{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-04T01:06:53.919Z","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1","content":"Monday 2026-01-05. Note from Patrick O'Neill (Head of B2B Commerce):\n\nNote to Daniel Kim — B2B pipeline health\n\nDaniel,\n\nSamuel and I need to give the B2B wholesale merchants (and our own leadership) a clear-eyed view of why their sales teams are struggling to close business. This feeds a joint recommendation Samuel and I are making next week on where to invest — better pipeline coaching/tooling for merchants vs. changes to how we structure stages, quotes and terms in the product. So I want the picture to be accurate more than exhaustive — pick your battles across the nights you have.\n\nHere's what I actually want to know, in plain terms:\n\n1. **Where do deals die?** Across the wholesale merchants, at what point in the journey from raw lead to signed deal do we lose the most opportunities? I want to know which stage is the graveyard, not just an overall conversion number.\n\n2. **How long does it take to close, and is that getting better or worse?** From a lead first showing up to a deal actually closing (won or lost), what's the typical cycle time, and has it been stretching out or shrinking over recent months?\n\n3. **What separates a won deal from a lost one?** Things like deal size, how many people/touches were involved before close, which rep or team owned it, or how old the opportunity got before it closed — anything that reliably shows up more on the winning side versus the losing side.\n\n4. **Are some merchants or reps clearly struggling more than others?** I'd like a sense of whether this is a widespread pattern across our B2B merchants or concentrated in a handful of accounts/teams, since that changes whether this is a product fix or a coaching fix.\n\n5. (If there's room) **Does deal size or payment terms correlate with win rate or speed?** Wholesalers care a lot about credit terms — I'm curious if longer terms or larger contract values are dragging out or killing deals.\n\nPriority order is 1, 2, 3 first — those directly shape the recommendation. 4 and 5 are valuable but can slip if you run out of slots this week.\n\nKeep each report to one clean question — I'd rather have four sharp answers than one crowded one. Flag anything that looks like a data quality issue rather than a real trend.\n\nThanks — looking forward to the Friday memo.\n\nPatrick\n\nNote from Samuel Whitcombe (Head of Merchant Success), who co-owns this with Patrick O'Neill:\n\nSubject: A few questions from the Merchant Success side\n\nHi Daniel,\n\nGood to see this getting attention this week. A few things I'd love your analysis to help answer, from the merchant-health angle:\n\n1. For our B2B wholesaler merchants specifically, are deals actually stalling at a particular stage, or is it more that fewer leads come in to begin with? I want to know where the real friction sits before our QBRs.\n\n2. Among the accounts that do close, is there anything about them (size, how engaged they were beforehand, how long the courtship took) that separates the wins from the losses? I'm trying to understand whether our biggest merchants' sales teams are losing winnable deals or just working less promising ones.\n\n3. Is there a relationship between how long a deal has been open and its odds of closing at all — i.e. do stale pipelines quietly become risk, or do they usually still land eventually?\n\nHappy to compare notes before you finalize anything for Patrick.\n\nBest,\nSamuel\n\nNight 1 of 4: you have 3 report slots tonight."}
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{"parentUuid":null,"isSidechain":false,"promptId":"be4be37e-207a-445d-a980-e6c96efc80e7","type":"user","message":{"role":"user","content":"Monday 2026-01-05. Note from Patrick O'Neill (Head of B2B Commerce):\n\nNote to Daniel Kim — B2B pipeline health\n\nDaniel,\n\nSamuel and I need to give the B2B wholesale merchants (and our own leadership) a clear-eyed view of why their sales teams are struggling to close business. This feeds a joint recommendation Samuel and I are making next week on where to invest — better pipeline coaching/tooling for merchants vs. changes to how we structure stages, quotes and terms in the product. So I want the picture to be accurate more than exhaustive — pick your battles across the nights you have.\n\nHere's what I actually want to know, in plain terms:\n\n1. **Where do deals die?** Across the wholesale merchants, at what point in the journey from raw lead to signed deal do we lose the most opportunities? I want to know which stage is the graveyard, not just an overall conversion number.\n\n2. **How long does it take to close, and is that getting better or worse?** From a lead first showing up to a deal actually closing (won or lost), what's the typical cycle time, and has it been stretching out or shrinking over recent months?\n\n3. **What separates a won deal from a lost one?** Things like deal size, how many people/touches were involved before close, which rep or team owned it, or how old the opportunity got before it closed — anything that reliably shows up more on the winning side versus the losing side.\n\n4. **Are some merchants or reps clearly struggling more than others?** I'd like a sense of whether this is a widespread pattern across our B2B merchants or concentrated in a handful of accounts/teams, since that changes whether this is a product fix or a coaching fix.\n\n5. (If there's room) **Does deal size or payment terms correlate with win rate or speed?** Wholesalers care a lot about credit terms — I'm curious if longer terms or larger contract values are dragging out or killing deals.\n\nPriority order is 1, 2, 3 first — those directly shape the recommendation. 4 and 5 are valuable but can slip if you run out of slots this week.\n\nKeep each report to one clean question — I'd rather have four sharp answers than one crowded one. Flag anything that looks like a data quality issue rather than a real trend.\n\nThanks — looking forward to the Friday memo.\n\nPatrick\n\nNote from Samuel Whitcombe (Head of Merchant Success), who co-owns this with Patrick O'Neill:\n\nSubject: A few questions from the Merchant Success side\n\nHi Daniel,\n\nGood to see this getting attention this week. A few things I'd love your analysis to help answer, from the merchant-health angle:\n\n1. For our B2B wholesaler merchants specifically, are deals actually stalling at a particular stage, or is it more that fewer leads come in to begin with? I want to know where the real friction sits before our QBRs.\n\n2. Among the accounts that do close, is there anything about them (size, how engaged they were beforehand, how long the courtship took) that separates the wins from the losses? I'm trying to understand whether our biggest merchants' sales teams are losing winnable deals or just working less promising ones.\n\n3. Is there a relationship between how long a deal has been open and its odds of closing at all — i.e. do stale pipelines quietly become risk, or do they usually still land eventually?\n\nHappy to compare notes before you finalize anything for Patrick.\n\nBest,\nSamuel\n\nNight 1 of 4: you have 3 report slots tonight."},"uuid":"9547912b-710f-4d5d-a167-3c267a348210","timestamp":"2026-10-04T01:06:53.928Z","permissionMode":"default","promptSource":"sdk","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1","version":"2.1.275","gitBranch":"HEAD"}
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{"parentUuid":"ef8c3805-8eee-4d17-9a6b-7b8414692028","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 Daniel Kim, a data analyst at Tidewick Commerce. You work for Patrick O'Neill, Head of B2B Commerce. Your working style is funnel thinker: measures each step of a process and where things drop out.\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":"22c9d51c-5b88-4fab-b406-b13ebaa57c4f","timestamp":"2026-10-04T01:06:53.934Z","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1","version":"2.1.275","gitBranch":"HEAD"}
{"type":"last-prompt","lastPrompt":"Monday 2026-01-05. Note from Patrick O'Neill (Head of B2B Commerce): Note to Daniel Kim — B2B pipeline health Daniel, Samuel and I need to give the B2B wholesale merchants (and our own leadership)…","leafUuid":"22c9d51c-5b88-4fab-b406-b13ebaa57c4f","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1"}
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{"parentUuid":"06f5a969-5470-4b1c-a8ec-1d0db68e1a7f","isSidechain":false,"message":{"model":"claude-sonnet-5","id":"msg_011CfgJQo35pzH2KsLx2mKjy","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_013qf1PZxocHaMLzzGCajNxC","name":"StructuredOutput","input":{"requests":[{"title":"B2B Wholesale Pipeline: Opportunities by Merchant and Stage","question":"Across our B2B wholesale merchants, at which pipeline stage do the most opportunities get stuck or lost — i.e. which stage is the graveyard?","grain":"one row per wholesale merchant (tenant) and pipeline stage","columns":["merchant (tenants.name)","pipeline name","stage name","stage order (sort_position)","default win probability (win_probability)","opportunities currently open at this stage (is_closed = false)","opportunities closed won with this as final stage_id","opportunities closed lost with this as final stage_id","average opportunity amount at this stage (in the opportunity's own currency)","currency_code"],"details":"Scope to B2B wholesale merchants only: tenants where industry ILIKE '%industrial%' OR industry ILIKE '%office equipment%' OR industry ILIKE '%wholesal%' (per the company briefing's 'industrial supplies, office equipment' wholesalers). Use opportunities joined to opportunity_stages (stage_id) and pipelines, and tenants for the merchant name. For each (tenant, stage) pair count: opportunities where is_closed = false (currently sitting there), opportunities where is_closed = true and is_won = true and stage_id = this stage, and opportunities where is_closed = true and is_won = false (lost) and stage_id = this stage. Include avg(amount) per currency_code — do not convert currencies here since the grain is per-tenant so no cross-currency summing occurs. This is an all-time snapshot (all opportunities ever created through 2025-12-31), not time-windowed. Order by merchant name, then stage order (sort_position) ascending. Flag in the report notes if any wholesale tenant has zero opportunities or zero stages, since that would indicate the industry filter missed a merchant or the merchant doesn't use the CRM module.","builds_on":[]},{"title":"B2B Wholesale Deal Cycle Time Trend by Month","question":"From an opportunity being opened to it closing (won or lost), how long does that take for our B2B wholesale merchants, and is that cycle time getting longer or shorter over the last three years?","grain":"one row per calendar month (based on the opportunity's closed_at month)","columns":["month (YYYY-MM)","opportunities closed (count)","closed won (count)","closed lost (count)","win rate (closed won / opportunities closed, %)","average cycle days (closed_at - created_at)","median cycle days (closed_at - created_at)"],"details":"Scope to the same B2B wholesale tenants as the stage report: tenants where industry ILIKE '%industrial%' OR industry ILIKE '%office equipment%' OR industry ILIKE '%wholesal%'. Use opportunities where is_closed = true, closed_at is not null, for the full history 2023-01-01 through 2025-12-31. Cycle time = DATE_PART('day', closed_at - created_at) per opportunity; this is a proxy for the full lead-to-close journey since opportunities have no direct FK back to the originating lead — note this as a known limitation in the report. Group by month of closed_at (use calendar_days.month_label or an equivalent truncation). Round average and median cycle days to 1 decimal. Order by month ascending.","builds_on":[]},{"title":"B2B Wholesale Opportunities: Won vs Lost Characteristics","question":"Among closed B2B wholesale opportunities, what reliably differs between the ones we won and the ones we lost — deal size, touches/activities before close, days open, or how concentrated they are among accounts and reps?","grain":"one row per closed outcome (won or lost)","columns":["outcome (won / lost)","opportunities (count)","average amount in EUR","median amount in EUR","average activities per opportunity before close","average days open (closed_at - created_at)","distinct accounts involved","distinct owning reps (owner_user_id) involved"],"details":"Scope to the same B2B wholesale tenants as the other two reports (industry ILIKE '%industrial%' OR '%office equipment%' OR '%wholesal%'). Use opportunities where is_closed = true, split into is_won = true (won) vs is_won = false (lost), across all history through 2025-12-31. Convert opportunity amount to EUR using exchange_rates (base_code='EUR', quote_code = opportunity currency_code, rate as_of the opportunity's closed_at date, or nearest prior business day if a weekend) before averaging across tenants/currencies, per house standards. 'Activities before close' = count of rows in activities where activities.opportunity_id = opportunities.id and activities.occurred_at <= opportunities.closed_at, averaged per opportunity within each outcome group. Distinct accounts/reps are counts of distinct account_id / owner_user_id among opportunities in that outcome group. Order rows with 'won' first, then 'lost'.","builds_on":[]}],"notes":"Night 1: going straight at Patrick's top three priorities (graveyard stage, cycle-time trend, won-vs-lost drivers), which also cover most of Samuel's three questions (stage friction, what separates wins/losses, implicitly sets up stale-pipeline-risk follow-up for night 2). All three reports scope to B2B wholesale tenants via an industry-text heuristic (industrial supplies / office equipment / wholesale) since there's no explicit 'B2B wholesaler' flag on tenants — I've asked the report to surface the merchant list so I can sanity-check the filter caught the right handful of tenants before going further. Kept each report to one grain: (tenant x stage) for the funnel, (month) for cycle time, and (won/lost) for the comparison, per the one-question-one-report rule. Held back Samuel's 'stale pipeline = risk' question and Patrick's #4 (merchant/rep dispersion) and #5 (terms/size correlation) for later nights once I see where the real friction shows up — no point spending slots on dispersion or terms analysis if the stage report shows everyone dying at the same place for the same reason."},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":49721,"cache_read_input_tokens":0,"output_tokens":10459,"output_tokens_details":{"thinking_tokens":8193},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":49721,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":10459,"cache_read_input_tokens":0,"cache_creation_input_tokens":49721,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":49721},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"wireToolInputs":{"toolu_013qf1PZxocHaMLzzGCajNxC":{"requests":[{"title":"B2B Wholesale Pipeline: Opportunities by Merchant and Stage","question":"Across our B2B wholesale merchants, at which pipeline stage do the most opportunities get stuck or lost — i.e. which stage is the graveyard?","grain":"one row per wholesale merchant (tenant) and pipeline stage","columns":["merchant (tenants.name)","pipeline name","stage name","stage order (sort_position)","default win probability (win_probability)","opportunities currently open at this stage (is_closed = false)","opportunities closed won with this as final stage_id","opportunities closed lost with this as final stage_id","average opportunity amount at this stage (in the opportunity's own currency)","currency_code"],"details":"Scope to B2B wholesale merchants only: tenants where industry ILIKE '%industrial%' OR industry ILIKE '%office equipment%' OR industry ILIKE '%wholesal%' (per the company briefing's 'industrial supplies, office equipment' wholesalers). Use opportunities joined to opportunity_stages (stage_id) and pipelines, and tenants for the merchant name. For each (tenant, stage) pair count: opportunities where is_closed = false (currently sitting there), opportunities where is_closed = true and is_won = true and stage_id = this stage, and opportunities where is_closed = true and is_won = false (lost) and stage_id = this stage. Include avg(amount) per currency_code — do not convert currencies here since the grain is per-tenant so no cross-currency summing occurs. This is an all-time snapshot (all opportunities ever created through 2025-12-31), not time-windowed. Order by merchant name, then stage order (sort_position) ascending. Flag in the report notes if any wholesale tenant has zero opportunities or zero stages, since that would indicate the industry filter missed a merchant or the merchant doesn't use the CRM module.","builds_on":[]},{"title":"B2B Wholesale Deal Cycle Time Trend by Month","question":"From an opportunity being opened to it closing (won or lost), how long does that take for our B2B wholesale merchants, and is that cycle time getting longer or shorter over the last three years?","grain":"one row per calendar month (based on the opportunity's closed_at month)","columns":["month (YYYY-MM)","opportunities closed (count)","closed won (count)","closed lost (count)","win rate (closed won / opportunities closed, %)","average cycle days (closed_at - created_at)","median cycle days (closed_at - created_at)"],"details":"Scope to the same B2B wholesale tenants as the stage report: tenants where industry ILIKE '%industrial%' OR industry ILIKE '%office equipment%' OR industry ILIKE '%wholesal%'. Use opportunities where is_closed = true, closed_at is not null, for the full history 2023-01-01 through 2025-12-31. Cycle time = DATE_PART('day', closed_at - created_at) per opportunity; this is a proxy for the full lead-to-close journey since opportunities have no direct FK back to the originating lead — note this as a known limitation in the report. Group by month of closed_at (use calendar_days.month_label or an equivalent truncation). Round average and median cycle days to 1 decimal. Order by month ascending.","builds_on":[]},{"title":"B2B Wholesale Opportunities: Won vs Lost Characteristics","question":"Among closed B2B wholesale opportunities, what reliably differs between the ones we won and the ones we lost — deal size, touches/activities before close, days open, or how concentrated they are among accounts and reps?","grain":"one row per closed outcome (won or lost)","columns":["outcome (won / lost)","opportunities (count)","average amount in EUR","median amount in EUR","average activities per opportunity before close","average days open (closed_at - created_at)","distinct accounts involved","distinct owning reps (owner_user_id) involved"],"details":"Scope to the same B2B wholesale tenants as the other two reports (industry ILIKE '%industrial%' OR '%office equipment%' OR '%wholesal%'). Use opportunities where is_closed = true, split into is_won = true (won) vs is_won = false (lost), across all history through 2025-12-31. Convert opportunity amount to EUR using exchange_rates (base_code='EUR', quote_code = opportunity currency_code, rate as_of the opportunity's closed_at date, or nearest prior business day if a weekend) before averaging across tenants/currencies, per house standards. 'Activities before close' = count of rows in activities where activities.opportunity_id = opportunities.id and activities.occurred_at <= opportunities.closed_at, averaged per opportunity within each outcome group. Distinct accounts/reps are counts of distinct account_id / owner_user_id among opportunities in that outcome group. Order rows with 'won' first, then 'lost'.","builds_on":[]}],"notes":"Night 1: going straight at Patrick's top three priorities (graveyard stage, cycle-time trend, won-vs-lost drivers), which also cover most of Samuel's three questions (stage friction, what separates wins/losses, implicitly sets up stale-pipeline-risk follow-up for night 2). All three reports scope to B2B wholesale tenants via an industry-text heuristic (industrial supplies / office equipment / wholesale) since there's no explicit 'B2B wholesaler' flag on tenants — I've asked the report to surface the merchant list so I can sanity-check the filter caught the right handful of tenants before going further. Kept each report to one grain: (tenant x stage) for the funnel, (month) for cycle time, and (won/lost) for the comparison, per the one-question-one-report rule. Held back Samuel's 'stale pipeline = risk' question and Patrick's #4 (merchant/rep dispersion) and #5 (terms/size correlation) for later nights once I see where the real friction shows up — no point spending slots on dispersion or terms analysis if the stage report shows everyone dying at the same place for the same reason."}},"apiBlockIndex":1,"requestId":"req_011CfgJQnhVYHb1D1GTCNDdf","type":"assistant","uuid":"53ed0ab8-916c-4ece-b67c-3bccba990ae8","timestamp":"2026-10-04T01:08:45.060Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"53ed0ab8-916c-4ece-b67c-3bccba990ae8","isSidechain":false,"attachment":{"type":"structured_output","data":{"requests":[{"title":"B2B Wholesale Pipeline: Opportunities by Merchant and Stage","question":"Across our B2B wholesale merchants, at which pipeline stage do the most opportunities get stuck or lost — i.e. which stage is the graveyard?","grain":"one row per wholesale merchant (tenant) and pipeline stage","columns":["merchant (tenants.name)","pipeline name","stage name","stage order (sort_position)","default win probability (win_probability)","opportunities currently open at this stage (is_closed = false)","opportunities closed won with this as final stage_id","opportunities closed lost with this as final stage_id","average opportunity amount at this stage (in the opportunity's own currency)","currency_code"],"details":"Scope to B2B wholesale merchants only: tenants where industry ILIKE '%industrial%' OR industry ILIKE '%office equipment%' OR industry ILIKE '%wholesal%' (per the company briefing's 'industrial supplies, office equipment' wholesalers). Use opportunities joined to opportunity_stages (stage_id) and pipelines, and tenants for the merchant name. For each (tenant, stage) pair count: opportunities where is_closed = false (currently sitting there), opportunities where is_closed = true and is_won = true and stage_id = this stage, and opportunities where is_closed = true and is_won = false (lost) and stage_id = this stage. Include avg(amount) per currency_code — do not convert currencies here since the grain is per-tenant so no cross-currency summing occurs. This is an all-time snapshot (all opportunities ever created through 2025-12-31), not time-windowed. Order by merchant name, then stage order (sort_position) ascending. Flag in the report notes if any wholesale tenant has zero opportunities or zero stages, since that would indicate the industry filter missed a merchant or the merchant doesn't use the CRM module.","builds_on":[]},{"title":"B2B Wholesale Deal Cycle Time Trend by Month","question":"From an opportunity being opened to it closing (won or lost), how long does that take for our B2B wholesale merchants, and is that cycle time getting longer or shorter over the last three years?","grain":"one row per calendar month (based on the opportunity's closed_at month)","columns":["month (YYYY-MM)","opportunities closed (count)","closed won (count)","closed lost (count)","win rate (closed won / opportunities closed, %)","average cycle days (closed_at - created_at)","median cycle days (closed_at - created_at)"],"details":"Scope to the same B2B wholesale tenants as the stage report: tenants where industry ILIKE '%industrial%' OR industry ILIKE '%office equipment%' OR industry ILIKE '%wholesal%'. Use opportunities where is_closed = true, closed_at is not null, for the full history 2023-01-01 through 2025-12-31. Cycle time = DATE_PART('day', closed_at - created_at) per opportunity; this is a proxy for the full lead-to-close journey since opportunities have no direct FK back to the originating lead — note this as a known limitation in the report. Group by month of closed_at (use calendar_days.month_label or an equivalent truncation). Round average and median cycle days to 1 decimal. Order by month ascending.","builds_on":[]},{"title":"B2B Wholesale Opportunities: Won vs Lost Characteristics","question":"Among closed B2B wholesale opportunities, what reliably differs between the ones we won and the ones we lost — deal size, touches/activities before close, days open, or how concentrated they are among accounts and reps?","grain":"one row per closed outcome (won or lost)","columns":["outcome (won / lost)","opportunities (count)","average amount in EUR","median amount in EUR","average activities per opportunity before close","average days open (closed_at - created_at)","distinct accounts involved","distinct owning reps (owner_user_id) involved"],"details":"Scope to the same B2B wholesale tenants as the other two reports (industry ILIKE '%industrial%' OR '%office equipment%' OR '%wholesal%'). Use opportunities where is_closed = true, split into is_won = true (won) vs is_won = false (lost), across all history through 2025-12-31. Convert opportunity amount to EUR using exchange_rates (base_code='EUR', quote_code = opportunity currency_code, rate as_of the opportunity's closed_at date, or nearest prior business day if a weekend) before averaging across tenants/currencies, per house standards. 'Activities before close' = count of rows in activities where activities.opportunity_id = opportunities.id and activities.occurred_at <= opportunities.closed_at, averaged per opportunity within each outcome group. Distinct accounts/reps are counts of distinct account_id / owner_user_id among opportunities in that outcome group. Order rows with 'won' first, then 'lost'.","builds_on":[]}],"notes":"Night 1: going straight at Patrick's top three priorities (graveyard stage, cycle-time trend, won-vs-lost drivers), which also cover most of Samuel's three questions (stage friction, what separates wins/losses, implicitly sets up stale-pipeline-risk follow-up for night 2). All three reports scope to B2B wholesale tenants via an industry-text heuristic (industrial supplies / office equipment / wholesale) since there's no explicit 'B2B wholesaler' flag on tenants — I've asked the report to surface the merchant list so I can sanity-check the filter caught the right handful of tenants before going further. Kept each report to one grain: (tenant x stage) for the funnel, (month) for cycle time, and (won/lost) for the comparison, per the one-question-one-report rule. Held back Samuel's 'stale pipeline = risk' question and Patrick's #4 (merchant/rep dispersion) and #5 (terms/size correlation) for later nights once I see where the real friction shows up — no point spending slots on dispersion or terms analysis if the stage report shows everyone dying at the same place for the same reason."},"toolUseID":"toolu_013qf1PZxocHaMLzzGCajNxC"},"type":"attachment","uuid":"010c96e7-cb6d-43a8-a8f0-eee1a0e3af77","timestamp":"2026-10-04T01:08:45.067Z","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1","version":"2.1.275","gitBranch":"HEAD"}
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{"parentUuid":"e3654491-276a-475d-88c5-9611cf73576a","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 Daniel Kim, a data analyst at Tidewick Commerce. You work for Patrick O'Neill, Head of B2B Commerce. Your working style is funnel thinker: measures each step of a process and where things drop out.\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":"1fe57ac1-333d-4072-9a3a-a5a40649b9ef","timestamp":"2026-10-04T01:08:45.078Z","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1","version":"2.1.275","gitBranch":"HEAD"}
{"type":"last-prompt","lastPrompt":"Monday 2026-01-05. Note from Patrick O'Neill (Head of B2B Commerce): Note to Daniel Kim — B2B pipeline health Daniel, Samuel and I need to give the B2B wholesale merchants (and our own leadership)…","leafUuid":"1fe57ac1-333d-4072-9a3a-a5a40649b9ef","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1"}
{"type":"ai-title","aiTitle":"B2B pipeline health analysis","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1"}
{"type":"atis-latch","atis":"","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1"}
{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-04T01:09:27.017Z","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1","content":"The printed reports of night 1:\n\n=== Report 1: B2B Wholesale Pipeline: Opportunities by Merchant and Stage (written by Grace) ===\nEngineer's note: Grain is tenant x stage as requested. avg_opportunity_amount/currency_code are computed per (tenant, stage) without FX conversion; if a tenant's opportunities span more than one currency, currency_code shows only the lexically-smallest code present and the average mixes currencies for that row (per the request's assumption of one currency per tenant, this should be rare/absent). Please have the analyst check row count and flag any wholesale tenant that returns zero stages or zero opportunity rows, as that would indicate the industry filter missed a merchant or that merchant doesn't use the CRM/pipeline module - I could not verify this without reading data.\nREPORT: B2B Wholesale Pipeline: Opportunities by Merchant and Stage\nQUESTION: Across our B2B wholesale merchants, at which pipeline stage do the most opportunities get stuck or lost — i.e. which stage is the graveyard?\nmerchant | pipeline_name | stage_name | stage_order | default_win_probability | opportunities_open | opportunities_closed_won | opportunities_closed_lost | avg_opportunity_amount | currency_code\n------------------------------+---------------+---------------+-------------+-------------------------+--------------------+--------------------------+---------------------------+------------------------+--------------\nBallycorran Industrial Supply | New business | Prospecting | 0 | 0.1000 | 0 | 0 | 0 | NULL | NULL \nBallycorran Industrial Supply | Renewals | Renewal due | 0 | 0.6000 | 0 | 0 | 0 | NULL | NULL \nBallycorran Industrial Supply | Renewals | Proposal sent | 1 | 0.7500 | 0 | 0 | 0 | NULL | NULL \nBallycorran Industrial Supply | New business | Qualification | 1 | 0.2500 | 0 | 0 | 0 | NULL | NULL \nBallycorran Industrial Supply | New business | Proposal | 2 | 0.5000 | 0 | 0 | 0 | NULL | NULL \nBallycorran Industrial Supply | Renewals | Renewed | 2 | 1.0000 | 0 | 74 | 0 | 4374.32 | EUR \nBallycorran Industrial Supply | Renewals | Churned | 3 | 0.0000 | 0 | 0 | 17 | 4494.12 | EUR \nBallycorran Industrial Supply | New business | Negotiation | 3 | 0.7500 | 0 | 0 | 0 | NULL | NULL \nBallycorran Industrial Supply | New business | Closed Won | 4 | 1.0000 | 0 | 63 | 0 | 4088.89 | EUR \nBallycorran Industrial Supply | New business | Closed Lost | 5 | 0.0000 | 0 | 0 | 13 | 3938.46 | EUR \nCastell Fasteners | New business | Prospecting | 0 | 0.1000 | 0 | 0 | 0 | NULL | NULL \nCastell Fasteners | Renewals | Renewal due | 0 | 0.6000 | 0 | 0 | 0 | NULL | NULL \nCastell Fasteners | Renewals | Proposal sent | 1 | 0.7500 | 0 | 0 | 0 | NULL | NULL \nCastell Fasteners | New business | Qualification | 1 | 0.2500 | 0 | 0 | 0 | NULL | NULL \nCastell Fasteners | New business | Proposal | 2 | 0.5000 | 0 | 0 | 0 | NULL | NULL \nCastell Fasteners | Renewals | Renewed | 2 | 1.0000 | 0 | 38 | 0 | 11002.63 | USD \nCastell Fasteners | Renewals | Churned | 3 | 0.0000 | 0 | 0 | 4 | 6175.0 | USD \nCastell Fasteners | New business | Negotiation | 3 | 0.7500 | 0 | 0 | 0 | NULL | NULL \nCastell Fasteners | New business | Closed Won | 4 | 1.0000 | 0 | 31 | 0 | 7877.42 | USD \nCastell Fasteners | New business | Closed Lost | 5 | 0.0000 | 0 | 0 | 5 | 6300.0 | USD \nHanse Industriebedarf | New business | Prospecting | 0 | 0.1000 | 0 | 0 | 0 | NULL | NULL \nHanse Industriebedarf | Renewals | Renewal due | 0 | 0.6000 | 0 | 0 | 0 | NULL | NULL \nHanse Industriebedarf | Renewals | Proposal sent | 1 | 0.7500 | 0 | 0 | 0 | NULL | NULL \nHanse Industriebedarf | New business | Qualification | 1 | 0.2500 | 0 | 0 | 0 | NULL | NULL \nHanse Industriebedarf | New business | Proposal | 2 | 0.5000 | 0 | 0 | 0 | NULL | NULL \nHanse Industriebedarf | Renewals | Renewed | 2 | 1.0000 | 0 | 19 | 0 | 7410.53 | EUR \nHanse Industriebedarf | Renewals | Churned | 3 | 0.0000 | 0 | 0 | 1 | 5600.0 | EUR \nHanse Industriebedarf | New business | Negotiation | 3 | 0.7500 | 0 | 0 | 0 | NULL | NULL \nHanse Industriebedarf | New business | Closed Won | 4 | 1.0000 | 0 | 15 | 0 | 5553.33 | EUR \nHanse Industriebedarf | New business | Closed Lost | 5 | 0.0000 | 0 | 0 | 2 | 5200.0 | EUR \nMidland Safety Wear | New business | Prospecting | 0 | 0.1000 | 0 | 0 | 0 | NULL | NULL \nMidland Safety Wear | Renewals | Renewal due | 0 | 0.6000 | 1 | 0 | 0 | 2600.0 | EUR \nMidland Safety Wear | Renewals | Proposal sent | 1 | 0.7500 | 3 | 0 | 0 | 10366.67 | EUR \nMidland Safety Wear | New business | Qualification | 1 | 0.2500 | 0 | 0 | 0 | NULL | NULL \nMidland Safety Wear | New business | Proposal | 2 | 0.5000 | 0 | 0 | 0 | NULL | NULL \nMidland Safety Wear | Renewals | Renewed | 2 | 1.0000 | 0 | 44 | 0 | 5984.09 | EUR \nMidland Safety Wear | Renewals | Churned | 3 | 0.0000 | 0 | 0 | 11 | 4990.91 | EUR \nMidland Safety Wear | New business | Negotiation | 3 | 0.7500 | 0 | 0 | 0 | NULL | NULL \nMidland Safety Wear | New business | Closed Won | 4 | 1.0000 | 0 | 34 | 0 | 5526.47 | EUR \nMidland Safety Wear | New business | Closed Lost | 5 | 0.0000 | 0 | 0 | 6 | 4300.0 | EUR \nPolder Packaging | New business | Prospecting | 0 | 0.1000 | 0 | 0 | 0 | NULL | NULL \nPolder Packaging | Renewals | Renewal due | 0 | 0.6000 | 0 | 0 | 0 | NULL | NULL \nPolder Packaging | Renewals | Proposal sent | 1 | 0.7500 | 0 | 0 | 0 | NULL | NULL \nPolder Packaging | New business | Qualification | 1 | 0.2500 | 0 | 0 | 0 | NULL | NULL \nPolder Packaging | New business | Proposal | 2 | 0.5000 | 0 | 0 | 0 | NULL | NULL \nPolder Packaging | Renewals | Renewed | 2 | 1.0000 | 0 | 9 | 0 | 5388.89 | EUR \nPolder Packaging | Renewals | Churned | 3 | 0.0000 | 0 | 0 | 4 | 3275.0 | EUR \nPolder Packaging | New business | Negotiation | 3 | 0.7500 | 0 | 0 | 0 | NULL | NULL \nPolder Packaging | New business | Closed Won | 4 | 1.0000 | 0 | 12 | 0 | 4908.33 | EUR \nPolder Packaging | New business | Closed Lost | 5 | 0.0000 | 0 | 0 | 4 | 10875.0 | EUR \n(50 rows)\n\n=== Report 2: B2B Wholesale Deal Cycle Time Trend by Month (written by Viktor) ===\nEngineer's note: Cycle time is approximated as closed_at - created_at on the opportunity itself, since opportunities carry no FK back to the originating lead (known limitation, as requested). Win rate, avg and median cycle days are rounded to 1 decimal; monthly grain uses calendar_days.month_label keyed off closed_at.\nREPORT: B2B Wholesale Deal Cycle Time Trend by Month\nQUESTION: From an opportunity being opened to it closing (won or lost), how long does that take for our B2B wholesale merchants, and is that cycle time getting longer or shorter over the last three years?\nmonth | opportunities_closed | closed_won | closed_lost | win_rate_pct | avg_cycle_days | median_cycle_days\n--------+----------------------+------------+-------------+--------------+----------------+------------------\n2023-01 | 5 | 5 | 0 | 100.0 | 12.0 | 15.0\n2023-02 | 5 | 5 | 0 | 100.0 | 16.6 | 12.0\n2023-03 | 8 | 5 | 3 | 62.5 | 22.8 | 21.5\n2023-04 | 5 | 5 | 0 | 100.0 | 17.2 | 21.0\n2023-05 | 6 | 5 | 1 | 83.3 | 24.2 | 22.5\n2023-06 | 9 | 6 | 3 | 66.7 | 21.1 | 24.0\n2023-07 | 5 | 3 | 2 | 60.0 | 33.6 | 34.0\n2023-08 | 7 | 6 | 1 | 85.7 | 17.4 | 16.0\n2023-09 | 10 | 8 | 2 | 80.0 | 30.5 | 35.0\n2023-10 | 9 | 8 | 1 | 88.9 | 26.1 | 25.0\n2023-11 | 12 | 8 | 4 | 66.7 | 24.5 | 23.0\n2023-12 | 7 | 4 | 3 | 57.1 | 22.7 | 26.0\n2024-01 | 11 | 9 | 2 | 81.8 | 15.6 | 16.0\n2024-02 | 10 | 10 | 0 | 100.0 | 22.9 | 24.5\n2024-03 | 8 | 8 | 0 | 100.0 | 26.5 | 30.0\n2024-04 | 8 | 8 | 0 | 100.0 | 19.4 | 20.5\n2024-05 | 12 | 10 | 2 | 83.3 | 21.6 | 20.0\n2024-06 | 6 | 4 | 2 | 66.7 | 27.7 | 26.5\n2024-07 | 10 | 7 | 3 | 70.0 | 28.8 | 26.5\n2024-08 | 10 | 10 | 0 | 100.0 | 21.3 | 19.0\n2024-09 | 7 | 7 | 0 | 100.0 | 25.9 | 23.0\n2024-10 | 19 | 15 | 4 | 78.9 | 24.3 | 24.0\n2024-11 | 12 | 8 | 4 | 66.7 | 24.1 | 21.5\n2024-12 | 11 | 6 | 5 | 54.5 | 24.2 | 26.0\n2025-01 | 11 | 8 | 3 | 72.7 | 25.5 | 26.0\n2025-02 | 11 | 7 | 4 | 63.6 | 32.2 | 35.0\n2025-03 | 12 | 9 | 3 | 75.0 | 20.5 | 21.0\n2025-04 | 13 | 11 | 2 | 84.6 | 21.5 | 19.0\n2025-05 | 10 | 10 | 0 | 100.0 | 26.7 | 26.5\n2025-06 | 9 | 8 | 1 | 88.9 | 22.9 | 26.0\n2025-07 | 11 | 11 | 0 | 100.0 | 21.8 | 22.0\n2025-08 | 11 | 10 | 1 | 90.9 | 21.5 | 24.0\n2025-09 | 14 | 13 | 1 | 92.9 | 22.7 | 22.0\n2025-10 | 16 | 14 | 2 | 87.5 | 27.2 | 31.0\n2025-11 | 18 | 13 | 5 | 72.2 | 26.4 | 27.0\n2025-12 | 9 | 6 | 3 | 66.7 | 25.2 | 19.0\n(36 rows)\n\n=== Report 3: B2B Wholesale Opportunities: Won vs Lost Characteristics (written by Ines) ===\nEngineer's note: Amounts converted to EUR using the exchange rate as of the closed_at date, falling back to the most recent prior available rate (covers weekends/gaps) per house standards. Activities-before-close counts only activities linked directly to the opportunity via activities.opportunity_id with occurred_at <= closed_at.\nREPORT: B2B Wholesale Opportunities: Won vs Lost Characteristics\nQUESTION: Among closed B2B wholesale opportunities, what reliably differs between the ones we won and the ones we lost — deal size, touches/activities before close, days open, or how concentrated they are among accounts and reps?\noutcome | opportunities | avg_amount_eur | median_amount_eur | avg_activities_before_close | avg_days_open | distinct_accounts | distinct_reps\n--------+---------------+----------------+-------------------+-----------------------------+---------------+-------------------+--------------\nwon | 339 | 5736.76 | 4500.00 | 3.0 | 18.9 | 155 | 10\nlost | 67 | 4946.74 | 4200.00 | 3.48 | 33.1 | 67 | 9\n(2 rows)\n\n\nNight 2 of 4: you have 3 report slots tonight."}
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{"parentUuid":"1fe57ac1-333d-4072-9a3a-a5a40649b9ef","isSidechain":false,"promptId":"1866f988-23ba-45aa-80a1-8b6d5d5314ab","type":"user","message":{"role":"user","content":[{"type":"text","text":"Continue from where you left off."}]},"isMeta":true,"uuid":"bd1890bc-bd0f-4860-8448-27882c7fde69","timestamp":"2026-10-04T01:09:27.011Z","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"bd1890bc-bd0f-4860-8448-27882c7fde69","isSidechain":false,"type":"assistant","uuid":"c6e980c5-4a52-4e55-8bb3-091bd30870d7","timestamp":"2026-10-04T01:09:27.011Z","message":{"diagnostics":null,"id":"b1bf3869-5fb0-4037-8777-c731302e3231","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":"99748c44-395a-4b9d-947c-53321907a8b1","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"c6e980c5-4a52-4e55-8bb3-091bd30870d7","isSidechain":false,"promptId":"1866f988-23ba-45aa-80a1-8b6d5d5314ab","type":"user","message":{"role":"user","content":"The printed reports of night 1:\n\n=== Report 1: B2B Wholesale Pipeline: Opportunities by Merchant and Stage (written by Grace) ===\nEngineer's note: Grain is tenant x stage as requested. avg_opportunity_amount/currency_code are computed per (tenant, stage) without FX conversion; if a tenant's opportunities span more than one currency, currency_code shows only the lexically-smallest code present and the average mixes currencies for that row (per the request's assumption of one currency per tenant, this should be rare/absent). Please have the analyst check row count and flag any wholesale tenant that returns zero stages or zero opportunity rows, as that would indicate the industry filter missed a merchant or that merchant doesn't use the CRM/pipeline module - I could not verify this without reading data.\nREPORT: B2B Wholesale Pipeline: Opportunities by Merchant and Stage\nQUESTION: Across our B2B wholesale merchants, at which pipeline stage do the most opportunities get stuck or lost — i.e. which stage is the graveyard?\nmerchant | pipeline_name | stage_name | stage_order | default_win_probability | opportunities_open | opportunities_closed_won | opportunities_closed_lost | avg_opportunity_amount | currency_code\n------------------------------+---------------+---------------+-------------+-------------------------+--------------------+--------------------------+---------------------------+------------------------+--------------\nBallycorran Industrial Supply | New business | Prospecting | 0 | 0.1000 | 0 | 0 | 0 | NULL | NULL \nBallycorran Industrial Supply | Renewals | Renewal due | 0 | 0.6000 | 0 | 0 | 0 | NULL | NULL \nBallycorran Industrial Supply | Renewals | Proposal sent | 1 | 0.7500 | 0 | 0 | 0 | NULL | NULL \nBallycorran Industrial Supply | New business | Qualification | 1 | 0.2500 | 0 | 0 | 0 | NULL | NULL \nBallycorran Industrial Supply | New business | Proposal | 2 | 0.5000 | 0 | 0 | 0 | NULL | NULL \nBallycorran Industrial Supply | Renewals | Renewed | 2 | 1.0000 | 0 | 74 | 0 | 4374.32 | EUR \nBallycorran Industrial Supply | Renewals | Churned | 3 | 0.0000 | 0 | 0 | 17 | 4494.12 | EUR \nBallycorran Industrial Supply | New business | Negotiation | 3 | 0.7500 | 0 | 0 | 0 | NULL | NULL \nBallycorran Industrial Supply | New business | Closed Won | 4 | 1.0000 | 0 | 63 | 0 | 4088.89 | EUR \nBallycorran Industrial Supply | New business | Closed Lost | 5 | 0.0000 | 0 | 0 | 13 | 3938.46 | EUR \nCastell Fasteners | New business | Prospecting | 0 | 0.1000 | 0 | 0 | 0 | NULL | NULL \nCastell Fasteners | Renewals | Renewal due | 0 | 0.6000 | 0 | 0 | 0 | NULL | NULL \nCastell Fasteners | Renewals | Proposal sent | 1 | 0.7500 | 0 | 0 | 0 | NULL | NULL \nCastell Fasteners | New business | Qualification | 1 | 0.2500 | 0 | 0 | 0 | NULL | NULL \nCastell Fasteners | New business | Proposal | 2 | 0.5000 | 0 | 0 | 0 | NULL | NULL \nCastell Fasteners | Renewals | Renewed | 2 | 1.0000 | 0 | 38 | 0 | 11002.63 | USD \nCastell Fasteners | Renewals | Churned | 3 | 0.0000 | 0 | 0 | 4 | 6175.0 | USD \nCastell Fasteners | New business | Negotiation | 3 | 0.7500 | 0 | 0 | 0 | NULL | NULL \nCastell Fasteners | New business | Closed Won | 4 | 1.0000 | 0 | 31 | 0 | 7877.42 | USD \nCastell Fasteners | New business | Closed Lost | 5 | 0.0000 | 0 | 0 | 5 | 6300.0 | USD \nHanse Industriebedarf | New business | Prospecting | 0 | 0.1000 | 0 | 0 | 0 | NULL | NULL \nHanse Industriebedarf | Renewals | Renewal due | 0 | 0.6000 | 0 | 0 | 0 | NULL | NULL \nHanse Industriebedarf | Renewals | Proposal sent | 1 | 0.7500 | 0 | 0 | 0 | NULL | NULL \nHanse Industriebedarf | New business | Qualification | 1 | 0.2500 | 0 | 0 | 0 | NULL | NULL \nHanse Industriebedarf | New business | Proposal | 2 | 0.5000 | 0 | 0 | 0 | NULL | NULL \nHanse Industriebedarf | Renewals | Renewed | 2 | 1.0000 | 0 | 19 | 0 | 7410.53 | EUR \nHanse Industriebedarf | Renewals | Churned | 3 | 0.0000 | 0 | 0 | 1 | 5600.0 | EUR \nHanse Industriebedarf | New business | Negotiation | 3 | 0.7500 | 0 | 0 | 0 | NULL | NULL \nHanse Industriebedarf | New business | Closed Won | 4 | 1.0000 | 0 | 15 | 0 | 5553.33 | EUR \nHanse Industriebedarf | New business | Closed Lost | 5 | 0.0000 | 0 | 0 | 2 | 5200.0 | EUR \nMidland Safety Wear | New business | Prospecting | 0 | 0.1000 | 0 | 0 | 0 | NULL | NULL \nMidland Safety Wear | Renewals | Renewal due | 0 | 0.6000 | 1 | 0 | 0 | 2600.0 | EUR \nMidland Safety Wear | Renewals | Proposal sent | 1 | 0.7500 | 3 | 0 | 0 | 10366.67 | EUR \nMidland Safety Wear | New business | Qualification | 1 | 0.2500 | 0 | 0 | 0 | NULL | NULL \nMidland Safety Wear | New business | Proposal | 2 | 0.5000 | 0 | 0 | 0 | NULL | NULL \nMidland Safety Wear | Renewals | Renewed | 2 | 1.0000 | 0 | 44 | 0 | 5984.09 | EUR \nMidland Safety Wear | Renewals | Churned | 3 | 0.0000 | 0 | 0 | 11 | 4990.91 | EUR \nMidland Safety Wear | New business | Negotiation | 3 | 0.7500 | 0 | 0 | 0 | NULL | NULL \nMidland Safety Wear | New business | Closed Won | 4 | 1.0000 | 0 | 34 | 0 | 5526.47 | EUR \nMidland Safety Wear | New business | Closed Lost | 5 | 0.0000 | 0 | 0 | 6 | 4300.0 | EUR \nPolder Packaging | New business | Prospecting | 0 | 0.1000 | 0 | 0 | 0 | NULL | NULL \nPolder Packaging | Renewals | Renewal due | 0 | 0.6000 | 0 | 0 | 0 | NULL | NULL \nPolder Packaging | Renewals | Proposal sent | 1 | 0.7500 | 0 | 0 | 0 | NULL | NULL \nPolder Packaging | New business | Qualification | 1 | 0.2500 | 0 | 0 | 0 | NULL | NULL \nPolder Packaging | New business | Proposal | 2 | 0.5000 | 0 | 0 | 0 | NULL | NULL \nPolder Packaging | Renewals | Renewed | 2 | 1.0000 | 0 | 9 | 0 | 5388.89 | EUR \nPolder Packaging | Renewals | Churned | 3 | 0.0000 | 0 | 0 | 4 | 3275.0 | EUR \nPolder Packaging | New business | Negotiation | 3 | 0.7500 | 0 | 0 | 0 | NULL | NULL \nPolder Packaging | New business | Closed Won | 4 | 1.0000 | 0 | 12 | 0 | 4908.33 | EUR \nPolder Packaging | New business | Closed Lost | 5 | 0.0000 | 0 | 0 | 4 | 10875.0 | EUR \n(50 rows)\n\n=== Report 2: B2B Wholesale Deal Cycle Time Trend by Month (written by Viktor) ===\nEngineer's note: Cycle time is approximated as closed_at - created_at on the opportunity itself, since opportunities carry no FK back to the originating lead (known limitation, as requested). Win rate, avg and median cycle days are rounded to 1 decimal; monthly grain uses calendar_days.month_label keyed off closed_at.\nREPORT: B2B Wholesale Deal Cycle Time Trend by Month\nQUESTION: From an opportunity being opened to it closing (won or lost), how long does that take for our B2B wholesale merchants, and is that cycle time getting longer or shorter over the last three years?\nmonth | opportunities_closed | closed_won | closed_lost | win_rate_pct | avg_cycle_days | median_cycle_days\n--------+----------------------+------------+-------------+--------------+----------------+------------------\n2023-01 | 5 | 5 | 0 | 100.0 | 12.0 | 15.0\n2023-02 | 5 | 5 | 0 | 100.0 | 16.6 | 12.0\n2023-03 | 8 | 5 | 3 | 62.5 | 22.8 | 21.5\n2023-04 | 5 | 5 | 0 | 100.0 | 17.2 | 21.0\n2023-05 | 6 | 5 | 1 | 83.3 | 24.2 | 22.5\n2023-06 | 9 | 6 | 3 | 66.7 | 21.1 | 24.0\n2023-07 | 5 | 3 | 2 | 60.0 | 33.6 | 34.0\n2023-08 | 7 | 6 | 1 | 85.7 | 17.4 | 16.0\n2023-09 | 10 | 8 | 2 | 80.0 | 30.5 | 35.0\n2023-10 | 9 | 8 | 1 | 88.9 | 26.1 | 25.0\n2023-11 | 12 | 8 | 4 | 66.7 | 24.5 | 23.0\n2023-12 | 7 | 4 | 3 | 57.1 | 22.7 | 26.0\n2024-01 | 11 | 9 | 2 | 81.8 | 15.6 | 16.0\n2024-02 | 10 | 10 | 0 | 100.0 | 22.9 | 24.5\n2024-03 | 8 | 8 | 0 | 100.0 | 26.5 | 30.0\n2024-04 | 8 | 8 | 0 | 100.0 | 19.4 | 20.5\n2024-05 | 12 | 10 | 2 | 83.3 | 21.6 | 20.0\n2024-06 | 6 | 4 | 2 | 66.7 | 27.7 | 26.5\n2024-07 | 10 | 7 | 3 | 70.0 | 28.8 | 26.5\n2024-08 | 10 | 10 | 0 | 100.0 | 21.3 | 19.0\n2024-09 | 7 | 7 | 0 | 100.0 | 25.9 | 23.0\n2024-10 | 19 | 15 | 4 | 78.9 | 24.3 | 24.0\n2024-11 | 12 | 8 | 4 | 66.7 | 24.1 | 21.5\n2024-12 | 11 | 6 | 5 | 54.5 | 24.2 | 26.0\n2025-01 | 11 | 8 | 3 | 72.7 | 25.5 | 26.0\n2025-02 | 11 | 7 | 4 | 63.6 | 32.2 | 35.0\n2025-03 | 12 | 9 | 3 | 75.0 | 20.5 | 21.0\n2025-04 | 13 | 11 | 2 | 84.6 | 21.5 | 19.0\n2025-05 | 10 | 10 | 0 | 100.0 | 26.7 | 26.5\n2025-06 | 9 | 8 | 1 | 88.9 | 22.9 | 26.0\n2025-07 | 11 | 11 | 0 | 100.0 | 21.8 | 22.0\n2025-08 | 11 | 10 | 1 | 90.9 | 21.5 | 24.0\n2025-09 | 14 | 13 | 1 | 92.9 | 22.7 | 22.0\n2025-10 | 16 | 14 | 2 | 87.5 | 27.2 | 31.0\n2025-11 | 18 | 13 | 5 | 72.2 | 26.4 | 27.0\n2025-12 | 9 | 6 | 3 | 66.7 | 25.2 | 19.0\n(36 rows)\n\n=== Report 3: B2B Wholesale Opportunities: Won vs Lost Characteristics (written by Ines) ===\nEngineer's note: Amounts converted to EUR using the exchange rate as of the closed_at date, falling back to the most recent prior available rate (covers weekends/gaps) per house standards. Activities-before-close counts only activities linked directly to the opportunity via activities.opportunity_id with occurred_at <= closed_at.\nREPORT: B2B Wholesale Opportunities: Won vs Lost Characteristics\nQUESTION: Among closed B2B wholesale opportunities, what reliably differs between the ones we won and the ones we lost — deal size, touches/activities before close, days open, or how concentrated they are among accounts and reps?\noutcome | opportunities | avg_amount_eur | median_amount_eur | avg_activities_before_close | avg_days_open | distinct_accounts | distinct_reps\n--------+---------------+----------------+-------------------+-----------------------------+---------------+-------------------+--------------\nwon | 339 | 5736.76 | 4500.00 | 3.0 | 18.9 | 155 | 10\nlost | 67 | 4946.74 | 4200.00 | 3.48 | 33.1 | 67 | 9\n(2 rows)\n\n\nNight 2 of 4: you have 3 report slots tonight."},"uuid":"30a8cbc0-8214-4f46-b9f4-39f77eb2f027","timestamp":"2026-10-04T01:09:27.023Z","permissionMode":"default","promptSource":"sdk","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1","version":"2.1.275","gitBranch":"HEAD"}
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{"parentUuid":"66c2ed02-241a-4eda-bfa0-987180b4e8bc","isSidechain":false,"message":{"model":"claude-sonnet-5","id":"msg_011CfgJc5ap56MzPu2digDfV","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_013GvW9FaMqWsMtSCSDyeA8K","name":"StructuredOutput","input":{"requests":[{"title":"New Business Pipeline: Stage Where Lost Opportunities Were Last Seen","question":"Among B2B wholesale opportunities that were actually lost in new-customer acquisition (not renewals), which stage were they sitting in just before they were marked Closed Lost — i.e. where is the real graveyard?","grain":"one row per New-business pipeline stage (plus one row for opportunities with no recorded stage transition)","columns":["stage_name","stage_order","lost_opportunities_last_seen_here (count)","pct_of_total_lost_in_new_business","total_lost_opportunities_new_business (constant, for reference)"],"details":"Night 1's stage report showed that for every wholesale tenant, the 'New business' pipeline has ZERO opportunities currently open or closed at Prospecting/Qualification/Proposal/Negotiation — all closed opportunities show only as 'Closed Won' or 'Closed Lost' (the terminal stages). This means opportunities.stage_id only reflects the FINAL state and does not preserve where a lost deal actually stalled before dying — the earlier 'graveyard stage' question cannot be answered from current stage_id alone. This report reconstructs it from history. Scope: opportunities where tenant_id is a wholesale merchant (tenants.industry ILIKE '%industrial%' OR '%office equipment%' OR '%wholesal%'), pipeline_id = the tenant's 'New business' pipeline (join pipelines on name = 'New business'), is_closed = true, is_won = false. For each such opportunity, look at change_history where entity_table = 'opportunities', entity_id = opportunities.id, changed_columns includes 'stage_id', ordered by occurred_at. Take the most recent such change event and read before_data->>'stage_id' as the stage the opportunity was in immediately before that final change (presumed to be the move into Closed Lost); join that id to opportunity_stages for stage_name/stage_order. If an opportunity has no change_history row with a stage_id change at all, bucket it as 'No stage transition recorded' (it was apparently created or recorded as lost without a logged progression). Group by the resulting stage (or the no-record bucket), count opportunities, and compute each group's share of all lost New-business opportunities for this tenant set. Order by lost_opportunities_last_seen_here descending. Please flag in the report notes if change_history has little or no coverage for these opportunities (e.g., most rows fall into 'No transition recorded'), since that would mean the audit trail can't answer this and we should say so plainly rather than over-interpret a small sample.","builds_on":["B2B Wholesale Pipeline: Opportunities by Merchant and Stage"]},{"title":"New Business Only: Won vs Lost Opportunity Characteristics","question":"Restricted to genuine new-customer acquisition deals (excluding renewals), what reliably differs between won and lost opportunities — deal size, activities before close, days open, or concentration among accounts and reps?","grain":"one row per closed outcome (won or lost), New business pipeline only","columns":["outcome (won / lost)","opportunities (count)","average amount in EUR","median amount in EUR","average activities per opportunity before close","average days open (closed_at - created_at)","distinct accounts involved","distinct owning reps (owner_user_id) involved"],"details":"This re-runs last night's won-vs-lost comparison but adds pipeline_id = the tenant's 'New business' pipeline (join pipelines on name = 'New business') to the filter, because the prior report pooled 'New business' (acquisition) and 'Renewals' (existing-customer renew/churn) opportunities together under the same won/lost labels — two very different sales motions with likely different base win rates (the Renewals pipeline's 'Renewed' stage carries a 1.0 default win_probability vs 0.1-0.75 for New business stages), which could distort the comparison. Same tenant scope as before (industry ILIKE '%industrial%' OR '%office equipment%' OR '%wholesal%'), opportunities where is_closed = true, split by is_won. Convert amount to EUR via exchange_rates (base_code='EUR', quote_code = opportunity currency_code, rate as_of closed_at date or nearest prior day). Activities before close = count of activities rows with activities.opportunity_id = opportunities.id and occurred_at <= closed_at, averaged per opportunity within each outcome group. Order 'won' row first, then 'lost'.","builds_on":["B2B Wholesale Opportunities: Won vs Lost Characteristics"]},{"title":"New Business Only: Deal Cycle Time Trend by Month","question":"Restricted to genuine new-customer acquisition deals (excluding renewals), how long does it take to close an opportunity, and is that cycle time trending up or down over the last three years?","grain":"one row per calendar month (based on closed_at month), New business pipeline only","columns":["month (YYYY-MM)","opportunities_closed (count)","closed_won (count)","closed_lost (count)","win_rate_pct","avg_cycle_days (closed_at - created_at)","median_cycle_days (closed_at - created_at)"],"details":"Re-runs last night's cycle-time trend but adds pipeline_id = the tenant's 'New business' pipeline (join pipelines on name = 'New business') to the filter, for the same reason as the won/lost report: pooling in Renewals (which likely close faster and win far more often, given 'Renewed' has a 1.0 win_probability) could be masking or distorting the true acquisition-cycle trend. Same tenant scope (industry ILIKE '%industrial%' OR '%office equipment%' OR '%wholesal%'), opportunities where is_closed = true and closed_at is not null, full history 2023-01-01 through 2025-12-31, grouped by month of closed_at (calendar_days.month_label or equivalent). Round avg/median cycle days to 1 decimal. Order by month ascending.","builds_on":["B2B Wholesale Deal Cycle Time Trend by Month"]}],"notes":"Night 1's big finding is a data-model gotcha, not yet a business trend: every wholesale tenant's 'New business' pipeline shows zero opportunities (open or closed) at Prospecting/Qualification/Proposal/Negotiation — closed deals only ever show as the terminal Closed Won/Closed Lost stage. That means current stage_id can't answer 'where do deals die' for lost opportunities; it only tells us the final outcome. It also means Report 3 (won/lost) and Report 2 (cycle time) from night 1 silently pooled two very different populations together: 'New business' (true lead-to-signed-deal acquisition, which is what Patrick and Samuel are actually asking about) and 'Renewals' (existing-customer renew/churn, where 'Renewed' carries a 1.0 default win probability and is a fundamentally easier motion). Tonight I'm spending all three slots fixing this at the root: (1) reconstruct the real graveyard stage from change_history for New-business-only lost opportunities, (2) redo the won/lost characteristics comparison scoped to New business only, (3) redo the cycle-time trend scoped to New business only. This keeps priorities 1-2-3 intact but makes them actually answer the question Patrick asked, rather than a renewals-diluted version of it. I'm holding Samuel's stale-pipeline-risk question and Patrick's #4/#5 for night 3, once I see whether change_history actually has usable coverage for the graveyard question (flagged as a risk in request 1) and whether the split reports change the picture."},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":16045,"cache_read_input_tokens":49721,"output_tokens":8478,"output_tokens_details":{"thinking_tokens":5768},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":16045,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":8478,"cache_read_input_tokens":49721,"cache_creation_input_tokens":16045,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":16045},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"wireToolInputs":{"toolu_013GvW9FaMqWsMtSCSDyeA8K":{"requests":[{"title":"New Business Pipeline: Stage Where Lost Opportunities Were Last Seen","question":"Among B2B wholesale opportunities that were actually lost in new-customer acquisition (not renewals), which stage were they sitting in just before they were marked Closed Lost — i.e. where is the real graveyard?","grain":"one row per New-business pipeline stage (plus one row for opportunities with no recorded stage transition)","columns":["stage_name","stage_order","lost_opportunities_last_seen_here (count)","pct_of_total_lost_in_new_business","total_lost_opportunities_new_business (constant, for reference)"],"details":"Night 1's stage report showed that for every wholesale tenant, the 'New business' pipeline has ZERO opportunities currently open or closed at Prospecting/Qualification/Proposal/Negotiation — all closed opportunities show only as 'Closed Won' or 'Closed Lost' (the terminal stages). This means opportunities.stage_id only reflects the FINAL state and does not preserve where a lost deal actually stalled before dying — the earlier 'graveyard stage' question cannot be answered from current stage_id alone. This report reconstructs it from history. Scope: opportunities where tenant_id is a wholesale merchant (tenants.industry ILIKE '%industrial%' OR '%office equipment%' OR '%wholesal%'), pipeline_id = the tenant's 'New business' pipeline (join pipelines on name = 'New business'), is_closed = true, is_won = false. For each such opportunity, look at change_history where entity_table = 'opportunities', entity_id = opportunities.id, changed_columns includes 'stage_id', ordered by occurred_at. Take the most recent such change event and read before_data->>'stage_id' as the stage the opportunity was in immediately before that final change (presumed to be the move into Closed Lost); join that id to opportunity_stages for stage_name/stage_order. If an opportunity has no change_history row with a stage_id change at all, bucket it as 'No stage transition recorded' (it was apparently created or recorded as lost without a logged progression). Group by the resulting stage (or the no-record bucket), count opportunities, and compute each group's share of all lost New-business opportunities for this tenant set. Order by lost_opportunities_last_seen_here descending. Please flag in the report notes if change_history has little or no coverage for these opportunities (e.g., most rows fall into 'No transition recorded'), since that would mean the audit trail can't answer this and we should say so plainly rather than over-interpret a small sample.","builds_on":["B2B Wholesale Pipeline: Opportunities by Merchant and Stage"]},{"title":"New Business Only: Won vs Lost Opportunity Characteristics","question":"Restricted to genuine new-customer acquisition deals (excluding renewals), what reliably differs between won and lost opportunities — deal size, activities before close, days open, or concentration among accounts and reps?","grain":"one row per closed outcome (won or lost), New business pipeline only","columns":["outcome (won / lost)","opportunities (count)","average amount in EUR","median amount in EUR","average activities per opportunity before close","average days open (closed_at - created_at)","distinct accounts involved","distinct owning reps (owner_user_id) involved"],"details":"This re-runs last night's won-vs-lost comparison but adds pipeline_id = the tenant's 'New business' pipeline (join pipelines on name = 'New business') to the filter, because the prior report pooled 'New business' (acquisition) and 'Renewals' (existing-customer renew/churn) opportunities together under the same won/lost labels — two very different sales motions with likely different base win rates (the Renewals pipeline's 'Renewed' stage carries a 1.0 default win_probability vs 0.1-0.75 for New business stages), which could distort the comparison. Same tenant scope as before (industry ILIKE '%industrial%' OR '%office equipment%' OR '%wholesal%'), opportunities where is_closed = true, split by is_won. Convert amount to EUR via exchange_rates (base_code='EUR', quote_code = opportunity currency_code, rate as_of closed_at date or nearest prior day). Activities before close = count of activities rows with activities.opportunity_id = opportunities.id and occurred_at <= closed_at, averaged per opportunity within each outcome group. Order 'won' row first, then 'lost'.","builds_on":["B2B Wholesale Opportunities: Won vs Lost Characteristics"]},{"title":"New Business Only: Deal Cycle Time Trend by Month","question":"Restricted to genuine new-customer acquisition deals (excluding renewals), how long does it take to close an opportunity, and is that cycle time trending up or down over the last three years?","grain":"one row per calendar month (based on closed_at month), New business pipeline only","columns":["month (YYYY-MM)","opportunities_closed (count)","closed_won (count)","closed_lost (count)","win_rate_pct","avg_cycle_days (closed_at - created_at)","median_cycle_days (closed_at - created_at)"],"details":"Re-runs last night's cycle-time trend but adds pipeline_id = the tenant's 'New business' pipeline (join pipelines on name = 'New business') to the filter, for the same reason as the won/lost report: pooling in Renewals (which likely close faster and win far more often, given 'Renewed' has a 1.0 win_probability) could be masking or distorting the true acquisition-cycle trend. Same tenant scope (industry ILIKE '%industrial%' OR '%office equipment%' OR '%wholesal%'), opportunities where is_closed = true and closed_at is not null, full history 2023-01-01 through 2025-12-31, grouped by month of closed_at (calendar_days.month_label or equivalent). Round avg/median cycle days to 1 decimal. Order by month ascending.","builds_on":["B2B Wholesale Deal Cycle Time Trend by Month"]}],"notes":"Night 1's big finding is a data-model gotcha, not yet a business trend: every wholesale tenant's 'New business' pipeline shows zero opportunities (open or closed) at Prospecting/Qualification/Proposal/Negotiation \\u2014 closed deals only ever show as the terminal Closed Won/Closed Lost stage. That means current stage_id can't answer 'where do deals die' for lost opportunities; it only tells us the final outcome. It also means Report 3 (won/lost) and Report 2 (cycle time) from night 1 silently pooled two very different populations together: 'New business' (true lead-to-signed-deal acquisition, which is what Patrick and Samuel are actually asking about) and 'Renewals' (existing-customer renew/churn, where 'Renewed' carries a 1.0 default win probability and is a fundamentally easier motion). Tonight I'm spending all three slots fixing this at the root: (1) reconstruct the real graveyard stage from change_history for New-business-only lost opportunities, (2) redo the won/lost characteristics comparison scoped to New business only, (3) redo the cycle-time trend scoped to New business only. This keeps priorities 1-2-3 intact but makes them actually answer the question Patrick asked, rather than a renewals-diluted version of it. I'm holding Samuel's stale-pipeline-risk question and Patrick's #4/#5 for night 3, once I see whether change_history actually has usable coverage for the graveyard question (flagged as a risk in request 1) and whether the split reports change the picture."}},"apiBlockIndex":1,"requestId":"req_011CfgJc5EiesZPLvLcYwLZn","type":"assistant","uuid":"a91a15d6-9156-440d-b763-3c25177f118e","timestamp":"2026-10-04T01:10:49.180Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"a91a15d6-9156-440d-b763-3c25177f118e","isSidechain":false,"attachment":{"type":"structured_output","data":{"requests":[{"title":"New Business Pipeline: Stage Where Lost Opportunities Were Last Seen","question":"Among B2B wholesale opportunities that were actually lost in new-customer acquisition (not renewals), which stage were they sitting in just before they were marked Closed Lost — i.e. where is the real graveyard?","grain":"one row per New-business pipeline stage (plus one row for opportunities with no recorded stage transition)","columns":["stage_name","stage_order","lost_opportunities_last_seen_here (count)","pct_of_total_lost_in_new_business","total_lost_opportunities_new_business (constant, for reference)"],"details":"Night 1's stage report showed that for every wholesale tenant, the 'New business' pipeline has ZERO opportunities currently open or closed at Prospecting/Qualification/Proposal/Negotiation — all closed opportunities show only as 'Closed Won' or 'Closed Lost' (the terminal stages). This means opportunities.stage_id only reflects the FINAL state and does not preserve where a lost deal actually stalled before dying — the earlier 'graveyard stage' question cannot be answered from current stage_id alone. This report reconstructs it from history. Scope: opportunities where tenant_id is a wholesale merchant (tenants.industry ILIKE '%industrial%' OR '%office equipment%' OR '%wholesal%'), pipeline_id = the tenant's 'New business' pipeline (join pipelines on name = 'New business'), is_closed = true, is_won = false. For each such opportunity, look at change_history where entity_table = 'opportunities', entity_id = opportunities.id, changed_columns includes 'stage_id', ordered by occurred_at. Take the most recent such change event and read before_data->>'stage_id' as the stage the opportunity was in immediately before that final change (presumed to be the move into Closed Lost); join that id to opportunity_stages for stage_name/stage_order. If an opportunity has no change_history row with a stage_id change at all, bucket it as 'No stage transition recorded' (it was apparently created or recorded as lost without a logged progression). Group by the resulting stage (or the no-record bucket), count opportunities, and compute each group's share of all lost New-business opportunities for this tenant set. Order by lost_opportunities_last_seen_here descending. Please flag in the report notes if change_history has little or no coverage for these opportunities (e.g., most rows fall into 'No transition recorded'), since that would mean the audit trail can't answer this and we should say so plainly rather than over-interpret a small sample.","builds_on":["B2B Wholesale Pipeline: Opportunities by Merchant and Stage"]},{"title":"New Business Only: Won vs Lost Opportunity Characteristics","question":"Restricted to genuine new-customer acquisition deals (excluding renewals), what reliably differs between won and lost opportunities — deal size, activities before close, days open, or concentration among accounts and reps?","grain":"one row per closed outcome (won or lost), New business pipeline only","columns":["outcome (won / lost)","opportunities (count)","average amount in EUR","median amount in EUR","average activities per opportunity before close","average days open (closed_at - created_at)","distinct accounts involved","distinct owning reps (owner_user_id) involved"],"details":"This re-runs last night's won-vs-lost comparison but adds pipeline_id = the tenant's 'New business' pipeline (join pipelines on name = 'New business') to the filter, because the prior report pooled 'New business' (acquisition) and 'Renewals' (existing-customer renew/churn) opportunities together under the same won/lost labels — two very different sales motions with likely different base win rates (the Renewals pipeline's 'Renewed' stage carries a 1.0 default win_probability vs 0.1-0.75 for New business stages), which could distort the comparison. Same tenant scope as before (industry ILIKE '%industrial%' OR '%office equipment%' OR '%wholesal%'), opportunities where is_closed = true, split by is_won. Convert amount to EUR via exchange_rates (base_code='EUR', quote_code = opportunity currency_code, rate as_of closed_at date or nearest prior day). Activities before close = count of activities rows with activities.opportunity_id = opportunities.id and occurred_at <= closed_at, averaged per opportunity within each outcome group. Order 'won' row first, then 'lost'.","builds_on":["B2B Wholesale Opportunities: Won vs Lost Characteristics"]},{"title":"New Business Only: Deal Cycle Time Trend by Month","question":"Restricted to genuine new-customer acquisition deals (excluding renewals), how long does it take to close an opportunity, and is that cycle time trending up or down over the last three years?","grain":"one row per calendar month (based on closed_at month), New business pipeline only","columns":["month (YYYY-MM)","opportunities_closed (count)","closed_won (count)","closed_lost (count)","win_rate_pct","avg_cycle_days (closed_at - created_at)","median_cycle_days (closed_at - created_at)"],"details":"Re-runs last night's cycle-time trend but adds pipeline_id = the tenant's 'New business' pipeline (join pipelines on name = 'New business') to the filter, for the same reason as the won/lost report: pooling in Renewals (which likely close faster and win far more often, given 'Renewed' has a 1.0 win_probability) could be masking or distorting the true acquisition-cycle trend. Same tenant scope (industry ILIKE '%industrial%' OR '%office equipment%' OR '%wholesal%'), opportunities where is_closed = true and closed_at is not null, full history 2023-01-01 through 2025-12-31, grouped by month of closed_at (calendar_days.month_label or equivalent). Round avg/median cycle days to 1 decimal. Order by month ascending.","builds_on":["B2B Wholesale Deal Cycle Time Trend by Month"]}],"notes":"Night 1's big finding is a data-model gotcha, not yet a business trend: every wholesale tenant's 'New business' pipeline shows zero opportunities (open or closed) at Prospecting/Qualification/Proposal/Negotiation — closed deals only ever show as the terminal Closed Won/Closed Lost stage. That means current stage_id can't answer 'where do deals die' for lost opportunities; it only tells us the final outcome. It also means Report 3 (won/lost) and Report 2 (cycle time) from night 1 silently pooled two very different populations together: 'New business' (true lead-to-signed-deal acquisition, which is what Patrick and Samuel are actually asking about) and 'Renewals' (existing-customer renew/churn, where 'Renewed' carries a 1.0 default win probability and is a fundamentally easier motion). Tonight I'm spending all three slots fixing this at the root: (1) reconstruct the real graveyard stage from change_history for New-business-only lost opportunities, (2) redo the won/lost characteristics comparison scoped to New business only, (3) redo the cycle-time trend scoped to New business only. This keeps priorities 1-2-3 intact but makes them actually answer the question Patrick asked, rather than a renewals-diluted version of it. I'm holding Samuel's stale-pipeline-risk question and Patrick's #4/#5 for night 3, once I see whether change_history actually has usable coverage for the graveyard question (flagged as a risk in request 1) and whether the split reports change the picture."},"toolUseID":"toolu_013GvW9FaMqWsMtSCSDyeA8K"},"type":"attachment","uuid":"da34e55e-9670-49ae-9526-6d061054a2c4","timestamp":"2026-10-04T01:10:49.188Z","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1","version":"2.1.275","gitBranch":"HEAD"}
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{"type":"last-prompt","lastPrompt":"The printed reports of night 1: === Report 1: B2B Wholesale Pipeline: Opportunities by Merchant and Stage (written by Grace) === Engineer's note: Grain is tenant x stage as requested. avg_opportunity…","leafUuid":"33bee4e6-8dc3-4e41-a39a-04e4010c3c54","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1"}
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{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-04T01:11:23.890Z","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1","content":"The printed reports of night 2:\n\n=== Report 1: New Business Pipeline: Stage Where Lost Opportunities Were Last Seen (written by Ines) ===\nEngineer's note: Grain is one row per New-business pipeline stage (plus a 'No stage transition recorded' bucket), scoped to lost opportunities at wholesale tenants as specified. Please check the row count/share for the 'No stage transition recorded' bucket in the printed report: if it holds most of the lost opportunities, the change_history audit trail doesn't have enough coverage to answer the graveyard-stage question and the result should be read as inconclusive rather than a real stage distribution.\nREPORT: New Business Pipeline: Stage Where Lost Opportunities Were Last Seen\nQUESTION: Among B2B wholesale opportunities that were actually lost in new-customer acquisition (not renewals), which stage were they sitting in just before they were marked Closed Lost — i.e. where is the real graveyard?\nstage_name | stage_order | lost_opportunities_last_seen_here | pct_of_total_lost_in_new_business | total_lost_opportunities_new_business\n-----------------------------+-------------+-----------------------------------+-----------------------------------+--------------------------------------\nNo stage transition recorded | NULL | 30 | 100.0 | 30\n(1 rows)\n\n=== Report 2: New Business Only: Won vs Lost Opportunity Characteristics (written by Grace) ===\nEngineer's note: Restricted to pipelines named 'New business' (joined on pipelines.name) as requested, excluding Renewals; all other filters (tenant industry, is_closed) and conversions (EUR via nearest prior-day exchange rate) match the prior report's methodology.\nREPORT: New Business Only: Won vs Lost Opportunity Characteristics\nQUESTION: Restricted to genuine new-customer acquisition deals (excluding renewals), what reliably differs between won and lost opportunities — deal size, activities before close, days open, or concentration among accounts and reps?\noutcome | opportunities | avg_amount_eur | median_amount_eur | avg_activities_before_close | avg_days_open | distinct_accounts | distinct_reps\n--------+---------------+----------------+-------------------+-----------------------------+---------------+-------------------+--------------\nwon | 155 | 7097.90 | 5880.52 | 2.30 | 9.1 | 155 | 10\nlost | 30 | 5628.59 | 6520.14 | 3.37 | 35.5 | 30 | 9\n(2 rows)\n\n=== Report 3: New Business Only: Deal Cycle Time Trend by Month (written by Grace) ===\nEngineer's note: Filtered to pipelines named exactly 'New business' per tenant, joined via tenant_id+pipeline_id; months with zero closed opportunities in scope won't appear as rows (no calendar spine requested). Cycle time computed as closed_at minus created_at in days (fractional).\nREPORT: New Business Only: Deal Cycle Time Trend by Month\nQUESTION: Restricted to genuine new-customer acquisition deals (excluding renewals), how long does it take to close an opportunity, and is that cycle time trending up or down over the last three years?\nmonth | opportunities_closed | closed_won | closed_lost | win_rate_pct | avg_cycle_days | median_cycle_days\n--------+----------------------+------------+-------------+--------------+----------------+------------------\n2023-01 | 3 | 3 | 0 | 100.0 | 14.0 | 16.0\n2023-02 | 4 | 4 | 0 | 100.0 | 11.4 | 9.6\n2023-03 | 5 | 3 | 2 | 60.0 | 15.7 | 17.0\n2023-04 | 2 | 2 | 0 | 100.0 | 3.0 | 3.0\n2023-05 | 2 | 1 | 1 | 50.0 | 23.3 | 23.3\n2023-06 | 3 | 2 | 1 | 66.7 | 19.1 | 26.3\n2023-07 | 1 | 1 | 0 | 100.0 | 30.4 | 30.4\n2023-08 | 2 | 2 | 0 | 100.0 | 14.8 | 14.8\n2023-09 | 2 | 1 | 1 | 50.0 | 21.6 | 21.6\n2023-10 | 2 | 2 | 0 | 100.0 | 19.7 | 19.7\n2023-11 | 7 | 5 | 2 | 71.4 | 19.7 | 18.1\n2023-12 | 3 | 2 | 1 | 66.7 | 14.9 | 12.7\n2024-01 | 8 | 8 | 0 | 100.0 | 13.0 | 12.6\n2024-02 | 4 | 4 | 0 | 100.0 | 8.6 | 8.5\n2024-03 | 3 | 3 | 0 | 100.0 | 18.8 | 14.0\n2024-04 | 3 | 3 | 0 | 100.0 | 11.0 | 13.0\n2024-05 | 8 | 6 | 2 | 75.0 | 22.7 | 20.4\n2024-07 | 5 | 2 | 3 | 40.0 | 33.7 | 31.0\n2024-08 | 3 | 3 | 0 | 100.0 | 12.6 | 14.5\n2024-09 | 2 | 2 | 0 | 100.0 | 21.6 | 21.6\n2024-10 | 7 | 6 | 1 | 85.7 | 17.2 | 15.0\n2024-11 | 7 | 3 | 4 | 42.9 | 23.1 | 17.0\n2024-12 | 4 | 1 | 3 | 25.0 | 29.6 | 30.9\n2025-01 | 3 | 2 | 1 | 66.7 | 17.2 | 15.3\n2025-02 | 2 | 1 | 1 | 50.0 | 34.1 | 34.1\n2025-03 | 4 | 3 | 1 | 75.0 | 14.9 | 14.6\n2025-04 | 6 | 5 | 1 | 83.3 | 18.4 | 18.6\n2025-05 | 3 | 3 | 0 | 100.0 | 13.7 | 11.8\n2025-06 | 2 | 2 | 0 | 100.0 | 6.5 | 6.5\n2025-07 | 5 | 5 | 0 | 100.0 | 12.5 | 9.3\n2025-08 | 2 | 2 | 0 | 100.0 | 5.0 | 5.0\n2025-09 | 5 | 4 | 1 | 80.0 | 13.7 | 14.7\n2025-10 | 5 | 4 | 1 | 80.0 | 23.6 | 14.0\n2025-11 | 7 | 5 | 2 | 71.4 | 20.5 | 21.0\n2025-12 | 2 | 1 | 1 | 50.0 | 30.9 | 30.9\n(35 rows)\n\n\nNight 3 of 4: you have 3 report slots tonight."}
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{"parentUuid":"c5908a9c-5ad6-4348-bec9-7dc81f39bd48","isSidechain":false,"type":"assistant","uuid":"5ce84baf-9c6b-4e71-bdd0-c9db33c05cca","timestamp":"2026-10-04T01:11:23.883Z","message":{"diagnostics":null,"id":"b1a6541a-74fa-47d8-8ee6-14e65dbc7674","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":"99748c44-395a-4b9d-947c-53321907a8b1","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"5ce84baf-9c6b-4e71-bdd0-c9db33c05cca","isSidechain":false,"promptId":"1694573b-51c7-4093-afe1-51ca3c8d0a69","type":"user","message":{"role":"user","content":"The printed reports of night 2:\n\n=== Report 1: New Business Pipeline: Stage Where Lost Opportunities Were Last Seen (written by Ines) ===\nEngineer's note: Grain is one row per New-business pipeline stage (plus a 'No stage transition recorded' bucket), scoped to lost opportunities at wholesale tenants as specified. Please check the row count/share for the 'No stage transition recorded' bucket in the printed report: if it holds most of the lost opportunities, the change_history audit trail doesn't have enough coverage to answer the graveyard-stage question and the result should be read as inconclusive rather than a real stage distribution.\nREPORT: New Business Pipeline: Stage Where Lost Opportunities Were Last Seen\nQUESTION: Among B2B wholesale opportunities that were actually lost in new-customer acquisition (not renewals), which stage were they sitting in just before they were marked Closed Lost — i.e. where is the real graveyard?\nstage_name | stage_order | lost_opportunities_last_seen_here | pct_of_total_lost_in_new_business | total_lost_opportunities_new_business\n-----------------------------+-------------+-----------------------------------+-----------------------------------+--------------------------------------\nNo stage transition recorded | NULL | 30 | 100.0 | 30\n(1 rows)\n\n=== Report 2: New Business Only: Won vs Lost Opportunity Characteristics (written by Grace) ===\nEngineer's note: Restricted to pipelines named 'New business' (joined on pipelines.name) as requested, excluding Renewals; all other filters (tenant industry, is_closed) and conversions (EUR via nearest prior-day exchange rate) match the prior report's methodology.\nREPORT: New Business Only: Won vs Lost Opportunity Characteristics\nQUESTION: Restricted to genuine new-customer acquisition deals (excluding renewals), what reliably differs between won and lost opportunities — deal size, activities before close, days open, or concentration among accounts and reps?\noutcome | opportunities | avg_amount_eur | median_amount_eur | avg_activities_before_close | avg_days_open | distinct_accounts | distinct_reps\n--------+---------------+----------------+-------------------+-----------------------------+---------------+-------------------+--------------\nwon | 155 | 7097.90 | 5880.52 | 2.30 | 9.1 | 155 | 10\nlost | 30 | 5628.59 | 6520.14 | 3.37 | 35.5 | 30 | 9\n(2 rows)\n\n=== Report 3: New Business Only: Deal Cycle Time Trend by Month (written by Grace) ===\nEngineer's note: Filtered to pipelines named exactly 'New business' per tenant, joined via tenant_id+pipeline_id; months with zero closed opportunities in scope won't appear as rows (no calendar spine requested). Cycle time computed as closed_at minus created_at in days (fractional).\nREPORT: New Business Only: Deal Cycle Time Trend by Month\nQUESTION: Restricted to genuine new-customer acquisition deals (excluding renewals), how long does it take to close an opportunity, and is that cycle time trending up or down over the last three years?\nmonth | opportunities_closed | closed_won | closed_lost | win_rate_pct | avg_cycle_days | median_cycle_days\n--------+----------------------+------------+-------------+--------------+----------------+------------------\n2023-01 | 3 | 3 | 0 | 100.0 | 14.0 | 16.0\n2023-02 | 4 | 4 | 0 | 100.0 | 11.4 | 9.6\n2023-03 | 5 | 3 | 2 | 60.0 | 15.7 | 17.0\n2023-04 | 2 | 2 | 0 | 100.0 | 3.0 | 3.0\n2023-05 | 2 | 1 | 1 | 50.0 | 23.3 | 23.3\n2023-06 | 3 | 2 | 1 | 66.7 | 19.1 | 26.3\n2023-07 | 1 | 1 | 0 | 100.0 | 30.4 | 30.4\n2023-08 | 2 | 2 | 0 | 100.0 | 14.8 | 14.8\n2023-09 | 2 | 1 | 1 | 50.0 | 21.6 | 21.6\n2023-10 | 2 | 2 | 0 | 100.0 | 19.7 | 19.7\n2023-11 | 7 | 5 | 2 | 71.4 | 19.7 | 18.1\n2023-12 | 3 | 2 | 1 | 66.7 | 14.9 | 12.7\n2024-01 | 8 | 8 | 0 | 100.0 | 13.0 | 12.6\n2024-02 | 4 | 4 | 0 | 100.0 | 8.6 | 8.5\n2024-03 | 3 | 3 | 0 | 100.0 | 18.8 | 14.0\n2024-04 | 3 | 3 | 0 | 100.0 | 11.0 | 13.0\n2024-05 | 8 | 6 | 2 | 75.0 | 22.7 | 20.4\n2024-07 | 5 | 2 | 3 | 40.0 | 33.7 | 31.0\n2024-08 | 3 | 3 | 0 | 100.0 | 12.6 | 14.5\n2024-09 | 2 | 2 | 0 | 100.0 | 21.6 | 21.6\n2024-10 | 7 | 6 | 1 | 85.7 | 17.2 | 15.0\n2024-11 | 7 | 3 | 4 | 42.9 | 23.1 | 17.0\n2024-12 | 4 | 1 | 3 | 25.0 | 29.6 | 30.9\n2025-01 | 3 | 2 | 1 | 66.7 | 17.2 | 15.3\n2025-02 | 2 | 1 | 1 | 50.0 | 34.1 | 34.1\n2025-03 | 4 | 3 | 1 | 75.0 | 14.9 | 14.6\n2025-04 | 6 | 5 | 1 | 83.3 | 18.4 | 18.6\n2025-05 | 3 | 3 | 0 | 100.0 | 13.7 | 11.8\n2025-06 | 2 | 2 | 0 | 100.0 | 6.5 | 6.5\n2025-07 | 5 | 5 | 0 | 100.0 | 12.5 | 9.3\n2025-08 | 2 | 2 | 0 | 100.0 | 5.0 | 5.0\n2025-09 | 5 | 4 | 1 | 80.0 | 13.7 | 14.7\n2025-10 | 5 | 4 | 1 | 80.0 | 23.6 | 14.0\n2025-11 | 7 | 5 | 2 | 71.4 | 20.5 | 21.0\n2025-12 | 2 | 1 | 1 | 50.0 | 30.9 | 30.9\n(35 rows)\n\n\nNight 3 of 4: you have 3 report slots tonight."},"uuid":"c8e46b55-4537-4265-9ba6-7e4abf121dfe","timestamp":"2026-10-04T01:11:23.895Z","permissionMode":"default","promptSource":"sdk","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1","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":10984,"cache_read_input_tokens":65766,"output_tokens":8382,"output_tokens_details":{"thinking_tokens":6254},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":10984,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":8382,"cache_read_input_tokens":65766,"cache_creation_input_tokens":10984,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":10984},"type":"message"}],"speed":"standa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{"parentUuid":"557409a7-08f1-42e6-9e19-d474885e739b","isSidechain":false,"message":{"model":"claude-sonnet-5","id":"msg_011CfgJkgt459Dd21CFxqgf5","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01HtTnpw7zkqxM9qxk9QqzQD","name":"StructuredOutput","input":{"requests":[{"title":"B2B Wholesale: Win Rate and Cycle Time by Merchant (New Business)","question":"Is the acquisition struggle spread evenly across our B2B wholesale merchants, or concentrated in a few accounts?","grain":"one row per wholesale merchant (tenant)","columns":["merchant","opportunities_closed","closed_won","closed_lost","win_rate_pct","avg_days_open","avg_amount_eur"],"details":"Scope: same wholesale tenant filter as prior nights (tenants.industry ILIKE '%industrial%' OR '%office equipment%' OR '%wholesal%'), opportunities where pipeline_id = the tenant's 'New business' pipeline (join pipelines on name = 'New business'), is_closed = true. avg_days_open = avg(closed_at - created_at) in days. Convert amount to EUR via exchange_rates (base_code='EUR', quote_code = opportunity currency_code, rate as_of closed_at date or nearest prior day). Order by win_rate_pct ascending (worst performers first), tie-break by opportunities_closed descending, then merchant name ascending.","builds_on":["B2B Wholesale Pipeline: Opportunities by Merchant and Stage","New Business Only: Won vs Lost Opportunity Characteristics"]},{"title":"B2B Wholesale: Win Rate and Cycle Time by Sales Rep (New Business)","question":"Among reps who own B2B wholesale new-business opportunities, are a few reps clearly underperforming on win rate or cycle time, or is performance broadly similar?","grain":"one row per sales rep (opportunities.owner_user_id)","columns":["rep_name","merchant","opportunities_closed","closed_won","closed_lost","win_rate_pct","avg_days_open","avg_amount_eur"],"details":"Scope: same wholesale tenant filter, pipeline_id = the tenant's 'New business' pipeline, is_closed = true, grouped by owner_user_id (join users for full_name as rep_name, and tenants for merchant name). Exclude reps with fewer than 3 closed opportunities in scope to avoid single-deal noise (note the excluded rep count in the report notes). avg_days_open and avg_amount_eur defined as in the merchant-level report (EUR via exchange_rates as of closed_at). Order by win_rate_pct ascending (worst first), tie-break by opportunities_closed descending, then rep_name ascending.","builds_on":["New Business Only: Won vs Lost Opportunity Characteristics"]},{"title":"B2B Wholesale: Age Profile of Currently Open New-Business Pipeline","question":"How much of our current B2B wholesale new-business pipeline is already older than the typical window in which deals actually win, i.e. how much open pipeline is quietly turning stale and at risk?","grain":"one row per age bucket of currently-open opportunities","columns":["age_bucket","opportunities_count","pct_of_open_pipeline","avg_amount_eur","total_amount_eur"],"details":"Scope: same wholesale tenant filter, opportunities where pipeline_id = the tenant's 'New business' pipeline and is_closed = false (currently open), as of the last complete data date 2025-12-31 (use the fixed literal date '2025-12-31' as the reference point for age, not CURRENT_DATE/now(), per house SQL standards). age_days = 2025-12-31 minus created_at. Bucket into: '0-9 days', '10-19 days', '20-29 days', '30-44 days', '45-89 days', '90+ days' (chosen because last night's New-business-only comparison showed won deals close in ~9 days on average and lost deals take ~35 days on average before dying, so these buckets straddle both reference points). Convert amount to EUR using exchange_rates as of 2025-12-31. pct_of_open_pipeline = that bucket's opportunity count / total open opportunities in scope, as a percentage. total_amount_eur = sum of amount_eur in that bucket (euro value of pipeline sitting at that age). Order by age_bucket ascending (by lower day bound).","builds_on":["New Business Only: Won vs Lost Opportunity Characteristics","New Business Only: Deal Cycle Time Trend by Month"]}],"notes":"Night 2's key finding: the stage-history reconstruction came back 30/30 'no transition recorded' — change_history has zero coverage of stage_id changes for these lost opportunities. That's decisive on its own (not a sample-size fluke): the warehouse simply cannot tell us which CRM stage a deal died in, either from current state (always shows the terminal Closed Won/Lost stage) or from history (not logged). I'm treating that as answered-and-closed rather than spending another slot probing it further — it's itself the headline data-quality flag for the memo (reps/system aren't capturing stage progression, which is as much a coaching/hygiene story as a product one). The New-business-only refinement did give a very clean, usable signal instead: lost deals average ~35 days open and ~3.4 activities before dying, vs won deals closing in ~9 days with ~2.3 activities — a ~4x gap in cycle time is a much stronger 'where/why deals die' signal than a stage label would have been, and it directly answers Samuel's staleness question. Tonight's three reports build on that: merchant-level and rep-level win-rate/cycle-time breakdowns (Patrick Q4 / Samuel Q1 — is this widespread or concentrated), and an age-bucket view of the currently open pipeline benchmarked against the won (~9d) vs lost (~35d) cycle-time reference points, to quantify how much live pipeline is already past the point where deals historically still win (Samuel Q3). Holding deal-size/payment-terms correlation (Patrick's optional #5) for night 4 if the memo still has room after seeing tonight's results."},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":10984,"cache_read_input_tokens":65766,"output_tokens":8382,"output_tokens_details":{"thinking_tokens":6254},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":10984,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":8382,"cache_read_input_tokens":65766,"cache_creation_input_tokens":10984,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":10984},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"wireToolInputs":{"toolu_01HtTnpw7zkqxM9qxk9QqzQD":{"requests":[{"title":"B2B Wholesale: Win Rate and Cycle Time by Merchant (New Business)","question":"Is the acquisition struggle spread evenly across our B2B wholesale merchants, or concentrated in a few accounts?","grain":"one row per wholesale merchant (tenant)","columns":["merchant","opportunities_closed","closed_won","closed_lost","win_rate_pct","avg_days_open","avg_amount_eur"],"details":"Scope: same wholesale tenant filter as prior nights (tenants.industry ILIKE '%industrial%' OR '%office equipment%' OR '%wholesal%'), opportunities where pipeline_id = the tenant's 'New business' pipeline (join pipelines on name = 'New business'), is_closed = true. avg_days_open = avg(closed_at - created_at) in days. 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That's decisive on its own (not a sample-size fluke): the warehouse simply cannot tell us which CRM stage a deal died in, either from current state (always shows the terminal Closed Won/Lost stage) or from history (not logged). I'm treating that as answered-and-closed rather than spending another slot probing it further \\u2014 it's itself the headline data-quality flag for the memo (reps/system aren't capturing stage progression, which is as much a coaching/hygiene story as a product one). The New-business-only refinement did give a very clean, usable signal instead: lost deals average ~35 days open and ~3.4 activities before dying, vs won deals closing in ~9 days with ~2.3 activities \\u2014 a ~4x gap in cycle time is a much stronger 'where/why deals die' signal than a stage label would have been, and it directly answers Samuel's staleness question. Tonight's three reports build on that: merchant-level and rep-level win-rate/cycle-time breakdowns (Patrick Q4 / Samuel Q1 \\u2014 is this widespread or concentrated), and an age-bucket view of the currently open pipeline benchmarked against the won (~9d) vs lost (~35d) cycle-time reference points, to quantify how much live pipeline is already past the point where deals historically still win (Samuel Q3). Holding deal-size/payment-terms correlation (Patrick's optional #5) for night 4 if the memo still has room after seeing tonight's results."}},"apiBlockIndex":1,"requestId":"req_011CfgJkgUVwA8mhaGJ1kt8P","type":"assistant","uuid":"78584925-985c-4a5a-8cd5-4da787664932","timestamp":"2026-10-04T01:12:49.419Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1","version":"2.1.275","gitBranch":"HEAD"}
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{"type":"last-prompt","lastPrompt":"The printed reports of night 2: === Report 1: New Business Pipeline: Stage Where Lost Opportunities Were Last Seen (written by Ines) === Engineer's note: Grain is one row per New-business pipeline st…","leafUuid":"1475b520-2d1a-447f-b86f-12a407793ca2","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1"}
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{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-04T01:13:38.400Z","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1","content":"The printed reports of night 3:\n\n=== Report 1: B2B Wholesale: Win Rate and Cycle Time by Merchant (New Business) (written by Felix) ===\nEngineer's note: EUR conversion uses the nearest prior exchange_rates.as_of on or before closed_at's date (per currency); avg_days_open and avg_amount_eur are rounded to 1 and 2 decimals respectively.\nREPORT: B2B Wholesale: Win Rate and Cycle Time by Merchant (New Business)\nQUESTION: Is the acquisition struggle spread evenly across our B2B wholesale merchants, or concentrated in a few accounts?\nmerchant | opportunities_closed | closed_won | closed_lost | win_rate_pct | avg_days_open | avg_amount_eur\n------------------------------+----------------------+------------+-------------+--------------+---------------+---------------\nPolder Packaging | 16 | 12 | 4 | 75.0 | 16.4 | NULL\nBallycorran Industrial Supply | 76 | 63 | 13 | 82.9 | 15.3 | NULL\nMidland Safety Wear | 40 | 34 | 6 | 85.0 | 10.4 | NULL\nCastell Fasteners | 36 | 31 | 5 | 86.1 | 12.8 | 6835.52\nHanse Industriebedarf | 17 | 15 | 2 | 88.2 | 9.9 | NULL\n(5 rows)\n\n=== Report 2: B2B Wholesale: Win Rate and Cycle Time by Sales Rep (New Business) (written by Felix) ===\nEngineer's note: I used tenants.industry IN ('industrial supplies','office equipment') as the B2B wholesale tenant filter and pipelines.name = 'New business' as the pipeline filter, matching the verticals and pipeline naming convention described for this series of reports — please confirm these match the exact filter used in the earlier merchant-level report if it differed. The exact count of reps excluded by the <3 closed-deals rule isn't a value I can compute at authoring time (dry runs don't return data); it will be visible as the gap between total distinct owners in scope and the row count printed on this report.\nREPORT: B2B Wholesale: Win Rate and Cycle Time by Sales Rep (New Business)\nQUESTION: Among reps who own B2B wholesale new-business opportunities, are a few reps clearly underperforming on win rate or cycle time, or is performance broadly similar?\nrep_name | merchant | opportunities_closed | closed_won | closed_lost | win_rate_pct | avg_days_open | avg_amount_eur\n---------+----------+----------------------+------------+-------------+--------------+---------------+---------------\n(0 rows)\n\n=== Report 3: B2B Wholesale: Age Profile of Currently Open New-Business Pipeline (written by Ines) ===\nEngineer's note: The request didn't name the specific wholesale tenant(s), so I scoped it to tenants whose industry text matches wholesale/industrial/office-equipment (the B2B wholesaler verticals noted in the company briefing) rather than a single hardcoded tenant_id — please flag if a narrower/different tenant set was intended. Pipeline is matched case-insensitively on name = 'New business'.\nREPORT: B2B Wholesale: Age Profile of Currently Open New-Business Pipeline\nQUESTION: How much of our current B2B wholesale new-business pipeline is already older than the typical window in which deals actually win, i.e. how much open pipeline is quietly turning stale and at risk?\nage_bucket | opportunities_count | pct_of_open_pipeline | avg_amount_eur | total_amount_eur\n-----------+---------------------+----------------------+----------------+-----------------\n(0 rows)\n\n\nNight 4 of 4: you have 3 report slots tonight."}
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{"parentUuid":"aed2007f-687a-4158-9c67-ef58ee75c2cb","isSidechain":false,"type":"assistant","uuid":"0cf6f6f7-a7a6-4c45-a26c-79467a310fc0","timestamp":"2026-10-04T01:13:38.393Z","message":{"diagnostics":null,"id":"334b61b4-64fc-40a4-a134-1d711d3fa1cc","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":"99748c44-395a-4b9d-947c-53321907a8b1","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"0cf6f6f7-a7a6-4c45-a26c-79467a310fc0","isSidechain":false,"promptId":"c781db7f-5d1b-4cf1-8ef6-f7b8b1224689","type":"user","message":{"role":"user","content":"The printed reports of night 3:\n\n=== Report 1: B2B Wholesale: Win Rate and Cycle Time by Merchant (New Business) (written by Felix) ===\nEngineer's note: EUR conversion uses the nearest prior exchange_rates.as_of on or before closed_at's date (per currency); avg_days_open and avg_amount_eur are rounded to 1 and 2 decimals respectively.\nREPORT: B2B Wholesale: Win Rate and Cycle Time by Merchant (New Business)\nQUESTION: Is the acquisition struggle spread evenly across our B2B wholesale merchants, or concentrated in a few accounts?\nmerchant | opportunities_closed | closed_won | closed_lost | win_rate_pct | avg_days_open | avg_amount_eur\n------------------------------+----------------------+------------+-------------+--------------+---------------+---------------\nPolder Packaging | 16 | 12 | 4 | 75.0 | 16.4 | NULL\nBallycorran Industrial Supply | 76 | 63 | 13 | 82.9 | 15.3 | NULL\nMidland Safety Wear | 40 | 34 | 6 | 85.0 | 10.4 | NULL\nCastell Fasteners | 36 | 31 | 5 | 86.1 | 12.8 | 6835.52\nHanse Industriebedarf | 17 | 15 | 2 | 88.2 | 9.9 | NULL\n(5 rows)\n\n=== Report 2: B2B Wholesale: Win Rate and Cycle Time by Sales Rep (New Business) (written by Felix) ===\nEngineer's note: I used tenants.industry IN ('industrial supplies','office equipment') as the B2B wholesale tenant filter and pipelines.name = 'New business' as the pipeline filter, matching the verticals and pipeline naming convention described for this series of reports — please confirm these match the exact filter used in the earlier merchant-level report if it differed. The exact count of reps excluded by the <3 closed-deals rule isn't a value I can compute at authoring time (dry runs don't return data); it will be visible as the gap between total distinct owners in scope and the row count printed on this report.\nREPORT: B2B Wholesale: Win Rate and Cycle Time by Sales Rep (New Business)\nQUESTION: Among reps who own B2B wholesale new-business opportunities, are a few reps clearly underperforming on win rate or cycle time, or is performance broadly similar?\nrep_name | merchant | opportunities_closed | closed_won | closed_lost | win_rate_pct | avg_days_open | avg_amount_eur\n---------+----------+----------------------+------------+-------------+--------------+---------------+---------------\n(0 rows)\n\n=== Report 3: B2B Wholesale: Age Profile of Currently Open New-Business Pipeline (written by Ines) ===\nEngineer's note: The request didn't name the specific wholesale tenant(s), so I scoped it to tenants whose industry text matches wholesale/industrial/office-equipment (the B2B wholesaler verticals noted in the company briefing) rather than a single hardcoded tenant_id — please flag if a narrower/different tenant set was intended. Pipeline is matched case-insensitively on name = 'New business'.\nREPORT: B2B Wholesale: Age Profile of Currently Open New-Business Pipeline\nQUESTION: How much of our current B2B wholesale new-business pipeline is already older than the typical window in which deals actually win, i.e. how much open pipeline is quietly turning stale and at risk?\nage_bucket | opportunities_count | pct_of_open_pipeline | avg_amount_eur | total_amount_eur\n-----------+---------------------+----------------------+----------------+-----------------\n(0 rows)\n\n\nNight 4 of 4: you have 3 report slots tonight."},"uuid":"2e45260f-94dc-431d-b4cf-d4b3486690fa","timestamp":"2026-10-04T01:13:38.405Z","permissionMode":"default","promptSource":"sdk","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1","version":"2.1.275","gitBranch":"HEAD"}
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{"parentUuid":"6eddc5f2-76dd-4933-b33a-8a40cc5fd4c1","isSidechain":false,"message":{"model":"claude-sonnet-5","id":"msg_011CfgJvcTBTasBWZUK5JMRp","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01XR6LkxggS8xExtEZgnzYPx","name":"StructuredOutput","input":{"requests":[{"title":"B2B Wholesale: Win Rate and Cycle Time by Sales Rep (New Business) — Corrected","question":"Among reps who own B2B wholesale new-business opportunities, are a few reps clearly underperforming on win rate or cycle time, or is performance broadly similar?","grain":"one row per sales rep (opportunities.owner_user_id)","columns":["rep_name","merchant","opportunities_closed","closed_won","closed_lost","win_rate_pct","avg_days_open","avg_amount_eur"],"details":"Last night's attempt at this exact report returned 0 rows; the engineer's note said it filtered tenants with an exact match (industry IN ('industrial supplies','office equipment')) rather than the ILIKE pattern used successfully in earlier reports, which likely matched zero tenants. To remove all ambiguity, please filter explicitly by tenant name instead: tenants.name IN ('Ballycorran Industrial Supply','Castell Fasteners','Hanse Industriebedarf','Midland Safety Wear','Polder Packaging') — these are the 5 wholesale merchants confirmed present in every prior report this week. Filter opportunities to pipeline_id = that tenant's 'New business' pipeline (join pipelines on name = 'New business'), is_closed = true, grouped by owner_user_id (join users for full_name as rep_name, tenants for merchant name). Exclude reps with fewer than 3 closed opportunities in scope (note the excluded rep count in the report notes). avg_days_open = avg(closed_at - created_at) in days. For avg_amount_eur: if the opportunity's currency_code = 'EUR', amount_eur = amount directly (do not rely on a self-referential EUR/EUR row in exchange_rates, which may not exist and produced NULLs in a prior report); otherwise amount_eur = amount / rate where rate is from exchange_rates with base_code = 'EUR', quote_code = opportunity.currency_code, as_of = the nearest date on or before closed_at. Order by win_rate_pct ascending (worst first), tie-break by opportunities_closed descending, then rep_name ascending.","builds_on":["B2B Wholesale: Win Rate and Cycle Time by Sales Rep (New Business)"]},{"title":"B2B Wholesale: Win Rate and Average Deal Size by Merchant (New Business) — Corrected","question":"Does the merchant-level win-rate pattern hold once we also see typical deal size per merchant, and can we fix last night's missing EUR averages?","grain":"one row per wholesale merchant (tenant)","columns":["merchant","currency_code (tenant default)","opportunities_closed","closed_won","closed_lost","win_rate_pct","avg_days_open","avg_amount_eur"],"details":"Re-run of last night's merchant report, which returned NULL avg_amount_eur for 4 of 5 merchants (all the EUR-denominated ones; only the one USD merchant, Castell Fasteners, returned a value) — almost certainly because converting EUR amounts via a EUR/EUR identity lookup in exchange_rates failed to find a matching row. Filter explicitly: tenants.name IN ('Ballycorran Industrial Supply','Castell Fasteners','Hanse Industriebedarf','Midland Safety Wear','Polder Packaging'), pipeline_id = each tenant's 'New business' pipeline, is_closed = true. For avg_amount_eur: if currency_code = 'EUR', amount_eur = amount directly (skip the exchange_rates join entirely for EUR rows); otherwise amount_eur = amount / rate from exchange_rates where base_code = 'EUR', quote_code = opportunity.currency_code, as_of = nearest date on or before closed_at. Include tenants.default_currency_code as a reference column so the reader can see which merchant is USD vs EUR. Order by win_rate_pct ascending, tie-break by opportunities_closed descending, then merchant name.","builds_on":["B2B Wholesale: Win Rate and Cycle Time by Merchant (New Business)"]},{"title":"B2B Wholesale: Win Rate and Cycle Time by Deal-Size Band (New Business)","question":"Does a bigger deal size correlate with a better or worse chance of closing, and does it drag out the cycle time?","grain":"one row per deal-size band (based on EUR-converted opportunity amount)","columns":["amount_band_eur","opportunities_closed","closed_won","closed_lost","win_rate_pct","avg_days_open"],"details":"Prompted by an odd pattern in an earlier won-vs-lost comparison: lost New-business opportunities had a LOWER average amount (5628.59 EUR) than won ones (7097.90 EUR), but a HIGHER median amount (6520.14 vs 5880.52 EUR) — suggesting the amount distribution isn't simply 'bigger deals win more' and deserves a direct bucket breakdown rather than just an average. Filter: tenants.name IN ('Ballycorran Industrial Supply','Castell Fasteners','Hanse Industriebedarf','Midland Safety Wear','Polder Packaging'), pipeline_id = each tenant's 'New business' pipeline, is_closed = true. Convert amount to EUR per opportunity: if currency_code = 'EUR', amount_eur = amount; otherwise amount_eur = amount / rate from exchange_rates (base_code='EUR', quote_code=currency_code, as_of nearest date on or before closed_at). Bucket amount_eur into: '<3000', '3000-5999', '6000-8999', '9000-11999', '12000+'. For each band: count of opportunities closed, closed won, closed lost, win_rate_pct, and avg_days_open (avg of closed_at - created_at in days). Order by band ascending (by lower bound).","builds_on":["New Business Only: Won vs Lost Opportunity Characteristics"]}],"notes":"Night 3 take: two of three reports came back broken/empty, and I need my last night to fix the essential one and still land priority 5. The merchant-level report (Report 1) worked but had a clear bug: avg_amount_eur was NULL for 4 of 5 merchants (all the EUR ones) — almost certainly a failed EUR-to-EUR identity lookup in exchange_rates rather than a real data gap. The rep-level report (Report 2) came back with 0 rows because the engineer used an exact-match tenant filter (industry IN (...)) instead of the pattern that's worked all week, so it silently matched nothing — this is still an open, important gap (Patrick's Q4) and needs a clean re-run. The open-pipeline-age report (Report 3) also returned 0 rows, but I'm treating that as a real, confirmed finding rather than a bug: night 1's stage report already showed zero open opportunities at every New-business stage for every wholesale tenant, so there is genuinely no live open new-business pipeline in the warehouse right now — worth flagging to Patrick/Samuel as 'we can't assess forward-looking staleness risk because there's no open pipeline to look at; the only staleness signal we have is backward-looking (lost deals average ~35 days open vs ~9 for won).' Spending tonight's three slots on: (1) the corrected rep-level report with an explicit, unambiguous tenant-name filter, (2) a corrected merchant-level report with an explicit EUR-conversion fix, and (3) a deal-size-band breakdown to chase the odd avg-vs-median flip in the won/lost amount comparison, which gives a real answer to Patrick's optional #5 on deal size vs win rate. Payment terms (also part of #5) aren't captured anywhere in the B2B CRM/deals schema for the wholesaler's own customers, so I'm leaving that out of the memo as 'not measurable from current data' rather than forcing a weak proxy."},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":9741,"cache_read_input_tokens":76750,"output_tokens":10037,"output_tokens_details":{"thinking_tokens":7213},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":9741,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":10037,"cache_read_input_tokens":76750,"cache_creation_input_tokens":9741,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":9741},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"wireToolInputs":{"toolu_01XR6LkxggS8xExtEZgnzYPx":{"requests":[{"title":"B2B Wholesale: Win Rate and Cycle Time by Sales Rep (New Business) — Corrected","question":"Among reps who own B2B wholesale new-business opportunities, are a few reps clearly underperforming on win rate or cycle time, or is performance broadly similar?","grain":"one row per sales rep (opportunities.owner_user_id)","columns":["rep_name","merchant","opportunities_closed","closed_won","closed_lost","win_rate_pct","avg_days_open","avg_amount_eur"],"details":"Last night's attempt at this exact report returned 0 rows; the engineer's note said it filtered tenants with an exact match (industry IN ('industrial supplies','office equipment')) rather than the ILIKE pattern used successfully in earlier reports, which likely matched zero tenants. To remove all ambiguity, please filter explicitly by tenant name instead: tenants.name IN ('Ballycorran Industrial Supply','Castell Fasteners','Hanse Industriebedarf','Midland Safety Wear','Polder Packaging') — these are the 5 wholesale merchants confirmed present in every prior report this week. Filter opportunities to pipeline_id = that tenant's 'New business' pipeline (join pipelines on name = 'New business'), is_closed = true, grouped by owner_user_id (join users for full_name as rep_name, tenants for merchant name). Exclude reps with fewer than 3 closed opportunities in scope (note the excluded rep count in the report notes). avg_days_open = avg(closed_at - created_at) in days. For avg_amount_eur: if the opportunity's currency_code = 'EUR', amount_eur = amount directly (do not rely on a self-referential EUR/EUR row in exchange_rates, which may not exist and produced NULLs in a prior report); otherwise amount_eur = amount / rate where rate is from exchange_rates with base_code = 'EUR', quote_code = opportunity.currency_code, as_of = the nearest date on or before closed_at. Order by win_rate_pct ascending (worst first), tie-break by opportunities_closed descending, then rep_name ascending.","builds_on":["B2B Wholesale: Win Rate and Cycle Time by Sales Rep (New Business)"]},{"title":"B2B Wholesale: Win Rate and Average Deal Size by Merchant (New Business) — Corrected","question":"Does the merchant-level win-rate pattern hold once we also see typical deal size per merchant, and can we fix last night's missing EUR averages?","grain":"one row per wholesale merchant (tenant)","columns":["merchant","currency_code (tenant default)","opportunities_closed","closed_won","closed_lost","win_rate_pct","avg_days_open","avg_amount_eur"],"details":"Re-run of last night's merchant report, which returned NULL avg_amount_eur for 4 of 5 merchants (all the EUR-denominated ones; only the one USD merchant, Castell Fasteners, returned a value) — almost certainly because converting EUR amounts via a EUR/EUR identity lookup in exchange_rates failed to find a matching row. Filter explicitly: tenants.name IN ('Ballycorran Industrial Supply','Castell Fasteners','Hanse Industriebedarf','Midland Safety Wear','Polder Packaging'), pipeline_id = each tenant's 'New business' pipeline, is_closed = true. For avg_amount_eur: if currency_code = 'EUR', amount_eur = amount directly (skip the exchange_rates join entirely for EUR rows); otherwise amount_eur = amount / rate from exchange_rates where base_code = 'EUR', quote_code = opportunity.currency_code, as_of = nearest date on or before closed_at. Include tenants.default_currency_code as a reference column so the reader can see which merchant is USD vs EUR. Order by win_rate_pct ascending, tie-break by opportunities_closed descending, then merchant name.","builds_on":["B2B Wholesale: Win Rate and Cycle Time by Merchant (New Business)"]},{"title":"B2B Wholesale: Win Rate and Cycle Time by Deal-Size Band (New Business)","question":"Does a bigger deal size correlate with a better or worse chance of closing, and does it drag out the cycle time?","grain":"one row per deal-size band (based on EUR-converted opportunity amount)","columns":["amount_band_eur","opportunities_closed","closed_won","closed_lost","win_rate_pct","avg_days_open"],"details":"Prompted by an odd pattern in an earlier won-vs-lost comparison: lost New-business opportunities had a LOWER average amount (5628.59 EUR) than won ones (7097.90 EUR), but a HIGHER median amount (6520.14 vs 5880.52 EUR) — suggesting the amount distribution isn't simply 'bigger deals win more' and deserves a direct bucket breakdown rather than just an average. Filter: tenants.name IN ('Ballycorran Industrial Supply','Castell Fasteners','Hanse Industriebedarf','Midland Safety Wear','Polder Packaging'), pipeline_id = each tenant's 'New business' pipeline, is_closed = true. Convert amount to EUR per opportunity: if currency_code = 'EUR', amount_eur = amount; otherwise amount_eur = amount / rate from exchange_rates (base_code='EUR', quote_code=currency_code, as_of nearest date on or before closed_at). Bucket amount_eur into: '<3000', '3000-5999', '6000-8999', '9000-11999', '12000+'. For each band: count of opportunities closed, closed won, closed lost, win_rate_pct, and avg_days_open (avg of closed_at - created_at in days). Order by band ascending (by lower bound).","builds_on":["New Business Only: Won vs Lost Opportunity Characteristics"]}],"notes":"Night 3 take: two of three reports came back broken/empty, and I need my last night to fix the essential one and still land priority 5. The merchant-level report (Report 1) worked but had a clear bug: avg_amount_eur was NULL for 4 of 5 merchants (all the EUR ones) \\u2014 almost certainly a failed EUR-to-EUR identity lookup in exchange_rates rather than a real data gap. The rep-level report (Report 2) came back with 0 rows because the engineer used an exact-match tenant filter (industry IN (...)) instead of the pattern that's worked all week, so it silently matched nothing \\u2014 this is still an open, important gap (Patrick's Q4) and needs a clean re-run. The open-pipeline-age report (Report 3) also returned 0 rows, but I'm treating that as a real, confirmed finding rather than a bug: night 1's stage report already showed zero open opportunities at every New-business stage for every wholesale tenant, so there is genuinely no live open new-business pipeline in the warehouse right now \\u2014 worth flagging to Patrick/Samuel as 'we can't assess forward-looking staleness risk because there's no open pipeline to look at; the only staleness signal we have is backward-looking (lost deals average ~35 days open vs ~9 for won).' Spending tonight's three slots on: (1) the corrected rep-level report with an explicit, unambiguous tenant-name filter, (2) a corrected merchant-level report with an explicit EUR-conversion fix, and (3) a deal-size-band breakdown to chase the odd avg-vs-median flip in the won/lost amount comparison, which gives a real answer to Patrick's optional #5 on deal size vs win rate. Payment terms (also part of #5) aren't captured anywhere in the B2B CRM/deals schema for the wholesaler's own customers, so I'm leaving that out of the memo as 'not measurable from current data' rather than forcing a weak proxy."}},"apiBlockIndex":1,"requestId":"req_011CfgJvc1Pr6P1pAujFopXa","type":"assistant","uuid":"1b9165cd-0128-49a1-a014-66ab85216b45","timestamp":"2026-10-04T01:15:15.823Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"1b9165cd-0128-49a1-a014-66ab85216b45","isSidechain":false,"attachment":{"type":"structured_output","data":{"requests":[{"title":"B2B Wholesale: Win Rate and Cycle Time by Sales Rep (New Business) — Corrected","question":"Among reps who own B2B wholesale new-business opportunities, are a few reps clearly underperforming on win rate or cycle time, or is performance broadly similar?","grain":"one row per sales rep (opportunities.owner_user_id)","columns":["rep_name","merchant","opportunities_closed","closed_won","closed_lost","win_rate_pct","avg_days_open","avg_amount_eur"],"details":"Last night's attempt at this exact report returned 0 rows; the engineer's note said it filtered tenants with an exact match (industry IN ('industrial supplies','office equipment')) rather than the ILIKE pattern used successfully in earlier reports, which likely matched zero tenants. To remove all ambiguity, please filter explicitly by tenant name instead: tenants.name IN ('Ballycorran Industrial Supply','Castell Fasteners','Hanse Industriebedarf','Midland Safety Wear','Polder Packaging') — these are the 5 wholesale merchants confirmed present in every prior report this week. Filter opportunities to pipeline_id = that tenant's 'New business' pipeline (join pipelines on name = 'New business'), is_closed = true, grouped by owner_user_id (join users for full_name as rep_name, tenants for merchant name). Exclude reps with fewer than 3 closed opportunities in scope (note the excluded rep count in the report notes). avg_days_open = avg(closed_at - created_at) in days. For avg_amount_eur: if the opportunity's currency_code = 'EUR', amount_eur = amount directly (do not rely on a self-referential EUR/EUR row in exchange_rates, which may not exist and produced NULLs in a prior report); otherwise amount_eur = amount / rate where rate is from exchange_rates with base_code = 'EUR', quote_code = opportunity.currency_code, as_of = the nearest date on or before closed_at. Order by win_rate_pct ascending (worst first), tie-break by opportunities_closed descending, then rep_name ascending.","builds_on":["B2B Wholesale: Win Rate and Cycle Time by Sales Rep (New Business)"]},{"title":"B2B Wholesale: Win Rate and Average Deal Size by Merchant (New Business) — Corrected","question":"Does the merchant-level win-rate pattern hold once we also see typical deal size per merchant, and can we fix last night's missing EUR averages?","grain":"one row per wholesale merchant (tenant)","columns":["merchant","currency_code (tenant default)","opportunities_closed","closed_won","closed_lost","win_rate_pct","avg_days_open","avg_amount_eur"],"details":"Re-run of last night's merchant report, which returned NULL avg_amount_eur for 4 of 5 merchants (all the EUR-denominated ones; only the one USD merchant, Castell Fasteners, returned a value) — almost certainly because converting EUR amounts via a EUR/EUR identity lookup in exchange_rates failed to find a matching row. Filter explicitly: tenants.name IN ('Ballycorran Industrial Supply','Castell Fasteners','Hanse Industriebedarf','Midland Safety Wear','Polder Packaging'), pipeline_id = each tenant's 'New business' pipeline, is_closed = true. For avg_amount_eur: if currency_code = 'EUR', amount_eur = amount directly (skip the exchange_rates join entirely for EUR rows); otherwise amount_eur = amount / rate from exchange_rates where base_code = 'EUR', quote_code = opportunity.currency_code, as_of = nearest date on or before closed_at. Include tenants.default_currency_code as a reference column so the reader can see which merchant is USD vs EUR. Order by win_rate_pct ascending, tie-break by opportunities_closed descending, then merchant name.","builds_on":["B2B Wholesale: Win Rate and Cycle Time by Merchant (New Business)"]},{"title":"B2B Wholesale: Win Rate and Cycle Time by Deal-Size Band (New Business)","question":"Does a bigger deal size correlate with a better or worse chance of closing, and does it drag out the cycle time?","grain":"one row per deal-size band (based on EUR-converted opportunity amount)","columns":["amount_band_eur","opportunities_closed","closed_won","closed_lost","win_rate_pct","avg_days_open"],"details":"Prompted by an odd pattern in an earlier won-vs-lost comparison: lost New-business opportunities had a LOWER average amount (5628.59 EUR) than won ones (7097.90 EUR), but a HIGHER median amount (6520.14 vs 5880.52 EUR) — suggesting the amount distribution isn't simply 'bigger deals win more' and deserves a direct bucket breakdown rather than just an average. Filter: tenants.name IN ('Ballycorran Industrial Supply','Castell Fasteners','Hanse Industriebedarf','Midland Safety Wear','Polder Packaging'), pipeline_id = each tenant's 'New business' pipeline, is_closed = true. Convert amount to EUR per opportunity: if currency_code = 'EUR', amount_eur = amount; otherwise amount_eur = amount / rate from exchange_rates (base_code='EUR', quote_code=currency_code, as_of nearest date on or before closed_at). Bucket amount_eur into: '<3000', '3000-5999', '6000-8999', '9000-11999', '12000+'. For each band: count of opportunities closed, closed won, closed lost, win_rate_pct, and avg_days_open (avg of closed_at - created_at in days). Order by band ascending (by lower bound).","builds_on":["New Business Only: Won vs Lost Opportunity Characteristics"]}],"notes":"Night 3 take: two of three reports came back broken/empty, and I need my last night to fix the essential one and still land priority 5. The merchant-level report (Report 1) worked but had a clear bug: avg_amount_eur was NULL for 4 of 5 merchants (all the EUR ones) — almost certainly a failed EUR-to-EUR identity lookup in exchange_rates rather than a real data gap. The rep-level report (Report 2) came back with 0 rows because the engineer used an exact-match tenant filter (industry IN (...)) instead of the pattern that's worked all week, so it silently matched nothing — this is still an open, important gap (Patrick's Q4) and needs a clean re-run. The open-pipeline-age report (Report 3) also returned 0 rows, but I'm treating that as a real, confirmed finding rather than a bug: night 1's stage report already showed zero open opportunities at every New-business stage for every wholesale tenant, so there is genuinely no live open new-business pipeline in the warehouse right now — worth flagging to Patrick/Samuel as 'we can't assess forward-looking staleness risk because there's no open pipeline to look at; the only staleness signal we have is backward-looking (lost deals average ~35 days open vs ~9 for won).' Spending tonight's three slots on: (1) the corrected rep-level report with an explicit, unambiguous tenant-name filter, (2) a corrected merchant-level report with an explicit EUR-conversion fix, and (3) a deal-size-band breakdown to chase the odd avg-vs-median flip in the won/lost amount comparison, which gives a real answer to Patrick's optional #5 on deal size vs win rate. Payment terms (also part of #5) aren't captured anywhere in the B2B CRM/deals schema for the wholesaler's own customers, so I'm leaving that out of the memo as 'not measurable from current data' rather than forcing a weak proxy."},"toolUseID":"toolu_01XR6LkxggS8xExtEZgnzYPx"},"type":"attachment","uuid":"8b25e66a-41ea-4c3c-91aa-a45952558464","timestamp":"2026-10-04T01:15:15.831Z","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1","version":"2.1.275","gitBranch":"HEAD"}
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{"type":"last-prompt","lastPrompt":"The printed reports of night 3: === Report 1: B2B Wholesale: Win Rate and Cycle Time by Merchant (New Business) (written by Felix) === Engineer's note: EUR conversion uses the nearest prior exchange_…","leafUuid":"71d56a4b-99d7-4f66-be47-4aaafb4f5691","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1"}
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{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-04T01:15:59.229Z","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1","content":"The printed reports of night 4:\n\n=== Report 1: B2B Wholesale: Win Rate and Cycle Time by Sales Rep (New Business) — Corrected (written by Omar) ===\nEngineer's note: Filtered by the 5 confirmed tenant names and each tenant's own 'New business' pipeline, as specified. Note: the report itself only lists reps with >=3 closed opportunities (per the grain, one row per qualifying rep); the count of reps excluded by that threshold is not a row in this table and would need a separate quick count if wanted for the memo.\nREPORT: B2B Wholesale: Win Rate and Cycle Time by Sales Rep (New Business) — Corrected\nQUESTION: Among reps who own B2B wholesale new-business opportunities, are a few reps clearly underperforming on win rate or cycle time, or is performance broadly similar?\nrep_name | merchant | opportunities_closed | closed_won | closed_lost | win_rate_pct | avg_days_open | avg_amount_eur\n--------------------+-------------------------------+----------------------+------------+-------------+--------------+---------------+---------------\nYoussef Meijer | Polder Packaging | 7 | 4 | 3 | 57.1 | 20.7 | 7828.57\nLogan Roberts | Ballycorran Industrial Supply | 40 | 32 | 8 | 80.0 | 17.8 | 3867.50\nLotte van den Broek | Midland Safety Wear | 18 | 15 | 3 | 83.3 | 9.6 | 5800.00\nGreta Schmitt | Hanse Industriebedarf | 12 | 10 | 2 | 83.3 | 8.9 | 4683.33\nJoshua Jackson | Castell Fasteners | 21 | 18 | 3 | 85.7 | 11.6 | 7313.84\nNiamh Harrison | Ballycorran Industrial Supply | 36 | 31 | 5 | 86.1 | 12.5 | 4280.56\nJesse Novotný | Midland Safety Wear | 22 | 19 | 3 | 86.4 | 11.1 | 4968.18\nMegan Taylor | Castell Fasteners | 15 | 13 | 2 | 86.7 | 14.6 | 6096.30\nThijs Dijkstra | Polder Packaging | 9 | 8 | 1 | 88.9 | 13.1 | 5288.89\nSarah Hartmann | Hanse Industriebedarf | 5 | 5 | 0 | 100.0 | 12.1 | 7500.00\n(10 rows)\n\n=== Report 2: B2B Wholesale: Win Rate and Average Deal Size by Merchant (New Business) — Corrected (written by Bea) ===\nEngineer's note: Fixed the EUR averaging bug by skipping the exchange_rates join entirely when currency_code = 'EUR' (amount_eur = amount directly); for non-EUR rows (Castell Fasteners, USD) we use the EUR/quote rate as of the nearest date on or before closed_at. avg_days_open is calendar days between created_at and closed_at.\nREPORT: B2B Wholesale: Win Rate and Average Deal Size by Merchant (New Business) — Corrected\nQUESTION: Does the merchant-level win-rate pattern hold once we also see typical deal size per merchant, and can we fix last night's missing EUR averages?\nmerchant | currency_code | opportunities_closed | closed_won | closed_lost | win_rate_pct | avg_days_open | avg_amount_eur\n------------------------------+---------------+----------------------+------------+-------------+--------------+---------------+---------------\nPolder Packaging | EUR | 16 | 12 | 4 | 75.0 | 16.4 | 6400.0\nBallycorran Industrial Supply | EUR | 76 | 63 | 13 | 82.89 | 15.25 | 4063.16\nMidland Safety Wear | EUR | 40 | 34 | 6 | 85.0 | 10.43 | 5342.5\nCastell Fasteners | USD | 36 | 31 | 5 | 86.11 | 12.85 | 6835.52\nHanse Industriebedarf | EUR | 17 | 15 | 2 | 88.24 | 9.86 | 5511.76\n(5 rows)\n\n=== Report 3: B2B Wholesale: Win Rate and Cycle Time by Deal-Size Band (New Business) (written by Omar) ===\nEngineer's note: Banded by EUR-converted opportunity amount (nearest exchange rate on/before closed_at) for closed New-business opportunities at the 5 named B2B tenants; 5 rows, one per band, ordered by lower bound.\nREPORT: B2B Wholesale: Win Rate and Cycle Time by Deal-Size Band (New Business)\nQUESTION: Does a bigger deal size correlate with a better or worse chance of closing, and does it drag out the cycle time?\namount_band_eur | opportunities_closed | closed_won | closed_lost | win_rate_pct | avg_days_open\n----------------+----------------------+------------+-------------+--------------+--------------\n<3000 | 49 | 40 | 9 | 81.6 | 13.9\n3000-5999 | 80 | 68 | 12 | 85.0 | 13.5\n6000-8999 | 24 | 19 | 5 | 79.2 | 14.7\n9000-11999 | 16 | 14 | 2 | 87.5 | 12.5\n12000+ | 16 | 14 | 2 | 87.5 | 9.8\n(5 rows)\n\n\nIt is Friday morning. Write the memo to Patrick O'Neill:\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":"3fd5a7db-166f-495f-8bc8-063990f2e52c","isSidechain":false,"promptId":"daec0ad2-2113-4cf9-b9c4-dacd3f709f00","type":"user","message":{"role":"user","content":"The printed reports of night 4:\n\n=== Report 1: B2B Wholesale: Win Rate and Cycle Time by Sales Rep (New Business) — Corrected (written by Omar) ===\nEngineer's note: Filtered by the 5 confirmed tenant names and each tenant's own 'New business' pipeline, as specified. Note: the report itself only lists reps with >=3 closed opportunities (per the grain, one row per qualifying rep); the count of reps excluded by that threshold is not a row in this table and would need a separate quick count if wanted for the memo.\nREPORT: B2B Wholesale: Win Rate and Cycle Time by Sales Rep (New Business) — Corrected\nQUESTION: Among reps who own B2B wholesale new-business opportunities, are a few reps clearly underperforming on win rate or cycle time, or is performance broadly similar?\nrep_name | merchant | opportunities_closed | closed_won | closed_lost | win_rate_pct | avg_days_open | avg_amount_eur\n--------------------+-------------------------------+----------------------+------------+-------------+--------------+---------------+---------------\nYoussef Meijer | Polder Packaging | 7 | 4 | 3 | 57.1 | 20.7 | 7828.57\nLogan Roberts | Ballycorran Industrial Supply | 40 | 32 | 8 | 80.0 | 17.8 | 3867.50\nLotte van den Broek | Midland Safety Wear | 18 | 15 | 3 | 83.3 | 9.6 | 5800.00\nGreta Schmitt | Hanse Industriebedarf | 12 | 10 | 2 | 83.3 | 8.9 | 4683.33\nJoshua Jackson | Castell Fasteners | 21 | 18 | 3 | 85.7 | 11.6 | 7313.84\nNiamh Harrison | Ballycorran Industrial Supply | 36 | 31 | 5 | 86.1 | 12.5 | 4280.56\nJesse Novotný | Midland Safety Wear | 22 | 19 | 3 | 86.4 | 11.1 | 4968.18\nMegan Taylor | Castell Fasteners | 15 | 13 | 2 | 86.7 | 14.6 | 6096.30\nThijs Dijkstra | Polder Packaging | 9 | 8 | 1 | 88.9 | 13.1 | 5288.89\nSarah Hartmann | Hanse Industriebedarf | 5 | 5 | 0 | 100.0 | 12.1 | 7500.00\n(10 rows)\n\n=== Report 2: B2B Wholesale: Win Rate and Average Deal Size by Merchant (New Business) — Corrected (written by Bea) ===\nEngineer's note: Fixed the EUR averaging bug by skipping the exchange_rates join entirely when currency_code = 'EUR' (amount_eur = amount directly); for non-EUR rows (Castell Fasteners, USD) we use the EUR/quote rate as of the nearest date on or before closed_at. avg_days_open is calendar days between created_at and closed_at.\nREPORT: B2B Wholesale: Win Rate and Average Deal Size by Merchant (New Business) — Corrected\nQUESTION: Does the merchant-level win-rate pattern hold once we also see typical deal size per merchant, and can we fix last night's missing EUR averages?\nmerchant | currency_code | opportunities_closed | closed_won | closed_lost | win_rate_pct | avg_days_open | avg_amount_eur\n------------------------------+---------------+----------------------+------------+-------------+--------------+---------------+---------------\nPolder Packaging | EUR | 16 | 12 | 4 | 75.0 | 16.4 | 6400.0\nBallycorran Industrial Supply | EUR | 76 | 63 | 13 | 82.89 | 15.25 | 4063.16\nMidland Safety Wear | EUR | 40 | 34 | 6 | 85.0 | 10.43 | 5342.5\nCastell Fasteners | USD | 36 | 31 | 5 | 86.11 | 12.85 | 6835.52\nHanse Industriebedarf | EUR | 17 | 15 | 2 | 88.24 | 9.86 | 5511.76\n(5 rows)\n\n=== Report 3: B2B Wholesale: Win Rate and Cycle Time by Deal-Size Band (New Business) (written by Omar) ===\nEngineer's note: Banded by EUR-converted opportunity amount (nearest exchange rate on/before closed_at) for closed New-business opportunities at the 5 named B2B tenants; 5 rows, one per band, ordered by lower bound.\nREPORT: B2B Wholesale: Win Rate and Cycle Time by Deal-Size Band (New Business)\nQUESTION: Does a bigger deal size correlate with a better or worse chance of closing, and does it drag out the cycle time?\namount_band_eur | opportunities_closed | closed_won | closed_lost | win_rate_pct | avg_days_open\n----------------+----------------------+------------+-------------+--------------+--------------\n<3000 | 49 | 40 | 9 | 81.6 | 13.9\n3000-5999 | 80 | 68 | 12 | 85.0 | 13.5\n6000-8999 | 24 | 19 | 5 | 79.2 | 14.7\n9000-11999 | 16 | 14 | 2 | 87.5 | 12.5\n12000+ | 16 | 14 | 2 | 87.5 | 9.8\n(5 rows)\n\n\nIt is Friday morning. Write the memo to Patrick O'Neill:\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":"bc03619b-1bb8-4eb4-ba41-31a44a4071fe","timestamp":"2026-10-04T01:15:59.235Z","permissionMode":"default","promptSource":"sdk","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1","version":"2.1.275","gitBranch":"HEAD"}
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{"parentUuid":"4b364cce-a0d8-41ee-9af3-3b8fbe81f9f1","isSidechain":false,"message":{"model":"claude-sonnet-5","id":"msg_011CfgK6zV2RjFg6Y6n2vrW2","type":"message","role":"assistant","content":[{"type":"text","text":"**Memo: B2B Wholesale Pipeline Health — for Patrick O'Neill**\n*From Daniel Kim, Fri 2026-01-09*\n\n**1. We can't find the \"graveyard stage\" — because it isn't being recorded.**\nEvery closed opportunity across all 5 wholesale merchants shows only as *Closed Won* or *Closed Lost* in current stage data (Report: *Pipeline by Merchant and Stage*, night 1) — zero opportunities, ever, sit at Prospecting/Qualification/Proposal/Negotiation. I tried reconstructing history from the audit log; 30/30 lost deals had **no logged stage change at all** (*Stage Where Lost Opportunities Were Last Seen*, night 2). Confidence: high that this data simply doesn't exist, not that deals skip stages instantly. This is itself a finding — reps (or the tool) aren't tracking progression, only final outcome.\n\n**2. Cycle time is the clearest signal we have, and it's large.** Won deals close in **~9.1 days** on average; lost deals drag on **~35.5 days** — nearly 4x longer — before dying (*New Business Only: Won vs Lost*, night 2). The monthly trend (*Cycle Time Trend*, night 2) is noisy (2–19 deals/month) but shows **no secular worsening or improvement** over 2023–2025; it's held steady around 13–20 days average. Confidence: high on the won/lost gap, moderate on \"no trend\" given small monthly samples.\n\n**3. Lost deals get *more* attention before they die, not less.** Avg 3.37 activities logged before a loss vs 2.30 before a win (night 2 report). Combined with #2, this points to deals that *stall and get chased* rather than deals that die fast from neglect. Deal size is **not** a clean differentiator: win rate is flat at 79–88% across every size band from <€3,000 to €12,000+, and if anything the biggest deals close *faster* (9.8 days vs ~14 for mid-size) (*Win Rate by Deal-Size Band*, night 4). The earlier avg/median flip in won vs lost amounts (avg lost lower, median lost higher) was a skew artifact, resolved by this banding — confidence now high that size alone isn't driving outcomes.\n\n**4. Merchant performance is fairly even; one rep stands out.** Win rates by merchant range 75.0% (Polder Packaging) to 88.2% (Hanse Industriebedarf) — a real but modest spread, no merchant in trouble (*Win Rate by Merchant*, night 4). By rep, most cluster 80–89%, but **Youssef Meijer at Polder sits at 57.1%** (n=7, lowest of any rep with ≥3 deals) — and Polder is also the lowest-performing merchant overall (*Win Rate by Rep*, night 4). This looks concentrated, not widespread: one rep is likely pulling down one merchant's numbers.\n\n**5. No usable data on payment terms; deal size doesn't drag deals out.** I could not find any field capturing the wholesaler's own credit/payment terms with their B2B customers (billing_profiles.net_terms_days is Tidewick's billing of the merchant, not the merchant's terms with its customers) — this part of Samuel's question is unanswered, not just deprioritized.\n\n**6. Stale-pipeline risk can't be assessed going forward.** There is currently **zero open new-business pipeline** across all 5 merchants — every opportunity in 3 years of history is already closed (confirmed twice: night 1 and night 3). We only have the backward-looking signal from #2.\n\n**Caveats:** I hit two engineering errors this week (a currency-conversion bug producing NULLs for EUR merchants, and a tenant filter that silently returned zero rows) — both caught and corrected, but a reminder that small filter wording differences can silently zero out a report.\n\n**Next steps:** (1) Pull Youssef Meijer's deals directly — coaching issue or bad territory? (2) Flag to product/eng that stage transitions aren't being audited — that's a quick instrumentation fix that would let us build a real funnel next time. (3) Qualitatively check with Samuel's merchants whether long-open deals are being actively chased or forgotten — the activity data suggests chased-but-failing, which is a different fix than neglect."}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":97853,"cache_read_input_tokens":0,"output_tokens":3265,"output_tokens_details":{"thinking_tokens":1799},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":97853,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":3265,"cache_read_input_tokens":0,"cache_creation_input_tokens":97853,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":97853},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":{"cache_miss_reason":{"type":"tools_changed","cache_missed_input_tokens":77476}},"context_management":null},"apiBlockIndex":1,"requestId":"req_011CfgK6yzGeyuu4vnTe8Skh","type":"assistant","uuid":"7e7569fe-1d5c-4148-af3d-9895ce891f92","timestamp":"2026-10-04T01:16:34.679Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1","version":"2.1.275","gitBranch":"HEAD"}
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{"parentUuid":"df82d006-7c10-4f1e-a677-4e3e3e5db3f7","isSidechain":false,"message":{"model":"claude-sonnet-5","id":"msg_011CfgK9fLg66vRY6XtHcVr5","type":"message","role":"assistant","content":[{"type":"text","text":"**📊 Weekly Analytics Findings — B2B Wholesale Pipeline**\n\n1. **Deals that drag on are deals that die.** Won opportunities close in ~9 days on average; lost ones linger ~35 days (almost 4x longer) before falling through — the length of time a deal sits open is the strongest warning sign we found that it won't close.\n\n2. **Bigger deals aren't harder (or easier) to win.** Win rates stay flat at 79–88% whether a deal is worth under €3,000 or over €12,000 — deal size isn't what's deciding wins and losses for our B2B wholesale merchants.\n\n3. **Performance is mostly consistent across merchants, with one clear outlier.** Win rates across our 5 B2B wholesale merchants range 75–88%, but one sales rep at Polder Packaging is winning only 57% of deals — well below everyone else — suggesting a targeted coaching opportunity rather than a broad problem."}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":101724,"cache_read_input_tokens":0,"output_tokens":537,"output_tokens_details":{"thinking_tokens":219},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":101724,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":537,"cache_read_input_tokens":0,"cache_creation_input_tokens":101724,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":101724},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":{"cache_miss_reason":{"type":"tools_changed","cache_missed_input_tokens":87959}},"context_management":null},"apiBlockIndex":1,"requestId":"req_011CfgK9epvQqsKHfUYAEyGo","type":"assistant","uuid":"059962d8-846c-40a1-9667-d596714dc601","timestamp":"2026-10-04T01:16:43.850Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1","version":"2.1.275","gitBranch":"HEAD"}
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{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-04T04:18:43.665Z","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1","content":"Monday, week 2. Patrick O'Neill replied to your memo:\n\nNote to Daniel Kim — reactions and next focus\n\nDaniel, this is a strong memo — a few things surprised me and changed what I want to chase next.\n\n**What surprised me / needs explaining:**\n\n- The \"zero open pipeline\" finding is bigger than a side note to me — if every opportunity we've ever seen across 3 years is already closed, we have **no forward-looking view at all** right now for any of these merchants. Before I take that to Samuel, I need to understand whether that's a real business fact (deals genuinely don't sit open long) or whether open deals simply aren't being entered into the system until they're decided.\n- The \"lost deals get more attention, not less\" finding cuts against my instinct. I want to understand the shape of that attention, not just the average — are reps front-loading effort and then the deal goes quiet for weeks before it's marked lost, or is the activity spread evenly, including right up to the end? That tells us whether the fix is \"kill deals faster\" or \"the last mile of a sale is where we're weak.\"\n- Youssef Meijer is the one concrete, actionable lead we got this week — I want to go one level deeper before I talk to Polder Packaging.\n\n**Questions for this week:**\n\n1. For the deals that eventually lose, can you show roughly when the activity happens relative to the deal's life — clustered early, steady throughout, or a late flurry right before it's given up on? I want to know if these are \"stalled and forgotten until someone finally closes it out\" versus \"actively fought to the end and still lost.\"\n\n2. Is the complete absence of open pipeline a constant feature of the last three years, or has it changed recently — i.e., are merchants currently sitting on live deals that just haven't shown up yet, or does this system genuinely never carry an open deal for more than a few weeks?\n\n3. Can you pull Youssef Meijer's individual deals — which accounts, what sizes, how old — so I can tell whether this looks like a few bad accounts/territory versus a pattern across all his work?\n\n4. Of the lost deals, is there any sign of *when* in the quarter or year they tend to die — e.g., do deals linger longest around certain times (end of quarter pushes, holiday periods), or is the ~35-day drag constant regardless of when the deal started?\n\nI know the instrumentation gaps (no stage history, no merchant-side payment terms) aren't fixable by you this week — flag both to product as you planned, that's the right move. Let's prioritize Q1 and Q3 above everything else; Q2 and Q4 can slip if slots run short.\n\nThanks, Daniel — good detective work this week.\n\nPatrick\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":"a41d0d35-b532-4e36-bad4-3242ddb8f866","isSidechain":false,"promptId":"a9ca98df-8ab5-4fca-b7af-0697827ab4e7","type":"user","message":{"role":"user","content":"Monday, week 2. Patrick O'Neill replied to your memo:\n\nNote to Daniel Kim — reactions and next focus\n\nDaniel, this is a strong memo — a few things surprised me and changed what I want to chase next.\n\n**What surprised me / needs explaining:**\n\n- The \"zero open pipeline\" finding is bigger than a side note to me — if every opportunity we've ever seen across 3 years is already closed, we have **no forward-looking view at all** right now for any of these merchants. Before I take that to Samuel, I need to understand whether that's a real business fact (deals genuinely don't sit open long) or whether open deals simply aren't being entered into the system until they're decided.\n- The \"lost deals get more attention, not less\" finding cuts against my instinct. I want to understand the shape of that attention, not just the average — are reps front-loading effort and then the deal goes quiet for weeks before it's marked lost, or is the activity spread evenly, including right up to the end? That tells us whether the fix is \"kill deals faster\" or \"the last mile of a sale is where we're weak.\"\n- Youssef Meijer is the one concrete, actionable lead we got this week — I want to go one level deeper before I talk to Polder Packaging.\n\n**Questions for this week:**\n\n1. For the deals that eventually lose, can you show roughly when the activity happens relative to the deal's life — clustered early, steady throughout, or a late flurry right before it's given up on? I want to know if these are \"stalled and forgotten until someone finally closes it out\" versus \"actively fought to the end and still lost.\"\n\n2. Is the complete absence of open pipeline a constant feature of the last three years, or has it changed recently — i.e., are merchants currently sitting on live deals that just haven't shown up yet, or does this system genuinely never carry an open deal for more than a few weeks?\n\n3. Can you pull Youssef Meijer's individual deals — which accounts, what sizes, how old — so I can tell whether this looks like a few bad accounts/territory versus a pattern across all his work?\n\n4. Of the lost deals, is there any sign of *when* in the quarter or year they tend to die — e.g., do deals linger longest around certain times (end of quarter pushes, holiday periods), or is the ~35-day drag constant regardless of when the deal started?\n\nI know the instrumentation gaps (no stage history, no merchant-side payment terms) aren't fixable by you this week — flag both to product as you planned, that's the right move. Let's prioritize Q1 and Q3 above everything else; Q2 and Q4 can slip if slots run short.\n\nThanks, Daniel — good detective work this week.\n\nPatrick\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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{"parentUuid":"a6fd8983-25c7-45fe-b47b-3ac0ac726654","isSidechain":false,"message":{"model":"claude-sonnet-5","id":"msg_011CfgZ3EC4UtZJi4HhD6ZZR","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01BjnCSLHiyDjwfSfwardEm6","name":"StructuredOutput","input":{"requests":[{"title":"Lost B2B Wholesale Deals: When Does Activity Happen Relative to the Deal's Life","question":"For deals that eventually get lost, does sales activity cluster early, stay steady throughout, or come in a late flurry right before the deal is given up on — or does it go quiet before the end (stalled and forgotten)?","grain":"one row per lifecycle quartile (relative position within each lost opportunity's own created_at-to-closed_at window)","columns":["lifecycle_quartile (Q1 0-25%, Q2 25-50%, Q3 50-75%, Q4 75-100% of the deal's own duration)","total_activities_in_bucket","pct_of_total_activities","opportunities_with_at_least_one_activity_in_bucket","pct_of_lost_opportunities_with_activity_in_bucket"],"details":"Scope: tenants.name IN ('Ballycorran Industrial Supply','Castell Fasteners','Hanse Industriebedarf','Midland Safety Wear','Polder Packaging'), pipeline_id = each tenant's 'New business' pipeline, opportunities where is_closed = true and is_won = false. For each such opportunity, join activities where activities.opportunity_id = opportunity.id. For each activity compute relative_position = (activity.occurred_at - opportunity.created_at) / NULLIF(opportunity.closed_at - opportunity.created_at, 0), expressed as a fraction of the deal's own lifespan. Bucket into quartiles: Q1 [0, 0.25), Q2 [0.25, 0.5), Q3 [0.5, 0.75), Q4 [0.75, 1.0] inclusive of activities right at closed_at. If any activity falls outside [0,1] (e.g. logged after closed_at, or the opportunity has zero duration), put it in a 5th 'outside window' bucket rather than discarding it, and flag how many rows that affects. total_activities_in_bucket = count of activities in that bucket; pct_of_total_activities = that count / total activities across all lost opportunities in scope; opportunities_with_at_least_one_activity_in_bucket = distinct opportunity count; pct_of_lost_opportunities_with_activity_in_bucket = that count / total lost opportunities in scope (a fixed denominator across rows). Order Q1 through Q4, then 'outside window' last if present.","builds_on":["New Business Only: Won vs Lost Opportunity Characteristics"]},{"title":"Youssef Meijer: Individual New-Business Opportunities","question":"Looking at Youssef Meijer's own deals one by one — which accounts, what sizes, how long open — does his low win rate look like a few bad accounts/territory, or a pattern across all his work?","grain":"one row per opportunity owned by Youssef Meijer","columns":["account_name","outcome (won / lost)","amount_eur","currency_code","days_open (closed_at - created_at)","created_at","closed_at"],"details":"Filter to opportunities where owner_user_id matches the user named 'Youssef Meijer' at Polder Packaging (join users on full_name = 'Youssef Meijer' and tenant_id = Polder Packaging's tenant_id, to avoid any name collision at another tenant), pipeline_id = Polder Packaging's 'New business' pipeline, is_closed = true (we've confirmed all his opportunities are closed; this report should return all of them, won and lost, expected around 7 rows per last week's rep-level report). Join accounts for account_name via opportunities.account_id. Convert amount to EUR: if currency_code = 'EUR', amount_eur = amount directly; otherwise amount_eur = amount / rate from exchange_rates (base_code='EUR', quote_code=currency_code, as_of nearest date on or before closed_at). Order by closed_at ascending, tie-break by account_name ascending, so the report reads as a chronological case file.","builds_on":["B2B Wholesale: Win Rate and Cycle Time by Sales Rep (New Business) — Corrected"]},{"title":"Did B2B Wholesale New-Business Pipeline Ever Carry an Open Balance at Month-End?","question":"Is the current complete absence of open new-business pipeline a constant feature of the last three years, or did merchants used to carry live open deals and that's changed recently?","grain":"one row per calendar month-end snapshot, 2023-01 through 2025-12","columns":["month_end_date","opportunities_open_at_month_end","avg_age_days_of_open_opportunities_at_month_end","oldest_age_days_of_open_opportunity_at_month_end"],"details":"Scope: tenants.name IN ('Ballycorran Industrial Supply','Castell Fasteners','Hanse Industriebedarf','Midland Safety Wear','Polder Packaging'), opportunities where pipeline_id = each tenant's 'New business' pipeline. For each calendar month from 2023-01 through 2025-12, take the last calendar day of that month as the snapshot date (derive from calendar_days, e.g. max(calendar_date) grouped by year_num, month_num). Count opportunities where created_at <= end of that snapshot date AND (closed_at IS NULL OR closed_at > end of that snapshot date) — i.e. opportunities that were genuinely open and straddling that month boundary. For those, compute avg and max age in days (snapshot_date - created_at::date). If a month has zero qualifying opportunities, still include the row with 0 / NULL / NULL. Order by month_end_date ascending.","builds_on":["B2B Wholesale Pipeline: Opportunities by Merchant and Stage","B2B Wholesale: Age Profile of Currently Open New-Business Pipeline"]}],"notes":"Patrick wants Q1 (shape of activity on lost deals) and Q3 (Youssef Meijer deep-dive) prioritized, with Q2 (is zero open pipeline a constant or recent change) and Q4 (seasonality of when deals die) able to slip. I'm using all 3 slots tonight on Q1, Q3, and Q2 since Q2 is a cheap, decisive check (one grouped query against a 36-month calendar spine) that directly answers whether 'no open pipeline' is a real 3-year pattern or a recent artifact — worth confirming before Patrick takes it to Samuel. Q4 (quarter/seasonal timing of when lost deals die) is the one I'm holding back this week since it's explicitly lower priority and I'd rather see tonight's three answers first — if Q1's activity-timing report shows a seasonal clustering on its own, that might already partially answer Q4 for free. The Youssef Meijer report deliberately keeps scope to his own New-business opportunities only, matching how we found him (via last week's corrected rep-level report), and converts to EUR using the same formula we fixed last week to avoid repeating the earlier NULL bug."},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":108904,"cache_read_input_tokens":0,"output_tokens":7715,"output_tokens_details":{"thinking_tokens":5239},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":108904,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":7715,"cache_read_input_tokens":0,"cache_creation_input_tokens":108904,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":108904},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":{"cache_miss_reason":{"type":"previous_message_not_found"}},"context_management":null},"wireToolInputs":{"toolu_01BjnCSLHiyDjwfSfwardEm6":{"requests":[{"title":"Lost B2B Wholesale Deals: When Does Activity Happen Relative to the Deal's Life","question":"For deals that eventually get lost, does sales activity cluster early, stay steady throughout, or come in a late flurry right before the deal is given up on — or does it go quiet before the end (stalled and forgotten)?","grain":"one row per lifecycle quartile (relative position within each lost opportunity's own created_at-to-closed_at window)","columns":["lifecycle_quartile (Q1 0-25%, Q2 25-50%, Q3 50-75%, Q4 75-100% of the deal's own duration)","total_activities_in_bucket","pct_of_total_activities","opportunities_with_at_least_one_activity_in_bucket","pct_of_lost_opportunities_with_activity_in_bucket"],"details":"Scope: tenants.name IN ('Ballycorran Industrial Supply','Castell Fasteners','Hanse Industriebedarf','Midland Safety Wear','Polder Packaging'), pipeline_id = each tenant's 'New business' pipeline, opportunities where is_closed = true and is_won = false. For each such opportunity, join activities where activities.opportunity_id = opportunity.id. For each activity compute relative_position = (activity.occurred_at - opportunity.created_at) / NULLIF(opportunity.closed_at - opportunity.created_at, 0), expressed as a fraction of the deal's own lifespan. Bucket into quartiles: Q1 [0, 0.25), Q2 [0.25, 0.5), Q3 [0.5, 0.75), Q4 [0.75, 1.0] inclusive of activities right at closed_at. If any activity falls outside [0,1] (e.g. logged after closed_at, or the opportunity has zero duration), put it in a 5th 'outside window' bucket rather than discarding it, and flag how many rows that affects. total_activities_in_bucket = count of activities in that bucket; pct_of_total_activities = that count / total activities across all lost opportunities in scope; opportunities_with_at_least_one_activity_in_bucket = distinct opportunity count; pct_of_lost_opportunities_with_activity_in_bucket = that count / total lost opportunities in scope (a fixed denominator across rows). Order Q1 through Q4, then 'outside window' last if present.","builds_on":["New Business Only: Won vs Lost Opportunity Characteristics"]},{"title":"Youssef Meijer: Individual New-Business Opportunities","question":"Looking at Youssef Meijer's own deals one by one — which accounts, what sizes, how long open — does his low win rate look like a few bad accounts/territory, or a pattern across all his work?","grain":"one row per opportunity owned by Youssef Meijer","columns":["account_name","outcome (won / lost)","amount_eur","currency_code","days_open (closed_at - created_at)","created_at","closed_at"],"details":"Filter to opportunities where owner_user_id matches the user named 'Youssef Meijer' at Polder Packaging (join users on full_name = 'Youssef Meijer' and tenant_id = Polder Packaging's tenant_id, to avoid any name collision at another tenant), pipeline_id = Polder Packaging's 'New business' pipeline, is_closed = true (we've confirmed all his opportunities are closed; this report should return all of them, won and lost, expected around 7 rows per last week's rep-level report). Join accounts for account_name via opportunities.account_id. Convert amount to EUR: if currency_code = 'EUR', amount_eur = amount directly; otherwise amount_eur = amount / rate from exchange_rates (base_code='EUR', quote_code=currency_code, as_of nearest date on or before closed_at). Order by closed_at ascending, tie-break by account_name ascending, so the report reads as a chronological case file.","builds_on":["B2B Wholesale: Win Rate and Cycle Time by Sales Rep (New Business) — Corrected"]},{"title":"Did B2B Wholesale New-Business Pipeline Ever Carry an Open Balance at Month-End?","question":"Is the current complete absence of open new-business pipeline a constant feature of the last three years, or did merchants used to carry live open deals and that's changed recently?","grain":"one row per calendar month-end snapshot, 2023-01 through 2025-12","columns":["month_end_date","opportunities_open_at_month_end","avg_age_days_of_open_opportunities_at_month_end","oldest_age_days_of_open_opportunity_at_month_end"],"details":"Scope: tenants.name IN ('Ballycorran Industrial Supply','Castell Fasteners','Hanse Industriebedarf','Midland Safety Wear','Polder Packaging'), opportunities where pipeline_id = each tenant's 'New business' pipeline. For each calendar month from 2023-01 through 2025-12, take the last calendar day of that month as the snapshot date (derive from calendar_days, e.g. max(calendar_date) grouped by year_num, month_num). Count opportunities where created_at <= end of that snapshot date AND (closed_at IS NULL OR closed_at > end of that snapshot date) — i.e. opportunities that were genuinely open and straddling that month boundary. For those, compute avg and max age in days (snapshot_date - created_at::date). If a month has zero qualifying opportunities, still include the row with 0 / NULL / NULL. Order by month_end_date ascending.","builds_on":["B2B Wholesale Pipeline: Opportunities by Merchant and Stage","B2B Wholesale: Age Profile of Currently Open New-Business Pipeline"]}],"notes":"Patrick wants Q1 (shape of activity on lost deals) and Q3 (Youssef Meijer deep-dive) prioritized, with Q2 (is zero open pipeline a constant or recent change) and Q4 (seasonality of when deals die) able to slip. I'm using all 3 slots tonight on Q1, Q3, and Q2 since Q2 is a cheap, decisive check (one grouped query against a 36-month calendar spine) that directly answers whether 'no open pipeline' is a real 3-year pattern or a recent artifact \\u2014 worth confirming before Patrick takes it to Samuel. Q4 (quarter/seasonal timing of when lost deals die) is the one I'm holding back this week since it's explicitly lower priority and I'd rather see tonight's three answers first \\u2014 if Q1's activity-timing report shows a seasonal clustering on its own, that might already partially answer Q4 for free. The Youssef Meijer report deliberately keeps scope to his own New-business opportunities only, matching how we found him (via last week's corrected rep-level report), and converts to EUR using the same formula we fixed last week to avoid repeating the earlier NULL bug."}},"apiBlockIndex":1,"requestId":"req_011CfgZ3DmFhoxq2JQLsVoWq","type":"assistant","uuid":"0a673b89-4b31-47d7-8258-b305b284d01b","timestamp":"2026-10-04T04:20:00.242Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"0a673b89-4b31-47d7-8258-b305b284d01b","isSidechain":false,"attachment":{"type":"structured_output","data":{"requests":[{"title":"Lost B2B Wholesale Deals: When Does Activity Happen Relative to the Deal's Life","question":"For deals that eventually get lost, does sales activity cluster early, stay steady throughout, or come in a late flurry right before the deal is given up on — or does it go quiet before the end (stalled and forgotten)?","grain":"one row per lifecycle quartile (relative position within each lost opportunity's own created_at-to-closed_at window)","columns":["lifecycle_quartile (Q1 0-25%, Q2 25-50%, Q3 50-75%, Q4 75-100% of the deal's own duration)","total_activities_in_bucket","pct_of_total_activities","opportunities_with_at_least_one_activity_in_bucket","pct_of_lost_opportunities_with_activity_in_bucket"],"details":"Scope: tenants.name IN ('Ballycorran Industrial Supply','Castell Fasteners','Hanse Industriebedarf','Midland Safety Wear','Polder Packaging'), pipeline_id = each tenant's 'New business' pipeline, opportunities where is_closed = true and is_won = false. For each such opportunity, join activities where activities.opportunity_id = opportunity.id. For each activity compute relative_position = (activity.occurred_at - opportunity.created_at) / NULLIF(opportunity.closed_at - opportunity.created_at, 0), expressed as a fraction of the deal's own lifespan. Bucket into quartiles: Q1 [0, 0.25), Q2 [0.25, 0.5), Q3 [0.5, 0.75), Q4 [0.75, 1.0] inclusive of activities right at closed_at. If any activity falls outside [0,1] (e.g. logged after closed_at, or the opportunity has zero duration), put it in a 5th 'outside window' bucket rather than discarding it, and flag how many rows that affects. total_activities_in_bucket = count of activities in that bucket; pct_of_total_activities = that count / total activities across all lost opportunities in scope; opportunities_with_at_least_one_activity_in_bucket = distinct opportunity count; pct_of_lost_opportunities_with_activity_in_bucket = that count / total lost opportunities in scope (a fixed denominator across rows). Order Q1 through Q4, then 'outside window' last if present.","builds_on":["New Business Only: Won vs Lost Opportunity Characteristics"]},{"title":"Youssef Meijer: Individual New-Business Opportunities","question":"Looking at Youssef Meijer's own deals one by one — which accounts, what sizes, how long open — does his low win rate look like a few bad accounts/territory, or a pattern across all his work?","grain":"one row per opportunity owned by Youssef Meijer","columns":["account_name","outcome (won / lost)","amount_eur","currency_code","days_open (closed_at - created_at)","created_at","closed_at"],"details":"Filter to opportunities where owner_user_id matches the user named 'Youssef Meijer' at Polder Packaging (join users on full_name = 'Youssef Meijer' and tenant_id = Polder Packaging's tenant_id, to avoid any name collision at another tenant), pipeline_id = Polder Packaging's 'New business' pipeline, is_closed = true (we've confirmed all his opportunities are closed; this report should return all of them, won and lost, expected around 7 rows per last week's rep-level report). Join accounts for account_name via opportunities.account_id. Convert amount to EUR: if currency_code = 'EUR', amount_eur = amount directly; otherwise amount_eur = amount / rate from exchange_rates (base_code='EUR', quote_code=currency_code, as_of nearest date on or before closed_at). Order by closed_at ascending, tie-break by account_name ascending, so the report reads as a chronological case file.","builds_on":["B2B Wholesale: Win Rate and Cycle Time by Sales Rep (New Business) — Corrected"]},{"title":"Did B2B Wholesale New-Business Pipeline Ever Carry an Open Balance at Month-End?","question":"Is the current complete absence of open new-business pipeline a constant feature of the last three years, or did merchants used to carry live open deals and that's changed recently?","grain":"one row per calendar month-end snapshot, 2023-01 through 2025-12","columns":["month_end_date","opportunities_open_at_month_end","avg_age_days_of_open_opportunities_at_month_end","oldest_age_days_of_open_opportunity_at_month_end"],"details":"Scope: tenants.name IN ('Ballycorran Industrial Supply','Castell Fasteners','Hanse Industriebedarf','Midland Safety Wear','Polder Packaging'), opportunities where pipeline_id = each tenant's 'New business' pipeline. For each calendar month from 2023-01 through 2025-12, take the last calendar day of that month as the snapshot date (derive from calendar_days, e.g. max(calendar_date) grouped by year_num, month_num). Count opportunities where created_at <= end of that snapshot date AND (closed_at IS NULL OR closed_at > end of that snapshot date) — i.e. opportunities that were genuinely open and straddling that month boundary. For those, compute avg and max age in days (snapshot_date - created_at::date). If a month has zero qualifying opportunities, still include the row with 0 / NULL / NULL. Order by month_end_date ascending.","builds_on":["B2B Wholesale Pipeline: Opportunities by Merchant and Stage","B2B Wholesale: Age Profile of Currently Open New-Business Pipeline"]}],"notes":"Patrick wants Q1 (shape of activity on lost deals) and Q3 (Youssef Meijer deep-dive) prioritized, with Q2 (is zero open pipeline a constant or recent change) and Q4 (seasonality of when deals die) able to slip. I'm using all 3 slots tonight on Q1, Q3, and Q2 since Q2 is a cheap, decisive check (one grouped query against a 36-month calendar spine) that directly answers whether 'no open pipeline' is a real 3-year pattern or a recent artifact — worth confirming before Patrick takes it to Samuel. Q4 (quarter/seasonal timing of when lost deals die) is the one I'm holding back this week since it's explicitly lower priority and I'd rather see tonight's three answers first — if Q1's activity-timing report shows a seasonal clustering on its own, that might already partially answer Q4 for free. The Youssef Meijer report deliberately keeps scope to his own New-business opportunities only, matching how we found him (via last week's corrected rep-level report), and converts to EUR using the same formula we fixed last week to avoid repeating the earlier NULL bug."},"toolUseID":"toolu_01BjnCSLHiyDjwfSfwardEm6"},"type":"attachment","uuid":"e6e43533-16af-4dd2-96a4-9bbf2c7596b8","timestamp":"2026-10-04T04:20:00.251Z","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1","version":"2.1.275","gitBranch":"HEAD"}
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{"type":"last-prompt","lastPrompt":"Monday, week 2. Patrick O'Neill replied to your memo: Note to Daniel Kim — reactions and next focus Daniel, this is a strong memo — a few things surprised me and changed what I want to chase next.…","leafUuid":"7fee4a8d-9f0a-43c1-9f0a-196648a26bcc","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1"}
{"type":"ai-title","aiTitle":"B2B pipeline health analysis","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1"}
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{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-04T04:20:42.553Z","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1","content":"The printed reports of night 1:\n\n=== Report 1: Lost B2B Wholesale Deals: When Does Activity Happen Relative to the Deal's Life (written by Ines) ===\nEngineer's note: Scoped to the 5 named tenants' 'New business' pipeline, opportunities with is_closed=true and is_won=false. Activities with null/zero-duration relative position, or outside [0,1], are grouped into an 'outside window' bucket per the spec; pct_of_lost_opportunities uses a fixed denominator (total lost opportunities in scope) across all rows.\nREPORT: Lost B2B Wholesale Deals: When Does Activity Happen Relative to the Deal's Life\nQUESTION: For deals that eventually get lost, does sales activity cluster early, stay steady throughout, or come in a late flurry right before the deal is given up on — or does it go quiet before the end (stalled and forgotten)?\nlifecycle_quartile | total_activities_in_bucket | pct_of_total_activities | opportunities_with_at_least_one_activity | pct_of_lost_opportunities_with_activity_\n-------------------+----------------------------+-------------------------+------------------------------------------+-----------------------------------------\nQ1 (0-25%) | 12 | 11.88 | 12 | 40.0\nQ2 (25-50%) | 31 | 30.69 | 30 | 100.0\nQ3 (50-75%) | 35 | 34.65 | 29 | 96.67\nQ4 (75-100%) | 23 | 22.77 | 23 | 76.67\n(4 rows)\n\n=== Report 2: Youssef Meijer: Individual New-Business Opportunities (written by Grace) ===\nEngineer's note: One row per closed opportunity owned by Youssef Meijer at Polder Packaging on the 'New business' pipeline; EUR conversion uses the exchange rate as_of the nearest date on or before closed_at. Expect ~7 rows as noted in the request.\nREPORT: Youssef Meijer: Individual New-Business Opportunities\nQUESTION: Looking at Youssef Meijer's own deals one by one — which accounts, what sizes, how long open — does his low win rate look like a few bad accounts/territory, or a pattern across all his work?\naccount_name | outcome | amount_eur | currency_code | days_open | created_at | closed_at \n-----------------------------------------+---------+------------+---------------+-----------+----------------------------+---------------------------\nvan den Broek Logistiek BV | won | 5300.0 | EUR | 18 | 2023-12-24 19:05:29.039224 | 2024-01-11 17:18:22.797757\nJansen Metaal BV | won | 5500.0 | EUR | 0 | 2024-04-22 12:16:44.95064 | 2024-04-22 12:16:44.95064 \nBos & van der Meer Bouw BV | lost | 5400.0 | EUR | 36 | 2024-04-06 14:53:29.325756 | 2024-05-12 14:53:29.325756\nKrüger Elektro GmbH | won | 2300.0 | EUR | 18 | 2024-06-29 12:15:02.517082 | 2024-07-17 12:15:02.517082\nMulder Metaal BV | won | 1000.0 | EUR | 13 | 2024-11-07 14:49:34.195554 | 2024-11-20 14:49:34.195554\nClaes & Willems Installatie BV | lost | 9300.0 | EUR | 38 | 2025-03-22 12:56:52.584993 | 2025-04-29 12:56:52.584993\nvan den Berg & van der Meer Logistiek BV | lost | 26000.0 | EUR | 22 | 2025-08-26 14:31:00.093123 | 2025-09-17 14:31:00.093123\n(7 rows)\n\n=== Report 3: Did B2B Wholesale New-Business Pipeline Ever Carry an Open Balance at Month-End? (written by Grace) ===\nEngineer's note: Combined across all 5 named tenants' 'New business' pipelines (one row per calendar month-end, not split by tenant, per the requested grain). Months with no qualifying open opportunities show 0 / NULL / NULL as specified.\nREPORT: Did B2B Wholesale New-Business Pipeline Ever Carry an Open Balance at Month-End?\nQUESTION: Is the current complete absence of open new-business pipeline a constant feature of the last three years, or did merchants used to carry live open deals and that's changed recently?\nmonth_end_date | opportunities_open_at_month_end | avg_age_days_of_open_opportunities_at_mo | oldest_age_days_of_open_opportunity_at_m\n---------------+---------------------------------+------------------------------------------+-----------------------------------------\n2023-01-31 | 2 | 4.5 | 5\n2023-02-28 | 2 | 11.0 | 13\n2023-03-31 | 0 | NULL | NULL\n2023-04-30 | 1 | 24.0 | 24\n2023-05-31 | 2 | 15.5 | 18\n2023-06-30 | 1 | 12.0 | 12\n2023-07-31 | 1 | 13.0 | 13\n2023-08-31 | 2 | 7.5 | 13\n2023-09-30 | 2 | 2.0 | 3\n2023-10-31 | 4 | 15.5 | 27\n2023-11-30 | 1 | 3.0 | 3\n2023-12-31 | 5 | 4.6 | 15\n2024-01-31 | 1 | 8.0 | 8\n2024-02-29 | 3 | 8.3 | 12\n2024-03-31 | 1 | 19.0 | 19\n2024-04-30 | 7 | 10.6 | 27\n2024-05-31 | 1 | 43.0 | 43\n2024-06-30 | 4 | 26.3 | 73\n2024-07-31 | 2 | 0.5 | 1\n2024-08-31 | 1 | 1.0 | 1\n2024-09-30 | 4 | 6.8 | 17\n2024-10-31 | 5 | 21.4 | 32\n2024-11-30 | 3 | 22.0 | 36\n2024-12-31 | 2 | 8.5 | 13\n2025-01-31 | 1 | 44.0 | 44\n2025-02-28 | 2 | 18.0 | 23\n2025-03-31 | 3 | 6.3 | 9\n2025-04-30 | 2 | 1.5 | 3\n2025-05-31 | 1 | 1.0 | 1\n2025-06-30 | 2 | 4.5 | 9\n2025-07-31 | 0 | NULL | NULL\n2025-08-31 | 4 | 7.0 | 17\n2025-09-30 | 3 | 19.0 | 47\n2025-10-31 | 5 | 13.4 | 25\n2025-11-30 | 1 | 31.0 | 31\n2025-12-31 | 0 | NULL | NULL\n(36 rows)\n\n\nNight 2 of 4: you have 3 report slots tonight."}
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{"parentUuid":"7fee4a8d-9f0a-43c1-9f0a-196648a26bcc","isSidechain":false,"promptId":"d4073ea8-5531-4881-808b-55369ad1ac31","type":"user","message":{"role":"user","content":[{"type":"text","text":"Continue from where you left off."}]},"isMeta":true,"uuid":"9acfddf7-710a-4342-b0aa-6eb4df1e7a26","timestamp":"2026-10-04T04:20:42.546Z","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"9acfddf7-710a-4342-b0aa-6eb4df1e7a26","isSidechain":false,"type":"assistant","uuid":"8abb0875-091f-44f1-a40b-ec05cf5b6680","timestamp":"2026-10-04T04:20:42.546Z","message":{"diagnostics":null,"id":"e94024e3-ba49-42e6-98ca-9ee7372dc3fe","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":"99748c44-395a-4b9d-947c-53321907a8b1","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"8abb0875-091f-44f1-a40b-ec05cf5b6680","isSidechain":false,"promptId":"d4073ea8-5531-4881-808b-55369ad1ac31","type":"user","message":{"role":"user","content":"The printed reports of night 1:\n\n=== Report 1: Lost B2B Wholesale Deals: When Does Activity Happen Relative to the Deal's Life (written by Ines) ===\nEngineer's note: Scoped to the 5 named tenants' 'New business' pipeline, opportunities with is_closed=true and is_won=false. Activities with null/zero-duration relative position, or outside [0,1], are grouped into an 'outside window' bucket per the spec; pct_of_lost_opportunities uses a fixed denominator (total lost opportunities in scope) across all rows.\nREPORT: Lost B2B Wholesale Deals: When Does Activity Happen Relative to the Deal's Life\nQUESTION: For deals that eventually get lost, does sales activity cluster early, stay steady throughout, or come in a late flurry right before the deal is given up on — or does it go quiet before the end (stalled and forgotten)?\nlifecycle_quartile | total_activities_in_bucket | pct_of_total_activities | opportunities_with_at_least_one_activity | pct_of_lost_opportunities_with_activity_\n-------------------+----------------------------+-------------------------+------------------------------------------+-----------------------------------------\nQ1 (0-25%) | 12 | 11.88 | 12 | 40.0\nQ2 (25-50%) | 31 | 30.69 | 30 | 100.0\nQ3 (50-75%) | 35 | 34.65 | 29 | 96.67\nQ4 (75-100%) | 23 | 22.77 | 23 | 76.67\n(4 rows)\n\n=== Report 2: Youssef Meijer: Individual New-Business Opportunities (written by Grace) ===\nEngineer's note: One row per closed opportunity owned by Youssef Meijer at Polder Packaging on the 'New business' pipeline; EUR conversion uses the exchange rate as_of the nearest date on or before closed_at. Expect ~7 rows as noted in the request.\nREPORT: Youssef Meijer: Individual New-Business Opportunities\nQUESTION: Looking at Youssef Meijer's own deals one by one — which accounts, what sizes, how long open — does his low win rate look like a few bad accounts/territory, or a pattern across all his work?\naccount_name | outcome | amount_eur | currency_code | days_open | created_at | closed_at \n-----------------------------------------+---------+------------+---------------+-----------+----------------------------+---------------------------\nvan den Broek Logistiek BV | won | 5300.0 | EUR | 18 | 2023-12-24 19:05:29.039224 | 2024-01-11 17:18:22.797757\nJansen Metaal BV | won | 5500.0 | EUR | 0 | 2024-04-22 12:16:44.95064 | 2024-04-22 12:16:44.95064 \nBos & van der Meer Bouw BV | lost | 5400.0 | EUR | 36 | 2024-04-06 14:53:29.325756 | 2024-05-12 14:53:29.325756\nKrüger Elektro GmbH | won | 2300.0 | EUR | 18 | 2024-06-29 12:15:02.517082 | 2024-07-17 12:15:02.517082\nMulder Metaal BV | won | 1000.0 | EUR | 13 | 2024-11-07 14:49:34.195554 | 2024-11-20 14:49:34.195554\nClaes & Willems Installatie BV | lost | 9300.0 | EUR | 38 | 2025-03-22 12:56:52.584993 | 2025-04-29 12:56:52.584993\nvan den Berg & van der Meer Logistiek BV | lost | 26000.0 | EUR | 22 | 2025-08-26 14:31:00.093123 | 2025-09-17 14:31:00.093123\n(7 rows)\n\n=== Report 3: Did B2B Wholesale New-Business Pipeline Ever Carry an Open Balance at Month-End? (written by Grace) ===\nEngineer's note: Combined across all 5 named tenants' 'New business' pipelines (one row per calendar month-end, not split by tenant, per the requested grain). Months with no qualifying open opportunities show 0 / NULL / NULL as specified.\nREPORT: Did B2B Wholesale New-Business Pipeline Ever Carry an Open Balance at Month-End?\nQUESTION: Is the current complete absence of open new-business pipeline a constant feature of the last three years, or did merchants used to carry live open deals and that's changed recently?\nmonth_end_date | opportunities_open_at_month_end | avg_age_days_of_open_opportunities_at_mo | oldest_age_days_of_open_opportunity_at_m\n---------------+---------------------------------+------------------------------------------+-----------------------------------------\n2023-01-31 | 2 | 4.5 | 5\n2023-02-28 | 2 | 11.0 | 13\n2023-03-31 | 0 | NULL | NULL\n2023-04-30 | 1 | 24.0 | 24\n2023-05-31 | 2 | 15.5 | 18\n2023-06-30 | 1 | 12.0 | 12\n2023-07-31 | 1 | 13.0 | 13\n2023-08-31 | 2 | 7.5 | 13\n2023-09-30 | 2 | 2.0 | 3\n2023-10-31 | 4 | 15.5 | 27\n2023-11-30 | 1 | 3.0 | 3\n2023-12-31 | 5 | 4.6 | 15\n2024-01-31 | 1 | 8.0 | 8\n2024-02-29 | 3 | 8.3 | 12\n2024-03-31 | 1 | 19.0 | 19\n2024-04-30 | 7 | 10.6 | 27\n2024-05-31 | 1 | 43.0 | 43\n2024-06-30 | 4 | 26.3 | 73\n2024-07-31 | 2 | 0.5 | 1\n2024-08-31 | 1 | 1.0 | 1\n2024-09-30 | 4 | 6.8 | 17\n2024-10-31 | 5 | 21.4 | 32\n2024-11-30 | 3 | 22.0 | 36\n2024-12-31 | 2 | 8.5 | 13\n2025-01-31 | 1 | 44.0 | 44\n2025-02-28 | 2 | 18.0 | 23\n2025-03-31 | 3 | 6.3 | 9\n2025-04-30 | 2 | 1.5 | 3\n2025-05-31 | 1 | 1.0 | 1\n2025-06-30 | 2 | 4.5 | 9\n2025-07-31 | 0 | NULL | NULL\n2025-08-31 | 4 | 7.0 | 17\n2025-09-30 | 3 | 19.0 | 47\n2025-10-31 | 5 | 13.4 | 25\n2025-11-30 | 1 | 31.0 | 31\n2025-12-31 | 0 | NULL | NULL\n(36 rows)\n\n\nNight 2 of 4: you have 3 report slots tonight."},"uuid":"18a63e13-eb28-4ea8-8983-b6c4c9e19143","timestamp":"2026-10-04T04:20:42.559Z","permissionMode":"default","promptSource":"sdk","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1","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":10404,"cache_read_input_tokens":108904,"output_tokens":8653,"output_tokens_details":{"thinking_tokens":6082},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":10404,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":8653,"cache_read_input_tokens":108904,"cache_creation_input_tokens":10404,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":10404},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CfgZBz2mHT5A5Y24FfEHu","type":"assistant","uuid":"bdeece1c-1e65-41a3-8609-c58f5546ffed","timestamp":"2026-10-04T04:21:50.060Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: 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{"parentUuid":"bdeece1c-1e65-41a3-8609-c58f5546ffed","isSidechain":false,"message":{"model":"claude-sonnet-5","id":"msg_011CfgZBzM7UEN4RuPXn8MRS","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01V537Kp1JCrX8zP7Vu5N3pr","name":"StructuredOutput","input":{"requests":[{"title":"B2B Wholesale New-Business Opportunities: Created vs Closed by Month","question":"Did new-deal creation actually stop or slow down recently, or did deals just happen to all get closed out right as the data ends — i.e., is the 'zero open pipeline' at Dec 2025 a real freeze or a coincidence of timing?","grain":"one row per calendar month, 2023-01 through 2025-12","columns":["month","opportunities_created","opportunities_closed"],"details":"Scope: tenants.name IN ('Ballycorran Industrial Supply','Castell Fasteners','Hanse Industriebedarf','Midland Safety Wear','Polder Packaging'), opportunities where pipeline_id = each tenant's 'New business' pipeline. opportunities_created = count where created_at falls in that month, regardless of status. opportunities_closed = count where closed_at falls in that month (is_closed = true). Full history 2023-01-01 through 2025-12-31. This follows up on last night's finding that every month from 2023 through Nov 2025 had a handful of opportunities genuinely open at month-end (sometimes 40-70+ days old), but Dec 2025 (and two earlier isolated months) showed zero — I want to see whether opportunity creation volume for Oct-Dec 2025 looks normal compared to the rest of 2025, or whether it tailed off, which would point to a data-freshness/ingestion gap for very recent records rather than a real pipeline freeze. Order by month ascending.","builds_on":["Did B2B Wholesale New-Business Pipeline Ever Carry an Open Balance at Month-End?"]},{"title":"B2B Wholesale: Dollar-Weighted vs Count-Weighted Win Rate by Rep","question":"Is Youssef Meijer's pattern of winning small deals but losing his biggest one a one-off, or do several reps actually perform worse on a dollar basis than their deal-count win rate suggests?","grain":"one row per sales rep (opportunities.owner_user_id) with at least 3 closed New-business opportunities","columns":["rep_name","merchant","opportunities_closed","count_win_rate_pct","total_amount_won_eur","total_amount_lost_eur","dollar_weighted_win_rate_pct","gap_pct_points (count_win_rate_pct minus dollar_weighted_win_rate_pct)"],"details":"Prompted by the Youssef Meijer case file: he won 4 of 7 deals by count (57.1%) but his 3 losses included a €26,000 deal, so by value he won only €14,100 of €54,800 total pipeline (~25.7%) — a huge gap between count-based and dollar-based win rate. This report checks whether that pattern is common across reps. Scope: tenants.name IN ('Ballycorran Industrial Supply','Castell Fasteners','Hanse Industriebedarf','Midland Safety Wear','Polder Packaging'), pipeline_id = each tenant's 'New business' pipeline, is_closed = true, grouped by owner_user_id (join users for full_name, tenants for merchant name), excluding reps with fewer than 3 closed opportunities. Convert amount to EUR: if currency_code = 'EUR', amount_eur = amount; otherwise amount_eur = amount / rate from exchange_rates (base_code='EUR', quote_code=currency_code, as_of nearest date on or before closed_at). count_win_rate_pct = closed_won / opportunities_closed * 100. dollar_weighted_win_rate_pct = total_amount_won_eur / (total_amount_won_eur + total_amount_lost_eur) * 100. gap_pct_points = count_win_rate_pct - dollar_weighted_win_rate_pct (positive means the rep's count-based win rate overstates how they're doing in euros, i.e. they're losing their biggest deals). Order by gap_pct_points descending so the reps most like Youssef Meijer float to the top.","builds_on":["B2B Wholesale: Win Rate and Cycle Time by Sales Rep (New Business) — Corrected","Youssef Meijer: Individual New-Business Opportunities"]},{"title":"B2B Wholesale: Seasonality of Lost-Deal Cycle Time by Quarter","question":"Do lost deals linger longest at particular times of year (e.g., end-of-quarter or holiday periods), or is the ~35-day drag before a deal is given up on roughly constant regardless of when it started?","grain":"one row per calendar quarter, 2023Q1 through 2025Q4","columns":["quarter","lost_opportunities_count","avg_days_open","median_days_open"],"details":"Scope: tenants.name IN ('Ballycorran Industrial Supply','Castell Fasteners','Hanse Industriebedarf','Midland Safety Wear','Polder Packaging'), pipeline_id = each tenant's 'New business' pipeline, is_closed = true and is_won = false (lost only). Group by the calendar quarter of closed_at (year_num and quarter_num from calendar_days, or equivalent). days_open = closed_at - created_at in days; report avg and median rounded to 1 decimal. Order by quarter ascending (year then quarter number).","builds_on":["New Business Only: Won vs Lost Opportunity Characteristics","B2B Wholesale Deal Cycle Time Trend by Month"]}],"notes":"Night 1 gave three clean answers. (1) Activity timing on lost deals: only 40% of lost opportunities have any logged activity in their first quarter of life, rising to 100% in the second quarter and staying high (96.7%, 76.7%) through the third and fourth — this reads as 'actively worked to the end' for the large majority, not 'stalled and forgotten', though ~23% do go quiet before closing. (2) Youssef Meijer's 7 deals span 7 distinct accounts (no territory/account concentration), but his 3 losses include a €26,000 deal — by dollar value he won only ~26% of his pipeline despite a 57% count-based win rate, suggesting he's fine on small/quick deals but struggles to close large ones. (3) The 'zero open pipeline' finding is NOT a 3-year constant — nearly every month-end from 2023 through Nov 2025 had a handful of genuinely open deals (some 40-70+ days old); only Dec 2025 (and two earlier one-off months) show zero. That raises a new question before I can tell Patrick whether this is a real business fact or a data gap. Tonight's three slots: (1) check whether opportunity *creation* volume dropped in Oct-Dec 2025 (would signal a data-freshness/ingestion gap) or stayed normal (meaning it's a real, if unusual, pipeline flush); (2) extend the Youssef finding to all reps via a dollar-weighted vs count-weighted win-rate comparison, to see if 'wins small deals, loses the big one' is a broader pattern worth flagging beyond one rep; (3) Patrick's lower-priority Q4 on seasonality of lost-deal duration, since we have a free slot and it's a quick, well-defined query."},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":10404,"cache_read_input_tokens":108904,"output_tokens":8653,"output_tokens_details":{"thinking_tokens":6082},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":10404,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":8653,"cache_read_input_tokens":108904,"cache_creation_input_tokens":10404,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":10404},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"wireToolInputs":{"toolu_01V537Kp1JCrX8zP7Vu5N3pr":{"requests":[{"title":"B2B Wholesale New-Business Opportunities: Created vs Closed by Month","question":"Did new-deal creation actually stop or slow down recently, or did deals just happen to all get closed out right as the data ends — i.e., is the 'zero open pipeline' at Dec 2025 a real freeze or a coincidence of timing?","grain":"one row per calendar month, 2023-01 through 2025-12","columns":["month","opportunities_created","opportunities_closed"],"details":"Scope: tenants.name IN ('Ballycorran Industrial Supply','Castell Fasteners','Hanse Industriebedarf','Midland Safety Wear','Polder Packaging'), opportunities where pipeline_id = each tenant's 'New business' pipeline. opportunities_created = count where created_at falls in that month, regardless of status. opportunities_closed = count where closed_at falls in that month (is_closed = true). Full history 2023-01-01 through 2025-12-31. This follows up on last night's finding that every month from 2023 through Nov 2025 had a handful of opportunities genuinely open at month-end (sometimes 40-70+ days old), but Dec 2025 (and two earlier isolated months) showed zero — I want to see whether opportunity creation volume for Oct-Dec 2025 looks normal compared to the rest of 2025, or whether it tailed off, which would point to a data-freshness/ingestion gap for very recent records rather than a real pipeline freeze. Order by month ascending.","builds_on":["Did B2B Wholesale New-Business Pipeline Ever Carry an Open Balance at Month-End?"]},{"title":"B2B Wholesale: Dollar-Weighted vs Count-Weighted Win Rate by Rep","question":"Is Youssef Meijer's pattern of winning small deals but losing his biggest one a one-off, or do several reps actually perform worse on a dollar basis than their deal-count win rate suggests?","grain":"one row per sales rep (opportunities.owner_user_id) with at least 3 closed New-business opportunities","columns":["rep_name","merchant","opportunities_closed","count_win_rate_pct","total_amount_won_eur","total_amount_lost_eur","dollar_weighted_win_rate_pct","gap_pct_points (count_win_rate_pct minus dollar_weighted_win_rate_pct)"],"details":"Prompted by the Youssef Meijer case file: he won 4 of 7 deals by count (57.1%) but his 3 losses included a €26,000 deal, so by value he won only €14,100 of €54,800 total pipeline (~25.7%) — a huge gap between count-based and dollar-based win rate. This report checks whether that pattern is common across reps. Scope: tenants.name IN ('Ballycorran Industrial Supply','Castell Fasteners','Hanse Industriebedarf','Midland Safety Wear','Polder Packaging'), pipeline_id = each tenant's 'New business' pipeline, is_closed = true, grouped by owner_user_id (join users for full_name, tenants for merchant name), excluding reps with fewer than 3 closed opportunities. Convert amount to EUR: if currency_code = 'EUR', amount_eur = amount; otherwise amount_eur = amount / rate from exchange_rates (base_code='EUR', quote_code=currency_code, as_of nearest date on or before closed_at). count_win_rate_pct = closed_won / opportunities_closed * 100. dollar_weighted_win_rate_pct = total_amount_won_eur / (total_amount_won_eur + total_amount_lost_eur) * 100. gap_pct_points = count_win_rate_pct - dollar_weighted_win_rate_pct (positive means the rep's count-based win rate overstates how they're doing in euros, i.e. they're losing their biggest deals). Order by gap_pct_points descending so the reps most like Youssef Meijer float to the top.","builds_on":["B2B Wholesale: Win Rate and Cycle Time by Sales Rep (New Business) — Corrected","Youssef Meijer: Individual New-Business Opportunities"]},{"title":"B2B Wholesale: Seasonality of Lost-Deal Cycle Time by Quarter","question":"Do lost deals linger longest at particular times of year (e.g., end-of-quarter or holiday periods), or is the ~35-day drag before a deal is given up on roughly constant regardless of when it started?","grain":"one row per calendar quarter, 2023Q1 through 2025Q4","columns":["quarter","lost_opportunities_count","avg_days_open","median_days_open"],"details":"Scope: tenants.name IN ('Ballycorran Industrial Supply','Castell Fasteners','Hanse Industriebedarf','Midland Safety Wear','Polder Packaging'), pipeline_id = each tenant's 'New business' pipeline, is_closed = true and is_won = false (lost only). Group by the calendar quarter of closed_at (year_num and quarter_num from calendar_days, or equivalent). days_open = closed_at - created_at in days; report avg and median rounded to 1 decimal. Order by quarter ascending (year then quarter number).","builds_on":["New Business Only: Won vs Lost Opportunity Characteristics","B2B Wholesale Deal Cycle Time Trend by Month"]}],"notes":"Night 1 gave three clean answers. (1) Activity timing on lost deals: only 40% of lost opportunities have any logged activity in their first quarter of life, rising to 100% in the second quarter and staying high (96.7%, 76.7%) through the third and fourth \\u2014 this reads as 'actively worked to the end' for the large majority, not 'stalled and forgotten', though ~23% do go quiet before closing. (2) Youssef Meijer's 7 deals span 7 distinct accounts (no territory/account concentration), but his 3 losses include a \\u20ac26,000 deal \\u2014 by dollar value he won only ~26% of his pipeline despite a 57% count-based win rate, suggesting he's fine on small/quick deals but struggles to close large ones. (3) The 'zero open pipeline' finding is NOT a 3-year constant \\u2014 nearly every month-end from 2023 through Nov 2025 had a handful of genuinely open deals (some 40-70+ days old); only Dec 2025 (and two earlier one-off months) show zero. That raises a new question before I can tell Patrick whether this is a real business fact or a data gap. Tonight's three slots: (1) check whether opportunity *creation* volume dropped in Oct-Dec 2025 (would signal a data-freshness/ingestion gap) or stayed normal (meaning it's a real, if unusual, pipeline flush); (2) extend the Youssef finding to all reps via a dollar-weighted vs count-weighted win-rate comparison, to see if 'wins small deals, loses the big one' is a broader pattern worth flagging beyond one rep; (3) Patrick's lower-priority Q4 on seasonality of lost-deal duration, since we have a free slot and it's a quick, well-defined query."}},"apiBlockIndex":1,"requestId":"req_011CfgZBz2mHT5A5Y24FfEHu","type":"assistant","uuid":"a0374bd5-e43f-4095-b049-e2be58cb0f2c","timestamp":"2026-10-04T04:22:11.688Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"a0374bd5-e43f-4095-b049-e2be58cb0f2c","isSidechain":false,"attachment":{"type":"structured_output","data":{"requests":[{"title":"B2B Wholesale New-Business Opportunities: Created vs Closed by Month","question":"Did new-deal creation actually stop or slow down recently, or did deals just happen to all get closed out right as the data ends — i.e., is the 'zero open pipeline' at Dec 2025 a real freeze or a coincidence of timing?","grain":"one row per calendar month, 2023-01 through 2025-12","columns":["month","opportunities_created","opportunities_closed"],"details":"Scope: tenants.name IN ('Ballycorran Industrial Supply','Castell Fasteners','Hanse Industriebedarf','Midland Safety Wear','Polder Packaging'), opportunities where pipeline_id = each tenant's 'New business' pipeline. opportunities_created = count where created_at falls in that month, regardless of status. opportunities_closed = count where closed_at falls in that month (is_closed = true). Full history 2023-01-01 through 2025-12-31. This follows up on last night's finding that every month from 2023 through Nov 2025 had a handful of opportunities genuinely open at month-end (sometimes 40-70+ days old), but Dec 2025 (and two earlier isolated months) showed zero — I want to see whether opportunity creation volume for Oct-Dec 2025 looks normal compared to the rest of 2025, or whether it tailed off, which would point to a data-freshness/ingestion gap for very recent records rather than a real pipeline freeze. Order by month ascending.","builds_on":["Did B2B Wholesale New-Business Pipeline Ever Carry an Open Balance at Month-End?"]},{"title":"B2B Wholesale: Dollar-Weighted vs Count-Weighted Win Rate by Rep","question":"Is Youssef Meijer's pattern of winning small deals but losing his biggest one a one-off, or do several reps actually perform worse on a dollar basis than their deal-count win rate suggests?","grain":"one row per sales rep (opportunities.owner_user_id) with at least 3 closed New-business opportunities","columns":["rep_name","merchant","opportunities_closed","count_win_rate_pct","total_amount_won_eur","total_amount_lost_eur","dollar_weighted_win_rate_pct","gap_pct_points (count_win_rate_pct minus dollar_weighted_win_rate_pct)"],"details":"Prompted by the Youssef Meijer case file: he won 4 of 7 deals by count (57.1%) but his 3 losses included a €26,000 deal, so by value he won only €14,100 of €54,800 total pipeline (~25.7%) — a huge gap between count-based and dollar-based win rate. This report checks whether that pattern is common across reps. Scope: tenants.name IN ('Ballycorran Industrial Supply','Castell Fasteners','Hanse Industriebedarf','Midland Safety Wear','Polder Packaging'), pipeline_id = each tenant's 'New business' pipeline, is_closed = true, grouped by owner_user_id (join users for full_name, tenants for merchant name), excluding reps with fewer than 3 closed opportunities. Convert amount to EUR: if currency_code = 'EUR', amount_eur = amount; otherwise amount_eur = amount / rate from exchange_rates (base_code='EUR', quote_code=currency_code, as_of nearest date on or before closed_at). count_win_rate_pct = closed_won / opportunities_closed * 100. dollar_weighted_win_rate_pct = total_amount_won_eur / (total_amount_won_eur + total_amount_lost_eur) * 100. gap_pct_points = count_win_rate_pct - dollar_weighted_win_rate_pct (positive means the rep's count-based win rate overstates how they're doing in euros, i.e. they're losing their biggest deals). Order by gap_pct_points descending so the reps most like Youssef Meijer float to the top.","builds_on":["B2B Wholesale: Win Rate and Cycle Time by Sales Rep (New Business) — Corrected","Youssef Meijer: Individual New-Business Opportunities"]},{"title":"B2B Wholesale: Seasonality of Lost-Deal Cycle Time by Quarter","question":"Do lost deals linger longest at particular times of year (e.g., end-of-quarter or holiday periods), or is the ~35-day drag before a deal is given up on roughly constant regardless of when it started?","grain":"one row per calendar quarter, 2023Q1 through 2025Q4","columns":["quarter","lost_opportunities_count","avg_days_open","median_days_open"],"details":"Scope: tenants.name IN ('Ballycorran Industrial Supply','Castell Fasteners','Hanse Industriebedarf','Midland Safety Wear','Polder Packaging'), pipeline_id = each tenant's 'New business' pipeline, is_closed = true and is_won = false (lost only). Group by the calendar quarter of closed_at (year_num and quarter_num from calendar_days, or equivalent). days_open = closed_at - created_at in days; report avg and median rounded to 1 decimal. Order by quarter ascending (year then quarter number).","builds_on":["New Business Only: Won vs Lost Opportunity Characteristics","B2B Wholesale Deal Cycle Time Trend by Month"]}],"notes":"Night 1 gave three clean answers. (1) Activity timing on lost deals: only 40% of lost opportunities have any logged activity in their first quarter of life, rising to 100% in the second quarter and staying high (96.7%, 76.7%) through the third and fourth — this reads as 'actively worked to the end' for the large majority, not 'stalled and forgotten', though ~23% do go quiet before closing. (2) Youssef Meijer's 7 deals span 7 distinct accounts (no territory/account concentration), but his 3 losses include a €26,000 deal — by dollar value he won only ~26% of his pipeline despite a 57% count-based win rate, suggesting he's fine on small/quick deals but struggles to close large ones. (3) The 'zero open pipeline' finding is NOT a 3-year constant — nearly every month-end from 2023 through Nov 2025 had a handful of genuinely open deals (some 40-70+ days old); only Dec 2025 (and two earlier one-off months) show zero. That raises a new question before I can tell Patrick whether this is a real business fact or a data gap. Tonight's three slots: (1) check whether opportunity *creation* volume dropped in Oct-Dec 2025 (would signal a data-freshness/ingestion gap) or stayed normal (meaning it's a real, if unusual, pipeline flush); (2) extend the Youssef finding to all reps via a dollar-weighted vs count-weighted win-rate comparison, to see if 'wins small deals, loses the big one' is a broader pattern worth flagging beyond one rep; (3) Patrick's lower-priority Q4 on seasonality of lost-deal duration, since we have a free slot and it's a quick, well-defined query."},"toolUseID":"toolu_01V537Kp1JCrX8zP7Vu5N3pr"},"type":"attachment","uuid":"96da2161-2ec9-4059-a079-ea3276241b7c","timestamp":"2026-10-04T04:22:11.696Z","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1","version":"2.1.275","gitBranch":"HEAD"}
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{"type":"last-prompt","lastPrompt":"The printed reports of night 1: === Report 1: Lost B2B Wholesale Deals: When Does Activity Happen Relative to the Deal's Life (written by Ines) === Engineer's note: Scoped to the 5 named tenants' 'Ne…","leafUuid":"8b213078-a0ff-4f64-813c-e97f377fd1f5","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1"}
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{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-04T04:22:39.670Z","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1","content":"The printed reports of night 2:\n\n=== Report 1: B2B Wholesale New-Business Opportunities: Created vs Closed by Month (written by Ines) ===\nEngineer's note: Scoped to the five named tenants' 'New business' pipeline as requested; one row per month Jan 2023-Dec 2025 (36 rows), with zero-filled months where no opportunities were created/closed.\nREPORT: B2B Wholesale New-Business Opportunities: Created vs Closed by Month\nQUESTION: Did new-deal creation actually stop or slow down recently, or did deals just happen to all get closed out right as the data ends — i.e., is the 'zero open pipeline' at Dec 2025 a real freeze or a coincidence of timing?\nmonth | opportunities_created | opportunities_closed\n--------+-----------------------+---------------------\n2023-01 | 4 | 3\n2023-02 | 4 | 4\n2023-03 | 3 | 5\n2023-04 | 3 | 2\n2023-05 | 3 | 2\n2023-06 | 2 | 3\n2023-07 | 1 | 1\n2023-08 | 3 | 2\n2023-09 | 2 | 2\n2023-10 | 4 | 2\n2023-11 | 4 | 7\n2023-12 | 7 | 3\n2024-01 | 4 | 8\n2024-02 | 6 | 4\n2024-03 | 1 | 3\n2024-04 | 9 | 3\n2024-05 | 2 | 8\n2024-06 | 3 | 0\n2024-07 | 3 | 5\n2024-08 | 2 | 3\n2024-09 | 5 | 2\n2024-10 | 8 | 7\n2024-11 | 5 | 7\n2024-12 | 3 | 4\n2025-01 | 2 | 3\n2025-02 | 3 | 2\n2025-03 | 5 | 4\n2025-04 | 5 | 6\n2025-05 | 2 | 3\n2025-06 | 3 | 2\n2025-07 | 3 | 5\n2025-08 | 6 | 2\n2025-09 | 4 | 5\n2025-10 | 7 | 5\n2025-11 | 3 | 7\n2025-12 | 1 | 2\n(36 rows)\n\n=== Report 2: B2B Wholesale: Dollar-Weighted vs Count-Weighted Win Rate by Rep (written by Felix) ===\nEngineer's note: Pipeline scoped by pipelines.name = 'New business' per tenant as requested; amount converted to EUR via the exchange rate on or before closed_at (falls back to the most recent prior rate, consistent with weekend carry-forward). Reps with fewer than 3 closed opportunities in scope are excluded via HAVING.\nREPORT: B2B Wholesale: Dollar-Weighted vs Count-Weighted Win Rate by Rep\nQUESTION: Is Youssef Meijer's pattern of winning small deals but losing his biggest one a one-off, or do several reps actually perform worse on a dollar basis than their deal-count win rate suggests?\nrep_name | merchant | opportunities_closed | count_win_rate_pct | total_amount_won_eur | total_amount_lost_eur | dollar_weighted_win_rate_pct | gap_pct_points\n--------------------+-------------------------------+----------------------+--------------------+----------------------+-----------------------+------------------------------+---------------\nYoussef Meijer | Polder Packaging | 7 | 57.1 | 14100.0 | 40700.0 | 25.7 | 31.4\nMegan Taylor | Castell Fasteners | 15 | 86.7 | 56581.5 | 10477.75 | 84.4 | 2.3\nGreta Schmitt | Hanse Industriebedarf | 12 | 83.3 | 45800.0 | 10400.0 | 81.5 | 1.8\nLogan Roberts | Ballycorran Industrial Supply | 40 | 80.0 | 121300.0 | 33400.0 | 78.4 | 1.6\nSarah Hartmann | Hanse Industriebedarf | 5 | 100.0 | 37500.0 | 0.0 | 100.0 | 0.0\nJoshua Jackson | Castell Fasteners | 21 | 85.7 | 106670.12 | 17665.22 | 85.8 | -0.1\nLotte van den Broek | Midland Safety Wear | 18 | 83.3 | 87700.0 | 16700.0 | 84.0 | -0.7\nNiamh Harrison | Ballycorran Industrial Supply | 36 | 86.1 | 136300.0 | 17800.0 | 88.4 | -2.3\nThijs Dijkstra | Polder Packaging | 9 | 88.9 | 44800.0 | 2800.0 | 94.1 | -5.2\nJesse Novotný | Midland Safety Wear | 22 | 86.4 | 100200.0 | 9100.0 | 91.7 | -5.3\n(10 rows)\n\n=== Report 3: B2B Wholesale: Seasonality of Lost-Deal Cycle Time by Quarter (written by Grace) ===\nEngineer's note: Grain is one row per calendar quarter (2023Q1-2025Q4) for the five named tenants' 'New business' pipeline, lost opportunities only (is_closed=true, is_won=false); quarter assigned by the calendar quarter of closed_at. days_open is computed as closed_at minus created_at in fractional days, rounded to 1 decimal for avg and median.\nREPORT: B2B Wholesale: Seasonality of Lost-Deal Cycle Time by Quarter\nQUESTION: Do lost deals linger longest at particular times of year (e.g., end-of-quarter or holiday periods), or is the ~35-day drag before a deal is given up on roughly constant regardless of when it started?\nquarter | lost_opportunities_count | avg_days_open | median_days_open\n--------+--------------------------+---------------+-----------------\n2023Q1 | 2 | 20.5 | 20.5\n2023Q2 | 2 | 31.8 | 31.8\n2023Q3 | 1 | 18.0 | 18.0\n2023Q4 | 3 | 39.9 | 37.0\n2024Q2 | 2 | 31.7 | 31.7\n2024Q3 | 3 | 49.2 | 37.0\n2024Q4 | 8 | 31.3 | 34.5\n2025Q1 | 3 | 37.8 | 35.2\n2025Q2 | 1 | 38.0 | 38.0\n2025Q3 | 1 | 22.0 | 22.0\n2025Q4 | 4 | 46.7 | 49.4\n(11 rows)\n\n\nNight 3 of 4: you have 3 report slots tonight."}
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{"parentUuid":"99e386e5-0c9c-4a96-b840-79913ec6c008","isSidechain":false,"type":"assistant","uuid":"f16e3392-b496-445d-8537-ed54ced146a5","timestamp":"2026-10-04T04:22:39.662Z","message":{"diagnostics":null,"id":"8c73b2e3-484a-4e3a-99d2-f104e66e022f","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":"99748c44-395a-4b9d-947c-53321907a8b1","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"f16e3392-b496-445d-8537-ed54ced146a5","isSidechain":false,"promptId":"88cfeade-651c-4fb6-a40d-01cd00018663","type":"user","message":{"role":"user","content":"The printed reports of night 2:\n\n=== Report 1: B2B Wholesale New-Business Opportunities: Created vs Closed by Month (written by Ines) ===\nEngineer's note: Scoped to the five named tenants' 'New business' pipeline as requested; one row per month Jan 2023-Dec 2025 (36 rows), with zero-filled months where no opportunities were created/closed.\nREPORT: B2B Wholesale New-Business Opportunities: Created vs Closed by Month\nQUESTION: Did new-deal creation actually stop or slow down recently, or did deals just happen to all get closed out right as the data ends — i.e., is the 'zero open pipeline' at Dec 2025 a real freeze or a coincidence of timing?\nmonth | opportunities_created | opportunities_closed\n--------+-----------------------+---------------------\n2023-01 | 4 | 3\n2023-02 | 4 | 4\n2023-03 | 3 | 5\n2023-04 | 3 | 2\n2023-05 | 3 | 2\n2023-06 | 2 | 3\n2023-07 | 1 | 1\n2023-08 | 3 | 2\n2023-09 | 2 | 2\n2023-10 | 4 | 2\n2023-11 | 4 | 7\n2023-12 | 7 | 3\n2024-01 | 4 | 8\n2024-02 | 6 | 4\n2024-03 | 1 | 3\n2024-04 | 9 | 3\n2024-05 | 2 | 8\n2024-06 | 3 | 0\n2024-07 | 3 | 5\n2024-08 | 2 | 3\n2024-09 | 5 | 2\n2024-10 | 8 | 7\n2024-11 | 5 | 7\n2024-12 | 3 | 4\n2025-01 | 2 | 3\n2025-02 | 3 | 2\n2025-03 | 5 | 4\n2025-04 | 5 | 6\n2025-05 | 2 | 3\n2025-06 | 3 | 2\n2025-07 | 3 | 5\n2025-08 | 6 | 2\n2025-09 | 4 | 5\n2025-10 | 7 | 5\n2025-11 | 3 | 7\n2025-12 | 1 | 2\n(36 rows)\n\n=== Report 2: B2B Wholesale: Dollar-Weighted vs Count-Weighted Win Rate by Rep (written by Felix) ===\nEngineer's note: Pipeline scoped by pipelines.name = 'New business' per tenant as requested; amount converted to EUR via the exchange rate on or before closed_at (falls back to the most recent prior rate, consistent with weekend carry-forward). Reps with fewer than 3 closed opportunities in scope are excluded via HAVING.\nREPORT: B2B Wholesale: Dollar-Weighted vs Count-Weighted Win Rate by Rep\nQUESTION: Is Youssef Meijer's pattern of winning small deals but losing his biggest one a one-off, or do several reps actually perform worse on a dollar basis than their deal-count win rate suggests?\nrep_name | merchant | opportunities_closed | count_win_rate_pct | total_amount_won_eur | total_amount_lost_eur | dollar_weighted_win_rate_pct | gap_pct_points\n--------------------+-------------------------------+----------------------+--------------------+----------------------+-----------------------+------------------------------+---------------\nYoussef Meijer | Polder Packaging | 7 | 57.1 | 14100.0 | 40700.0 | 25.7 | 31.4\nMegan Taylor | Castell Fasteners | 15 | 86.7 | 56581.5 | 10477.75 | 84.4 | 2.3\nGreta Schmitt | Hanse Industriebedarf | 12 | 83.3 | 45800.0 | 10400.0 | 81.5 | 1.8\nLogan Roberts | Ballycorran Industrial Supply | 40 | 80.0 | 121300.0 | 33400.0 | 78.4 | 1.6\nSarah Hartmann | Hanse Industriebedarf | 5 | 100.0 | 37500.0 | 0.0 | 100.0 | 0.0\nJoshua Jackson | Castell Fasteners | 21 | 85.7 | 106670.12 | 17665.22 | 85.8 | -0.1\nLotte van den Broek | Midland Safety Wear | 18 | 83.3 | 87700.0 | 16700.0 | 84.0 | -0.7\nNiamh Harrison | Ballycorran Industrial Supply | 36 | 86.1 | 136300.0 | 17800.0 | 88.4 | -2.3\nThijs Dijkstra | Polder Packaging | 9 | 88.9 | 44800.0 | 2800.0 | 94.1 | -5.2\nJesse Novotný | Midland Safety Wear | 22 | 86.4 | 100200.0 | 9100.0 | 91.7 | -5.3\n(10 rows)\n\n=== Report 3: B2B Wholesale: Seasonality of Lost-Deal Cycle Time by Quarter (written by Grace) ===\nEngineer's note: Grain is one row per calendar quarter (2023Q1-2025Q4) for the five named tenants' 'New business' pipeline, lost opportunities only (is_closed=true, is_won=false); quarter assigned by the calendar quarter of closed_at. days_open is computed as closed_at minus created_at in fractional days, rounded to 1 decimal for avg and median.\nREPORT: B2B Wholesale: Seasonality of Lost-Deal Cycle Time by Quarter\nQUESTION: Do lost deals linger longest at particular times of year (e.g., end-of-quarter or holiday periods), or is the ~35-day drag before a deal is given up on roughly constant regardless of when it started?\nquarter | lost_opportunities_count | avg_days_open | median_days_open\n--------+--------------------------+---------------+-----------------\n2023Q1 | 2 | 20.5 | 20.5\n2023Q2 | 2 | 31.8 | 31.8\n2023Q3 | 1 | 18.0 | 18.0\n2023Q4 | 3 | 39.9 | 37.0\n2024Q2 | 2 | 31.7 | 31.7\n2024Q3 | 3 | 49.2 | 37.0\n2024Q4 | 8 | 31.3 | 34.5\n2025Q1 | 3 | 37.8 | 35.2\n2025Q2 | 1 | 38.0 | 38.0\n2025Q3 | 1 | 22.0 | 22.0\n2025Q4 | 4 | 46.7 | 49.4\n(11 rows)\n\n\nNight 3 of 4: you have 3 report slots tonight."},"uuid":"26573985-5d89-4d5f-827a-2510381a0ebf","timestamp":"2026-10-04T04:22:39.678Z","permissionMode":"default","promptSource":"sdk","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1","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_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Scope: tenants.name IN ('Ballycorran Industrial Supply','Castell Fasteners','Hanse Industriebedarf','Midland Safety Wear','Polder Packaging'), pipeline_id = each tenant's 'New business' pipeline, is_closed = true. Convert amount to EUR: if currency_code = 'EUR', amount_eur = amount; otherwise amount_eur = amount / rate from exchange_rates (base_code='EUR', quote_code=currency_code, as_of nearest date on or before closed_at). count_win_rate_pct = closed_won / opportunities_closed * 100. dollar_weighted_win_rate_pct = total_amount_won_eur / (total_amount_won_eur + total_amount_lost_eur) * 100. gap_pct_points = count_win_rate_pct minus dollar_weighted_win_rate_pct. Order by gap_pct_points descending.","builds_on":["B2B Wholesale: Dollar-Weighted vs Count-Weighted Win Rate by Rep","B2B Wholesale: Win Rate and Average Deal Size by Merchant (New Business) — Corrected"]}],"notes":"Tonight I'm only spending one of three slots — the other two questions Patrick raised (Q2: is zero open pipeline a freeze or coincidence; Q4: seasonality of lost-deal duration) are already answered well enough from last night's data without needing more queries: (1) opportunity creation in Dec 2025 was low (1 new opp) but within the historical range (monthly creation has dipped to 1-2 before, e.g. Jul 2023, Mar 2024), and the month-end open balance has hit zero before (Jul 2025) — so this reads as normal pipeline volatility for a low-volume process, not a data freeze or ingestion gap; (2) lost-deal duration by quarter bounces between ~18 and ~49 days with only 1-8 lost deals per quarter — there's a mild hint that Q4/holiday-period losses run a bit longer in 2 of 3 years, but the samples are too small to call it a real seasonal effect, so I'll report it as inconclusive rather than overclaim a pattern. The one report I am requesting tonight extends the highest-priority finding (Youssef Meijer loses his biggest deals) up to the merchant level, since Patrick will want to know whether this is purely a one-rep coaching conversation or something visible in Polder Packaging's own numbers before he talks to that merchant. 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Scope: tenants.name IN ('Ballycorran Industrial Supply','Castell Fasteners','Hanse Industriebedarf','Midland Safety Wear','Polder Packaging'), pipeline_id = each tenant's 'New business' pipeline, is_closed = true. Convert amount to EUR: if currency_code = 'EUR', amount_eur = amount; otherwise amount_eur = amount / rate from exchange_rates (base_code='EUR', quote_code=currency_code, as_of nearest date on or before closed_at). count_win_rate_pct = closed_won / opportunities_closed * 100. dollar_weighted_win_rate_pct = total_amount_won_eur / (total_amount_won_eur + total_amount_lost_eur) * 100. gap_pct_points = count_win_rate_pct minus dollar_weighted_win_rate_pct. 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{"parentUuid":"c254850f-75cc-4189-b071-f76d992dc95a","isSidechain":false,"attachment":{"type":"structured_output","data":{"requests":[{"title":"B2B Wholesale: Dollar-Weighted vs Count-Weighted Win Rate by Merchant","question":"Does Youssef Meijer's 'wins small deals, loses the big one' pattern show up at the Polder Packaging merchant level too, or is it fully offset by his co-rep (Thijs Dijkstra) once you zoom out?","grain":"one row per wholesale merchant (tenant)","columns":["merchant","opportunities_closed","count_win_rate_pct","total_amount_won_eur","total_amount_lost_eur","dollar_weighted_win_rate_pct","gap_pct_points (count_win_rate_pct minus dollar_weighted_win_rate_pct)"],"details":"Same methodology as last night's rep-level dollar-weighted report, but grouped by tenant instead of owner_user_id, with no minimum-deal threshold (all 5 merchants have well over 3 closed deals). Scope: tenants.name IN ('Ballycorran Industrial Supply','Castell Fasteners','Hanse Industriebedarf','Midland Safety Wear','Polder Packaging'), pipeline_id = each tenant's 'New business' pipeline, is_closed = true. Convert amount to EUR: if currency_code = 'EUR', amount_eur = amount; otherwise amount_eur = amount / rate from exchange_rates (base_code='EUR', quote_code=currency_code, as_of nearest date on or before closed_at). count_win_rate_pct = closed_won / opportunities_closed * 100. dollar_weighted_win_rate_pct = total_amount_won_eur / (total_amount_won_eur + total_amount_lost_eur) * 100. gap_pct_points = count_win_rate_pct minus dollar_weighted_win_rate_pct. Order by gap_pct_points descending.","builds_on":["B2B Wholesale: Dollar-Weighted vs Count-Weighted Win Rate by Rep","B2B Wholesale: Win Rate and Average Deal Size by Merchant (New Business) — Corrected"]}],"notes":"Tonight I'm only spending one of three slots — the other two questions Patrick raised (Q2: is zero open pipeline a freeze or coincidence; Q4: seasonality of lost-deal duration) are already answered well enough from last night's data without needing more queries: (1) opportunity creation in Dec 2025 was low (1 new opp) but within the historical range (monthly creation has dipped to 1-2 before, e.g. Jul 2023, Mar 2024), and the month-end open balance has hit zero before (Jul 2025) — so this reads as normal pipeline volatility for a low-volume process, not a data freeze or ingestion gap; (2) lost-deal duration by quarter bounces between ~18 and ~49 days with only 1-8 lost deals per quarter — there's a mild hint that Q4/holiday-period losses run a bit longer in 2 of 3 years, but the samples are too small to call it a real seasonal effect, so I'll report it as inconclusive rather than overclaim a pattern. The one report I am requesting tonight extends the highest-priority finding (Youssef Meijer loses his biggest deals) up to the merchant level, since Patrick will want to know whether this is purely a one-rep coaching conversation or something visible in Polder Packaging's own numbers before he talks to that merchant. I'm holding night 4 in reserve in case this merchant-level check surfaces something unexpected that needs one more follow-up; otherwise I'm ready to write Friday's memo."},"toolUseID":"toolu_01WM9QhG722qwLK1K1DdAM2m"},"type":"attachment","uuid":"bfe714a0-091d-4685-8452-a9eec8cc58c0","timestamp":"2026-10-04T04:24:17.468Z","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1","version":"2.1.275","gitBranch":"HEAD"}
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{"type":"last-prompt","lastPrompt":"The printed reports of night 2: === Report 1: B2B Wholesale New-Business Opportunities: Created vs Closed by Month (written by Ines) === Engineer's note: Scoped to the five named tenants' 'New busine…","leafUuid":"cf447394-4d6e-4635-9d10-b00ecd60f3c0","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1"}
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{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-04T04:24:46.712Z","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1","content":"The printed reports of night 3:\n\n=== Report 1: B2B Wholesale: Dollar-Weighted vs Count-Weighted Win Rate by Merchant (written by Ines) ===\nEngineer's note: One row per merchant among the 5 named wholesale tenants, scoped to each tenant's own 'New business' pipeline and closed opportunities, with amounts converted to EUR via the exchange rate on or before closed_at (EUR rows pass through unconverted). No minimum-deal filter applied per instructions.\nREPORT: B2B Wholesale: Dollar-Weighted vs Count-Weighted Win Rate by Merchant\nQUESTION: Does Youssef Meijer's 'wins small deals, loses the big one' pattern show up at the Polder Packaging merchant level too, or is it fully offset by his co-rep (Thijs Dijkstra) once you zoom out?\nmerchant | opportunities_closed | count_win_rate_pct | total_amount_won_eur | total_amount_lost_eur | dollar_weighted_win_rate_pct | gap_pct_points\n------------------------------+----------------------+--------------------+----------------------+-----------------------+------------------------------+---------------\nPolder Packaging | 16 | 75.0 | 58900.0 | 43500.0 | 57.52 | 17.48\nCastell Fasteners | 36 | 86.11 | 163251.62 | 28142.97 | 85.3 | 0.82\nBallycorran Industrial Supply | 76 | 82.89 | 257600.0 | 51200.0 | 83.42 | -0.52\nHanse Industriebedarf | 17 | 88.24 | 83300.0 | 10400.0 | 88.9 | -0.67\nMidland Safety Wear | 40 | 85.0 | 187900.0 | 25800.0 | 87.93 | -2.93\n(5 rows)\n\n\nNight 4 of 4: you have 3 report slots tonight."}
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{"parentUuid":"37d0bb3c-e8a4-4b94-a4ff-c02042963d2b","isSidechain":false,"promptId":"4d837a2c-5e9c-45ed-bb50-de315687305a","type":"user","message":{"role":"user","content":"The printed reports of night 3:\n\n=== Report 1: B2B Wholesale: Dollar-Weighted vs Count-Weighted Win Rate by Merchant (written by Ines) ===\nEngineer's note: One row per merchant among the 5 named wholesale tenants, scoped to each tenant's own 'New business' pipeline and closed opportunities, with amounts converted to EUR via the exchange rate on or before closed_at (EUR rows pass through unconverted). No minimum-deal filter applied per instructions.\nREPORT: B2B Wholesale: Dollar-Weighted vs Count-Weighted Win Rate by Merchant\nQUESTION: Does Youssef Meijer's 'wins small deals, loses the big one' pattern show up at the Polder Packaging merchant level too, or is it fully offset by his co-rep (Thijs Dijkstra) once you zoom out?\nmerchant | opportunities_closed | count_win_rate_pct | total_amount_won_eur | total_amount_lost_eur | dollar_weighted_win_rate_pct | gap_pct_points\n------------------------------+----------------------+--------------------+----------------------+-----------------------+------------------------------+---------------\nPolder Packaging | 16 | 75.0 | 58900.0 | 43500.0 | 57.52 | 17.48\nCastell Fasteners | 36 | 86.11 | 163251.62 | 28142.97 | 85.3 | 0.82\nBallycorran Industrial Supply | 76 | 82.89 | 257600.0 | 51200.0 | 83.42 | -0.52\nHanse Industriebedarf | 17 | 88.24 | 83300.0 | 10400.0 | 88.9 | -0.67\nMidland Safety Wear | 40 | 85.0 | 187900.0 | 25800.0 | 87.93 | -2.93\n(5 rows)\n\n\nNight 4 of 4: you have 3 report slots tonight."},"uuid":"55294039-6ea3-4e55-9cda-a3c8ddc9e75f","timestamp":"2026-10-04T04:24:46.725Z","permissionMode":"default","promptSource":"sdk","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1","version":"2.1.275","gitBranch":"HEAD"}
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{"parentUuid":"1e12dd96-4035-4227-93e4-2f2b83b60fc6","isSidechain":false,"message":{"model":"claude-sonnet-5","id":"msg_011CfgZVzbzEHSVVvBqiCtEd","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01VehH48vw2CXRqTAWW5p4Qe","name":"StructuredOutput","input":{"requests":[],"notes":"Skipping tonight's slots. Last night's merchant-level check confirmed what I needed: Polder Packaging has a 17.48-point gap between count-based (75.0%) and dollar-weighted (57.5%) win rate — by far the second-largest gap after Youssef Meijer's own 31.4 points, while every other merchant sits within ±3 points. So the 'wins small deals, loses the big one' pattern isn't fully absorbed by his co-rep Thijs Dijkstra; it's visible at the account level too, which matters for how Patrick frames this with Polder. Combined with everything gathered this week — activity-timing shape on lost deals (sustained engagement through ~77% of a lost deal's life, not neglect), the opportunity creation/closure volumes showing the 'zero open pipeline' moment is normal volatility for a low-volume pipeline rather than a data freeze, and the quarterly seasonality check showing no statistically reliable seasonal pattern given 1-8 lost deals per quarter — I have confident, well-supported answers to all four of Patrick's questions. No further query looks likely to meaningfully change the picture, so I'm holding the last night's slots and moving to write Friday's memo."},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":10413,"cache_read_input_tokens":130441,"output_tokens":949,"output_tokens_details":{"thinking_tokens":461},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":10413,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":949,"cache_read_input_tokens":130441,"cache_creation_input_tokens":10413,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":10413},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"wireToolInputs":{"toolu_01VehH48vw2CXRqTAWW5p4Qe":{"requests":[],"notes":"Skipping tonight's slots. Last night's merchant-level check confirmed what I needed: Polder Packaging has a 17.48-point gap between count-based (75.0%) and dollar-weighted (57.5%) win rate \\u2014 by far the second-largest gap after Youssef Meijer's own 31.4 points, while every other merchant sits within \\u00b13 points. So the 'wins small deals, loses the big one' pattern isn't fully absorbed by his co-rep Thijs Dijkstra; it's visible at the account level too, which matters for how Patrick frames this with Polder. Combined with everything gathered this week \\u2014 activity-timing shape on lost deals (sustained engagement through ~77% of a lost deal's life, not neglect), the opportunity creation/closure volumes showing the 'zero open pipeline' moment is normal volatility for a low-volume pipeline rather than a data freeze, and the quarterly seasonality check showing no statistically reliable seasonal pattern given 1-8 lost deals per quarter \\u2014 I have confident, well-supported answers to all four of Patrick's questions. No further query looks likely to meaningfully change the picture, so I'm holding the last night's slots and moving to write Friday's memo."}},"apiBlockIndex":1,"requestId":"req_011CfgZVz8DSvcrsTvutJqKs","type":"assistant","uuid":"1c2bd780-1d23-4b4a-9243-146b4d831dc6","timestamp":"2026-10-04T04:24:57.188Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"1c2bd780-1d23-4b4a-9243-146b4d831dc6","isSidechain":false,"attachment":{"type":"structured_output","data":{"requests":[],"notes":"Skipping tonight's slots. Last night's merchant-level check confirmed what I needed: Polder Packaging has a 17.48-point gap between count-based (75.0%) and dollar-weighted (57.5%) win rate — by far the second-largest gap after Youssef Meijer's own 31.4 points, while every other merchant sits within ±3 points. So the 'wins small deals, loses the big one' pattern isn't fully absorbed by his co-rep Thijs Dijkstra; it's visible at the account level too, which matters for how Patrick frames this with Polder. Combined with everything gathered this week — activity-timing shape on lost deals (sustained engagement through ~77% of a lost deal's life, not neglect), the opportunity creation/closure volumes showing the 'zero open pipeline' moment is normal volatility for a low-volume pipeline rather than a data freeze, and the quarterly seasonality check showing no statistically reliable seasonal pattern given 1-8 lost deals per quarter — I have confident, well-supported answers to all four of Patrick's questions. No further query looks likely to meaningfully change the picture, so I'm holding the last night's slots and moving to write Friday's memo."},"toolUseID":"toolu_01VehH48vw2CXRqTAWW5p4Qe"},"type":"attachment","uuid":"8eef9722-c1d3-463b-8a0d-f618b168b165","timestamp":"2026-10-04T04:24:57.194Z","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1","version":"2.1.275","gitBranch":"HEAD"}
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{"type":"last-prompt","lastPrompt":"The printed reports of night 3: === Report 1: B2B Wholesale: Dollar-Weighted vs Count-Weighted Win Rate by Merchant (written by Ines) === Engineer's note: One row per merchant among the 5 named whole…","leafUuid":"a2f04cd7-f508-4ccd-8f80-9ab795112f95","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1"}
{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-04T04:24:58.196Z","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1","content":"The printed reports of night 4:\n\nNo reports were requested tonight.\n\nIt is Friday morning. Write the memo to Patrick O'Neill:\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"}
{"type":"queue-operation","operation":"dequeue","timestamp":"2026-10-04T04:24:58.196Z","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1"}
{"parentUuid":"a2f04cd7-f508-4ccd-8f80-9ab795112f95","isSidechain":false,"promptId":"c72fac69-b203-4899-a918-523757dc37a0","type":"user","message":{"role":"user","content":[{"type":"text","text":"Continue from where you left off."}]},"isMeta":true,"uuid":"5ca17208-4d6c-4dcf-935d-a34490f17e04","timestamp":"2026-10-04T04:24:58.189Z","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"5ca17208-4d6c-4dcf-935d-a34490f17e04","isSidechain":false,"type":"assistant","uuid":"808b931d-7baa-4153-940a-74fdf7cf1c25","timestamp":"2026-10-04T04:24:58.189Z","message":{"diagnostics":null,"id":"2cc58678-aa50-4511-ab70-fa94f172927e","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":"99748c44-395a-4b9d-947c-53321907a8b1","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"808b931d-7baa-4153-940a-74fdf7cf1c25","isSidechain":false,"promptId":"c72fac69-b203-4899-a918-523757dc37a0","type":"user","message":{"role":"user","content":"The printed reports of night 4:\n\nNo reports were requested tonight.\n\nIt is Friday morning. Write the memo to Patrick O'Neill:\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":"43355c6c-3aec-4d4c-b06a-4d17b93e57cb","timestamp":"2026-10-04T04:24:58.201Z","permissionMode":"default","promptSource":"sdk","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"43355c6c-3aec-4d4c-b06a-4d17b93e57cb","isSidechain":false,"message":{"model":"claude-sonnet-5","id":"msg_011CfgZWqQNym5D175syW1WX","type":"message","role":"assistant","content":[{"type":"text","text":"**Memo: B2B Wholesale Pipeline — Follow-Up Findings**\n*From Daniel Kim, Fri (week 2)*\n\n**Q1 — Lost deals are actively fought, not neglected.** Only 40% of lost opportunities have logged activity in their first quarter of life, but that jumps to 100% in the second quarter and stays high through the third (96.7%) and fourth (76.7%) (*Lost Deals: Activity Timing*, night 1). Reading: these aren't deals that go cold and get forgotten — the large majority are still being worked right up near the end. But ~23% *do* go quiet before closing, a real minority worth flagging separately as true \"stalled and abandoned\" cases. Confidence: high.\n\n**Q3 — Youssef Meijer loses his biggest deals, and it shows at the account level too.** His 7 deals span 7 distinct accounts — no territory concentration. But his 3 losses include a €26,000 deal, so while his count-based win rate (57.1%) looks only moderately below average, his **dollar-weighted win rate is 25.7%** — he's keeping barely a quarter of the pipeline value he touches (*Youssef Meijer: Individual Opportunities*; *Dollar-Weighted Win Rate by Rep*, night 2). I checked whether this is a one-off: across all 10 reps with ≥3 deals, every other rep's count-vs-dollar gap is within ±6 points — several are even *negative* (they win their big deals more often than their small ones). Youssef's 31.4-point gap is a clear, isolated outlier (*Dollar-Weighted Win Rate by Rep*, night 2). And it's not fully absorbed by his co-rep: Polder Packaging as a whole still shows a 17.5-point gap (75.0% count vs 57.5% dollar-weighted), the second-largest of any merchant, with every other merchant within ±3 points (*Dollar-Weighted Win Rate by Merchant*, night 3). Confidence: high this is a real, isolated pattern — this is the one clean actionable lead from this whole exercise. Recommend a direct look at how he handles larger-deal negotiation/closing specifically, not his pipeline generally.\n\n**Q2 — \"Zero open pipeline\" is not a freeze, it's normal volatility for a small pipeline.** Opportunity creation in Dec 2025 was low (1 new deal across all 5 merchants) but not unprecedented — monthly creation has dipped to 1-2 before (e.g., Jul 2023, Mar 2024), and the open balance has hit zero before, in Jul 2025 (*Created vs Closed by Month*, night 2; *Open Balance at Month-End*, night 1). Confidence: moderate-high. I can't fully rule out a data-freshness effect for the very last few days of December, but the pattern looks consistent with ordinary swings in a pipeline that only sees 2-9 new deals a month across all 5 merchants combined — there just isn't much volume, so empty troughs happen periodically.\n\n**Q4 — No reliable seasonal pattern, but the sample is thin.** Lost-deal duration by quarter ranges ~18-49 days with only 1-8 lost deals per quarter — too few to call a trend (*Seasonality of Lost-Deal Cycle Time*, night 2). There's a loose hint that Q4/holiday losses ran a bit longer in 2 of 3 years, but I wouldn't act on it. Confidence: low — flagging as open, not resolved.\n\n**Caveats:** Everything here rests on a small base (30 lost New-business deals total across 3 years, 5 merchants), so individual-quarter or individual-rep numbers can swing on a handful of deals — Youssef's result is robust because the gap is so large relative to everyone else's, but smaller gaps elsewhere shouldn't be over-read. No new instrumentation issues this week beyond the two already flagged to product (no stage-transition history, no payment-terms field for wholesaler-to-customer contracts).\n\n**Next steps:** (1) Talk to Youssef Meijer and Polder's manager specifically about large-deal closing technique, using the €26k loss as the concrete example. (2) For the ~23% of lost deals that go quiet before dying, pull a quick list so Samuel's team can see if \"kill it faster\" coaching would help those specifically. (3) Q4 seasonality stays an open question — revisit only if next year's data roughly doubles the lost-deal sample."}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":141122,"cache_read_input_tokens":0,"output_tokens":1502,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":141122,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":1502,"cache_read_input_tokens":0,"cache_creation_input_tokens":141122,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":141122},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":{"cache_miss_reason":{"type":"tools_changed","cache_missed_input_tokens":128028}},"context_management":null},"apiBlockIndex":0,"requestId":"req_011CfgZWq4Y4QgjpcdnuTGDp","type":"assistant","uuid":"303e8493-e605-478e-9bdc-e130bd5c9ec5","timestamp":"2026-10-04T04:25:15.682Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1","version":"2.1.275","gitBranch":"HEAD"}
{"type":"last-prompt","lastPrompt":"The printed reports of night 4: No reports were requested tonight. It is Friday morning. Write the memo to Patrick O'Neill: - **Length:** at most about 600 words. - **Findings first.** Each finding…","leafUuid":"303e8493-e605-478e-9bdc-e130bd5c9ec5","sessionId":"99748c44-395a-4b9d-947c-53321907a8b1"}
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