all shipped shipments regardless of delivery status.\nREPORT: Carrier performance, full year 2025 (complete)\nQUESTION: What is each carrier's full-year transit reliability and volume share, so I can estimate how much overall unreliability would disappear if the weak carriers matched the good ones?\ncarrier | shipments_shipped | shipments_delivered | avg_transit_days | median_transit_days | pct_within_3_days | share_of_total_shipments_pct\n---------------------+-------------------+---------------------+------------------+---------------------+-------------------+-----------------------------\nStrombogen Kurier | 2972 | 2944 | 2.14 | 2.00 | 93.8 | 13.9\nAlbion Parcel | 2676 | 2661 | 3.46 | 2.00 | 80.7 | 12.5\nLowlands Express | 2561 | 2524 | 3.56 | 3.00 | 57.9 | 11.9\nLiberty Ground | 2343 | 2319 | 2.23 | 2.00 | 90.1 | 10.9\nAlpenpaket | 2022 | 2014 | 2.17 | 2.00 | 92.5 | 9.4\nLandfall Freight | 1906 | 1878 | 4.89 | 5.00 | 25.7 | 8.9\nTrailhead Express | 1879 | 1860 | 2.46 | 2.00 | 83.7 | 8.8\nSkerrylane Logistics | 1124 | 1116 | 2.50 | 2.00 | 81.0 | 5.2\nKennet Freight | 909 | 898 | 2.31 | 2.00 | 88.1 | 4.2\nPolder Post | 902 | 892 | 2.57 | 2.00 | 79.0 | 4.2\nRegent Parcels | 798 | 789 | 2.30 | 2.00 | 88.7 | 3.7\nPrairie Line Express | 748 | 741 | 2.54 | 2.00 | 81.5 | 3.5\nLiffey Couriers | 323 | 322 | 2.71 | 2.00 | 76.1 | 1.5\nGallia Colis | 284 | 280 | 2.82 | 2.00 | 74.6 | 1.3\n(14 rows)\n\n=== Report 2: Purchase order replenishment performance by quarter, 2025 (mature POs only, non-censored) (written by Felix) ===\nEngineer's note: Aggregated across all tenants/POs platform-wide (no merchant filter requested). As noted in the request, Q4's matured subset only covers POs ordered in October since Nov/Dec POs aren't yet 60 days old as of the 2025-12-31 cutoff - this is expected, not a data gap.\nREPORT: Purchase order replenishment performance by quarter, 2025 (mature POs only, non-censored)\nQUESTION: Once we only look at purchase orders old enough to have fully played out, are we genuinely behind on restocking, or was the Q4 backlog just a year-end timing artifact?\nquarter | pos_ordered_total | pos_matured | pos_matured_received | pct_matured_still_open | avg_actual_lead_days | pct_received_late | avg_days_late\n--------+-------------------+-------------+----------------------+------------------------+----------------------+-------------------+--------------\n2025-Q1 | 1183 | 1183 | 1183 | 0.0 | 22.4 | 52.9 | 6.1\n2025-Q2 | 1209 | 1209 | 1209 | 0.0 | 20.0 | 47.4 | 3.8\n2025-Q3 | 1238 | 1238 | 1238 | 0.0 | 20.8 | 54.0 | 4.8\n2025-Q4 | 1588 | 464 | 460 | 0.9 | 20.7 | 59.3 | 4.5\n(4 rows)\n\n=== Report 3: Fulfillment shortfall and delivery speed by merchant vertical, 2025 (written by Bea) ===\nEngineer's note: \"Fully delivered\" requires the order to have at least one linked shipment (via fulfillments) and none of them null delivered_at; orders with status='fulfilled' but no shipment rows at all are excluded from that subset rather than counted as delivered. Tenants with a null industry are excluded since the grain is per-vertical.\nREPORT: Fulfillment shortfall and delivery speed by merchant vertical, 2025\nQUESTION: Do apparel/footwear merchants, where picking discrepancies are concentrated, actually see more orders go unfulfilled or delivered slowly -- or is that purely an inventory/accounting issue with no customer-facing effect?\nindustry | orders_placed_2025 | pct_orders_with_fulfillment_shortfall | orders_fully_delivered | avg_delivery_days | pct_within_5_days\n--------------------------+--------------------+---------------------------------------+------------------------+-------------------+------------------\nBeauty | 1561 | 4.7 | 1451 | 4.55 | 73.8\nApparel & Footwear | 7149 | 4.5 | 6264 | 3.84 | 82.4\nSports & Outdoor | 1014 | 4.2 | 916 | 4.05 | 77.8\nHome & Living | 1658 | 4.0 | 1519 | 4.43 | 73.7\nJewelry & Accessories | 50 | 4.0 | 46 | 4.23 | 73.9\nConsumer Electronics | 575 | 3.7 | 534 | 4.08 | 76.2\nBooks & Stationery | 159 | 3.1 | 151 | 4.16 | 80.1\nPet Supplies | 1448 | 3.0 | 1385 | 4.28 | 77.8\nOffice Supplies (B2B) | 526 | 1.9 | 512 | 3.95 | 77.3\nFood & Coffee | 7210 | 1.7 | 6997 | 3.93 | 80.5\nIndustrial Supplies (B2B) | 773 | 1.6 | 758 | 4.26 | 72.2\n(11 rows)\n\n\nNight 3 of 4: you have 3 report slots tonight."}
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{"parentUuid":"342f8fed-2428-4a33-8dd6-93b8214e9f8f","isSidechain":false,"type":"assistant","uuid":"0a5fbaf6-ab48-4ac6-b5f1-9ecfab4bed9c","timestamp":"2026-10-04T02:05:54.069Z","message":{"diagnostics":null,"id":"1e8e0bc3-d53a-4847-8e2a-ae26d28c10e3","container":null,"model":"","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":"e0b165aa-32dc-460c-8e74-dc2db9d40120","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"0a5fbaf6-ab48-4ac6-b5f1-9ecfab4bed9c","isSidechain":false,"promptId":"e3b8bfbe-8b27-4ef7-9cdc-482012ed1bf9","type":"user","message":{"role":"user","content":"The printed reports of night 2:\n\n=== Report 1: Carrier performance, full year 2025 (complete) (written by Ines) ===\nEngineer's note: transit_days computed as delivered_at::date - shipped_at::date (whole calendar days); avg/median/pct_within_3_days are based only on the delivered subset per carrier, while shipments_shipped and share_of_total_shipments_pct cover all shipped shipments regardless of delivery status.\nREPORT: Carrier performance, full year 2025 (complete)\nQUESTION: What is each carrier's full-year transit reliability and volume share, so I can estimate how much overall unreliability would disappear if the weak carriers matched the good ones?\ncarrier | shipments_shipped | shipments_delivered | avg_transit_days | median_transit_days | pct_within_3_days | share_of_total_shipments_pct\n---------------------+-------------------+---------------------+------------------+---------------------+-------------------+-----------------------------\nStrombogen Kurier | 2972 | 2944 | 2.14 | 2.00 | 93.8 | 13.9\nAlbion Parcel | 2676 | 2661 | 3.46 | 2.00 | 80.7 | 12.5\nLowlands Express | 2561 | 2524 | 3.56 | 3.00 | 57.9 | 11.9\nLiberty Ground | 2343 | 2319 | 2.23 | 2.00 | 90.1 | 10.9\nAlpenpaket | 2022 | 2014 | 2.17 | 2.00 | 92.5 | 9.4\nLandfall Freight | 1906 | 1878 | 4.89 | 5.00 | 25.7 | 8.9\nTrailhead Express | 1879 | 1860 | 2.46 | 2.00 | 83.7 | 8.8\nSkerrylane Logistics | 1124 | 1116 | 2.50 | 2.00 | 81.0 | 5.2\nKennet Freight | 909 | 898 | 2.31 | 2.00 | 88.1 | 4.2\nPolder Post | 902 | 892 | 2.57 | 2.00 | 79.0 | 4.2\nRegent Parcels | 798 | 789 | 2.30 | 2.00 | 88.7 | 3.7\nPrairie Line Express | 748 | 741 | 2.54 | 2.00 | 81.5 | 3.5\nLiffey Couriers | 323 | 322 | 2.71 | 2.00 | 76.1 | 1.5\nGallia Colis | 284 | 280 | 2.82 | 2.00 | 74.6 | 1.3\n(14 rows)\n\n=== Report 2: Purchase order replenishment performance by quarter, 2025 (mature POs only, non-censored) (written by Felix) ===\nEngineer's note: Aggregated across all tenants/POs platform-wide (no merchant filter requested). As noted in the request, Q4's matured subset only covers POs ordered in October since Nov/Dec POs aren't yet 60 days old as of the 2025-12-31 cutoff - this is expected, not a data gap.\nREPORT: Purchase order replenishment performance by quarter, 2025 (mature POs only, non-censored)\nQUESTION: Once we only look at purchase orders old enough to have fully played out, are we genuinely behind on restocking, or was the Q4 backlog just a year-end timing artifact?\nquarter | pos_ordered_total | pos_matured | pos_matured_received | pct_matured_still_open | avg_actual_lead_days | pct_received_late | avg_days_late\n--------+-------------------+-------------+----------------------+------------------------+----------------------+-------------------+--------------\n2025-Q1 | 1183 | 1183 | 1183 | 0.0 | 22.4 | 52.9 | 6.1\n2025-Q2 | 1209 | 1209 | 1209 | 0.0 | 20.0 | 47.4 | 3.8\n2025-Q3 | 1238 | 1238 | 1238 | 0.0 | 20.8 | 54.0 | 4.8\n2025-Q4 | 1588 | 464 | 460 | 0.9 | 20.7 | 59.3 | 4.5\n(4 rows)\n\n=== Report 3: Fulfillment shortfall and delivery speed by merchant vertical, 2025 (written by Bea) ===\nEngineer's note: \"Fully delivered\" requires the order to have at least one linked shipment (via fulfillments) and none of them null delivered_at; orders with status='fulfilled' but no shipment rows at all are excluded from that subset rather than counted as delivered. Tenants with a null industry are excluded since the grain is per-vertical.\nREPORT: Fulfillment shortfall and delivery speed by merchant vertical, 2025\nQUESTION: Do apparel/footwear merchants, where picking discrepancies are concentrated, actually see more orders go unfulfilled or delivered slowly -- or is that purely an inventory/accounting issue with no customer-facing effect?\nindustry | orders_placed_2025 | pct_orders_with_fulfillment_shortfall | orders_fully_delivered | avg_delivery_days | pct_within_5_days\n--------------------------+--------------------+---------------------------------------+------------------------+-------------------+------------------\nBeauty | 1561 | 4.7 | 1451 | 4.55 | 73.8\nApparel & Footwear | 7149 | 4.5 | 6264 | 3.84 | 82.4\nSports & Outdoor | 1014 | 4.2 | 916 | 4.05 | 77.8\nHome & Living | 1658 | 4.0 | 1519 | 4.43 | 73.7\nJewelry & Accessories | 50 | 4.0 | 46 | 4.23 | 73.9\nConsumer Electronics | 575 | 3.7 | 534 | 4.08 | 76.2\nBooks & Stationery | 159 | 3.1 | 151 | 4.16 | 80.1\nPet Supplies | 1448 | 3.0 | 1385 | 4.28 | 77.8\nOffice Supplies (B2B) | 526 | 1.9 | 512 | 3.95 | 77.3\nFood & Coffee | 7210 | 1.7 | 6997 | 3.93 | 80.5\nIndustrial Supplies (B2B) | 773 | 1.6 | 758 | 4.26 | 72.2\n(11 rows)\n\n\nNight 3 of 4: you have 3 report slots tonight."},"uuid":"05928b26-9f6b-4107-b69b-de364b069642","timestamp":"2026-10-04T02:05:54.089Z","permissionMode":"default","promptSource":"sdk","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120","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":11975,"cache_read_input_tokens":120725,"output_tokens":10228,"output_tokens_details":{"thinking_tokens":8024},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":11975,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":10228,"cache_read_input_tokens":120725,"cache_creation_input_tokens":11975,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":11975},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CfgNukTas5qu1CRj4LiLj","type":"assistant","uuid":"4ed388a6-16cb-4072-a4a7-fdc9ed2ad8a2","timestamp":"2026-10-04T02:07:11.582Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: 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{"parentUuid":"4ed388a6-16cb-4072-a4a7-fdc9ed2ad8a2","isSidechain":false,"message":{"model":"claude-sonnet-5","id":"msg_011CfgNukngcqaVKdLHiPULu","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01LP9Qu9b9JhDk3Ux9vjKiuY","name":"StructuredOutput","input":{"notes":"I can already answer most of priorities #2-#4 by hand from data I have: (a) the full-year carrier table lets me compute that today's overall transit on-time rate (pct within 3 days) is ~78%, and if the three weak carriers matched the ~88% weighted average of the other 11 carriers, overall on-time would rise to ~88% -- i.e. roughly 45% of our current unreliability sits with these three carriers, the rest is baseline slop even among good carriers. (b) the non-censored PO report fully resolves #3: pct_matured_still_open is ~0% in every quarter including the mature Oct-only slice of Q4 -- there is no real backlog, the 418 \"open\" POs were purely a right-censoring artifact. The late-delivery rate itself (47-59%) is real and chronic, but it's a persistent promise-miss rate, not a growing queue -- worth flagging as its own issue. (c) the vertical report resolves #4: Beauty and Apparel & Footwear have the highest fulfillment-shortfall rates (4.5-4.7% of orders), but Apparel is actually the *fastest* and most reliable vertical once an order fully delivers (3.84 avg days, 82.4% within 5) -- so the picking-discrepancy issue looks like a separate, real completeness problem (customers getting short quantities), not a delivery-speed problem. Tonight I want to re-express the carrier \"ceiling\" story in the exact metric Priya already quotes (order-level pct-within-5-days, not shipment transit), and isolate how much of the Nov-Dec dip is specifically attributable to Albion vs. everything else, plus quantify the Lowlands lane-fix opportunity -- these three numbers should make the carrier narrative airtight and actionable.","requests":[{"title":"Order-level delivery reliability: weak carriers vs rest, 2025","question":"In the same order-level metric I've already reported to Priya (pct within 5 days), how much better would our overall delivery reliability be if the three weak carriers matched everyone else?","grain":"one row per carrier_group: 'Weak three' (Albion Parcel, Landfall Freight, Lowlands Express) vs 'All other carriers'","columns":["carrier_group","orders_fully_delivered","avg_delivery_days (placed_at to delivered_at)","pct_within_5_days","share_of_orders_pct"],"details":"Scope: orders with placed_at in 2025-01-01 through 2025-12-31, status='fulfilled', restricted to orders with exactly one row in fulfillments and that fulfillment linked to exactly one row in shipments, with that shipment's delivered_at not null (clean single-carrier attribution). delivery_days = DATEDIFF(day, orders.placed_at, shipments.delivered_at). carrier_group = 'Weak three' if shipments.carrier in ('Albion Parcel','Landfall Freight','Lowlands Express'), else 'All other carriers'. Round avg_delivery_days to 2 decimals, pct columns to 1 decimal. Order by carrier_group ascending. Expect 2 rows.","builds_on":["Carrier performance, full year 2025 (complete)","Delivery time trend, monthly (2024-2025)"]},{"title":"Order-level delivery reliability: Albion Parcel vs rest, baseline vs collapse, 2025","question":"How much of the November-December delivery slowdown is specifically attributable to Albion Parcel, versus everything else?","grain":"one row per carrier_group ('Albion Parcel' vs 'All other carriers') and period ('baseline': placed_at Jan-Oct 2025; 'collapse': placed_at Nov-Dec 2025)","columns":["carrier_group","period","orders_fully_delivered","avg_delivery_days","pct_within_5_days","share_of_period_orders_pct"],"details":"Same scoping and single-carrier attribution rule as the weak-carriers-vs-rest report (orders placed_at in 2025, status='fulfilled', exactly one fulfillment with exactly one shipment, delivered_at not null). period is based on orders.placed_at (baseline = 2025-01-01 to 2025-10-31; collapse = 2025-11-01 to 2025-12-31). carrier_group = 'Albion Parcel' if shipments.carrier = 'Albion Parcel', else 'All other carriers'. Round avg_delivery_days to 2 decimals, pct columns to 1 decimal. Order by period ascending, then carrier_group ascending. Expect 4 rows.","builds_on":["Albion Parcel performance by destination country, baseline vs collapse","Albion Parcel performance by origin warehouse, baseline vs collapse"]},{"title":"Order-level delivery reliability: Lowlands Express by destination lane, 2025","question":"How much better would Lowlands Express's orders perform, and how much would that help overall, if we only used it on its strong home lanes (Netherlands, Belgium, France) and moved everything else off it?","grain":"one row per lane_group: 'Lowlands core (NL/BE/FR)', 'Lowlands non-core (other countries)', 'All other carriers'","columns":["lane_group","orders_fully_delivered","avg_delivery_days","pct_within_5_days","share_of_orders_pct"],"details":"Same scoping and single-carrier attribution rule as the other two reports in this set (orders placed_at in 2025, status='fulfilled', exactly one fulfillment with exactly one shipment, delivered_at not null). For orders where shipments.carrier = 'Lowlands Express', further split by the order's destination country (orders.shipping_address_id -> addresses -> countries): 'Lowlands core' if country in ('Netherlands','Belgium','France'), else 'Lowlands non-core'. All other orders (any other carrier) go to 'All other carriers'. Round avg_delivery_days to 2 decimals, pct columns to 1 decimal. Order: 'Lowlands core' first, then 'Lowlands non-core', then 'All other carriers'. Expect 3 rows.","builds_on":["Landfall Freight & Lowlands Express performance by destination country, full year 2025"]}]},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":11975,"cache_read_input_tokens":120725,"output_tokens":10228,"output_tokens_details":{"thinking_tokens":8024},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":11975,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":10228,"cache_read_input_tokens":120725,"cache_creation_input_tokens":11975,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":11975},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"wireToolInputs":{"toolu_01LP9Qu9b9JhDk3Ux9vjKiuY":{"notes":"I can already answer most of priorities #2-#4 by hand from data I have: (a) the full-year carrier table lets me compute that today's overall transit on-time rate (pct within 3 days) is ~78%, and if the three weak carriers matched the ~88% weighted average of the other 11 carriers, overall on-time would rise to ~88% -- i.e. roughly 45% of our current unreliability sits with these three carriers, the rest is baseline slop even among good carriers. (b) the non-censored PO report fully resolves #3: pct_matured_still_open is ~0% in every quarter including the mature Oct-only slice of Q4 -- there is no real backlog, the 418 \"open\" POs were purely a right-censoring artifact. The late-delivery rate itself (47-59%) is real and chronic, but it's a persistent promise-miss rate, not a growing queue -- worth flagging as its own issue. (c) the vertical report resolves #4: Beauty and Apparel & Footwear have the highest fulfillment-shortfall rates (4.5-4.7% of orders), but Apparel is actually the *fastest* and most reliable vertical once an order fully delivers (3.84 avg days, 82.4% within 5) -- so the picking-discrepancy issue looks like a separate, real completeness problem (customers getting short quantities), not a delivery-speed problem. Tonight I want to re-express the carrier \"ceiling\" story in the exact metric Priya already quotes (order-level pct-within-5-days, not shipment transit), and isolate how much of the Nov-Dec dip is specifically attributable to Albion vs. everything else, plus quantify the Lowlands lane-fix opportunity -- these three numbers should make the carrier narrative airtight and actionable.","requests":[{"title":"Order-level delivery reliability: weak carriers vs rest, 2025","question":"In the same order-level metric I've already reported to Priya (pct within 5 days), how much better would our overall delivery reliability be if the three weak carriers matched everyone else?","grain":"one row per carrier_group: 'Weak three' (Albion Parcel, Landfall Freight, Lowlands Express) vs 'All other carriers'","columns":["carrier_group","orders_fully_delivered","avg_delivery_days (placed_at to delivered_at)","pct_within_5_days","share_of_orders_pct"],"details":"Scope: orders with placed_at in 2025-01-01 through 2025-12-31, status='fulfilled', restricted to orders with exactly one row in fulfillments and that fulfillment linked to exactly one row in shipments, with that shipment's delivered_at not null (clean single-carrier attribution). delivery_days = DATEDIFF(day, orders.placed_at, shipments.delivered_at). carrier_group = 'Weak three' if shipments.carrier in ('Albion Parcel','Landfall Freight','Lowlands Express'), else 'All other carriers'. Round avg_delivery_days to 2 decimals, pct columns to 1 decimal. Order by carrier_group ascending. Expect 2 rows.","builds_on":["Carrier performance, full year 2025 (complete)","Delivery time trend, monthly (2024-2025)"]},{"title":"Order-level delivery reliability: Albion Parcel vs rest, baseline vs collapse, 2025","question":"How much of the November-December delivery slowdown is specifically attributable to Albion Parcel, versus everything else?","grain":"one row per carrier_group ('Albion Parcel' vs 'All other carriers') and period ('baseline': placed_at Jan-Oct 2025; 'collapse': placed_at Nov-Dec 2025)","columns":["carrier_group","period","orders_fully_delivered","avg_delivery_days","pct_within_5_days","share_of_period_orders_pct"],"details":"Same scoping and single-carrier attribution rule as the weak-carriers-vs-rest report (orders placed_at in 2025, status='fulfilled', exactly one fulfillment with exactly one shipment, delivered_at not null). period is based on orders.placed_at (baseline = 2025-01-01 to 2025-10-31; collapse = 2025-11-01 to 2025-12-31). carrier_group = 'Albion Parcel' if shipments.carrier = 'Albion Parcel', else 'All other carriers'. Round avg_delivery_days to 2 decimals, pct columns to 1 decimal. Order by period ascending, then carrier_group ascending. Expect 4 rows.","builds_on":["Albion Parcel performance by destination country, baseline vs collapse","Albion Parcel performance by origin warehouse, baseline vs collapse"]},{"title":"Order-level delivery reliability: Lowlands Express by destination lane, 2025","question":"How much better would Lowlands Express's orders perform, and how much would that help overall, if we only used it on its strong home lanes (Netherlands, Belgium, France) and moved everything else off it?","grain":"one row per lane_group: 'Lowlands core (NL/BE/FR)', 'Lowlands non-core (other countries)', 'All other carriers'","columns":["lane_group","orders_fully_delivered","avg_delivery_days","pct_within_5_days","share_of_orders_pct"],"details":"Same scoping and single-carrier attribution rule as the other two reports in this set (orders placed_at in 2025, status='fulfilled', exactly one fulfillment with exactly one shipment, delivered_at not null). For orders where shipments.carrier = 'Lowlands Express', further split by the order's destination country (orders.shipping_address_id -> addresses -> countries): 'Lowlands core' if country in ('Netherlands','Belgium','France'), else 'Lowlands non-core'. All other orders (any other carrier) go to 'All other carriers'. Round avg_delivery_days to 2 decimals, pct columns to 1 decimal. Order: 'Lowlands core' first, then 'Lowlands non-core', then 'All other carriers'. Expect 3 rows.","builds_on":["Landfall Freight & Lowlands Express performance by destination country, full year 2025"]}]}},"apiBlockIndex":1,"requestId":"req_011CfgNukTas5qu1CRj4LiLj","type":"assistant","uuid":"31474304-90dd-421e-885d-c1c22c12dbce","timestamp":"2026-10-04T02:07:28.796Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"31474304-90dd-421e-885d-c1c22c12dbce","isSidechain":false,"attachment":{"type":"structured_output","data":{"notes":"I can already answer most of priorities #2-#4 by hand from data I have: (a) the full-year carrier table lets me compute that today's overall transit on-time rate (pct within 3 days) is ~78%, and if the three weak carriers matched the ~88% weighted average of the other 11 carriers, overall on-time would rise to ~88% -- i.e. roughly 45% of our current unreliability sits with these three carriers, the rest is baseline slop even among good carriers. (b) the non-censored PO report fully resolves #3: pct_matured_still_open is ~0% in every quarter including the mature Oct-only slice of Q4 -- there is no real backlog, the 418 \"open\" POs were purely a right-censoring artifact. The late-delivery rate itself (47-59%) is real and chronic, but it's a persistent promise-miss rate, not a growing queue -- worth flagging as its own issue. (c) the vertical report resolves #4: Beauty and Apparel & Footwear have the highest fulfillment-shortfall rates (4.5-4.7% of orders), but Apparel is actually the *fastest* and most reliable vertical once an order fully delivers (3.84 avg days, 82.4% within 5) -- so the picking-discrepancy issue looks like a separate, real completeness problem (customers getting short quantities), not a delivery-speed problem. Tonight I want to re-express the carrier \"ceiling\" story in the exact metric Priya already quotes (order-level pct-within-5-days, not shipment transit), and isolate how much of the Nov-Dec dip is specifically attributable to Albion vs. everything else, plus quantify the Lowlands lane-fix opportunity -- these three numbers should make the carrier narrative airtight and actionable.","requests":[{"title":"Order-level delivery reliability: weak carriers vs rest, 2025","question":"In the same order-level metric I've already reported to Priya (pct within 5 days), how much better would our overall delivery reliability be if the three weak carriers matched everyone else?","grain":"one row per carrier_group: 'Weak three' (Albion Parcel, Landfall Freight, Lowlands Express) vs 'All other carriers'","columns":["carrier_group","orders_fully_delivered","avg_delivery_days (placed_at to delivered_at)","pct_within_5_days","share_of_orders_pct"],"details":"Scope: orders with placed_at in 2025-01-01 through 2025-12-31, status='fulfilled', restricted to orders with exactly one row in fulfillments and that fulfillment linked to exactly one row in shipments, with that shipment's delivered_at not null (clean single-carrier attribution). delivery_days = DATEDIFF(day, orders.placed_at, shipments.delivered_at). carrier_group = 'Weak three' if shipments.carrier in ('Albion Parcel','Landfall Freight','Lowlands Express'), else 'All other carriers'. Round avg_delivery_days to 2 decimals, pct columns to 1 decimal. Order by carrier_group ascending. Expect 2 rows.","builds_on":["Carrier performance, full year 2025 (complete)","Delivery time trend, monthly (2024-2025)"]},{"title":"Order-level delivery reliability: Albion Parcel vs rest, baseline vs collapse, 2025","question":"How much of the November-December delivery slowdown is specifically attributable to Albion Parcel, versus everything else?","grain":"one row per carrier_group ('Albion Parcel' vs 'All other carriers') and period ('baseline': placed_at Jan-Oct 2025; 'collapse': placed_at Nov-Dec 2025)","columns":["carrier_group","period","orders_fully_delivered","avg_delivery_days","pct_within_5_days","share_of_period_orders_pct"],"details":"Same scoping and single-carrier attribution rule as the weak-carriers-vs-rest report (orders placed_at in 2025, status='fulfilled', exactly one fulfillment with exactly one shipment, delivered_at not null). period is based on orders.placed_at (baseline = 2025-01-01 to 2025-10-31; collapse = 2025-11-01 to 2025-12-31). carrier_group = 'Albion Parcel' if shipments.carrier = 'Albion Parcel', else 'All other carriers'. Round avg_delivery_days to 2 decimals, pct columns to 1 decimal. Order by period ascending, then carrier_group ascending. Expect 4 rows.","builds_on":["Albion Parcel performance by destination country, baseline vs collapse","Albion Parcel performance by origin warehouse, baseline vs collapse"]},{"title":"Order-level delivery reliability: Lowlands Express by destination lane, 2025","question":"How much better would Lowlands Express's orders perform, and how much would that help overall, if we only used it on its strong home lanes (Netherlands, Belgium, France) and moved everything else off it?","grain":"one row per lane_group: 'Lowlands core (NL/BE/FR)', 'Lowlands non-core (other countries)', 'All other carriers'","columns":["lane_group","orders_fully_delivered","avg_delivery_days","pct_within_5_days","share_of_orders_pct"],"details":"Same scoping and single-carrier attribution rule as the other two reports in this set (orders placed_at in 2025, status='fulfilled', exactly one fulfillment with exactly one shipment, delivered_at not null). For orders where shipments.carrier = 'Lowlands Express', further split by the order's destination country (orders.shipping_address_id -> addresses -> countries): 'Lowlands core' if country in ('Netherlands','Belgium','France'), else 'Lowlands non-core'. All other orders (any other carrier) go to 'All other carriers'. Round avg_delivery_days to 2 decimals, pct columns to 1 decimal. Order: 'Lowlands core' first, then 'Lowlands non-core', then 'All other carriers'. Expect 3 rows.","builds_on":["Landfall Freight & Lowlands Express performance by destination country, full year 2025"]}]},"toolUseID":"toolu_01LP9Qu9b9JhDk3Ux9vjKiuY"},"type":"attachment","uuid":"626f7524-dbd1-4935-8e59-b2f8a7f69b6d","timestamp":"2026-10-04T02:07:28.804Z","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120","version":"2.1.275","gitBranch":"HEAD"}
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{"type":"last-prompt","lastPrompt":"The printed reports of night 2: === Report 1: Carrier performance, full year 2025 (complete) (written by Ines) === Engineer's note: transit_days computed as delivered_at::date - shipped_at::date (who…","leafUuid":"281fa65f-7f1a-4305-9c44-a44af8755a6e","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120"}
{"type":"ai-title","aiTitle":"Fulfillment delivery performance analysis","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120"}
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{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-04T02:08:08.624Z","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120","content":"The printed reports of night 3:\n\n=== Report 1: Order-level delivery reliability: weak carriers vs rest, 2025 (written by Bea) ===\nEngineer's note: Delivery days computed as calendar-date difference (delivered_at::date - placed_at::date), matching the DATEDIFF(day, ...) definition used in Priya's report. Two rows expected, one per carrier_group.\nREPORT: Order-level delivery reliability: weak carriers vs rest, 2025\nQUESTION: In the same order-level metric I've already reported to Priya (pct within 5 days), how much better would our overall delivery reliability be if the three weak carriers matched everyone else?\ncarrier_group | orders_fully_delivered | avg_delivery_days | pct_within_5_days | share_of_orders_pct\n-------------------+------------------------+-------------------+-------------------+--------------------\nAll other carriers | 13696 | 3.44 | 93.4 | 66.7\nWeak three | 6837 | 5.19 | 66.8 | 33.3\n(2 rows)\n\n=== Report 2: Order-level delivery reliability: Albion Parcel vs rest, baseline vs collapse, 2025 (written by Ines) ===\nEngineer's note: Aggregated across all tenants/merchants (not merchant-specific); delivery_days computed as delivered_at minus placed_at in fractional days using the single qualifying shipment per order, per the same attribution rule as the weak-carriers-vs-rest report.\nREPORT: Order-level delivery reliability: Albion Parcel vs rest, baseline vs collapse, 2025\nQUESTION: How much of the November-December delivery slowdown is specifically attributable to Albion Parcel, versus everything else?\ncarrier_group | period | orders_fully_delivered | avg_delivery_days | pct_within_5_days | share_of_period_orders_pct\n-------------------+----------+------------------------+-------------------+-------------------+---------------------------\nAlbion Parcel | baseline | 2052 | 3.4 | 92.3 | 13.3\nAll other carriers | baseline | 13409 | 3.97 | 78.9 | 86.7\nAlbion Parcel | collapse | 512 | 9.82 | 31.3 | 10.1\nAll other carriers | collapse | 4560 | 3.85 | 80.0 | 89.9\n(4 rows)\n\n=== Report 3: Order-level delivery reliability: Lowlands Express by destination lane, 2025 (written by Bea) ===\nEngineer's note: Delivery days computed as delivered_at minus placed_at (no other timestamp was specified for \"delivery\"); destination country taken from orders.shipping_address_id -> addresses -> countries.name as requested. All three lane_group rows are returned as expected (3 rows total, no truncation).\nREPORT: Order-level delivery reliability: Lowlands Express by destination lane, 2025\nQUESTION: How much better would Lowlands Express's orders perform, and how much would that help overall, if we only used it on its strong home lanes (Netherlands, Belgium, France) and moved everything else off it?\nlane_group | orders_fully_delivered | avg_delivery_days | pct_within_5_days | share_of_orders_pct\n------------------------------------+------------------------+-------------------+-------------------+--------------------\nLowlands core (NL/BE/FR) | 1581 | 4.34 | 72.9 | 7.7\nLowlands non-core (other countries) | 864 | 5.96 | 34.1 | 4.2\nAll other carriers | 18088 | 3.92 | 82.0 | 88.1\n(3 rows)\n\n\nNight 4 of 4: you have 3 report slots tonight."}
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{"parentUuid":"e06e7b0d-c2f8-4750-a26d-bf06765d3e79","isSidechain":false,"type":"assistant","uuid":"321730d9-f1e3-4241-bf51-69f5cfacf82f","timestamp":"2026-10-04T02:08:08.617Z","message":{"diagnostics":null,"id":"b815ddcf-3abf-4ad0-a01c-a3cc08d519c0","container":null,"model":"","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":"e0b165aa-32dc-460c-8e74-dc2db9d40120","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"321730d9-f1e3-4241-bf51-69f5cfacf82f","isSidechain":false,"promptId":"2f492d33-6933-4622-a807-8990b263b5fe","type":"user","message":{"role":"user","content":"The printed reports of night 3:\n\n=== Report 1: Order-level delivery reliability: weak carriers vs rest, 2025 (written by Bea) ===\nEngineer's note: Delivery days computed as calendar-date difference (delivered_at::date - placed_at::date), matching the DATEDIFF(day, ...) definition used in Priya's report. Two rows expected, one per carrier_group.\nREPORT: Order-level delivery reliability: weak carriers vs rest, 2025\nQUESTION: In the same order-level metric I've already reported to Priya (pct within 5 days), how much better would our overall delivery reliability be if the three weak carriers matched everyone else?\ncarrier_group | orders_fully_delivered | avg_delivery_days | pct_within_5_days | share_of_orders_pct\n-------------------+------------------------+-------------------+-------------------+--------------------\nAll other carriers | 13696 | 3.44 | 93.4 | 66.7\nWeak three | 6837 | 5.19 | 66.8 | 33.3\n(2 rows)\n\n=== Report 2: Order-level delivery reliability: Albion Parcel vs rest, baseline vs collapse, 2025 (written by Ines) ===\nEngineer's note: Aggregated across all tenants/merchants (not merchant-specific); delivery_days computed as delivered_at minus placed_at in fractional days using the single qualifying shipment per order, per the same attribution rule as the weak-carriers-vs-rest report.\nREPORT: Order-level delivery reliability: Albion Parcel vs rest, baseline vs collapse, 2025\nQUESTION: How much of the November-December delivery slowdown is specifically attributable to Albion Parcel, versus everything else?\ncarrier_group | period | orders_fully_delivered | avg_delivery_days | pct_within_5_days | share_of_period_orders_pct\n-------------------+----------+------------------------+-------------------+-------------------+---------------------------\nAlbion Parcel | baseline | 2052 | 3.4 | 92.3 | 13.3\nAll other carriers | baseline | 13409 | 3.97 | 78.9 | 86.7\nAlbion Parcel | collapse | 512 | 9.82 | 31.3 | 10.1\nAll other carriers | collapse | 4560 | 3.85 | 80.0 | 89.9\n(4 rows)\n\n=== Report 3: Order-level delivery reliability: Lowlands Express by destination lane, 2025 (written by Bea) ===\nEngineer's note: Delivery days computed as delivered_at minus placed_at (no other timestamp was specified for \"delivery\"); destination country taken from orders.shipping_address_id -> addresses -> countries.name as requested. All three lane_group rows are returned as expected (3 rows total, no truncation).\nREPORT: Order-level delivery reliability: Lowlands Express by destination lane, 2025\nQUESTION: How much better would Lowlands Express's orders perform, and how much would that help overall, if we only used it on its strong home lanes (Netherlands, Belgium, France) and moved everything else off it?\nlane_group | orders_fully_delivered | avg_delivery_days | pct_within_5_days | share_of_orders_pct\n------------------------------------+------------------------+-------------------+-------------------+--------------------\nLowlands core (NL/BE/FR) | 1581 | 4.34 | 72.9 | 7.7\nLowlands non-core (other countries) | 864 | 5.96 | 34.1 | 4.2\nAll other carriers | 18088 | 3.92 | 82.0 | 88.1\n(3 rows)\n\n\nNight 4 of 4: you have 3 report slots tonight."},"uuid":"58b7750d-54b5-4ad3-98ce-8a7cf99f95e7","timestamp":"2026-10-04T02:08:08.637Z","permissionMode":"default","promptSource":"sdk","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120","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":11536,"cache_read_input_tokens":132700,"output_tokens":16203,"output_tokens_details":{"thinking_tokens":13778},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":11536,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":16203,"cache_read_input_tokens":132700,"cache_creation_input_tokens":11536,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":11536},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CfgP5fbucAH99MfLxDj5X","type":"assistant","uuid":"a1e61633-6146-410b-9270-aec1b028c0bb","timestamp":"2026-10-04T02:10:27.921Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: 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{"parentUuid":"a1e61633-6146-410b-9270-aec1b028c0bb","isSidechain":false,"message":{"model":"claude-sonnet-5","id":"msg_011CfgP5fxz44MkoEPqeJLZQ","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Y5Kdz68tQaxGv611g5DYck","name":"StructuredOutput","input":{"notes":"Before I write anything, I cross-checked the three night-3 order-level reports against each other by hand, and they don't reconcile: Report 1 says the three weak carriers blend to 66.8% within 5 days; Report 2's Albion baseline/collapse figures imply Albion alone blends to ~80.1%, which would force Landfall+Lowlands down to ~58.8% to match Report 1's total. But Report 3's \"all other carriers\" (82.0%, 18,088 orders) combined with Report 1's \"good eleven\" (93.4%, 13,696 orders) and Report 2's Albion (~80.1%, 2,564 orders) overshoots the reported total on its own -- it would require Landfall's own rate to be negative, which is impossible. So at least one of these three reports has an inconsistency (likely a scoping or computation difference across the three separate queries), and I'm not willing to hand Priya a \"ceiling\" number or a \"fix Lowlands' lanes\" recommendation until it's resolved -- this is exactly the kind of number I'd expect her or Daniel's/Tomasz's teams to poke at. Tonight (last night) I'm spending all 3 slots getting a single, internally-consistent source of truth in one query each, rather than stitching across reports: (1) one full-year carrier-level breakdown to anchor the ceiling calculation, (2) one carrier-group-by-quarter breakdown (Albion / Landfall / Lowlands / everyone else) to nail down the timing story cleanly, and (3) one Lowlands-by-lane breakdown done the same consistent way, so the lane-reassignment number is trustworthy. I'll reconcile against this week's earlier reports once these are back and flag any remaining discrepancy explicitly in the memo rather than paper over it.","requests":[{"title":"Order-level delivery reliability by carrier, full year 2025 (unified source)","question":"What is each carrier's order-level delivery reliability (orders placed to orders delivered, within 5 days), in one single consistent query I can use as the ground truth for a reliability ceiling calculation?","grain":"one row per carrier, full year 2025","columns":["carrier","orders_fully_delivered","avg_delivery_days (placed_at to delivered_at)","pct_within_5_days","share_of_orders_pct"],"details":"Scope: orders with placed_at between 2025-01-01 and 2025-12-31, status='fulfilled', restricted to orders with exactly one row in fulfillments and that fulfillment linked to exactly one row in shipments, with that shipment's delivered_at not null (one carrier per order, cleanly attributed). delivery_days = DATEDIFF(day, orders.placed_at, shipments.delivered_at). Group by shipments.carrier. Please also show the grand total as a final row labeled 'ALL CARRIERS' (sum of orders_fully_delivered across all carriers, and the overall pct_within_5_days across that full population) so I can sanity-check that individual carrier rows sum correctly. Round avg_delivery_days to 2 decimals, pct columns to 1 decimal. Order by orders_fully_delivered descending, with the 'ALL CARRIERS' total row last. Expect about 15 rows (14 carriers + 1 total).","builds_on":["Order-level delivery reliability: weak carriers vs rest, 2025","Order-level delivery reliability: Lowlands Express by destination lane, 2025"]},{"title":"Order-level delivery reliability by carrier group and quarter, 2025 (unified source)","question":"Did Albion Parcel's reliability break down specifically in Q4 while Landfall Freight and Lowlands Express stayed consistently weak all year, when measured in one single consistent query?","grain":"one row per carrier_group and calendar quarter of placed_at, 2025","columns":["carrier_group ('Albion Parcel', 'Landfall Freight', 'Lowlands Express', or 'All other carriers')","quarter (e.g. 2025-Q1)","orders_fully_delivered","avg_delivery_days","pct_within_5_days","share_of_quarter_orders_pct"],"details":"Same scoping and single-carrier attribution rule as the full-year carrier report (orders placed_at in 2025, status='fulfilled', exactly one fulfillment with exactly one shipment, delivered_at not null). Group by quarter of orders.placed_at (use calendar_days.quarter_num/quarter_start) and by carrier_group, where carrier_group = the shipment's carrier name if it is 'Albion Parcel', 'Landfall Freight', or 'Lowlands Express', else 'All other carriers' (all remaining carriers summed together). Round avg_delivery_days to 2 decimals, pct columns to 1 decimal. Order by quarter ascending, then carrier_group in this fixed order: Albion Parcel, Landfall Freight, Lowlands Express, All other carriers. Expect 16 rows (4 quarters x 4 groups).","builds_on":["Order-level delivery reliability: Albion Parcel vs rest, baseline vs collapse, 2025"]},{"title":"Order-level delivery reliability: Lowlands Express by destination lane, full year 2025 (unified re-check)","question":"Confirmed and reconciled: how much does Lowlands Express's order-level reliability differ between its home lanes (Netherlands/Belgium/France) and everywhere else, versus the rest of our carriers combined?","grain":"one row per lane_group: 'Lowlands core (NL/BE/FR)', 'Lowlands non-core (other countries)', 'All other carriers combined'","columns":["lane_group","orders_fully_delivered","avg_delivery_days","pct_within_5_days","share_of_orders_pct"],"details":"Same scoping and single-carrier attribution rule as the other two reports in this set (orders placed_at in 2025, status='fulfilled', exactly one fulfillment with exactly one shipment, delivered_at not null). For orders where shipments.carrier = 'Lowlands Express', split by destination country (orders.shipping_address_id -> addresses -> countries.name): 'Lowlands core (NL/BE/FR)' if country in ('Netherlands','Belgium','France'), else 'Lowlands non-core (other countries)'. All remaining orders (any other carrier) go to 'All other carriers combined'. Please also report the raw count of orders in each group and confirm the three groups sum to the same total order count as the full-year carrier report in this batch, so I can verify consistency across the two reports. Round avg_delivery_days to 2 decimals, pct columns to 1 decimal. Order: Lowlands core first, then Lowlands non-core, then All other carriers combined. Expect 3 rows.","builds_on":["Order-level delivery reliability: Lowlands Express by destination lane, 2025"]}]},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":11536,"cache_read_input_tokens":132700,"output_tokens":16203,"output_tokens_details":{"thinking_tokens":13778},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":11536,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":16203,"cache_read_input_tokens":132700,"cache_creation_input_tokens":11536,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":11536},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"wireToolInputs":{"toolu_01Y5Kdz68tQaxGv611g5DYck":{"notes":"Before I write anything, I cross-checked the three night-3 order-level reports against each other by hand, and they don't reconcile: Report 1 says the three weak carriers blend to 66.8% within 5 days; Report 2's Albion baseline/collapse figures imply Albion alone blends to ~80.1%, which would force Landfall+Lowlands down to ~58.8% to match Report 1's total. But Report 3's \"all other carriers\" (82.0%, 18,088 orders) combined with Report 1's \"good eleven\" (93.4%, 13,696 orders) and Report 2's Albion (~80.1%, 2,564 orders) overshoots the reported total on its own -- it would require Landfall's own rate to be negative, which is impossible. So at least one of these three reports has an inconsistency (likely a scoping or computation difference across the three separate queries), and I'm not willing to hand Priya a \"ceiling\" number or a \"fix Lowlands' lanes\" recommendation until it's resolved -- this is exactly the kind of number I'd expect her or Daniel's/Tomasz's teams to poke at. Tonight (last night) I'm spending all 3 slots getting a single, internally-consistent source of truth in one query each, rather than stitching across reports: (1) one full-year carrier-level breakdown to anchor the ceiling calculation, (2) one carrier-group-by-quarter breakdown (Albion / Landfall / Lowlands / everyone else) to nail down the timing story cleanly, and (3) one Lowlands-by-lane breakdown done the same consistent way, so the lane-reassignment number is trustworthy. I'll reconcile against this week's earlier reports once these are back and flag any remaining discrepancy explicitly in the memo rather than paper over it.","requests":[{"title":"Order-level delivery reliability by carrier, full year 2025 (unified source)","question":"What is each carrier's order-level delivery reliability (orders placed to orders delivered, within 5 days), in one single consistent query I can use as the ground truth for a reliability ceiling calculation?","grain":"one row per carrier, full year 2025","columns":["carrier","orders_fully_delivered","avg_delivery_days (placed_at to delivered_at)","pct_within_5_days","share_of_orders_pct"],"details":"Scope: orders with placed_at between 2025-01-01 and 2025-12-31, status='fulfilled', restricted to orders with exactly one row in fulfillments and that fulfillment linked to exactly one row in shipments, with that shipment's delivered_at not null (one carrier per order, cleanly attributed). delivery_days = DATEDIFF(day, orders.placed_at, shipments.delivered_at). Group by shipments.carrier. Please also show the grand total as a final row labeled 'ALL CARRIERS' (sum of orders_fully_delivered across all carriers, and the overall pct_within_5_days across that full population) so I can sanity-check that individual carrier rows sum correctly. Round avg_delivery_days to 2 decimals, pct columns to 1 decimal. Order by orders_fully_delivered descending, with the 'ALL CARRIERS' total row last. Expect about 15 rows (14 carriers + 1 total).","builds_on":["Order-level delivery reliability: weak carriers vs rest, 2025","Order-level delivery reliability: Lowlands Express by destination lane, 2025"]},{"title":"Order-level delivery reliability by carrier group and quarter, 2025 (unified source)","question":"Did Albion Parcel's reliability break down specifically in Q4 while Landfall Freight and Lowlands Express stayed consistently weak all year, when measured in one single consistent query?","grain":"one row per carrier_group and calendar quarter of placed_at, 2025","columns":["carrier_group ('Albion Parcel', 'Landfall Freight', 'Lowlands Express', or 'All other carriers')","quarter (e.g. 2025-Q1)","orders_fully_delivered","avg_delivery_days","pct_within_5_days","share_of_quarter_orders_pct"],"details":"Same scoping and single-carrier attribution rule as the full-year carrier report (orders placed_at in 2025, status='fulfilled', exactly one fulfillment with exactly one shipment, delivered_at not null). Group by quarter of orders.placed_at (use calendar_days.quarter_num/quarter_start) and by carrier_group, where carrier_group = the shipment's carrier name if it is 'Albion Parcel', 'Landfall Freight', or 'Lowlands Express', else 'All other carriers' (all remaining carriers summed together). Round avg_delivery_days to 2 decimals, pct columns to 1 decimal. Order by quarter ascending, then carrier_group in this fixed order: Albion Parcel, Landfall Freight, Lowlands Express, All other carriers. Expect 16 rows (4 quarters x 4 groups).","builds_on":["Order-level delivery reliability: Albion Parcel vs rest, baseline vs collapse, 2025"]},{"title":"Order-level delivery reliability: Lowlands Express by destination lane, full year 2025 (unified re-check)","question":"Confirmed and reconciled: how much does Lowlands Express's order-level reliability differ between its home lanes (Netherlands/Belgium/France) and everywhere else, versus the rest of our carriers combined?","grain":"one row per lane_group: 'Lowlands core (NL/BE/FR)', 'Lowlands non-core (other countries)', 'All other carriers combined'","columns":["lane_group","orders_fully_delivered","avg_delivery_days","pct_within_5_days","share_of_orders_pct"],"details":"Same scoping and single-carrier attribution rule as the other two reports in this set (orders placed_at in 2025, status='fulfilled', exactly one fulfillment with exactly one shipment, delivered_at not null). For orders where shipments.carrier = 'Lowlands Express', split by destination country (orders.shipping_address_id -> addresses -> countries.name): 'Lowlands core (NL/BE/FR)' if country in ('Netherlands','Belgium','France'), else 'Lowlands non-core (other countries)'. All remaining orders (any other carrier) go to 'All other carriers combined'. Please also report the raw count of orders in each group and confirm the three groups sum to the same total order count as the full-year carrier report in this batch, so I can verify consistency across the two reports. Round avg_delivery_days to 2 decimals, pct columns to 1 decimal. Order: Lowlands core first, then Lowlands non-core, then All other carriers combined. Expect 3 rows.","builds_on":["Order-level delivery reliability: Lowlands Express by destination lane, 2025"]}]}},"apiBlockIndex":1,"requestId":"req_011CfgP5fbucAH99MfLxDj5X","type":"assistant","uuid":"81f401a0-19c1-4984-8671-741e1a44515d","timestamp":"2026-10-04T02:10:47.282Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"81f401a0-19c1-4984-8671-741e1a44515d","isSidechain":false,"attachment":{"type":"structured_output","data":{"notes":"Before I write anything, I cross-checked the three night-3 order-level reports against each other by hand, and they don't reconcile: Report 1 says the three weak carriers blend to 66.8% within 5 days; Report 2's Albion baseline/collapse figures imply Albion alone blends to ~80.1%, which would force Landfall+Lowlands down to ~58.8% to match Report 1's total. But Report 3's \"all other carriers\" (82.0%, 18,088 orders) combined with Report 1's \"good eleven\" (93.4%, 13,696 orders) and Report 2's Albion (~80.1%, 2,564 orders) overshoots the reported total on its own -- it would require Landfall's own rate to be negative, which is impossible. So at least one of these three reports has an inconsistency (likely a scoping or computation difference across the three separate queries), and I'm not willing to hand Priya a \"ceiling\" number or a \"fix Lowlands' lanes\" recommendation until it's resolved -- this is exactly the kind of number I'd expect her or Daniel's/Tomasz's teams to poke at. Tonight (last night) I'm spending all 3 slots getting a single, internally-consistent source of truth in one query each, rather than stitching across reports: (1) one full-year carrier-level breakdown to anchor the ceiling calculation, (2) one carrier-group-by-quarter breakdown (Albion / Landfall / Lowlands / everyone else) to nail down the timing story cleanly, and (3) one Lowlands-by-lane breakdown done the same consistent way, so the lane-reassignment number is trustworthy. I'll reconcile against this week's earlier reports once these are back and flag any remaining discrepancy explicitly in the memo rather than paper over it.","requests":[{"title":"Order-level delivery reliability by carrier, full year 2025 (unified source)","question":"What is each carrier's order-level delivery reliability (orders placed to orders delivered, within 5 days), in one single consistent query I can use as the ground truth for a reliability ceiling calculation?","grain":"one row per carrier, full year 2025","columns":["carrier","orders_fully_delivered","avg_delivery_days (placed_at to delivered_at)","pct_within_5_days","share_of_orders_pct"],"details":"Scope: orders with placed_at between 2025-01-01 and 2025-12-31, status='fulfilled', restricted to orders with exactly one row in fulfillments and that fulfillment linked to exactly one row in shipments, with that shipment's delivered_at not null (one carrier per order, cleanly attributed). delivery_days = DATEDIFF(day, orders.placed_at, shipments.delivered_at). Group by shipments.carrier. Please also show the grand total as a final row labeled 'ALL CARRIERS' (sum of orders_fully_delivered across all carriers, and the overall pct_within_5_days across that full population) so I can sanity-check that individual carrier rows sum correctly. Round avg_delivery_days to 2 decimals, pct columns to 1 decimal. Order by orders_fully_delivered descending, with the 'ALL CARRIERS' total row last. Expect about 15 rows (14 carriers + 1 total).","builds_on":["Order-level delivery reliability: weak carriers vs rest, 2025","Order-level delivery reliability: Lowlands Express by destination lane, 2025"]},{"title":"Order-level delivery reliability by carrier group and quarter, 2025 (unified source)","question":"Did Albion Parcel's reliability break down specifically in Q4 while Landfall Freight and Lowlands Express stayed consistently weak all year, when measured in one single consistent query?","grain":"one row per carrier_group and calendar quarter of placed_at, 2025","columns":["carrier_group ('Albion Parcel', 'Landfall Freight', 'Lowlands Express', or 'All other carriers')","quarter (e.g. 2025-Q1)","orders_fully_delivered","avg_delivery_days","pct_within_5_days","share_of_quarter_orders_pct"],"details":"Same scoping and single-carrier attribution rule as the full-year carrier report (orders placed_at in 2025, status='fulfilled', exactly one fulfillment with exactly one shipment, delivered_at not null). Group by quarter of orders.placed_at (use calendar_days.quarter_num/quarter_start) and by carrier_group, where carrier_group = the shipment's carrier name if it is 'Albion Parcel', 'Landfall Freight', or 'Lowlands Express', else 'All other carriers' (all remaining carriers summed together). Round avg_delivery_days to 2 decimals, pct columns to 1 decimal. Order by quarter ascending, then carrier_group in this fixed order: Albion Parcel, Landfall Freight, Lowlands Express, All other carriers. Expect 16 rows (4 quarters x 4 groups).","builds_on":["Order-level delivery reliability: Albion Parcel vs rest, baseline vs collapse, 2025"]},{"title":"Order-level delivery reliability: Lowlands Express by destination lane, full year 2025 (unified re-check)","question":"Confirmed and reconciled: how much does Lowlands Express's order-level reliability differ between its home lanes (Netherlands/Belgium/France) and everywhere else, versus the rest of our carriers combined?","grain":"one row per lane_group: 'Lowlands core (NL/BE/FR)', 'Lowlands non-core (other countries)', 'All other carriers combined'","columns":["lane_group","orders_fully_delivered","avg_delivery_days","pct_within_5_days","share_of_orders_pct"],"details":"Same scoping and single-carrier attribution rule as the other two reports in this set (orders placed_at in 2025, status='fulfilled', exactly one fulfillment with exactly one shipment, delivered_at not null). For orders where shipments.carrier = 'Lowlands Express', split by destination country (orders.shipping_address_id -> addresses -> countries.name): 'Lowlands core (NL/BE/FR)' if country in ('Netherlands','Belgium','France'), else 'Lowlands non-core (other countries)'. All remaining orders (any other carrier) go to 'All other carriers combined'. Please also report the raw count of orders in each group and confirm the three groups sum to the same total order count as the full-year carrier report in this batch, so I can verify consistency across the two reports. Round avg_delivery_days to 2 decimals, pct columns to 1 decimal. Order: Lowlands core first, then Lowlands non-core, then All other carriers combined. Expect 3 rows.","builds_on":["Order-level delivery reliability: Lowlands Express by destination lane, 2025"]}]},"toolUseID":"toolu_01Y5Kdz68tQaxGv611g5DYck"},"type":"attachment","uuid":"f38d7e5b-4965-411a-b25c-8cc7770cdd33","timestamp":"2026-10-04T02:10:47.291Z","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120","version":"2.1.275","gitBranch":"HEAD"}
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{"type":"last-prompt","lastPrompt":"The printed reports of night 3: === Report 1: Order-level delivery reliability: weak carriers vs rest, 2025 (written by Bea) === Engineer's note: Delivery days computed as calendar-date difference (d…","leafUuid":"ca3ccf14-e200-4c5f-91c3-fb3f502404ce","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120"}
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{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-04T02:12:03.514Z","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120","content":"The printed reports of night 4:\n\n=== Report 1: Order-level delivery reliability by carrier, full year 2025 (unified source) (written by Felix) ===\nEngineer's note: Scoped exactly as specified: orders placed in 2025, status='fulfilled', exactly one fulfillment with exactly one shipment, delivered_at not null, delivery_days = date(delivered_at) - date(placed_at). share_of_orders_pct is each carrier's share of this clean population (denominator is the same filtered set, not all orders tenant-wide), and the ALL CARRIERS row re-aggregates the identical population so it sums correctly against the carrier rows.\nREPORT: Order-level delivery reliability by carrier, full year 2025 (unified source)\nQUESTION: What is each carrier's order-level delivery reliability (orders placed to orders delivered, within 5 days), in one single consistent query I can use as the ground truth for a reliability ceiling calculation?\ncarrier | orders_fully_delivered | avg_delivery_days | pct_within_5_days | share_of_orders_pct\n---------------------+------------------------+-------------------+-------------------+--------------------\nStrombogen Kurier | 2900 | 3.27 | 97.7 | 14.1\nAlbion Parcel | 2564 | 4.72 | 82.9 | 12.5\nLowlands Express | 2445 | 4.97 | 66.7 | 11.9\nLiberty Ground | 2196 | 3.13 | 96.5 | 10.7\nAlpenpaket | 1996 | 3.31 | 97.1 | 9.7\nLandfall Freight | 1828 | 6.15 | 44.5 | 8.9\nTrailhead Express | 1759 | 3.36 | 92.3 | 8.6\nSkerrylane Logistics | 1075 | 3.83 | 87.5 | 5.2\nKennet Freight | 876 | 3.60 | 91.8 | 4.3\nPolder Post | 855 | 4.17 | 80.6 | 4.2\nRegent Parcels | 759 | 3.55 | 92.4 | 3.7\nPrairie Line Express | 699 | 3.41 | 93.7 | 3.4\nLiffey Couriers | 319 | 3.95 | 84.3 | 1.6\nGallia Colis | 262 | 4.18 | 81.7 | 1.3\nALL CARRIERS | 20533 | 4.02 | 84.5 | 100.0\n(15 rows)\n\n=== Report 2: Order-level delivery reliability by carrier group and quarter, 2025 (unified source) (written by Bea) ===\nEngineer's note: Built as a single self-contained query per the request: qualifying orders (2025, status='fulfilled', exactly one fulfillment with exactly one shipment, delivered_at not null) are bucketed into the three named carrier groups vs. 'All other carriers' (summed), by calendar quarter of placed_at. share_of_quarter_orders_pct is each group's share of all qualifying orders in that quarter (the four group shares sum to 100%). If a named carrier has zero qualifying orders in some quarter, that row will be absent rather than printed as zero (did not observe this in a dry run since no data is read).\nREPORT: Order-level delivery reliability by carrier group and quarter, 2025 (unified source)\nQUESTION: Did Albion Parcel's reliability break down specifically in Q4 while Landfall Freight and Lowlands Express stayed consistently weak all year, when measured in one single consistent query?\ncarrier_group | quarter | orders_fully_delivered | avg_delivery_days | pct_within_5_days | share_of_quarter_orders_pct\n-------------------+---------+------------------------+-------------------+-------------------+----------------------------\nAlbion Parcel | 2025-Q1 | 614 | 3.18 | 95.0 | 13.4\nLandfall Freight | 2025-Q1 | 430 | 5.76 | 42.6 | 9.4\nLowlands Express | 2025-Q1 | 567 | 4.61 | 64.2 | 12.4\nAll other carriers | 2025-Q1 | 2980 | 3.35 | 91.4 | 64.9\nAlbion Parcel | 2025-Q2 | 587 | 3.58 | 88.8 | 13.1\nLandfall Freight | 2025-Q2 | 366 | 6.08 | 38.8 | 8.2\nLowlands Express | 2025-Q2 | 560 | 5.27 | 54.1 | 12.5\nAll other carriers | 2025-Q2 | 2957 | 3.51 | 87.6 | 66.2\nAlbion Parcel | 2025-Q3 | 612 | 3.40 | 93.1 | 13.2\nLandfall Freight | 2025-Q3 | 408 | 6.24 | 30.6 | 8.8\nLowlands Express | 2025-Q3 | 547 | 4.82 | 60.1 | 11.8\nAll other carriers | 2025-Q3 | 3066 | 3.60 | 86.8 | 66.2\nAlbion Parcel | 2025-Q4 | 751 | 7.80 | 50.5 | 11.0\nLandfall Freight | 2025-Q4 | 624 | 6.31 | 31.6 | 9.1\nLowlands Express | 2025-Q4 | 771 | 4.95 | 58.5 | 11.3\nAll other carriers | 2025-Q4 | 4693 | 3.46 | 88.6 | 68.6\n(16 rows)\n\n=== Report 3: Order-level delivery reliability: Lowlands Express by destination lane, full year 2025 (unified re-check) (written by Grace) ===\nEngineer's note: Applies the same eligibility rule (status='fulfilled', placed_at in 2025, exactly one fulfillment with exactly one shipment, delivered_at not null) as the other two reports in this set; the three lane_group totals sum to that same eligible-order population, so they should reconcile with the full-year carrier report's total.\nREPORT: Order-level delivery reliability: Lowlands Express by destination lane, full year 2025 (unified re-check)\nQUESTION: Confirmed and reconciled: how much does Lowlands Express's order-level reliability differ between its home lanes (Netherlands/Belgium/France) and everywhere else, versus the rest of our carriers combined?\nlane_group | orders_fully_delivered | avg_delivery_days | pct_within_5_days | share_of_orders_pct\n------------------------------------+------------------------+-------------------+-------------------+--------------------\nLowlands core (NL/BE/FR) | 1581 | 4.34 | 72.9 | 7.7\nLowlands non-core (other countries) | 864 | 5.96 | 34.1 | 4.2\nAll other carriers combined | 18088 | 3.92 | 82.0 | 88.1\n(3 rows)\n\n\nIt is Friday morning. Write the memo to Priya Raman:\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":"98e68428-597f-439a-bcac-933d826a5d27","isSidechain":false,"promptId":"17dad9c1-e392-4611-af95-b1c5e9bf5197","type":"user","message":{"role":"user","content":"The printed reports of night 4:\n\n=== Report 1: Order-level delivery reliability by carrier, full year 2025 (unified source) (written by Felix) ===\nEngineer's note: Scoped exactly as specified: orders placed in 2025, status='fulfilled', exactly one fulfillment with exactly one shipment, delivered_at not null, delivery_days = date(delivered_at) - date(placed_at). share_of_orders_pct is each carrier's share of this clean population (denominator is the same filtered set, not all orders tenant-wide), and the ALL CARRIERS row re-aggregates the identical population so it sums correctly against the carrier rows.\nREPORT: Order-level delivery reliability by carrier, full year 2025 (unified source)\nQUESTION: What is each carrier's order-level delivery reliability (orders placed to orders delivered, within 5 days), in one single consistent query I can use as the ground truth for a reliability ceiling calculation?\ncarrier | orders_fully_delivered | avg_delivery_days | pct_within_5_days | share_of_orders_pct\n---------------------+------------------------+-------------------+-------------------+--------------------\nStrombogen Kurier | 2900 | 3.27 | 97.7 | 14.1\nAlbion Parcel | 2564 | 4.72 | 82.9 | 12.5\nLowlands Express | 2445 | 4.97 | 66.7 | 11.9\nLiberty Ground | 2196 | 3.13 | 96.5 | 10.7\nAlpenpaket | 1996 | 3.31 | 97.1 | 9.7\nLandfall Freight | 1828 | 6.15 | 44.5 | 8.9\nTrailhead Express | 1759 | 3.36 | 92.3 | 8.6\nSkerrylane Logistics | 1075 | 3.83 | 87.5 | 5.2\nKennet Freight | 876 | 3.60 | 91.8 | 4.3\nPolder Post | 855 | 4.17 | 80.6 | 4.2\nRegent Parcels | 759 | 3.55 | 92.4 | 3.7\nPrairie Line Express | 699 | 3.41 | 93.7 | 3.4\nLiffey Couriers | 319 | 3.95 | 84.3 | 1.6\nGallia Colis | 262 | 4.18 | 81.7 | 1.3\nALL CARRIERS | 20533 | 4.02 | 84.5 | 100.0\n(15 rows)\n\n=== Report 2: Order-level delivery reliability by carrier group and quarter, 2025 (unified source) (written by Bea) ===\nEngineer's note: Built as a single self-contained query per the request: qualifying orders (2025, status='fulfilled', exactly one fulfillment with exactly one shipment, delivered_at not null) are bucketed into the three named carrier groups vs. 'All other carriers' (summed), by calendar quarter of placed_at. share_of_quarter_orders_pct is each group's share of all qualifying orders in that quarter (the four group shares sum to 100%). If a named carrier has zero qualifying orders in some quarter, that row will be absent rather than printed as zero (did not observe this in a dry run since no data is read).\nREPORT: Order-level delivery reliability by carrier group and quarter, 2025 (unified source)\nQUESTION: Did Albion Parcel's reliability break down specifically in Q4 while Landfall Freight and Lowlands Express stayed consistently weak all year, when measured in one single consistent query?\ncarrier_group | quarter | orders_fully_delivered | avg_delivery_days | pct_within_5_days | share_of_quarter_orders_pct\n-------------------+---------+------------------------+-------------------+-------------------+----------------------------\nAlbion Parcel | 2025-Q1 | 614 | 3.18 | 95.0 | 13.4\nLandfall Freight | 2025-Q1 | 430 | 5.76 | 42.6 | 9.4\nLowlands Express | 2025-Q1 | 567 | 4.61 | 64.2 | 12.4\nAll other carriers | 2025-Q1 | 2980 | 3.35 | 91.4 | 64.9\nAlbion Parcel | 2025-Q2 | 587 | 3.58 | 88.8 | 13.1\nLandfall Freight | 2025-Q2 | 366 | 6.08 | 38.8 | 8.2\nLowlands Express | 2025-Q2 | 560 | 5.27 | 54.1 | 12.5\nAll other carriers | 2025-Q2 | 2957 | 3.51 | 87.6 | 66.2\nAlbion Parcel | 2025-Q3 | 612 | 3.40 | 93.1 | 13.2\nLandfall Freight | 2025-Q3 | 408 | 6.24 | 30.6 | 8.8\nLowlands Express | 2025-Q3 | 547 | 4.82 | 60.1 | 11.8\nAll other carriers | 2025-Q3 | 3066 | 3.60 | 86.8 | 66.2\nAlbion Parcel | 2025-Q4 | 751 | 7.80 | 50.5 | 11.0\nLandfall Freight | 2025-Q4 | 624 | 6.31 | 31.6 | 9.1\nLowlands Express | 2025-Q4 | 771 | 4.95 | 58.5 | 11.3\nAll other carriers | 2025-Q4 | 4693 | 3.46 | 88.6 | 68.6\n(16 rows)\n\n=== Report 3: Order-level delivery reliability: Lowlands Express by destination lane, full year 2025 (unified re-check) (written by Grace) ===\nEngineer's note: Applies the same eligibility rule (status='fulfilled', placed_at in 2025, exactly one fulfillment with exactly one shipment, delivered_at not null) as the other two reports in this set; the three lane_group totals sum to that same eligible-order population, so they should reconcile with the full-year carrier report's total.\nREPORT: Order-level delivery reliability: Lowlands Express by destination lane, full year 2025 (unified re-check)\nQUESTION: Confirmed and reconciled: how much does Lowlands Express's order-level reliability differ between its home lanes (Netherlands/Belgium/France) and everywhere else, versus the rest of our carriers combined?\nlane_group | orders_fully_delivered | avg_delivery_days | pct_within_5_days | share_of_orders_pct\n------------------------------------+------------------------+-------------------+-------------------+--------------------\nLowlands core (NL/BE/FR) | 1581 | 4.34 | 72.9 | 7.7\nLowlands non-core (other countries) | 864 | 5.96 | 34.1 | 4.2\nAll other carriers combined | 18088 | 3.92 | 82.0 | 88.1\n(3 rows)\n\n\nIt is Friday morning. Write the memo to Priya Raman:\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":"bd8725f9-2db9-4968-a7fa-e54ee4c518a5","timestamp":"2026-10-04T02:12:03.518Z","permissionMode":"default","promptSource":"sdk","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120","version":"2.1.275","gitBranch":"HEAD"}
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{"parentUuid":"92e9d05e-c05b-48b8-9907-752f5a1de1bc","isSidechain":false,"message":{"model":"claude-sonnet-5","id":"msg_011CfgPP1Whzy3ZzWPRRw2ik","type":"message","role":"assistant","content":[{"type":"text","text":"**To:** Priya Raman\n**From:** Lena Hartmann\n**Re:** Carrier narrative — what's fixable, what's a ceiling, and two resolved side-questions\n\n**1. Three weak carriers need three different responses (confident on direction, caveat on precision — see below).** Albion Parcel's collapse is *not* localized: it hit both countries it serves (UK, Ireland) and every single origin warehouse uniformly the moment November started, with pct-within-3-days dropping from ~90%+ to 30-40% across the board (destination/warehouse breakdowns, Night 1) — and it's specifically a Q4 event: ~88-95% on-time (5-day, order-level) in Q1-Q3, collapsing to ~50% in Q4 and *not* recovering by year-end (carrier-by-quarter report, Night 4). That's a \"this carrier broke under load everywhere at once\" story — systemic, not one bad depot, but also not (yet) chronic. **Landfall Freight** is uniformly weak across all 18 countries it ships to, all year, every quarter (30-45% within 5 days consistently) — no lane or season explains it away; this looks like a relationship-level problem. **Lowlands Express** is genuinely better on its Benelux/France home lanes than elsewhere (roughly 60-73% vs 25-34% outside), but even on home turf it's still below the standard of our good carriers (which run 87-98%) — so reassigning its non-core volume helps, but it isn't a full fix on its own.\n\n**2. The reliability ceiling is real but I won't give you a precise number yet.** Every version of this calculation — three independent cuts across two weeks — agrees that fixing the three weak carriers would close a *substantial* chunk, not just the long tail, of our delivery-reliability gap: overall order-level on-time (within 5 days) sits around 84-85%, the \"good\" carriers run 88-97%, and the weak three cluster in the 35-67% range depending on exactly how the query dates are computed. **Caveat, and this matters:** when I asked for the same figures three different ways this week, the same carrier's pct-within-5-days came back meaningfully different each time (e.g. Landfall ranged 35-45%, Lowlands 59-67%, \"good\" carriers 89-93%) even though the underlying order counts matched exactly every time. I believe this is a day-boundary/rounding difference between queries (date-diff vs. timestamp-diff), not a real swing in performance, but I don't yet have a single trusted number. My honest estimate: closing the gap on these three carriers would eliminate **roughly 40-55%** of our current delivery unreliability — directionally large and worth acting on, but I want that number verified with one engineer, one explicit date-diff definition, before it goes in front of merchants or carriers.\n\n**3. Restocking is not falling behind (resolved).** Once I excluded purchase orders too recent to have resolved yet, the apparent Q4 \"backlog\" (418 open POs) vanished — the mature, comparable subset shows ~0% genuinely open in every quarter including Q4. What *is* real and unchanged by season: about half of all POs (47-59%, every quarter) arrive after their expected date — a chronic supplier promise-miss rate, not a growing queue. Worth a conversation with suppliers, but it's not getting worse.\n\n**4. Picking discrepancies are a separate track from delivery speed (resolved).** Apparel & Footwear and Beauty have the highest rates of orders shipped short of the full quantity ordered (4.5-4.7% vs 1.6-3% elsewhere), consistent with Tomasz's picking-accuracy finding. But Apparel is actually our *fastest, most reliable* vertical once an order does fully ship (82.4% within 5 days, best in the book). So this looks like a real, separate completeness/accuracy problem — customers sometimes getting short orders — not a delivery-delay problem. Recommend it stays its own workstream.\n\n**Next steps:** (a) get one engineer to re-run the carrier reliability numbers with a single locked-down date-diff definition so I can give you a trustworthy ceiling figure; (b) before any carrier conversation, confirm whether Albion's capacity issue is resolving in January; (c) returns/restock cycle time, still queued from two weeks ago."}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":162266,"cache_read_input_tokens":0,"output_tokens":6609,"output_tokens_details":{"thinking_tokens":5169},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":162266,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":6609,"cache_read_input_tokens":0,"cache_creation_input_tokens":162266,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":162266},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":{"cache_miss_reason":{"type":"tools_changed","cache_missed_input_tokens":131194}},"context_management":null},"apiBlockIndex":1,"requestId":"req_011CfgPP1C7bcvZHUnVopqN4","type":"assistant","uuid":"45e55d3f-05ba-459b-869c-5f2df19155c4","timestamp":"2026-10-04T02:13:10.569Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120","version":"2.1.275","gitBranch":"HEAD"}
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{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-04T02:13:11.414Z","sessionId":"e0b165aa-32dc-460c-8e74-dc2db9d40120","content":"Post this week's findings to the company's analytics channel, which every team reads: your 2 or 3 most important findings, one line each, with the key number. Write them so that people outside your team understand them.\n"}
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