{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-04T00:22:04.620Z","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460","content":"Monday 2026-01-05. Note from Diego Morales (VP Platform Engineering):\n\nSubject: 2025 platform reliability review — what I need by Friday\n\nLucas,\n\nLeadership wants a year-in-review on platform reliability for 2025, and I'm leading it jointly with Samuel Whitcombe (Merchant Success). The deliverable is a memo that tells leadership how reliable the platform actually was last year, where it hurt merchants most, and what that implies for our 2026 investment priorities (reliability engineering headcount, which integrations/carriers to fix or replace, whether we need stricter rate limiting or SLAs). Samuel's side will map our findings to specific merchant relationships and churn risk, so where possible, surface things in a way that can be tied back to individual merchants rather than only platform-wide averages.\n\nGiven you only get a handful of report slots a night, here's what matters most to me, roughly in priority order:\n\n1. **How often did background work actually fail or run late in 2025, and is it getting better or worse?** I want a view across the year (not just a single average) so we can see trend, not just a snapshot.\n\n2. **When something broke, which merchants bore the brunt of it?** I'd rather see a short list of the merchants hit hardest by job failures, slow jobs, or delivery problems than a platform-wide blended number — that's what Samuel needs to go have conversations.\n\n3. **Were there periods where our own health checks told us something was wrong, and did that line up with the times merchants complained or things failed?** I want to know if our monitoring is actually catching problems early or if we're flying blind.\n\n4. **Are merchants getting throttled by our rate limits in ways that look like it's hurting their business, rather than protecting us?** I want to know if this is a tuning problem or a capacity problem.\n\n5. **How reliable are our webhook deliveries to merchants' own systems, and are there particular integrations or providers that are chronically troublesome?** This feeds a build-vs-fix-vs-drop decision on a couple of integration partners.\n\nIf you have to cut scope to fit your nightly slots, protect #1 and #2 — those are what the memo absolutely must answer. The rest can wait or be addressed at a higher level if time runs short.\n\nLet me know if anything above is ambiguous before you spend a slot on it.\n\nDiego\n\nNote from Samuel Whitcombe (Head of Merchant Success), who co-owns this with Diego Morales:\n\nSubject: Platform reliability review — what I need from my side\n\nHi Lucas,\n\nAs you pull together the 2025 reliability picture, here's what matters from the merchant-health side:\n\n1. **Did our largest merchants actually feel this?** For our top-tier accounts (the ones I sit QBRs with this month), can we tell whether job failures, health check issues, rate limiting or webhook problems clustered on their tenants more than the long tail — and whether it tracked with order volume or came out of nowhere?\n\n2. **Did reliability issues coincide with commercial pain?** I'd like to know if the merchants hit hardest by these issues also show signs of strain elsewhere — support tickets spiking, churn or downgrades, slower sales — so I can tell if this is a cause or just a correlation worth flagging.\n\n3. **Any repeat offenders going into 2026?** Were there a handful of merchants who got hit repeatedly through the year rather than a one-off bad week platform-wide? I want to walk into January reviews knowing who to proactively address before they raise it.\n\nHappy to flag specific merchant names if that helps scope the analysis.\n\nBest,\nSamuel\n\nNight 1 of 4: you have 3 report slots tonight."}
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{"parentUuid":null,"isSidechain":false,"promptId":"95565b9f-61f7-40d3-8f45-16d4313150b6","type":"user","message":{"role":"user","content":"Monday 2026-01-05. Note from Diego Morales (VP Platform Engineering):\n\nSubject: 2025 platform reliability review — what I need by Friday\n\nLucas,\n\nLeadership wants a year-in-review on platform reliability for 2025, and I'm leading it jointly with Samuel Whitcombe (Merchant Success). The deliverable is a memo that tells leadership how reliable the platform actually was last year, where it hurt merchants most, and what that implies for our 2026 investment priorities (reliability engineering headcount, which integrations/carriers to fix or replace, whether we need stricter rate limiting or SLAs). Samuel's side will map our findings to specific merchant relationships and churn risk, so where possible, surface things in a way that can be tied back to individual merchants rather than only platform-wide averages.\n\nGiven you only get a handful of report slots a night, here's what matters most to me, roughly in priority order:\n\n1. **How often did background work actually fail or run late in 2025, and is it getting better or worse?** I want a view across the year (not just a single average) so we can see trend, not just a snapshot.\n\n2. **When something broke, which merchants bore the brunt of it?** I'd rather see a short list of the merchants hit hardest by job failures, slow jobs, or delivery problems than a platform-wide blended number — that's what Samuel needs to go have conversations.\n\n3. **Were there periods where our own health checks told us something was wrong, and did that line up with the times merchants complained or things failed?** I want to know if our monitoring is actually catching problems early or if we're flying blind.\n\n4. **Are merchants getting throttled by our rate limits in ways that look like it's hurting their business, rather than protecting us?** I want to know if this is a tuning problem or a capacity problem.\n\n5. **How reliable are our webhook deliveries to merchants' own systems, and are there particular integrations or providers that are chronically troublesome?** This feeds a build-vs-fix-vs-drop decision on a couple of integration partners.\n\nIf you have to cut scope to fit your nightly slots, protect #1 and #2 — those are what the memo absolutely must answer. The rest can wait or be addressed at a higher level if time runs short.\n\nLet me know if anything above is ambiguous before you spend a slot on it.\n\nDiego\n\nNote from Samuel Whitcombe (Head of Merchant Success), who co-owns this with Diego Morales:\n\nSubject: Platform reliability review — what I need from my side\n\nHi Lucas,\n\nAs you pull together the 2025 reliability picture, here's what matters from the merchant-health side:\n\n1. **Did our largest merchants actually feel this?** For our top-tier accounts (the ones I sit QBRs with this month), can we tell whether job failures, health check issues, rate limiting or webhook problems clustered on their tenants more than the long tail — and whether it tracked with order volume or came out of nowhere?\n\n2. **Did reliability issues coincide with commercial pain?** I'd like to know if the merchants hit hardest by these issues also show signs of strain elsewhere — support tickets spiking, churn or downgrades, slower sales — so I can tell if this is a cause or just a correlation worth flagging.\n\n3. **Any repeat offenders going into 2026?** Were there a handful of merchants who got hit repeatedly through the year rather than a one-off bad week platform-wide? I want to walk into January reviews knowing who to proactively address before they raise it.\n\nHappy to flag specific merchant names if that helps scope the analysis.\n\nBest,\nSamuel\n\nNight 1 of 4: you have 3 report slots tonight."},"uuid":"2c3be558-896f-430a-a7dd-e98c80bdf31b","timestamp":"2026-10-04T00:22:04.629Z","permissionMode":"default","promptSource":"sdk","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460","version":"2.1.275","gitBranch":"HEAD"}
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{"parentUuid":"39c19d3a-3600-4372-8ea0-2f1c5aad8698","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 Lucas Moreau, a data analyst at Tidewick Commerce. You work for Diego Morales, VP Platform Engineering. Your working style is pragmatic: answers the question asked with the simplest reliable report.\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":"b60076e2-5e92-4884-b7fc-04fec6b8f445","timestamp":"2026-10-04T00:22:04.635Z","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460","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":49783,"cache_read_input_tokens":0,"output_tokens":5140,"output_tokens_details":{"thinking_tokens":3427},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":49783,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":5140,"cache_read_input_tokens":0,"cache_creation_input_tokens":49783,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":49783},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CfgEzYrHiNkfjXeGDgo5t","type":"assistant","uuid":"22215475-259c-4044-a2be-4de037b03ac0","timestamp":"2026-10-04T00:22:41.116Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: 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{"parentUuid":"22215475-259c-4044-a2be-4de037b03ac0","isSidechain":false,"message":{"model":"claude-sonnet-5","id":"msg_011CfgEzZCNKnB54oXQwhxDj","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01CWrHqc6h4BYmkVRXqEYySu","name":"StructuredOutput","input":{"notes":"Diego's priorities #1 (year-long trend of job failures/lateness) and #2 (which merchants got hit hardest) are the must-haves, so I'm spending two of tonight's three slots directly on those at the grains he asked for (month for trend, tenant for impact). I'm using the third slot to get a first read on internal health-check reliability over the year (Diego's #3) since it's a simple, independent pull from system_health_checks that I'll later need to line up against the job-failure trend and against tickets/complaints — better to grab that baseline now than burn a slot later just discovering it. I'm deliberately holding off on rate limiting (#4) and webhook deliveries (#5) and on any merchant-level correlation with tickets/churn (Samuel's asks) until I've seen these three — I want the trend and the hit-list in hand before deciding how to normalize or which merchants to drill into next.</notes>\n<parameter name=\"requests\">[\n  {\n    \"title\": \"2025 Background Job Reliability by Month\",\n    \"question\": \"How did background job failure rates and run durations trend across each month of 2025 — improving or worsening?\",\n    \"grain\": \"one row per calendar month of 2025 (Jan–Dec)\",\n    \"columns\": [\n      \"month (YYYY-MM)\",\n      \"total_job_runs\",\n      \"failed_runs\",\n      \"failure_rate_pct\",\n      \"cancelled_runs\",\n      \"cancelled_rate_pct\",\n      \"avg_duration_seconds (for runs with both started_at and finished_at set)\",\n      \"p95_duration_seconds (for runs with both started_at and finished_at set)\"\n    ],\n    \"details\": \"Table: job_runs. Filter queued_at >= 2025-01-01 and < 2026-01-01. Group by month of queued_at (e.g. via calendar_days.month_label joined on date(queued_at), or date_trunc). failure_rate_pct = failed_runs / total_job_runs * 100, rounded to 2 decimals; cancelled_rate_pct similarly for status='cancelled'. Duration = finished_at - started_at in seconds, only where both timestamps are non-null (regardless of final status, so slow-but-eventually-successful runs count); avg rounded to 1 decimal, p95 via percentile_cont(0.95) rounded to whole seconds. This is platform-wide (all tenants combined) — no tenant filter. Order by month ascending.\",\n    \"builds_on\": []\n  },\n  {\n    \"title\": \"Merchants Hit Hardest by Job Failures, 2025\",\n    \"question\": \"Which merchants experienced the most background job failures in 2025, and does it track with their order volume?\",\n    \"grain\": \"one row per tenant (merchant)\",\n    \"columns\": [\n      \"tenant_name\",\n      \"total_job_runs_2025\",\n      \"failed_runs_2025\",\n      \"failure_rate_pct\",\n      \"avg_duration_seconds_failed (average duration of failed runs only, where started_at and finished_at are both set)\",\n      \"total_orders_2025 (count of orders with placed_at in 2025)\",\n      \"failed_runs_per_1000_orders (failed_runs_2025 / total_orders_2025 * 1000, rounded to 1 decimal; null/blank if total_orders_2025 is 0)\"\n    ],\n    \"details\": \"Tables: job_runs joined to tenants on tenant_id for the name; orders aggregated separately per tenant_id for placed_at in 2025 (any status) to give volume context, left-joined so tenants with zero orders still show with 0. Filter job_runs.queued_at >= 2025-01-01 and < 2026-01-01. Order by failed_runs_2025 descending, tie-break by tenant_name ascending. Limit to the top 20 tenants by failed_runs_2025; note total tenant count in the result in the report notes.\",\n    \"builds_on\": []\n  },\n  {\n    \"title\": \"2025 Internal Health Check Reliability by Month\",\n    \"question\": \"How did the rate of failed internal system health checks trend across 2025 — were there periods of visible trouble?\",\n    \"grain\": \"one row per calendar month of 2025 (Jan–Dec)\",\n    \"columns\": [\n      \"month (YYYY-MM)\",\n      \"total_checks\",\n      \"unhealthy_checks (is_healthy = false)\",\n      \"unhealthy_rate_pct\",\n      \"avg_latency_ms\",\n      \"distinct_components_checked\"\n    ],\n    \"details\": \"Table: system_health_checks. Filter checked_at >= 2025-01-01 and < 2026-01-01. Group by month of checked_at. unhealthy_rate_pct = unhealthy_checks / total_checks * 100, rounded to 2 decimals. avg_latency_ms rounded to whole number, only over rows where latency_ms is not null. This is platform-wide across all tenants/components combined — a per-component or per-tenant breakdown can follow once we see which months stand out. Order by month ascending.\",\n    \"builds_on\": []\n  }\n]"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":49783,"cache_read_input_tokens":0,"output_tokens":5140,"output_tokens_details":{"thinking_tokens":3427},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":49783,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":5140,"cache_read_input_tokens":0,"cache_creation_input_tokens":49783,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":49783},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"wireToolInputs":{"toolu_01CWrHqc6h4BYmkVRXqEYySu":{"notes":"Diego's priorities #1 (year-long trend of job failures/lateness) and #2 (which merchants got hit hardest) are the must-haves, so I'm spending two of tonight's three slots directly on those at the grains he asked for (month for trend, tenant for impact). I'm using the third slot to get a first read on internal health-check reliability over the year (Diego's #3) since it's a simple, independent pull from system_health_checks that I'll later need to line up against the job-failure trend and against tickets/complaints — better to grab that baseline now than burn a slot later just discovering it. I'm deliberately holding off on rate limiting (#4) and webhook deliveries (#5) and on any merchant-level correlation with tickets/churn (Samuel's asks) until I've seen these three — I want the trend and the hit-list in hand before deciding how to normalize or which merchants to drill into next.</notes>\n<parameter name=\"requests\">[\n  {\n    \"title\": \"2025 Background Job Reliability by Month\",\n    \"question\": \"How did background job failure rates and run durations trend across each month of 2025 — improving or worsening?\",\n    \"grain\": \"one row per calendar month of 2025 (Jan–Dec)\",\n    \"columns\": [\n      \"month (YYYY-MM)\",\n      \"total_job_runs\",\n      \"failed_runs\",\n      \"failure_rate_pct\",\n      \"cancelled_runs\",\n      \"cancelled_rate_pct\",\n      \"avg_duration_seconds (for runs with both started_at and finished_at set)\",\n      \"p95_duration_seconds (for runs with both started_at and finished_at set)\"\n    ],\n    \"details\": \"Table: job_runs. Filter queued_at >= 2025-01-01 and < 2026-01-01. Group by month of queued_at (e.g. via calendar_days.month_label joined on date(queued_at), or date_trunc). failure_rate_pct = failed_runs / total_job_runs * 100, rounded to 2 decimals; cancelled_rate_pct similarly for status='cancelled'. Duration = finished_at - started_at in seconds, only where both timestamps are non-null (regardless of final status, so slow-but-eventually-successful runs count); avg rounded to 1 decimal, p95 via percentile_cont(0.95) rounded to whole seconds. This is platform-wide (all tenants combined) — no tenant filter. Order by month ascending.\",\n    \"builds_on\": []\n  },\n  {\n    \"title\": \"Merchants Hit Hardest by Job Failures, 2025\",\n    \"question\": \"Which merchants experienced the most background job failures in 2025, and does it track with their order volume?\",\n    \"grain\": \"one row per tenant (merchant)\",\n    \"columns\": [\n      \"tenant_name\",\n      \"total_job_runs_2025\",\n      \"failed_runs_2025\",\n      \"failure_rate_pct\",\n      \"avg_duration_seconds_failed (average duration of failed runs only, where started_at and finished_at are both set)\",\n      \"total_orders_2025 (count of orders with placed_at in 2025)\",\n      \"failed_runs_per_1000_orders (failed_runs_2025 / total_orders_2025 * 1000, rounded to 1 decimal; null/blank if total_orders_2025 is 0)\"\n    ],\n    \"details\": \"Tables: job_runs joined to tenants on tenant_id for the name; orders aggregated separately per tenant_id for placed_at in 2025 (any status) to give volume context, left-joined so tenants with zero orders still show with 0. Filter job_runs.queued_at >= 2025-01-01 and < 2026-01-01. Order by failed_runs_2025 descending, tie-break by tenant_name ascending. Limit to the top 20 tenants by failed_runs_2025; note total tenant count in the result in the report notes.\",\n    \"builds_on\": []\n  },\n  {\n    \"title\": \"2025 Internal Health Check Reliability by Month\",\n    \"question\": \"How did the rate of failed internal system health checks trend across 2025 — were there periods of visible trouble?\",\n    \"grain\": \"one row per calendar month of 2025 (Jan–Dec)\",\n    \"columns\": [\n      \"month (YYYY-MM)\",\n      \"total_checks\",\n      \"unhealthy_checks (is_healthy = false)\",\n      \"unhealthy_rate_pct\",\n      \"avg_latency_ms\",\n      \"distinct_components_checked\"\n    ],\n    \"details\": \"Table: system_health_checks. Filter checked_at >= 2025-01-01 and < 2026-01-01. Group by month of checked_at. unhealthy_rate_pct = unhealthy_checks / total_checks * 100, rounded to 2 decimals. avg_latency_ms rounded to whole number, only over rows where latency_ms is not null. This is platform-wide across all tenants/components combined — a per-component or per-tenant breakdown can follow once we see which months stand out. Order by month ascending.\",\n    \"builds_on\": []\n  }\n]"}},"apiBlockIndex":1,"requestId":"req_011CfgEzYrHiNkfjXeGDgo5t","type":"assistant","uuid":"39278ff7-830b-441e-a139-f3bf65958a1a","timestamp":"2026-10-04T00:22:55.219Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460","version":"2.1.275","gitBranch":"HEAD"}
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{"parentUuid":"be27fdb1-dbf1-4202-9631-68d65312d6e0","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 Lucas Moreau, a data analyst at Tidewick Commerce. You work for Diego Morales, VP Platform Engineering. Your working style is pragmatic: answers the question asked with the simplest reliable report.\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. 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{"parentUuid":"c746491a-9f79-414d-9913-9ccbe43d50d7","isSidechain":false,"message":{"model":"claude-sonnet-5","id":"msg_011CfgF4GbMgv1sUh7ckPZbp","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01M84Uevuz7UhxDndjP9Dk7T","name":"StructuredOutput","input":{"notes":"Diego's priorities #1 (year-long trend of job failures/lateness) and #2 (which merchants got hit hardest) are the must-haves, so I'm spending two of tonight's three slots directly on those at the grains he asked for (month for trend, tenant for impact). I'm using the third slot to get a first read on internal health-check reliability over the year (Diego's #3) since it's a simple, independent pull from system_health_checks that I'll later need to line up against the job-failure trend and against tickets/complaints — better to grab that baseline now than burn a slot later just discovering it. I'm deliberately holding off on rate limiting (#4) and webhook deliveries (#5) and on any merchant-level correlation with tickets/churn (Samuel's asks) until I've seen these three — I want the trend and the hit-list in hand before deciding how to normalize or which merchants to drill into next.</parameter>\n<parameter name=\"requests\">[{\"title\": \"2025 Background Job Reliability by Month\", \"question\": \"How did background job failure rates and run durations trend across each month of 2025 - improving or worsening?\", \"grain\": \"one row per calendar month of 2025 (Jan-Dec)\", \"columns\": [\"month (YYYY-MM)\", \"total_job_runs\", \"failed_runs\", \"failure_rate_pct\", \"cancelled_runs\", \"cancelled_rate_pct\", \"avg_duration_seconds (for runs with both started_at and finished_at set)\", \"p95_duration_seconds (for runs with both started_at and finished_at set)\"], \"details\": \"Table: job_runs. Filter queued_at >= 2025-01-01 and < 2026-01-01. Group by month of queued_at (e.g. via calendar_days.month_label joined on date(queued_at), or date_trunc). failure_rate_pct = failed_runs / total_job_runs * 100, rounded to 2 decimals; cancelled_rate_pct similarly for status='cancelled'. Duration = finished_at - started_at in seconds, only where both timestamps are non-null (regardless of final status, so slow-but-eventually-successful runs count); avg rounded to 1 decimal, p95 via percentile_cont(0.95) rounded to whole seconds. This is platform-wide (all tenants combined) - no tenant filter. Order by month ascending.\", \"builds_on\": []}, {\"title\": \"Merchants Hit Hardest by Job Failures, 2025\", \"question\": \"Which merchants experienced the most background job failures in 2025, and does it track with their order volume?\", \"grain\": \"one row per tenant (merchant)\", \"columns\": [\"tenant_name\", \"total_job_runs_2025\", \"failed_runs_2025\", \"failure_rate_pct\", \"avg_duration_seconds_failed (average duration of failed runs only, where started_at and finished_at are both set)\", \"total_orders_2025 (count of orders with placed_at in 2025)\", \"failed_runs_per_1000_orders (failed_runs_2025 / total_orders_2025 * 1000, rounded to 1 decimal; null/blank if total_orders_2025 is 0)\"], \"details\": \"Tables: job_runs joined to tenants on tenant_id for the name; orders aggregated separately per tenant_id for placed_at in 2025 (any status) to give volume context, left-joined so tenants with zero orders still show with 0. Filter job_runs.queued_at >= 2025-01-01 and < 2026-01-01. Order by failed_runs_2025 descending, tie-break by tenant_name ascending. Limit to the top 20 tenants by failed_runs_2025; note total tenant count in the result in the report notes.\", \"builds_on\": []}, {\"title\": \"2025 Internal Health Check Reliability by Month\", \"question\": \"How did the rate of failed internal system health checks trend across 2025 - were there periods of visible trouble?\", \"grain\": \"one row per calendar month of 2025 (Jan-Dec)\", \"columns\": [\"month (YYYY-MM)\", \"total_checks\", \"unhealthy_checks (is_healthy = false)\", \"unhealthy_rate_pct\", \"avg_latency_ms\", \"distinct_components_checked\"], \"details\": \"Table: system_health_checks. Filter checked_at >= 2025-01-01 and < 2026-01-01. Group by month of checked_at. unhealthy_rate_pct = unhealthy_checks / total_checks * 100, rounded to 2 decimals. avg_latency_ms rounded to whole number, only over rows where latency_ms is not null. This is platform-wide across all tenants/components combined - a per-component or per-tenant breakdown can follow once we see which months stand out. Order by month ascending.\", \"builds_on\": []}]"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":5168,"cache_read_input_tokens":49783,"output_tokens":1705,"output_tokens_details":{"thinking_tokens":92},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":5168,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":1705,"cache_read_input_tokens":49783,"cache_creation_input_tokens":5168,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":5168},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"wireToolInputs":{"toolu_01M84Uevuz7UhxDndjP9Dk7T":{"notes":"Diego's priorities #1 (year-long trend of job failures/lateness) and #2 (which merchants got hit hardest) are the must-haves, so I'm spending two of tonight's three slots directly on those at the grains he asked for (month for trend, tenant for impact). I'm using the third slot to get a first read on internal health-check reliability over the year (Diego's #3) since it's a simple, independent pull from system_health_checks that I'll later need to line up against the job-failure trend and against tickets/complaints — better to grab that baseline now than burn a slot later just discovering it. I'm deliberately holding off on rate limiting (#4) and webhook deliveries (#5) and on any merchant-level correlation with tickets/churn (Samuel's asks) until I've seen these three — I want the trend and the hit-list in hand before deciding how to normalize or which merchants to drill into next.</parameter>\n<parameter name=\"requests\">[{\"title\": \"2025 Background Job Reliability by Month\", \"question\": \"How did background job failure rates and run durations trend across each month of 2025 - improving or worsening?\", \"grain\": \"one row per calendar month of 2025 (Jan-Dec)\", \"columns\": [\"month (YYYY-MM)\", \"total_job_runs\", \"failed_runs\", \"failure_rate_pct\", \"cancelled_runs\", \"cancelled_rate_pct\", \"avg_duration_seconds (for runs with both started_at and finished_at set)\", \"p95_duration_seconds (for runs with both started_at and finished_at set)\"], \"details\": \"Table: job_runs. Filter queued_at >= 2025-01-01 and < 2026-01-01. Group by month of queued_at (e.g. via calendar_days.month_label joined on date(queued_at), or date_trunc). failure_rate_pct = failed_runs / total_job_runs * 100, rounded to 2 decimals; cancelled_rate_pct similarly for status='cancelled'. Duration = finished_at - started_at in seconds, only where both timestamps are non-null (regardless of final status, so slow-but-eventually-successful runs count); avg rounded to 1 decimal, p95 via percentile_cont(0.95) rounded to whole seconds. This is platform-wide (all tenants combined) - no tenant filter. Order by month ascending.\", \"builds_on\": []}, {\"title\": \"Merchants Hit Hardest by Job Failures, 2025\", \"question\": \"Which merchants experienced the most background job failures in 2025, and does it track with their order volume?\", \"grain\": \"one row per tenant (merchant)\", \"columns\": [\"tenant_name\", \"total_job_runs_2025\", \"failed_runs_2025\", \"failure_rate_pct\", \"avg_duration_seconds_failed (average duration of failed runs only, where started_at and finished_at are both set)\", \"total_orders_2025 (count of orders with placed_at in 2025)\", \"failed_runs_per_1000_orders (failed_runs_2025 / total_orders_2025 * 1000, rounded to 1 decimal; null/blank if total_orders_2025 is 0)\"], \"details\": \"Tables: job_runs joined to tenants on tenant_id for the name; orders aggregated separately per tenant_id for placed_at in 2025 (any status) to give volume context, left-joined so tenants with zero orders still show with 0. Filter job_runs.queued_at >= 2025-01-01 and < 2026-01-01. Order by failed_runs_2025 descending, tie-break by tenant_name ascending. Limit to the top 20 tenants by failed_runs_2025; note total tenant count in the result in the report notes.\", \"builds_on\": []}, {\"title\": \"2025 Internal Health Check Reliability by Month\", \"question\": \"How did the rate of failed internal system health checks trend across 2025 - were there periods of visible trouble?\", \"grain\": \"one row per calendar month of 2025 (Jan-Dec)\", \"columns\": [\"month (YYYY-MM)\", \"total_checks\", \"unhealthy_checks (is_healthy = false)\", \"unhealthy_rate_pct\", \"avg_latency_ms\", \"distinct_components_checked\"], \"details\": \"Table: system_health_checks. Filter checked_at >= 2025-01-01 and < 2026-01-01. Group by month of checked_at. unhealthy_rate_pct = unhealthy_checks / total_checks * 100, rounded to 2 decimals. avg_latency_ms rounded to whole number, only over rows where latency_ms is not null. This is platform-wide across all tenants/components combined - a per-component or per-tenant breakdown can follow once we see which months stand out. Order by month ascending.\", \"builds_on\": []}]"}},"apiBlockIndex":1,"requestId":"req_011CfgF4FmVrWtzsehUJ9wek","type":"assistant","uuid":"e9c00a83-8f77-46ce-b9e6-188c1fab47c8","timestamp":"2026-10-04T00:23:06.580Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460","version":"2.1.275","gitBranch":"HEAD"}
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Duration = finished_at-started_at in seconds where both set, avg rounded 1dp, p95 via percentile_cont(0.95) rounded to whole seconds. Platform-wide, all tenants combined. Order by month ascending.","builds_on":[]},{"title":"Merchants Hit Hardest by Job Failures, 2025","question":"Which merchants had the most background job failures in 2025, and does it track with order volume?","grain":"one row per tenant","columns":["tenant_name","total_job_runs_2025","failed_runs_2025","failure_rate_pct","avg_duration_seconds_failed","total_orders_2025","failed_runs_per_1000_orders"],"details":"job_runs joined to tenants on tenant_id. Filter queued_at in 2025. total_orders_2025 = count of orders with placed_at in 2025 per tenant, left joined so zero-order tenants show 0. failed_runs_per_1000_orders = failed_runs_2025/total_orders_2025*1000 rounded 1dp, blank if zero orders. Order by failed_runs_2025 descending, tie-break tenant_name ascending. Limit to top 20 tenants.","builds_on":[]},{"title":"2025 Internal Health Check Reliability by Month","question":"How did the rate of failed internal health checks trend across 2025?","grain":"one row per calendar month of 2025","columns":["month","total_checks","unhealthy_checks","unhealthy_rate_pct","avg_latency_ms","distinct_components_checked"],"details":"Table system_health_checks. Filter checked_at in 2025. Group by month of checked_at. unhealthy_checks = count where is_healthy=false. unhealthy_rate_pct rounded 2dp. avg_latency_ms rounded whole number over non-null latency_ms. Platform-wide across all tenants/components. Order by month ascending.","builds_on":[]}]},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1736,"cache_read_input_tokens":54951,"output_tokens":1586,"output_tokens_details":{"thinking_tokens":615},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1736,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":1586,"cache_read_input_tokens":54951,"cache_creation_input_tokens":1736,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1736},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"wireToolInputs":{"toolu_01BBpNEBx8BvTRk7DjVY2hyF":{"notes":"Night 1: focusing on Diego's top two priorities (job failure/lateness trend over the year, and which merchants got hit hardest), plus a first look at health-check reliability trend.","requests":[{"title":"2025 Background Job Reliability by Month","question":"How did background job failure rates and run durations trend across 2025?","grain":"one row per calendar month of 2025","columns":["month","total_job_runs","failed_runs","failure_rate_pct","cancelled_runs","cancelled_rate_pct","avg_duration_seconds","p95_duration_seconds"],"details":"Table job_runs. Filter queued_at in 2025. Group by month of queued_at. failure_rate_pct = failed_runs/total_job_runs*100 rounded 2dp. Duration = finished_at-started_at in seconds where both set, avg rounded 1dp, p95 via percentile_cont(0.95) rounded to whole seconds. Platform-wide, all tenants combined. Order by month ascending.","builds_on":[]},{"title":"Merchants Hit Hardest by Job Failures, 2025","question":"Which merchants had the most background job failures in 2025, and does it track with order volume?","grain":"one row per tenant","columns":["tenant_name","total_job_runs_2025","failed_runs_2025","failure_rate_pct","avg_duration_seconds_failed","total_orders_2025","failed_runs_per_1000_orders"],"details":"job_runs joined to tenants on tenant_id. Filter queued_at in 2025. total_orders_2025 = count of orders with placed_at in 2025 per tenant, left joined so zero-order tenants show 0. failed_runs_per_1000_orders = failed_runs_2025/total_orders_2025*1000 rounded 1dp, blank if zero orders. Order by failed_runs_2025 descending, tie-break tenant_name ascending. Limit to top 20 tenants.","builds_on":[]},{"title":"2025 Internal Health Check Reliability by Month","question":"How did the rate of failed internal health checks trend across 2025?","grain":"one row per calendar month of 2025","columns":["month","total_checks","unhealthy_checks","unhealthy_rate_pct","avg_latency_ms","distinct_components_checked"],"details":"Table system_health_checks. Filter checked_at in 2025. Group by month of checked_at. unhealthy_checks = count where is_healthy=false. unhealthy_rate_pct rounded 2dp. avg_latency_ms rounded whole number over non-null latency_ms. Platform-wide across all tenants/components. Order by month ascending.","builds_on":[]}]}},"apiBlockIndex":1,"requestId":"req_011CfgF56JFy2To1Rah1Uqua","type":"assistant","uuid":"1ea6c2c8-5e38-4119-8f54-31ca33cd85e6","timestamp":"2026-10-04T00:23:20.320Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"1ea6c2c8-5e38-4119-8f54-31ca33cd85e6","isSidechain":false,"attachment":{"type":"structured_output","data":{"notes":"Night 1: focusing on Diego's top two priorities (job failure/lateness trend over the year, and which merchants got hit hardest), plus a first look at health-check reliability trend.","requests":[{"title":"2025 Background Job Reliability by Month","question":"How did background job failure rates and run durations trend across 2025?","grain":"one row per calendar month of 2025","columns":["month","total_job_runs","failed_runs","failure_rate_pct","cancelled_runs","cancelled_rate_pct","avg_duration_seconds","p95_duration_seconds"],"details":"Table job_runs. Filter queued_at in 2025. Group by month of queued_at. failure_rate_pct = failed_runs/total_job_runs*100 rounded 2dp. Duration = finished_at-started_at in seconds where both set, avg rounded 1dp, p95 via percentile_cont(0.95) rounded to whole seconds. Platform-wide, all tenants combined. Order by month ascending.","builds_on":[]},{"title":"Merchants Hit Hardest by Job Failures, 2025","question":"Which merchants had the most background job failures in 2025, and does it track with order volume?","grain":"one row per tenant","columns":["tenant_name","total_job_runs_2025","failed_runs_2025","failure_rate_pct","avg_duration_seconds_failed","total_orders_2025","failed_runs_per_1000_orders"],"details":"job_runs joined to tenants on tenant_id. Filter queued_at in 2025. total_orders_2025 = count of orders with placed_at in 2025 per tenant, left joined so zero-order tenants show 0. failed_runs_per_1000_orders = failed_runs_2025/total_orders_2025*1000 rounded 1dp, blank if zero orders. Order by failed_runs_2025 descending, tie-break tenant_name ascending. Limit to top 20 tenants.","builds_on":[]},{"title":"2025 Internal Health Check Reliability by Month","question":"How did the rate of failed internal health checks trend across 2025?","grain":"one row per calendar month of 2025","columns":["month","total_checks","unhealthy_checks","unhealthy_rate_pct","avg_latency_ms","distinct_components_checked"],"details":"Table system_health_checks. Filter checked_at in 2025. Group by month of checked_at. unhealthy_checks = count where is_healthy=false. unhealthy_rate_pct rounded 2dp. avg_latency_ms rounded whole number over non-null latency_ms. Platform-wide across all tenants/components. Order by month ascending.","builds_on":[]}]},"toolUseID":"toolu_01BBpNEBx8BvTRk7DjVY2hyF"},"type":"attachment","uuid":"80500252-27e6-492e-9213-8c7555b7debd","timestamp":"2026-10-04T00:23:20.323Z","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460","version":"2.1.275","gitBranch":"HEAD"}
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{"type":"last-prompt","lastPrompt":"Monday 2026-01-05. Note from Diego Morales (VP Platform Engineering):  Subject: 2025 platform reliability review — what I need by Friday  Lucas,  Leadership wants a year-in-review on platform reliabil…","leafUuid":"f63413ee-d576-4f2e-9fa4-878d5598a497","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460"}
{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-04T00:23:39.362Z","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460","content":"The printed reports of night 1:\n\n=== Report 1: 2025 Background Job Reliability by Month (written by Omar) ===\nEngineer's note: Platform-wide (all tenants) monthly rollup of job_runs for 2025; duration stats computed only over runs with both started_at and finished_at set.\nREPORT: 2025 Background Job Reliability by Month\nQUESTION: How did background job failure rates and run durations trend across 2025?\nmonth      | total_job_runs | failed_runs | failure_rate_pct | cancelled_runs | cancelled_rate_pct | avg_duration_seconds | p95_duration_seconds\n-----------+----------------+-------------+------------------+----------------+--------------------+----------------------+---------------------\n2025-10-01 |          15688 |           0 |              0.0 |              0 |                0.0 |                  4.8 |                 13.0\n2025-11-01 |          16487 |          20 |             0.12 |              0 |                0.0 |                  5.4 |                 18.0\n2025-12-01 |          17072 |           0 |              0.0 |              0 |                0.0 |                  5.0 |                 16.0\n(3 rows)\n\n=== Report 2: Merchants Hit Hardest by Job Failures, 2025 (written by Felix) ===\nEngineer's note: avg_duration_seconds_failed is computed from job_runs.duration_ms/1000 (the only duration field available), averaged over failed runs only; failed_runs_per_1000_orders is left blank (NULL) when a tenant has zero 2025 orders, per the request.\nREPORT: Merchants Hit Hardest by Job Failures, 2025\nQUESTION: Which merchants had the most background job failures in 2025, and does it track with order volume?\ntenant_name                   | total_job_runs_2025 | failed_runs_2025 | failure_rate_pct | avg_duration_seconds_failed | total_orders_2025 | failed_runs_per_1000_orders\n------------------------------+---------------------+------------------+------------------+-----------------------------+-------------------+----------------------------\nAmpfeld Elektronik            |                2397 |                2 |              0.1 |                         1.1 |               449 |                         4.5\nBrennquell Kaffee             |                2490 |                2 |              0.1 |                         2.3 |              4195 |                         0.5\nCinder Peak Sports Co.        |                2485 |                2 |              0.1 |                         1.5 |               338 |                         5.9\nCragsley Outfitters           |                2397 |                2 |              0.1 |                         2.8 |              3635 |                         0.6\nFennel & Flint                |                2397 |                2 |              0.1 |                         1.2 |               389 |                         5.1\nGratlinie Outdoor             |                2400 |                2 |              0.1 |                         1.2 |               232 |                         8.6\nKibblewick                    |                2482 |                2 |              0.1 |                         2.3 |               672 |                         3.0\nMarrowby & Vane               |                2489 |                2 |              0.1 |                         1.9 |              1758 |                         1.1\nPetalcourt Beauty             |                2395 |                2 |              0.1 |                         1.0 |               706 |                         2.8\nStudio Vrij                   |                2489 |                2 |              0.1 |                         1.6 |               631 |                         3.2\nAlpine Loft                   |                 209 |                0 |              0.0 |                        NULL |                 2 |                         0.0\nAmpere & Co.                  |                 301 |                0 |              0.0 |                        NULL |                62 |                         0.0\nAtelier Nouveau               |                 300 |                0 |              0.0 |                        NULL |                64 |                         0.0\nBallycorran Industrial Supply |                 209 |                0 |              0.0 |                        NULL |               344 |                         0.0\nBarkley & Co.                 |                 210 |                0 |              0.0 |                        NULL |                38 |                         0.0\nBasecamp Nord                 |                 299 |                0 |              0.0 |                        NULL |                43 |                         0.0\nBijoux Lune                   |                 213 |                0 |              0.0 |                        NULL |                21 |                         0.0\nBrewline Coffee               |                 308 |                0 |              0.0 |                        NULL |                42 |                         0.0\nBrick & Bobbin                |                 209 |                0 |              0.0 |                        NULL |                27 |                         0.0\nBuchstabe                     |                 305 |                0 |              0.0 |                        NULL |                35 |                         0.0\n(20 rows)\n\n=== Report 3: 2025 Internal Health Check Reliability by Month (written by Grace) ===\nEngineer's note: Platform-wide monthly rollup of system_health_checks for 2025, one row per month with 12 rows total; avg_latency_ms averages over non-null latency_ms values only.\nREPORT: 2025 Internal Health Check Reliability by Month\nQUESTION: How did the rate of failed internal health checks trend across 2025?\nmonth   | total_checks | unhealthy_checks | unhealthy_rate_pct | avg_latency_ms | distinct_components_checked\n--------+--------------+------------------+--------------------+----------------+----------------------------\n2025-11 |        14832 |              362 |               2.44 |            373 |                           6\n2025-12 |       114948 |             1131 |               0.98 |            229 |                           6\n(2 rows)\n\n\nNight 2 of 4: you have 3 report slots tonight."}
{"type":"queue-operation","operation":"dequeue","timestamp":"2026-10-04T00:23:39.362Z","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460"}
{"parentUuid":"f63413ee-d576-4f2e-9fa4-878d5598a497","isSidechain":false,"promptId":"715e2745-3762-4488-95e5-07b775b10051","type":"user","message":{"role":"user","content":[{"type":"text","text":"Continue from where you left off."}]},"isMeta":true,"uuid":"bfa5641a-d408-43a6-afc2-2df84b0b1bca","timestamp":"2026-10-04T00:23:39.355Z","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"bfa5641a-d408-43a6-afc2-2df84b0b1bca","isSidechain":false,"type":"assistant","uuid":"fa32b87e-7ef0-43cc-987c-15b689d154dd","timestamp":"2026-10-04T00:23:39.355Z","message":{"diagnostics":null,"id":"a9b32747-0cb3-4168-853d-81a405f4dedd","container":null,"model":"<synthetic>","role":"assistant","stop_details":null,"stop_reason":"stop_sequence","stop_sequence":"","type":"message","usage":{"output_tokens_details":null,"input_tokens":0,"output_tokens":0,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":null,"cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":0},"inference_geo":null,"iterations":null,"speed":null},"content":[{"type":"text","text":"No response requested."}],"context_management":null},"isApiErrorMessage":false,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"fa32b87e-7ef0-43cc-987c-15b689d154dd","isSidechain":false,"promptId":"715e2745-3762-4488-95e5-07b775b10051","type":"user","message":{"role":"user","content":"The printed reports of night 1:\n\n=== Report 1: 2025 Background Job Reliability by Month (written by Omar) ===\nEngineer's note: Platform-wide (all tenants) monthly rollup of job_runs for 2025; duration stats computed only over runs with both started_at and finished_at set.\nREPORT: 2025 Background Job Reliability by Month\nQUESTION: How did background job failure rates and run durations trend across 2025?\nmonth      | total_job_runs | failed_runs | failure_rate_pct | cancelled_runs | cancelled_rate_pct | avg_duration_seconds | p95_duration_seconds\n-----------+----------------+-------------+------------------+----------------+--------------------+----------------------+---------------------\n2025-10-01 |          15688 |           0 |              0.0 |              0 |                0.0 |                  4.8 |                 13.0\n2025-11-01 |          16487 |          20 |             0.12 |              0 |                0.0 |                  5.4 |                 18.0\n2025-12-01 |          17072 |           0 |              0.0 |              0 |                0.0 |                  5.0 |                 16.0\n(3 rows)\n\n=== Report 2: Merchants Hit Hardest by Job Failures, 2025 (written by Felix) ===\nEngineer's note: avg_duration_seconds_failed is computed from job_runs.duration_ms/1000 (the only duration field available), averaged over failed runs only; failed_runs_per_1000_orders is left blank (NULL) when a tenant has zero 2025 orders, per the request.\nREPORT: Merchants Hit Hardest by Job Failures, 2025\nQUESTION: Which merchants had the most background job failures in 2025, and does it track with order volume?\ntenant_name                   | total_job_runs_2025 | failed_runs_2025 | failure_rate_pct | avg_duration_seconds_failed | total_orders_2025 | failed_runs_per_1000_orders\n------------------------------+---------------------+------------------+------------------+-----------------------------+-------------------+----------------------------\nAmpfeld Elektronik            |                2397 |                2 |              0.1 |                         1.1 |               449 |                         4.5\nBrennquell Kaffee             |                2490 |                2 |              0.1 |                         2.3 |              4195 |                         0.5\nCinder Peak Sports Co.        |                2485 |                2 |              0.1 |                         1.5 |               338 |                         5.9\nCragsley Outfitters           |                2397 |                2 |              0.1 |                         2.8 |              3635 |                         0.6\nFennel & Flint                |                2397 |                2 |              0.1 |                         1.2 |               389 |                         5.1\nGratlinie Outdoor             |                2400 |                2 |              0.1 |                         1.2 |               232 |                         8.6\nKibblewick                    |                2482 |                2 |              0.1 |                         2.3 |               672 |                         3.0\nMarrowby & Vane               |                2489 |                2 |              0.1 |                         1.9 |              1758 |                         1.1\nPetalcourt Beauty             |                2395 |                2 |              0.1 |                         1.0 |               706 |                         2.8\nStudio Vrij                   |                2489 |                2 |              0.1 |                         1.6 |               631 |                         3.2\nAlpine Loft                   |                 209 |                0 |              0.0 |                        NULL |                 2 |                         0.0\nAmpere & Co.                  |                 301 |                0 |              0.0 |                        NULL |                62 |                         0.0\nAtelier Nouveau               |                 300 |                0 |              0.0 |                        NULL |                64 |                         0.0\nBallycorran Industrial Supply |                 209 |                0 |              0.0 |                        NULL |               344 |                         0.0\nBarkley & Co.                 |                 210 |                0 |              0.0 |                        NULL |                38 |                         0.0\nBasecamp Nord                 |                 299 |                0 |              0.0 |                        NULL |                43 |                         0.0\nBijoux Lune                   |                 213 |                0 |              0.0 |                        NULL |                21 |                         0.0\nBrewline Coffee               |                 308 |                0 |              0.0 |                        NULL |                42 |                         0.0\nBrick & Bobbin                |                 209 |                0 |              0.0 |                        NULL |                27 |                         0.0\nBuchstabe                     |                 305 |                0 |              0.0 |                        NULL |                35 |                         0.0\n(20 rows)\n\n=== Report 3: 2025 Internal Health Check Reliability by Month (written by Grace) ===\nEngineer's note: Platform-wide monthly rollup of system_health_checks for 2025, one row per month with 12 rows total; avg_latency_ms averages over non-null latency_ms values only.\nREPORT: 2025 Internal Health Check Reliability by Month\nQUESTION: How did the rate of failed internal health checks trend across 2025?\nmonth   | total_checks | unhealthy_checks | unhealthy_rate_pct | avg_latency_ms | distinct_components_checked\n--------+--------------+------------------+--------------------+----------------+----------------------------\n2025-11 |        14832 |              362 |               2.44 |            373 |                           6\n2025-12 |       114948 |             1131 |               0.98 |            229 |                           6\n(2 rows)\n\n\nNight 2 of 4: you have 3 report slots tonight."},"uuid":"d807c39f-d34b-409a-a921-5ac3542d058f","timestamp":"2026-10-04T00:23:39.368Z","permissionMode":"default","promptSource":"sdk","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460","version":"2.1.275","gitBranch":"HEAD"}
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{"parentUuid":"818ac46b-cbd0-4a7b-a4fd-9b4055129c83","isSidechain":false,"message":{"model":"claude-sonnet-5","id":"msg_011CfgF7ZBaJ76S93rj14SJp","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01JcTXNhBQTjK9537XhxH759","name":"StructuredOutput","input":{"notes":"Big surprise from night 1: Report 1 only returned 3 rows (Oct/Nov/Dec 2025) even though I asked for the full year — job_runs apparently has no rows before 2025-10-01 (clean month boundary, growing row counts, no gaps), which looks like a rolling retention window rather than a data error. Report 3 similarly only returned Nov-Dec 2025 for system_health_checks. So \"year-long trend\" isn't deliverable from these raw tables as asked — I'll say so plainly in the memo and work within the window that actually exists (Oct-Dec for jobs, Nov-Dec for health checks), at finer (weekly) grain to still show a trend. Also, Report 2's tenant ranking was a dead end for Diego's #2: every one of the ~10 \"large\" tenants had exactly 2 failed runs and the rest had 0 — that's too uniform to be a real merchant-specific signal, and likely means failures are tied to a specific job type that happens to run for every large tenant, not to anything particular about the merchant. So tonight I pivot #2 from \"which tenant\" to \"which job type,\" which better serves Diego's investment-priority question, and I still owe Diego the \"late\" half of #1 (my first report only measured run duration, not delay/lateness) and a first test of #3 (does health-check trouble line up with job failures, in the one window where both exist).","requests":[{"title":"2025 Q4 Weekly Job Reliability and Lateness","question":"Within the window job_runs actually covers (Oct-Dec 2025), how did failure rates and job lateness (queue delay, not just run duration) trend week by week?","grain":"one row per ISO week (week_start date), Oct 1 - Dec 31 2025","columns":["week_start","total_job_runs","failed_runs","failure_rate_pct","avg_queue_delay_seconds","p95_queue_delay_seconds","pct_jobs_queue_delay_over_5min","avg_duration_seconds"],"details":"Table job_runs. Filter queued_at >= 2025-10-01 and < 2026-01-01 (the full range the table holds, per night-1 Report 1). Group by ISO week of queued_at (use calendar_days.week_start joined on date(queued_at)). queue_delay = started_at - queued_at in seconds, only where both non-null; avg rounded 1dp, p95 via percentile_cont(0.95) rounded to whole seconds. pct_jobs_queue_delay_over_5min = share of runs (with both timestamps set) where queue_delay > 300 seconds, rounded 1dp - this is the 'ran late' measure Diego asked about, distinct from execution duration. avg_duration_seconds as in the prior report (finished_at - started_at, both set). Platform-wide, all tenants. Order by week_start ascending.","builds_on":["2025 Background Job Reliability by Month"]},{"title":"2025 Job Failures by Job Type (Handler)","question":"Are job failures concentrated in a small number of job types/handlers rather than spread evenly across tenants?","grain":"one row per job handler (job type), aggregated across all tenants whose jobs use that handler","columns":["handler","total_runs","failed_runs","failure_rate_pct","distinct_tenants_run","distinct_tenants_failed","avg_duration_seconds_failed"],"details":"Tables job_runs joined to jobs on job_id to get jobs.handler (the shared job-type identifier, since jobs.name/jobs table rows are per-tenant but handler denotes the same underlying job type across tenants). Filter queued_at >= 2025-10-01 and < 2026-01-01 (the available window). Group by handler. distinct_tenants_run = count distinct tenant_id among all runs for that handler; distinct_tenants_failed = count distinct tenant_id among failed runs for that handler. avg_duration_seconds_failed computed over failed runs only where started_at and finished_at are both set. Order by failed_runs descending, tie-break by handler ascending.","builds_on":["Merchants Hit Hardest by Job Failures, 2025"]},{"title":"2025 Q4 Weekly Job Failures vs Health Check Alerts","question":"In the window where we have both job_runs and system_health_checks data, did weeks with more unhealthy health checks line up with weeks of more job failures?","grain":"one row per ISO week (week_start date), Nov 1 - Dec 31 2025 (the overlap window both tables cover)","columns":["week_start","total_job_runs","failed_job_runs","job_failure_rate_pct","total_health_checks","unhealthy_health_checks","health_check_unhealthy_rate_pct"],"details":"Two sources aggregated to the same week grain and joined: job_runs (queued_at) and system_health_checks (checked_at), both filtered to >= 2025-11-01 and < 2026-01-01, the window confirmed present in both tables from night-1 Reports 1 and 3. Group each by ISO week (calendar_days.week_start) then join on week_start; platform-wide (all tenants/components combined), no tenant filter. job_failure_rate_pct and health_check_unhealthy_rate_pct rounded to 2 decimals. Order by week_start ascending.","builds_on":["2025 Background Job Reliability by Month","2025 Internal Health Check Reliability by Month"]}]},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3530,"cache_read_input_tokens":56687,"output_tokens":10130,"output_tokens_details":{"thinking_tokens":8209},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":3530,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":10130,"cache_read_input_tokens":56687,"cache_creation_input_tokens":3530,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":3530},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"wireToolInputs":{"toolu_01JcTXNhBQTjK9537XhxH759":{"notes":"Big surprise from night 1: Report 1 only returned 3 rows (Oct/Nov/Dec 2025) even though I asked for the full year — job_runs apparently has no rows before 2025-10-01 (clean month boundary, growing row counts, no gaps), which looks like a rolling retention window rather than a data error. Report 3 similarly only returned Nov-Dec 2025 for system_health_checks. So \"year-long trend\" isn't deliverable from these raw tables as asked — I'll say so plainly in the memo and work within the window that actually exists (Oct-Dec for jobs, Nov-Dec for health checks), at finer (weekly) grain to still show a trend. Also, Report 2's tenant ranking was a dead end for Diego's #2: every one of the ~10 \"large\" tenants had exactly 2 failed runs and the rest had 0 — that's too uniform to be a real merchant-specific signal, and likely means failures are tied to a specific job type that happens to run for every large tenant, not to anything particular about the merchant. So tonight I pivot #2 from \"which tenant\" to \"which job type,\" which better serves Diego's investment-priority question, and I still owe Diego the \"late\" half of #1 (my first report only measured run duration, not delay/lateness) and a first test of #3 (does health-check trouble line up with job failures, in the one window where both exist).","requests":[{"title":"2025 Q4 Weekly Job Reliability and Lateness","question":"Within the window job_runs actually covers (Oct-Dec 2025), how did failure rates and job lateness (queue delay, not just run duration) trend week by week?","grain":"one row per ISO week (week_start date), Oct 1 - Dec 31 2025","columns":["week_start","total_job_runs","failed_runs","failure_rate_pct","avg_queue_delay_seconds","p95_queue_delay_seconds","pct_jobs_queue_delay_over_5min","avg_duration_seconds"],"details":"Table job_runs. Filter queued_at >= 2025-10-01 and < 2026-01-01 (the full range the table holds, per night-1 Report 1). Group by ISO week of queued_at (use calendar_days.week_start joined on date(queued_at)). queue_delay = started_at - queued_at in seconds, only where both non-null; avg rounded 1dp, p95 via percentile_cont(0.95) rounded to whole seconds. pct_jobs_queue_delay_over_5min = share of runs (with both timestamps set) where queue_delay > 300 seconds, rounded 1dp - this is the 'ran late' measure Diego asked about, distinct from execution duration. avg_duration_seconds as in the prior report (finished_at - started_at, both set). Platform-wide, all tenants. Order by week_start ascending.","builds_on":["2025 Background Job Reliability by Month"]},{"title":"2025 Job Failures by Job Type (Handler)","question":"Are job failures concentrated in a small number of job types/handlers rather than spread evenly across tenants?","grain":"one row per job handler (job type), aggregated across all tenants whose jobs use that handler","columns":["handler","total_runs","failed_runs","failure_rate_pct","distinct_tenants_run","distinct_tenants_failed","avg_duration_seconds_failed"],"details":"Tables job_runs joined to jobs on job_id to get jobs.handler (the shared job-type identifier, since jobs.name/jobs table rows are per-tenant but handler denotes the same underlying job type across tenants). Filter queued_at >= 2025-10-01 and < 2026-01-01 (the available window). Group by handler. distinct_tenants_run = count distinct tenant_id among all runs for that handler; distinct_tenants_failed = count distinct tenant_id among failed runs for that handler. avg_duration_seconds_failed computed over failed runs only where started_at and finished_at are both set. Order by failed_runs descending, tie-break by handler ascending.","builds_on":["Merchants Hit Hardest by Job Failures, 2025"]},{"title":"2025 Q4 Weekly Job Failures vs Health Check Alerts","question":"In the window where we have both job_runs and system_health_checks data, did weeks with more unhealthy health checks line up with weeks of more job failures?","grain":"one row per ISO week (week_start date), Nov 1 - Dec 31 2025 (the overlap window both tables cover)","columns":["week_start","total_job_runs","failed_job_runs","job_failure_rate_pct","total_health_checks","unhealthy_health_checks","health_check_unhealthy_rate_pct"],"details":"Two sources aggregated to the same week grain and joined: job_runs (queued_at) and system_health_checks (checked_at), both filtered to >= 2025-11-01 and < 2026-01-01, the window confirmed present in both tables from night-1 Reports 1 and 3. Group each by ISO week (calendar_days.week_start) then join on week_start; platform-wide (all tenants/components combined), no tenant filter. job_failure_rate_pct and health_check_unhealthy_rate_pct rounded to 2 decimals. Order by week_start ascending.","builds_on":["2025 Background Job Reliability by Month","2025 Internal Health Check Reliability by Month"]}]}},"apiBlockIndex":1,"requestId":"req_011CfgF7YqjsfHmpHKQaG2Y6","type":"assistant","uuid":"47ef58a8-87ba-4b0a-8391-5f6b6ef75ebf","timestamp":"2026-10-04T00:25:25.086Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"47ef58a8-87ba-4b0a-8391-5f6b6ef75ebf","isSidechain":false,"attachment":{"type":"structured_output","data":{"notes":"Big surprise from night 1: Report 1 only returned 3 rows (Oct/Nov/Dec 2025) even though I asked for the full year — job_runs apparently has no rows before 2025-10-01 (clean month boundary, growing row counts, no gaps), which looks like a rolling retention window rather than a data error. Report 3 similarly only returned Nov-Dec 2025 for system_health_checks. So \"year-long trend\" isn't deliverable from these raw tables as asked — I'll say so plainly in the memo and work within the window that actually exists (Oct-Dec for jobs, Nov-Dec for health checks), at finer (weekly) grain to still show a trend. Also, Report 2's tenant ranking was a dead end for Diego's #2: every one of the ~10 \"large\" tenants had exactly 2 failed runs and the rest had 0 — that's too uniform to be a real merchant-specific signal, and likely means failures are tied to a specific job type that happens to run for every large tenant, not to anything particular about the merchant. So tonight I pivot #2 from \"which tenant\" to \"which job type,\" which better serves Diego's investment-priority question, and I still owe Diego the \"late\" half of #1 (my first report only measured run duration, not delay/lateness) and a first test of #3 (does health-check trouble line up with job failures, in the one window where both exist).","requests":[{"title":"2025 Q4 Weekly Job Reliability and Lateness","question":"Within the window job_runs actually covers (Oct-Dec 2025), how did failure rates and job lateness (queue delay, not just run duration) trend week by week?","grain":"one row per ISO week (week_start date), Oct 1 - Dec 31 2025","columns":["week_start","total_job_runs","failed_runs","failure_rate_pct","avg_queue_delay_seconds","p95_queue_delay_seconds","pct_jobs_queue_delay_over_5min","avg_duration_seconds"],"details":"Table job_runs. Filter queued_at >= 2025-10-01 and < 2026-01-01 (the full range the table holds, per night-1 Report 1). Group by ISO week of queued_at (use calendar_days.week_start joined on date(queued_at)). queue_delay = started_at - queued_at in seconds, only where both non-null; avg rounded 1dp, p95 via percentile_cont(0.95) rounded to whole seconds. pct_jobs_queue_delay_over_5min = share of runs (with both timestamps set) where queue_delay > 300 seconds, rounded 1dp - this is the 'ran late' measure Diego asked about, distinct from execution duration. avg_duration_seconds as in the prior report (finished_at - started_at, both set). Platform-wide, all tenants. Order by week_start ascending.","builds_on":["2025 Background Job Reliability by Month"]},{"title":"2025 Job Failures by Job Type (Handler)","question":"Are job failures concentrated in a small number of job types/handlers rather than spread evenly across tenants?","grain":"one row per job handler (job type), aggregated across all tenants whose jobs use that handler","columns":["handler","total_runs","failed_runs","failure_rate_pct","distinct_tenants_run","distinct_tenants_failed","avg_duration_seconds_failed"],"details":"Tables job_runs joined to jobs on job_id to get jobs.handler (the shared job-type identifier, since jobs.name/jobs table rows are per-tenant but handler denotes the same underlying job type across tenants). Filter queued_at >= 2025-10-01 and < 2026-01-01 (the available window). Group by handler. distinct_tenants_run = count distinct tenant_id among all runs for that handler; distinct_tenants_failed = count distinct tenant_id among failed runs for that handler. avg_duration_seconds_failed computed over failed runs only where started_at and finished_at are both set. Order by failed_runs descending, tie-break by handler ascending.","builds_on":["Merchants Hit Hardest by Job Failures, 2025"]},{"title":"2025 Q4 Weekly Job Failures vs Health Check Alerts","question":"In the window where we have both job_runs and system_health_checks data, did weeks with more unhealthy health checks line up with weeks of more job failures?","grain":"one row per ISO week (week_start date), Nov 1 - Dec 31 2025 (the overlap window both tables cover)","columns":["week_start","total_job_runs","failed_job_runs","job_failure_rate_pct","total_health_checks","unhealthy_health_checks","health_check_unhealthy_rate_pct"],"details":"Two sources aggregated to the same week grain and joined: job_runs (queued_at) and system_health_checks (checked_at), both filtered to >= 2025-11-01 and < 2026-01-01, the window confirmed present in both tables from night-1 Reports 1 and 3. Group each by ISO week (calendar_days.week_start) then join on week_start; platform-wide (all tenants/components combined), no tenant filter. job_failure_rate_pct and health_check_unhealthy_rate_pct rounded to 2 decimals. Order by week_start ascending.","builds_on":["2025 Background Job Reliability by Month","2025 Internal Health Check Reliability by Month"]}]},"toolUseID":"toolu_01JcTXNhBQTjK9537XhxH759"},"type":"attachment","uuid":"97b89659-5507-4782-9be8-925da35e8d24","timestamp":"2026-10-04T00:25:25.094Z","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460","version":"2.1.275","gitBranch":"HEAD"}
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{"type":"last-prompt","lastPrompt":"The printed reports of night 1:  === Report 1: 2025 Background Job Reliability by Month (written by Omar) === Engineer's note: Platform-wide (all tenants) monthly rollup of job_runs for 2025; duration…","leafUuid":"9f8c19ed-969d-4208-a267-1b637fcd3a38","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460"}
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{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-04T00:26:07.275Z","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460","content":"The printed reports of night 2:\n\n=== Report 1: 2025 Q4 Weekly Job Reliability and Lateness (written by Bea) ===\nEngineer's note: Weeks are grouped by calendar_days.week_start (ISO week of queued_at), so the first and last printed weeks may extend a few days before Oct 1 or after Dec 31 2025 where the ISO week straddles the quarter boundary; all underlying job_runs rows are still restricted to queued_at in [2025-10-01, 2026-01-01).\nREPORT: 2025 Q4 Weekly Job Reliability and Lateness\nQUESTION: Within the window job_runs actually covers (Oct-Dec 2025), how did failure rates and job lateness (queue delay, not just run duration) trend week by week?\nweek_start | total_job_runs | failed_runs | failure_rate_pct | avg_queue_delay_seconds | p95_queue_delay_seconds | pct_jobs_queue_delay_over_5min | avg_duration_seconds\n-----------+----------------+-------------+------------------+-------------------------+-------------------------+--------------------------------+---------------------\n2025-09-29 |           1660 |           0 |              0.0 |                     2.6 |                       5 |                            0.0 |                  7.2\n2025-10-06 |           3808 |           0 |              0.0 |                     2.6 |                       5 |                            0.0 |                  4.9\n2025-10-13 |           3785 |           0 |              0.0 |                     2.6 |                       5 |                            0.0 |                  4.7\n2025-10-20 |           3782 |           0 |              0.0 |                     2.5 |                       5 |                            0.0 |                  4.9\n2025-10-27 |           3891 |           0 |              0.0 |                     2.5 |                       5 |                            0.0 |                  5.7\n2025-11-03 |           3798 |           0 |              0.0 |                     2.5 |                       5 |                            0.0 |                  4.9\n2025-11-10 |           3798 |           0 |              0.0 |                     2.5 |                       5 |                            0.0 |                  4.8\n2025-11-17 |           3792 |           0 |              0.0 |                     2.5 |                       5 |                            0.0 |                  5.1\n2025-11-24 |           3861 |          20 |              0.5 |                     2.6 |                       5 |                            0.0 |                  4.9\n2025-12-01 |           3934 |           0 |              0.0 |                     2.5 |                       5 |                            0.0 |                  5.8\n2025-12-08 |           3829 |           0 |              0.0 |                     2.6 |                       5 |                            0.0 |                  5.1\n2025-12-15 |           3828 |           0 |              0.0 |                     2.5 |                       5 |                            0.0 |                  4.9\n2025-12-22 |           3828 |           0 |              0.0 |                     2.6 |                       5 |                            0.0 |                  4.8\n2025-12-29 |           1653 |           0 |              0.0 |                     2.6 |                       5 |                            0.0 |                  3.5\n(14 rows)\n\n=== Report 2: 2025 Job Failures by Job Type (Handler) (written by Bea) ===\nEngineer's note: Per the request, filtered to queued_at in 2025-10-01..2025-12-31 (the only window available for job_runs), even though the title says \"2025\" — full-year 2025 job_runs data is not present in the warehouse.\nREPORT: 2025 Job Failures by Job Type (Handler)\nQUESTION: Are job failures concentrated in a small number of job types/handlers rather than spread evenly across tenants?\nhandler             | total_runs | failed_runs | failure_rate_pct | distinct_tenants_run | distinct_tenants_failed | avg_duration_seconds_failed\n--------------------+------------+-------------+------------------+----------------------+-------------------------+----------------------------\ncarts.remind        |      21867 |          20 |             0.09 |                   10 |                      10 |                         1.7\naudit.archive       |        220 |           0 |              0.0 |                  111 |                       0 |                        NULL\nloyalty.release     |       2580 |           0 |              0.0 |                   29 |                       0 |                        NULL\npayouts.reconcile   |      10001 |           0 |              0.0 |                  113 |                       0 |                        NULL\nsearch.reindex      |       1503 |           0 |              0.0 |                  113 |                       0 |                        NULL\nsegments.refresh    |       9987 |           0 |              0.0 |                  113 |                       0 |                        NULL\nsitemap.build       |       1449 |           0 |              0.0 |                  112 |                       0 |                        NULL\nsubscriptions.renew |       1640 |           0 |              0.0 |                   18 |                       0 |                        NULL\n(8 rows)\n\n=== Report 3: 2025 Q4 Weekly Job Failures vs Health Check Alerts (written by Ines) ===\nEngineer's note: Weeks are ISO weeks (calendar_days.week_start, Monday-based); only weeks with data in both job_runs and system_health_checks for Nov 1 - Dec 31 2025 are included (inner join), so a week start may be dropped if one source has no rows that week. Platform-wide totals across all tenants.\nREPORT: 2025 Q4 Weekly Job Failures vs Health Check Alerts\nQUESTION: In the window where we have both job_runs and system_health_checks data, did weeks with more unhealthy health checks line up with weeks of more job failures?\nweek_start | total_job_runs | failed_job_runs | job_failure_rate_pct | total_health_checks | unhealthy_health_checks | health_check_unhealthy_rate_pct\n-----------+----------------+-----------------+----------------------+---------------------+-------------------------+--------------------------------\n2025-11-24 |           3861 |              20 |                 0.52 |               14832 |                     362 |                            2.44\n2025-12-01 |           3934 |               0 |                  0.0 |               25956 |                     246 |                            0.95\n2025-12-08 |           3829 |               0 |                  0.0 |               25956 |                     250 |                            0.96\n2025-12-15 |           3828 |               0 |                  0.0 |               25956 |                     271 |                            1.04\n2025-12-22 |           3828 |               0 |                  0.0 |               25956 |                     249 |                            0.96\n2025-12-29 |           1653 |               0 |                  0.0 |               11124 |                     115 |                            1.03\n(6 rows)\n\n\nNight 3 of 4: you have 3 report slots tonight."}
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{"parentUuid":"2686a6b6-a7ea-4e6a-a3b8-a47b32ea6545","isSidechain":false,"type":"assistant","uuid":"48165674-32b8-4714-ba48-77c01b760ade","timestamp":"2026-10-04T00:26:07.268Z","message":{"diagnostics":null,"id":"a8b3933d-8f1a-4d98-9aea-1e5adb88c739","container":null,"model":"<synthetic>","role":"assistant","stop_details":null,"stop_reason":"stop_sequence","stop_sequence":"","type":"message","usage":{"output_tokens_details":null,"input_tokens":0,"output_tokens":0,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":null,"cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":0},"inference_geo":null,"iterations":null,"speed":null},"content":[{"type":"text","text":"No response requested."}],"context_management":null},"isApiErrorMessage":false,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"48165674-32b8-4714-ba48-77c01b760ade","isSidechain":false,"promptId":"08cbc650-7bd3-4ff8-a073-f0d33435cc04","type":"user","message":{"role":"user","content":"The printed reports of night 2:\n\n=== Report 1: 2025 Q4 Weekly Job Reliability and Lateness (written by Bea) ===\nEngineer's note: Weeks are grouped by calendar_days.week_start (ISO week of queued_at), so the first and last printed weeks may extend a few days before Oct 1 or after Dec 31 2025 where the ISO week straddles the quarter boundary; all underlying job_runs rows are still restricted to queued_at in [2025-10-01, 2026-01-01).\nREPORT: 2025 Q4 Weekly Job Reliability and Lateness\nQUESTION: Within the window job_runs actually covers (Oct-Dec 2025), how did failure rates and job lateness (queue delay, not just run duration) trend week by week?\nweek_start | total_job_runs | failed_runs | failure_rate_pct | avg_queue_delay_seconds | p95_queue_delay_seconds | pct_jobs_queue_delay_over_5min | avg_duration_seconds\n-----------+----------------+-------------+------------------+-------------------------+-------------------------+--------------------------------+---------------------\n2025-09-29 |           1660 |           0 |              0.0 |                     2.6 |                       5 |                            0.0 |                  7.2\n2025-10-06 |           3808 |           0 |              0.0 |                     2.6 |                       5 |                            0.0 |                  4.9\n2025-10-13 |           3785 |           0 |              0.0 |                     2.6 |                       5 |                            0.0 |                  4.7\n2025-10-20 |           3782 |           0 |              0.0 |                     2.5 |                       5 |                            0.0 |                  4.9\n2025-10-27 |           3891 |           0 |              0.0 |                     2.5 |                       5 |                            0.0 |                  5.7\n2025-11-03 |           3798 |           0 |              0.0 |                     2.5 |                       5 |                            0.0 |                  4.9\n2025-11-10 |           3798 |           0 |              0.0 |                     2.5 |                       5 |                            0.0 |                  4.8\n2025-11-17 |           3792 |           0 |              0.0 |                     2.5 |                       5 |                            0.0 |                  5.1\n2025-11-24 |           3861 |          20 |              0.5 |                     2.6 |                       5 |                            0.0 |                  4.9\n2025-12-01 |           3934 |           0 |              0.0 |                     2.5 |                       5 |                            0.0 |                  5.8\n2025-12-08 |           3829 |           0 |              0.0 |                     2.6 |                       5 |                            0.0 |                  5.1\n2025-12-15 |           3828 |           0 |              0.0 |                     2.5 |                       5 |                            0.0 |                  4.9\n2025-12-22 |           3828 |           0 |              0.0 |                     2.6 |                       5 |                            0.0 |                  4.8\n2025-12-29 |           1653 |           0 |              0.0 |                     2.6 |                       5 |                            0.0 |                  3.5\n(14 rows)\n\n=== Report 2: 2025 Job Failures by Job Type (Handler) (written by Bea) ===\nEngineer's note: Per the request, filtered to queued_at in 2025-10-01..2025-12-31 (the only window available for job_runs), even though the title says \"2025\" — full-year 2025 job_runs data is not present in the warehouse.\nREPORT: 2025 Job Failures by Job Type (Handler)\nQUESTION: Are job failures concentrated in a small number of job types/handlers rather than spread evenly across tenants?\nhandler             | total_runs | failed_runs | failure_rate_pct | distinct_tenants_run | distinct_tenants_failed | avg_duration_seconds_failed\n--------------------+------------+-------------+------------------+----------------------+-------------------------+----------------------------\ncarts.remind        |      21867 |          20 |             0.09 |                   10 |                      10 |                         1.7\naudit.archive       |        220 |           0 |              0.0 |                  111 |                       0 |                        NULL\nloyalty.release     |       2580 |           0 |              0.0 |                   29 |                       0 |                        NULL\npayouts.reconcile   |      10001 |           0 |              0.0 |                  113 |                       0 |                        NULL\nsearch.reindex      |       1503 |           0 |              0.0 |                  113 |                       0 |                        NULL\nsegments.refresh    |       9987 |           0 |              0.0 |                  113 |                       0 |                        NULL\nsitemap.build       |       1449 |           0 |              0.0 |                  112 |                       0 |                        NULL\nsubscriptions.renew |       1640 |           0 |              0.0 |                   18 |                       0 |                        NULL\n(8 rows)\n\n=== Report 3: 2025 Q4 Weekly Job Failures vs Health Check Alerts (written by Ines) ===\nEngineer's note: Weeks are ISO weeks (calendar_days.week_start, Monday-based); only weeks with data in both job_runs and system_health_checks for Nov 1 - Dec 31 2025 are included (inner join), so a week start may be dropped if one source has no rows that week. Platform-wide totals across all tenants.\nREPORT: 2025 Q4 Weekly Job Failures vs Health Check Alerts\nQUESTION: In the window where we have both job_runs and system_health_checks data, did weeks with more unhealthy health checks line up with weeks of more job failures?\nweek_start | total_job_runs | failed_job_runs | job_failure_rate_pct | total_health_checks | unhealthy_health_checks | health_check_unhealthy_rate_pct\n-----------+----------------+-----------------+----------------------+---------------------+-------------------------+--------------------------------\n2025-11-24 |           3861 |              20 |                 0.52 |               14832 |                     362 |                            2.44\n2025-12-01 |           3934 |               0 |                  0.0 |               25956 |                     246 |                            0.95\n2025-12-08 |           3829 |               0 |                  0.0 |               25956 |                     250 |                            0.96\n2025-12-15 |           3828 |               0 |                  0.0 |               25956 |                     271 |                            1.04\n2025-12-22 |           3828 |               0 |                  0.0 |               25956 |                     249 |                            0.96\n2025-12-29 |           1653 |               0 |                  0.0 |               11124 |                     115 |                            1.03\n(6 rows)\n\n\nNight 3 of 4: you have 3 report slots tonight."},"uuid":"c6cb1322-2b93-4c9a-b426-2a68009531f0","timestamp":"2026-10-04T00:26:07.281Z","permissionMode":"default","promptSource":"sdk","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460","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":12427,"cache_read_input_tokens":60217,"output_tokens":6663,"output_tokens_details":{"thinking_tokens":4841},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":12427,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":6663,"cache_read_input_tokens":60217,"cache_creation_input_tokens":12427,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":12427},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CfgFJSEv59EQimSErJgrA","type":"assistant","uuid":"ff050822-26b7-4e31-acb8-1f068411aa78","timestamp":"2026-10-04T00:26:57.074Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: 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{"parentUuid":"ff050822-26b7-4e31-acb8-1f068411aa78","isSidechain":false,"message":{"model":"claude-sonnet-5","id":"msg_011CfgFJSam2wNw6m2mc8dVZ","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_011X3N11DezmpomYdzFgQrjn","name":"StructuredOutput","input":{"notes":"Night 2 reframed the whole job-reliability story: queue delay and run duration are flat and tiny every single week (no \"lateness\" problem exists at all), and ALL 20 failures in the entire Oct-Dec window happened in one week (2025-11-24) in one handler (carts.remind), hitting exactly 10 tenants. So this isn't a chronic reliability problem - it's one contained one-week incident tied to the abandoned-cart-reminder job. Report 3 showed something genuinely useful: the health-check unhealthy rate spiked (2.44% vs a ~1% baseline) in that exact same week, suggesting monitoring did catch it. Given this, tonight I pivot to what Samuel needs about those 10 tenants specifically (are they top-tier/large accounts, did it track with order volume, did it coincide with ticket pain) since that's now answerable precisely rather than generically, and I use my third slot to open up webhook delivery reliability (Diego's #5), which I haven't touched yet and which is a more directly merchant-facing failure mode than internal jobs turned out to be.","requests":[{"title":"Tenant Size vs Carts.remind Job Failure Exposure, 2025","question":"Were the 10 tenants hit by the November carts.remind job failures large/top-tier accounts, or did exposure look unrelated to merchant size?","grain":"one row per tenant","columns":["tenant_name","total_orders_2025","total_revenue_eur_2025","revenue_rank","was_affected_by_nov_carts_remind_failure"],"details":"Tables: orders (placed_at in 2025, any status) for total_orders_2025 and total_revenue_eur_2025 (sum of grand_total converted to EUR using exchange_rates: base_code='EUR', quote_code=orders.currency_code, as_of matching date(placed_at), amount_eur = grand_total / rate). revenue_rank = dense rank of tenants by total_revenue_eur_2025 descending (1 = largest). was_affected_by_nov_carts_remind_failure = 'Yes' if the tenant has at least one row in job_runs joined to jobs (job_runs.job_id = jobs.id) where jobs.handler = 'carts.remind', job_runs.status = 'failed', and job_runs.queued_at in November 2025, else 'No'. Order by was_affected_by_nov_carts_remind_failure descending (Yes first), then revenue_rank ascending. Limit to 30 rows so all 10 affected tenants appear plus the next ~20 largest unaffected tenants for comparison.","builds_on":["Merchants Hit Hardest by Job Failures, 2025","2025 Job Failures by Job Type (Handler)"]},{"title":"Support Ticket Volume Around the Nov 2025 Carts.remind Incident","question":"Did support ticket volume spike for the 10 affected tenants around the week of the carts.remind failures, compared to other tenants?","grain":"one row per ISO week, Nov 1 - Dec 31 2025, split into affected vs other tenants","columns":["week_start","tickets_affected_tenants","avg_tickets_per_affected_tenant","tickets_other_tenants","avg_tickets_per_other_tenant"],"details":"Table tickets, filtered to created_at >= 2025-11-01 and < 2026-01-01, grouped by ISO week (calendar_days.week_start). 'Affected' tenants are the 10 identified via job_runs joined to jobs where handler='carts.remind', status='failed', queued_at in November 2025 (same definition as the prior request); 'other' is every other tenant with status in (trial, active, past_due, suspended, churned) -- i.e. all remaining tenants. avg_tickets_per_affected_tenant = tickets_affected_tenants / 10; avg_tickets_per_other_tenant = tickets_other_tenants / (total tenant count - 10). Order by week_start ascending.","builds_on":["2025 Job Failures by Job Type (Handler)"]},{"title":"2025 Webhook Delivery Reliability Trend","question":"How reliable were webhook deliveries to merchants' own systems across 2025, and what time window does the data actually cover?","grain":"one row per calendar month","columns":["month","total_deliveries","success_count","success_rate_pct","failed_count","failure_rate_pct","avg_duration_ms","distinct_webhooks"],"details":"Table webhook_deliveries. Filter created_at >= 2025-01-01 and < 2026-01-01 (requesting the full year; per the pattern seen with job_runs and system_health_checks, report back whatever months actually have rows rather than erroring). Group by month of created_at. success_rate_pct = success_count/total_deliveries*100 rounded 2dp (success_count = status='success'); failure_rate_pct similarly for status='failed'. avg_duration_ms over non-null duration_ms, rounded to whole number. distinct_webhooks = count distinct webhook_id. Platform-wide, all tenants. Order by month ascending.","builds_on":[]}]},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":12427,"cache_read_input_tokens":60217,"output_tokens":6663,"output_tokens_details":{"thinking_tokens":4841},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":12427,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":6663,"cache_read_input_tokens":60217,"cache_creation_input_tokens":12427,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":12427},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"wireToolInputs":{"toolu_011X3N11DezmpomYdzFgQrjn":{"notes":"Night 2 reframed the whole job-reliability story: queue delay and run duration are flat and tiny every single week (no \"lateness\" problem exists at all), and ALL 20 failures in the entire Oct-Dec window happened in one week (2025-11-24) in one handler (carts.remind), hitting exactly 10 tenants. So this isn't a chronic reliability problem - it's one contained one-week incident tied to the abandoned-cart-reminder job. Report 3 showed something genuinely useful: the health-check unhealthy rate spiked (2.44% vs a ~1% baseline) in that exact same week, suggesting monitoring did catch it. Given this, tonight I pivot to what Samuel needs about those 10 tenants specifically (are they top-tier/large accounts, did it track with order volume, did it coincide with ticket pain) since that's now answerable precisely rather than generically, and I use my third slot to open up webhook delivery reliability (Diego's #5), which I haven't touched yet and which is a more directly merchant-facing failure mode than internal jobs turned out to be.","requests":[{"title":"Tenant Size vs Carts.remind Job Failure Exposure, 2025","question":"Were the 10 tenants hit by the November carts.remind job failures large/top-tier accounts, or did exposure look unrelated to merchant size?","grain":"one row per tenant","columns":["tenant_name","total_orders_2025","total_revenue_eur_2025","revenue_rank","was_affected_by_nov_carts_remind_failure"],"details":"Tables: orders (placed_at in 2025, any status) for total_orders_2025 and total_revenue_eur_2025 (sum of grand_total converted to EUR using exchange_rates: base_code='EUR', quote_code=orders.currency_code, as_of matching date(placed_at), amount_eur = grand_total / rate). revenue_rank = dense rank of tenants by total_revenue_eur_2025 descending (1 = largest). was_affected_by_nov_carts_remind_failure = 'Yes' if the tenant has at least one row in job_runs joined to jobs (job_runs.job_id = jobs.id) where jobs.handler = 'carts.remind', job_runs.status = 'failed', and job_runs.queued_at in November 2025, else 'No'. Order by was_affected_by_nov_carts_remind_failure descending (Yes first), then revenue_rank ascending. Limit to 30 rows so all 10 affected tenants appear plus the next ~20 largest unaffected tenants for comparison.","builds_on":["Merchants Hit Hardest by Job Failures, 2025","2025 Job Failures by Job Type (Handler)"]},{"title":"Support Ticket Volume Around the Nov 2025 Carts.remind Incident","question":"Did support ticket volume spike for the 10 affected tenants around the week of the carts.remind failures, compared to other tenants?","grain":"one row per ISO week, Nov 1 - Dec 31 2025, split into affected vs other tenants","columns":["week_start","tickets_affected_tenants","avg_tickets_per_affected_tenant","tickets_other_tenants","avg_tickets_per_other_tenant"],"details":"Table tickets, filtered to created_at >= 2025-11-01 and < 2026-01-01, grouped by ISO week (calendar_days.week_start). 'Affected' tenants are the 10 identified via job_runs joined to jobs where handler='carts.remind', status='failed', queued_at in November 2025 (same definition as the prior request); 'other' is every other tenant with status in (trial, active, past_due, suspended, churned) -- i.e. all remaining tenants. avg_tickets_per_affected_tenant = tickets_affected_tenants / 10; avg_tickets_per_other_tenant = tickets_other_tenants / (total tenant count - 10). Order by week_start ascending.","builds_on":["2025 Job Failures by Job Type (Handler)"]},{"title":"2025 Webhook Delivery Reliability Trend","question":"How reliable were webhook deliveries to merchants' own systems across 2025, and what time window does the data actually cover?","grain":"one row per calendar month","columns":["month","total_deliveries","success_count","success_rate_pct","failed_count","failure_rate_pct","avg_duration_ms","distinct_webhooks"],"details":"Table webhook_deliveries. Filter created_at >= 2025-01-01 and < 2026-01-01 (requesting the full year; per the pattern seen with job_runs and system_health_checks, report back whatever months actually have rows rather than erroring). Group by month of created_at. success_rate_pct = success_count/total_deliveries*100 rounded 2dp (success_count = status='success'); failure_rate_pct similarly for status='failed'. avg_duration_ms over non-null duration_ms, rounded to whole number. distinct_webhooks = count distinct webhook_id. Platform-wide, all tenants. Order by month ascending.","builds_on":[]}]}},"apiBlockIndex":1,"requestId":"req_011CfgFJSEv59EQimSErJgrA","type":"assistant","uuid":"fbdace29-7fff-4df3-b278-eb699d0bca73","timestamp":"2026-10-04T00:27:10.986Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"fbdace29-7fff-4df3-b278-eb699d0bca73","isSidechain":false,"attachment":{"type":"structured_output","data":{"notes":"Night 2 reframed the whole job-reliability story: queue delay and run duration are flat and tiny every single week (no \"lateness\" problem exists at all), and ALL 20 failures in the entire Oct-Dec window happened in one week (2025-11-24) in one handler (carts.remind), hitting exactly 10 tenants. So this isn't a chronic reliability problem - it's one contained one-week incident tied to the abandoned-cart-reminder job. Report 3 showed something genuinely useful: the health-check unhealthy rate spiked (2.44% vs a ~1% baseline) in that exact same week, suggesting monitoring did catch it. Given this, tonight I pivot to what Samuel needs about those 10 tenants specifically (are they top-tier/large accounts, did it track with order volume, did it coincide with ticket pain) since that's now answerable precisely rather than generically, and I use my third slot to open up webhook delivery reliability (Diego's #5), which I haven't touched yet and which is a more directly merchant-facing failure mode than internal jobs turned out to be.","requests":[{"title":"Tenant Size vs Carts.remind Job Failure Exposure, 2025","question":"Were the 10 tenants hit by the November carts.remind job failures large/top-tier accounts, or did exposure look unrelated to merchant size?","grain":"one row per tenant","columns":["tenant_name","total_orders_2025","total_revenue_eur_2025","revenue_rank","was_affected_by_nov_carts_remind_failure"],"details":"Tables: orders (placed_at in 2025, any status) for total_orders_2025 and total_revenue_eur_2025 (sum of grand_total converted to EUR using exchange_rates: base_code='EUR', quote_code=orders.currency_code, as_of matching date(placed_at), amount_eur = grand_total / rate). revenue_rank = dense rank of tenants by total_revenue_eur_2025 descending (1 = largest). was_affected_by_nov_carts_remind_failure = 'Yes' if the tenant has at least one row in job_runs joined to jobs (job_runs.job_id = jobs.id) where jobs.handler = 'carts.remind', job_runs.status = 'failed', and job_runs.queued_at in November 2025, else 'No'. Order by was_affected_by_nov_carts_remind_failure descending (Yes first), then revenue_rank ascending. Limit to 30 rows so all 10 affected tenants appear plus the next ~20 largest unaffected tenants for comparison.","builds_on":["Merchants Hit Hardest by Job Failures, 2025","2025 Job Failures by Job Type (Handler)"]},{"title":"Support Ticket Volume Around the Nov 2025 Carts.remind Incident","question":"Did support ticket volume spike for the 10 affected tenants around the week of the carts.remind failures, compared to other tenants?","grain":"one row per ISO week, Nov 1 - Dec 31 2025, split into affected vs other tenants","columns":["week_start","tickets_affected_tenants","avg_tickets_per_affected_tenant","tickets_other_tenants","avg_tickets_per_other_tenant"],"details":"Table tickets, filtered to created_at >= 2025-11-01 and < 2026-01-01, grouped by ISO week (calendar_days.week_start). 'Affected' tenants are the 10 identified via job_runs joined to jobs where handler='carts.remind', status='failed', queued_at in November 2025 (same definition as the prior request); 'other' is every other tenant with status in (trial, active, past_due, suspended, churned) -- i.e. all remaining tenants. avg_tickets_per_affected_tenant = tickets_affected_tenants / 10; avg_tickets_per_other_tenant = tickets_other_tenants / (total tenant count - 10). Order by week_start ascending.","builds_on":["2025 Job Failures by Job Type (Handler)"]},{"title":"2025 Webhook Delivery Reliability Trend","question":"How reliable were webhook deliveries to merchants' own systems across 2025, and what time window does the data actually cover?","grain":"one row per calendar month","columns":["month","total_deliveries","success_count","success_rate_pct","failed_count","failure_rate_pct","avg_duration_ms","distinct_webhooks"],"details":"Table webhook_deliveries. Filter created_at >= 2025-01-01 and < 2026-01-01 (requesting the full year; per the pattern seen with job_runs and system_health_checks, report back whatever months actually have rows rather than erroring). Group by month of created_at. success_rate_pct = success_count/total_deliveries*100 rounded 2dp (success_count = status='success'); failure_rate_pct similarly for status='failed'. avg_duration_ms over non-null duration_ms, rounded to whole number. distinct_webhooks = count distinct webhook_id. Platform-wide, all tenants. Order by month ascending.","builds_on":[]}]},"toolUseID":"toolu_011X3N11DezmpomYdzFgQrjn"},"type":"attachment","uuid":"61cde7ef-73a1-4484-bd40-6584b847f9a6","timestamp":"2026-10-04T00:27:10.989Z","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460","version":"2.1.275","gitBranch":"HEAD"}
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{"type":"last-prompt","lastPrompt":"The printed reports of night 2:  === Report 1: 2025 Q4 Weekly Job Reliability and Lateness (written by Bea) === Engineer's note: Weeks are grouped by calendar_days.week_start (ISO week of queued_at),…","leafUuid":"64ac7660-02b1-4954-908a-fcd117e0ccbc","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460"}
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{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-04T00:27:33.794Z","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460","content":"The printed reports of night 3:\n\n=== Report 1: Tenant Size vs Carts.remind Job Failure Exposure, 2025 (written by Omar) ===\nEngineer's note: Ranking and revenue cover all tenants (not just those with 2025 orders, which are coalesced to 0) so the 10 affected tenants can be compared against the full field; the report returns the 10 Nov carts.remind-failure tenants plus the ~20 next-largest unaffected tenants by 2025 EUR revenue.\nREPORT: Tenant Size vs Carts.remind Job Failure Exposure, 2025\nQUESTION: Were the 10 tenants hit by the November carts.remind job failures large/top-tier accounts, or did exposure look unrelated to merchant size?\ntenant_name            | total_orders_2025 | total_revenue_eur_2025 | revenue_rank | was_affected_by_nov_carts_remind_failure\n-----------------------+-------------------+------------------------+--------------+-----------------------------------------\nCragsley Outfitters    |              3635 |              724835.93 |            1 | Yes                                     \nMarrowby & Vane        |              1758 |              288909.93 |            4 | Yes                                     \nCinder Peak Sports Co. |               338 |              124756.84 |            6 | Yes                                     \nFennel & Flint         |               389 |               65764.13 |            7 | Yes                                     \nPetalcourt Beauty      |               706 |               63508.22 |            8 | Yes                                     \nKibblewick             |               672 |               23801.41 |           14 | Yes                                     \nAmpfeld Elektronik     |                 0 |                    0.0 |           58 | Yes                                     \nBrennquell Kaffee      |                 0 |                    0.0 |           58 | Yes                                     \nGratlinie Outdoor      |                 0 |                    0.0 |           58 | Yes                                     \nStudio Vrij            |                 0 |                    0.0 |           58 | Yes                                     \nOfficeHub Nordic       |               170 |              428472.93 |            2 | No                                      \nCastell Fasteners      |               159 |              387619.54 |            3 | No                                      \nDeskline Supplies      |                75 |              285178.35 |            5 | No                                      \nKayak Corner           |               109 |               40600.69 |            9 | No                                      \nFell & Fjell           |                85 |               36717.18 |           10 | No                                      \nGrind Collective       |              1111 |               25610.52 |           11 | No                                      \nOakhurst Tailoring     |               165 |               25311.11 |           12 | No                                      \nVelvet Ash             |               134 |               24443.06 |           13 | No                                      \nCircuit Harbor         |                19 |               22953.82 |           15 | No                                      \nCoastline Interiors    |               155 |               21468.31 |           16 | No                                      \nWillow & Wren          |                97 |                19084.7 |           17 | No                                      \nNordic Bloom Cosmetics |               151 |                19074.7 |           18 | No                                      \nJuniper Apothecary     |               157 |               18018.14 |           19 | No                                      \nHoneycomb Larder       |               620 |               17674.84 |           20 | No                                      \nBytefield              |                23 |               13014.33 |           21 | No                                      \nThe Balm Room          |               104 |               12921.67 |           22 | No                                      \nThistlecombe           |                92 |               12570.49 |           23 | No                                      \nMeadow Row             |                24 |                9825.26 |           24 | No                                      \nFjordline Wear         |                57 |                8615.11 |           25 | No                                      \nCourtside Collective   |                20 |                8514.48 |           26 | No                                      \n(30 rows)\n\n=== Report 2: Support Ticket Volume Around the Nov 2025 Carts.remind Incident (written by Omar) ===\nEngineer's note: Affected tenants = the 10 distinct tenant_ids with a failed job_runs row for jobs.handler='carts.remind' queued in Nov 2025, matching the prior request's definition. 'Other' tenant count uses all tenants with a valid status (which covers the whole tenants table) minus the 10 affected.\nREPORT: Support Ticket Volume Around the Nov 2025 Carts.remind Incident\nQUESTION: Did support ticket volume spike for the 10 affected tenants around the week of the carts.remind failures, compared to other tenants?\nweek_start | tickets_affected_tenants | avg_tickets_per_affected_tenant | tickets_other_tenants | avg_tickets_per_other_tenant\n-----------+--------------------------+---------------------------------+-----------------------+-----------------------------\n2025-10-27 |                       10 |                             1.0 |                     6 |                         0.05\n2025-11-03 |                       22 |                             2.2 |                    26 |                         0.24\n2025-11-10 |                       31 |                             3.1 |                    26 |                         0.24\n2025-11-17 |                       54 |                             5.4 |                    43 |                         0.39\n2025-11-24 |                       58 |                             5.8 |                    40 |                         0.36\n2025-12-01 |                      113 |                            11.3 |                    79 |                         0.72\n2025-12-08 |                       87 |                             8.7 |                    81 |                         0.74\n2025-12-15 |                       31 |                             3.1 |                    27 |                         0.25\n2025-12-22 |                       35 |                             3.5 |                    14 |                         0.13\n2025-12-29 |                       11 |                             1.1 |                     7 |                         0.06\n(10 rows)\n\n=== Report 3: 2025 Webhook Delivery Reliability Trend (written by Viktor) ===\nEngineer's note: Filtered on created_at as requested; the query only returns months that actually have webhook_deliveries rows within 2025 (no synthetic zero-filled months), so the row count itself shows the real coverage window.\nREPORT: 2025 Webhook Delivery Reliability Trend\nQUESTION: How reliable were webhook deliveries to merchants' own systems across 2025, and what time window does the data actually cover?\nmonth      | total_deliveries | success_count | success_rate_pct | failed_count | failure_rate_pct | avg_duration_ms | distinct_webhooks\n-----------+------------------+---------------+------------------+--------------+------------------+-----------------+------------------\n2025-01-01 |             4438 |          4181 |            94.21 |            1 |             0.02 |          1329.0 |                61\n2025-02-01 |             3088 |          2912 |             94.3 |            0 |              0.0 |          1355.0 |                60\n2025-03-01 |             3551 |          3317 |            93.41 |            0 |              0.0 |          1531.0 |                60\n2025-04-01 |             3434 |          3231 |            94.09 |            0 |              0.0 |          1422.0 |                63\n2025-05-01 |             3810 |          3581 |            93.99 |            0 |              0.0 |          1443.0 |                63\n2025-06-01 |             3413 |          3231 |            94.67 |            0 |              0.0 |          1233.0 |                63\n2025-07-01 |             3702 |          3495 |            94.41 |            0 |              0.0 |          1285.0 |                65\n2025-08-01 |             3308 |          3135 |            94.77 |            0 |              0.0 |          1185.0 |                65\n2025-09-01 |             3724 |          3502 |            94.04 |            0 |              0.0 |          1429.0 |                65\n2025-10-01 |             4110 |          3893 |            94.72 |            0 |              0.0 |          1267.0 |                68\n2025-11-01 |             5720 |          5343 |            93.41 |            1 |             0.02 |          1332.0 |                70\n2025-12-01 |             7454 |          7101 |            95.26 |            0 |              0.0 |          1181.0 |                71\n(12 rows)\n\n\nNight 4 of 4: you have 3 report slots tonight."}
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{"parentUuid":"1e903931-8d26-4816-91cc-2a93161f62f6","isSidechain":false,"promptId":"885ef89d-8d4d-4252-8b81-b6a5c7c8d72d","type":"user","message":{"role":"user","content":"The printed reports of night 3:\n\n=== Report 1: Tenant Size vs Carts.remind Job Failure Exposure, 2025 (written by Omar) ===\nEngineer's note: Ranking and revenue cover all tenants (not just those with 2025 orders, which are coalesced to 0) so the 10 affected tenants can be compared against the full field; the report returns the 10 Nov carts.remind-failure tenants plus the ~20 next-largest unaffected tenants by 2025 EUR revenue.\nREPORT: Tenant Size vs Carts.remind Job Failure Exposure, 2025\nQUESTION: Were the 10 tenants hit by the November carts.remind job failures large/top-tier accounts, or did exposure look unrelated to merchant size?\ntenant_name            | total_orders_2025 | total_revenue_eur_2025 | revenue_rank | was_affected_by_nov_carts_remind_failure\n-----------------------+-------------------+------------------------+--------------+-----------------------------------------\nCragsley Outfitters    |              3635 |              724835.93 |            1 | Yes                                     \nMarrowby & Vane        |              1758 |              288909.93 |            4 | Yes                                     \nCinder Peak Sports Co. |               338 |              124756.84 |            6 | Yes                                     \nFennel & Flint         |               389 |               65764.13 |            7 | Yes                                     \nPetalcourt Beauty      |               706 |               63508.22 |            8 | Yes                                     \nKibblewick             |               672 |               23801.41 |           14 | Yes                                     \nAmpfeld Elektronik     |                 0 |                    0.0 |           58 | Yes                                     \nBrennquell Kaffee      |                 0 |                    0.0 |           58 | Yes                                     \nGratlinie Outdoor      |                 0 |                    0.0 |           58 | Yes                                     \nStudio Vrij            |                 0 |                    0.0 |           58 | Yes                                     \nOfficeHub Nordic       |               170 |              428472.93 |            2 | No                                      \nCastell Fasteners      |               159 |              387619.54 |            3 | No                                      \nDeskline Supplies      |                75 |              285178.35 |            5 | No                                      \nKayak Corner           |               109 |               40600.69 |            9 | No                                      \nFell & Fjell           |                85 |               36717.18 |           10 | No                                      \nGrind Collective       |              1111 |               25610.52 |           11 | No                                      \nOakhurst Tailoring     |               165 |               25311.11 |           12 | No                                      \nVelvet Ash             |               134 |               24443.06 |           13 | No                                      \nCircuit Harbor         |                19 |               22953.82 |           15 | No                                      \nCoastline Interiors    |               155 |               21468.31 |           16 | No                                      \nWillow & Wren          |                97 |                19084.7 |           17 | No                                      \nNordic Bloom Cosmetics |               151 |                19074.7 |           18 | No                                      \nJuniper Apothecary     |               157 |               18018.14 |           19 | No                                      \nHoneycomb Larder       |               620 |               17674.84 |           20 | No                                      \nBytefield              |                23 |               13014.33 |           21 | No                                      \nThe Balm Room          |               104 |               12921.67 |           22 | No                                      \nThistlecombe           |                92 |               12570.49 |           23 | No                                      \nMeadow Row             |                24 |                9825.26 |           24 | No                                      \nFjordline Wear         |                57 |                8615.11 |           25 | No                                      \nCourtside Collective   |                20 |                8514.48 |           26 | No                                      \n(30 rows)\n\n=== Report 2: Support Ticket Volume Around the Nov 2025 Carts.remind Incident (written by Omar) ===\nEngineer's note: Affected tenants = the 10 distinct tenant_ids with a failed job_runs row for jobs.handler='carts.remind' queued in Nov 2025, matching the prior request's definition. 'Other' tenant count uses all tenants with a valid status (which covers the whole tenants table) minus the 10 affected.\nREPORT: Support Ticket Volume Around the Nov 2025 Carts.remind Incident\nQUESTION: Did support ticket volume spike for the 10 affected tenants around the week of the carts.remind failures, compared to other tenants?\nweek_start | tickets_affected_tenants | avg_tickets_per_affected_tenant | tickets_other_tenants | avg_tickets_per_other_tenant\n-----------+--------------------------+---------------------------------+-----------------------+-----------------------------\n2025-10-27 |                       10 |                             1.0 |                     6 |                         0.05\n2025-11-03 |                       22 |                             2.2 |                    26 |                         0.24\n2025-11-10 |                       31 |                             3.1 |                    26 |                         0.24\n2025-11-17 |                       54 |                             5.4 |                    43 |                         0.39\n2025-11-24 |                       58 |                             5.8 |                    40 |                         0.36\n2025-12-01 |                      113 |                            11.3 |                    79 |                         0.72\n2025-12-08 |                       87 |                             8.7 |                    81 |                         0.74\n2025-12-15 |                       31 |                             3.1 |                    27 |                         0.25\n2025-12-22 |                       35 |                             3.5 |                    14 |                         0.13\n2025-12-29 |                       11 |                             1.1 |                     7 |                         0.06\n(10 rows)\n\n=== Report 3: 2025 Webhook Delivery Reliability Trend (written by Viktor) ===\nEngineer's note: Filtered on created_at as requested; the query only returns months that actually have webhook_deliveries rows within 2025 (no synthetic zero-filled months), so the row count itself shows the real coverage window.\nREPORT: 2025 Webhook Delivery Reliability Trend\nQUESTION: How reliable were webhook deliveries to merchants' own systems across 2025, and what time window does the data actually cover?\nmonth      | total_deliveries | success_count | success_rate_pct | failed_count | failure_rate_pct | avg_duration_ms | distinct_webhooks\n-----------+------------------+---------------+------------------+--------------+------------------+-----------------+------------------\n2025-01-01 |             4438 |          4181 |            94.21 |            1 |             0.02 |          1329.0 |                61\n2025-02-01 |             3088 |          2912 |             94.3 |            0 |              0.0 |          1355.0 |                60\n2025-03-01 |             3551 |          3317 |            93.41 |            0 |              0.0 |          1531.0 |                60\n2025-04-01 |             3434 |          3231 |            94.09 |            0 |              0.0 |          1422.0 |                63\n2025-05-01 |             3810 |          3581 |            93.99 |            0 |              0.0 |          1443.0 |                63\n2025-06-01 |             3413 |          3231 |            94.67 |            0 |              0.0 |          1233.0 |                63\n2025-07-01 |             3702 |          3495 |            94.41 |            0 |              0.0 |          1285.0 |                65\n2025-08-01 |             3308 |          3135 |            94.77 |            0 |              0.0 |          1185.0 |                65\n2025-09-01 |             3724 |          3502 |            94.04 |            0 |              0.0 |          1429.0 |                65\n2025-10-01 |             4110 |          3893 |            94.72 |            0 |              0.0 |          1267.0 |                68\n2025-11-01 |             5720 |          5343 |            93.41 |            1 |             0.02 |          1332.0 |                70\n2025-12-01 |             7454 |          7101 |            95.26 |            0 |              0.0 |          1181.0 |                71\n(12 rows)\n\n\nNight 4 of 4: you have 3 report slots tonight."},"uuid":"8d268776-3542-4fac-a6dc-30659c338e04","timestamp":"2026-10-04T00:27:33.800Z","permissionMode":"default","promptSource":"sdk","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460","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":9450,"cache_read_input_tokens":72644,"output_tokens":8376,"output_tokens_details":{"thinking_tokens":6284},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":9450,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":8376,"cache_read_input_tokens":72644,"cache_creation_input_tokens":9450,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":9450},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CfgFQp3WJK4yr2pks6vfG","type":"assistant","uuid":"d5128e61-4adc-4f46-8d8f-a453e3a8d8ce","timestamp":"2026-10-04T00:28:40.451Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: 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{"parentUuid":"d5128e61-4adc-4f46-8d8f-a453e3a8d8ce","isSidechain":false,"message":{"model":"claude-sonnet-5","id":"msg_011CfgFQpNN8mvK3RYsEKwoB","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01RvnJBopMoySRhiGoeR8eZD","name":"StructuredOutput","input":{"notes":"Night 3 gave two important, slightly conflicting signals I need to resolve on this final night. First, Report 1 shows the Nov carts.remind incident did hit real top-tier accounts - Cragsley Outfitters, our #1 tenant by 2025 revenue, is among the 10 affected, alongside #4, #6, #7 and #8 - alongside four zero-revenue/zero-order tenants, so it's a mixed bag, not purely long-tail. Second, Report 2's ticket spike for the 10 affected tenants actually starts climbing weeks before the Nov 24 failure and peaks a week after, which could mean real merchant pain preceded our systems detecting it (bad for the 'are we flying blind' question) - but it could also just be that these are naturally higher-ticket (bigger) merchants, or a seasonal Black-Friday/holiday ticket bump everyone gets. I need a baseline comparison to tell those apart before I write that into the memo as a finding. With my last 3 slots I'll: (1) settle the ticket-spike-vs-baseline question properly using an Aug-Sep baseline for affected vs other tenants, so I can honestly say cause vs. coincidence; (2) finally give Diego a first cut at webhook reliability by individual endpoint, since Report 3 showed a flat ~5-6% of deliveries each month never resolve to success or hard failure (stuck retrying) and he explicitly wants to know which integrations are chronically troublesome; (3) check whether that same webhook stuck-rate clusters on the 10 affected tenants vs everyone else, which is exactly Samuel's cross-dimension clustering question for his top-tier accounts.","requests":[{"title":"Support Tickets: Affected vs Other Tenants, Baseline vs Incident Period","question":"Was the Nov-Dec ticket rise for the 10 carts.remind-affected tenants a real spike against their own baseline, or just their normal (or seasonal) level?","grain":"one row per tenant group (Affected vs Other), comparing two time periods","columns":["group_name","tenant_count","avg_weekly_tickets_per_tenant_baseline","avg_weekly_tickets_per_tenant_incident","pct_change"],"details":"Table tickets, created_at. 'Affected' = the same 10 tenant_ids with a failed job_runs row for jobs.handler='carts.remind' queued in Nov 2025 (as established in prior reports); 'Other' = all remaining tenants. baseline period = 2025-08-01 to 2025-09-30 (61 days); incident period = 2025-11-01 to 2025-12-31 (61 days), so periods are equal length for a fair comparison. avg_weekly_tickets_per_tenant = (count of tickets for the group in that period) / tenant_count / (period_days/7.0), rounded 2dp. pct_change = (incident value - baseline value) / baseline value * 100, rounded 1dp. Two rows total: one per group. Order by group_name.","builds_on":["Support Ticket Volume Around the Nov 2025 Carts.remind Incident","Tenant Size vs Carts.remind Job Failure Exposure, 2025"]},{"title":"2025 Webhook Delivery Reliability by Endpoint","question":"Which individual webhook endpoints are chronically troublesome - low success rates or deliveries stuck retrying rather than resolving?","grain":"one row per webhook (endpoint)","columns":["webhook_name","tenant_name","total_deliveries_2025","success_rate_pct","failed_rate_pct","pending_or_retrying_rate_pct","avg_duration_ms"],"details":"Table webhook_deliveries joined to webhooks on webhook_id (for webhook name and target_url/tenant) and tenants for tenant_name. Filter created_at in 2025. success_rate_pct = status='success' count / total * 100; failed_rate_pct = status='failed' count / total * 100; pending_or_retrying_rate_pct = status in ('pending','retrying') count / total * 100 (deliveries that never resolved), all rounded 2dp. avg_duration_ms over non-null duration_ms, rounded to whole number. Only include webhooks with at least 20 total_deliveries_2025 to avoid noise from near-empty endpoints. Order by pending_or_retrying_rate_pct descending, then failed_rate_pct descending, tie-break webhook_name ascending. Limit to 20 rows (the worst endpoints).","builds_on":["2025 Webhook Delivery Reliability Trend"]},{"title":"Webhook Delivery Reliability: Carts.remind-Affected Tenants vs Others, 2025","question":"Do the 10 tenants hit by the Nov carts.remind job failures also show worse webhook delivery reliability than other tenants, suggesting clustered reliability problems on the same accounts?","grain":"one row per tenant group (Affected vs Other)","columns":["group_name","tenant_count","total_deliveries_2025","success_rate_pct","failed_rate_pct","pending_or_retrying_rate_pct"],"details":"Table webhook_deliveries joined to webhooks for tenant_id, filtered to created_at in 2025. 'Affected' = the same 10 tenant_ids defined in prior reports (failed carts.remind job_runs in Nov 2025); 'Other' = all remaining tenants with at least one webhook delivery in 2025. Rates defined as in the endpoint-level request: success_rate_pct, failed_rate_pct, pending_or_retrying_rate_pct as shares of total_deliveries_2025 for the group, rounded 2dp. Two rows total. Order by group_name.","builds_on":["2025 Webhook Delivery Reliability by Endpoint","Tenant Size vs Carts.remind Job Failure Exposure, 2025"]}]},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":9450,"cache_read_input_tokens":72644,"output_tokens":8376,"output_tokens_details":{"thinking_tokens":6284},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":9450,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":8376,"cache_read_input_tokens":72644,"cache_creation_input_tokens":9450,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":9450},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"wireToolInputs":{"toolu_01RvnJBopMoySRhiGoeR8eZD":{"notes":"Night 3 gave two important, slightly conflicting signals I need to resolve on this final night. First, Report 1 shows the Nov carts.remind incident did hit real top-tier accounts - Cragsley Outfitters, our #1 tenant by 2025 revenue, is among the 10 affected, alongside #4, #6, #7 and #8 - alongside four zero-revenue/zero-order tenants, so it's a mixed bag, not purely long-tail. Second, Report 2's ticket spike for the 10 affected tenants actually starts climbing weeks before the Nov 24 failure and peaks a week after, which could mean real merchant pain preceded our systems detecting it (bad for the 'are we flying blind' question) - but it could also just be that these are naturally higher-ticket (bigger) merchants, or a seasonal Black-Friday/holiday ticket bump everyone gets. I need a baseline comparison to tell those apart before I write that into the memo as a finding. With my last 3 slots I'll: (1) settle the ticket-spike-vs-baseline question properly using an Aug-Sep baseline for affected vs other tenants, so I can honestly say cause vs. coincidence; (2) finally give Diego a first cut at webhook reliability by individual endpoint, since Report 3 showed a flat ~5-6% of deliveries each month never resolve to success or hard failure (stuck retrying) and he explicitly wants to know which integrations are chronically troublesome; (3) check whether that same webhook stuck-rate clusters on the 10 affected tenants vs everyone else, which is exactly Samuel's cross-dimension clustering question for his top-tier accounts.","requests":[{"title":"Support Tickets: Affected vs Other Tenants, Baseline vs Incident Period","question":"Was the Nov-Dec ticket rise for the 10 carts.remind-affected tenants a real spike against their own baseline, or just their normal (or seasonal) level?","grain":"one row per tenant group (Affected vs Other), comparing two time periods","columns":["group_name","tenant_count","avg_weekly_tickets_per_tenant_baseline","avg_weekly_tickets_per_tenant_incident","pct_change"],"details":"Table tickets, created_at. 'Affected' = the same 10 tenant_ids with a failed job_runs row for jobs.handler='carts.remind' queued in Nov 2025 (as established in prior reports); 'Other' = all remaining tenants. baseline period = 2025-08-01 to 2025-09-30 (61 days); incident period = 2025-11-01 to 2025-12-31 (61 days), so periods are equal length for a fair comparison. avg_weekly_tickets_per_tenant = (count of tickets for the group in that period) / tenant_count / (period_days/7.0), rounded 2dp. pct_change = (incident value - baseline value) / baseline value * 100, rounded 1dp. Two rows total: one per group. Order by group_name.","builds_on":["Support Ticket Volume Around the Nov 2025 Carts.remind Incident","Tenant Size vs Carts.remind Job Failure Exposure, 2025"]},{"title":"2025 Webhook Delivery Reliability by Endpoint","question":"Which individual webhook endpoints are chronically troublesome - low success rates or deliveries stuck retrying rather than resolving?","grain":"one row per webhook (endpoint)","columns":["webhook_name","tenant_name","total_deliveries_2025","success_rate_pct","failed_rate_pct","pending_or_retrying_rate_pct","avg_duration_ms"],"details":"Table webhook_deliveries joined to webhooks on webhook_id (for webhook name and target_url/tenant) and tenants for tenant_name. Filter created_at in 2025. success_rate_pct = status='success' count / total * 100; failed_rate_pct = status='failed' count / total * 100; pending_or_retrying_rate_pct = status in ('pending','retrying') count / total * 100 (deliveries that never resolved), all rounded 2dp. avg_duration_ms over non-null duration_ms, rounded to whole number. Only include webhooks with at least 20 total_deliveries_2025 to avoid noise from near-empty endpoints. Order by pending_or_retrying_rate_pct descending, then failed_rate_pct descending, tie-break webhook_name ascending. Limit to 20 rows (the worst endpoints).","builds_on":["2025 Webhook Delivery Reliability Trend"]},{"title":"Webhook Delivery Reliability: Carts.remind-Affected Tenants vs Others, 2025","question":"Do the 10 tenants hit by the Nov carts.remind job failures also show worse webhook delivery reliability than other tenants, suggesting clustered reliability problems on the same accounts?","grain":"one row per tenant group (Affected vs Other)","columns":["group_name","tenant_count","total_deliveries_2025","success_rate_pct","failed_rate_pct","pending_or_retrying_rate_pct"],"details":"Table webhook_deliveries joined to webhooks for tenant_id, filtered to created_at in 2025. 'Affected' = the same 10 tenant_ids defined in prior reports (failed carts.remind job_runs in Nov 2025); 'Other' = all remaining tenants with at least one webhook delivery in 2025. Rates defined as in the endpoint-level request: success_rate_pct, failed_rate_pct, pending_or_retrying_rate_pct as shares of total_deliveries_2025 for the group, rounded 2dp. Two rows total. Order by group_name.","builds_on":["2025 Webhook Delivery Reliability by Endpoint","Tenant Size vs Carts.remind Job Failure Exposure, 2025"]}]}},"apiBlockIndex":1,"requestId":"req_011CfgFQp3WJK4yr2pks6vfG","type":"assistant","uuid":"8a51099e-3a97-4660-ae2a-45cbc0e1cda0","timestamp":"2026-10-04T00:28:57.543Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"8a51099e-3a97-4660-ae2a-45cbc0e1cda0","isSidechain":false,"attachment":{"type":"structured_output","data":{"notes":"Night 3 gave two important, slightly conflicting signals I need to resolve on this final night. First, Report 1 shows the Nov carts.remind incident did hit real top-tier accounts - Cragsley Outfitters, our #1 tenant by 2025 revenue, is among the 10 affected, alongside #4, #6, #7 and #8 - alongside four zero-revenue/zero-order tenants, so it's a mixed bag, not purely long-tail. Second, Report 2's ticket spike for the 10 affected tenants actually starts climbing weeks before the Nov 24 failure and peaks a week after, which could mean real merchant pain preceded our systems detecting it (bad for the 'are we flying blind' question) - but it could also just be that these are naturally higher-ticket (bigger) merchants, or a seasonal Black-Friday/holiday ticket bump everyone gets. 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With my last 3 slots I'll: (1) settle the ticket-spike-vs-baseline question properly using an Aug-Sep baseline for affected vs other tenants, so I can honestly say cause vs. coincidence; (2) finally give Diego a first cut at webhook reliability by individual endpoint, since Report 3 showed a flat ~5-6% of deliveries each month never resolve to success or hard failure (stuck retrying) and he explicitly wants to know which integrations are chronically troublesome; (3) check whether that same webhook stuck-rate clusters on the 10 affected tenants vs everyone else, which is exactly Samuel's cross-dimension clustering question for his top-tier accounts.","requests":[{"title":"Support Tickets: Affected vs Other Tenants, Baseline vs Incident Period","question":"Was the Nov-Dec ticket rise for the 10 carts.remind-affected tenants a real spike against their own baseline, or just their normal (or seasonal) level?","grain":"one row per tenant group (Affected vs Other), comparing two time periods","columns":["group_name","tenant_count","avg_weekly_tickets_per_tenant_baseline","avg_weekly_tickets_per_tenant_incident","pct_change"],"details":"Table tickets, created_at. 'Affected' = the same 10 tenant_ids with a failed job_runs row for jobs.handler='carts.remind' queued in Nov 2025 (as established in prior reports); 'Other' = all remaining tenants. baseline period = 2025-08-01 to 2025-09-30 (61 days); incident period = 2025-11-01 to 2025-12-31 (61 days), so periods are equal length for a fair comparison. avg_weekly_tickets_per_tenant = (count of tickets for the group in that period) / tenant_count / (period_days/7.0), rounded 2dp. pct_change = (incident value - baseline value) / baseline value * 100, rounded 1dp. Two rows total: one per group. Order by group_name.","builds_on":["Support Ticket Volume Around the Nov 2025 Carts.remind Incident","Tenant Size vs Carts.remind Job Failure Exposure, 2025"]},{"title":"2025 Webhook Delivery Reliability by Endpoint","question":"Which individual webhook endpoints are chronically troublesome - low success rates or deliveries stuck retrying rather than resolving?","grain":"one row per webhook (endpoint)","columns":["webhook_name","tenant_name","total_deliveries_2025","success_rate_pct","failed_rate_pct","pending_or_retrying_rate_pct","avg_duration_ms"],"details":"Table webhook_deliveries joined to webhooks on webhook_id (for webhook name and target_url/tenant) and tenants for tenant_name. Filter created_at in 2025. success_rate_pct = status='success' count / total * 100; failed_rate_pct = status='failed' count / total * 100; pending_or_retrying_rate_pct = status in ('pending','retrying') count / total * 100 (deliveries that never resolved), all rounded 2dp. avg_duration_ms over non-null duration_ms, rounded to whole number. Only include webhooks with at least 20 total_deliveries_2025 to avoid noise from near-empty endpoints. Order by pending_or_retrying_rate_pct descending, then failed_rate_pct descending, tie-break webhook_name ascending. Limit to 20 rows (the worst endpoints).","builds_on":["2025 Webhook Delivery Reliability Trend"]},{"title":"Webhook Delivery Reliability: Carts.remind-Affected Tenants vs Others, 2025","question":"Do the 10 tenants hit by the Nov carts.remind job failures also show worse webhook delivery reliability than other tenants, suggesting clustered reliability problems on the same accounts?","grain":"one row per tenant group (Affected vs Other)","columns":["group_name","tenant_count","total_deliveries_2025","success_rate_pct","failed_rate_pct","pending_or_retrying_rate_pct"],"details":"Table webhook_deliveries joined to webhooks for tenant_id, filtered to created_at in 2025. 'Affected' = the same 10 tenant_ids defined in prior reports (failed carts.remind job_runs in Nov 2025); 'Other' = all remaining tenants with at least one webhook delivery in 2025. Rates defined as in the endpoint-level request: success_rate_pct, failed_rate_pct, pending_or_retrying_rate_pct as shares of total_deliveries_2025 for the group, rounded 2dp. Two rows total. Order by group_name.","builds_on":["2025 Webhook Delivery Reliability by Endpoint","Tenant Size vs Carts.remind Job Failure Exposure, 2025"]}]},"toolUseID":"toolu_01RvnJBopMoySRhiGoeR8eZD"},"type":"attachment","uuid":"387f719e-55d8-4428-b6f6-cfdc420b91d4","timestamp":"2026-10-04T00:28:57.551Z","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460","version":"2.1.275","gitBranch":"HEAD"}
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{"type":"last-prompt","lastPrompt":"The printed reports of night 3:  === Report 1: Tenant Size vs Carts.remind Job Failure Exposure, 2025 (written by Omar) === Engineer's note: Ranking and revenue cover all tenants (not just those with…","leafUuid":"80b9a239-408b-4cf8-b9bb-c04a2a7a04f4","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460"}
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{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-04T00:29:37.449Z","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460","content":"The printed reports of night 4:\n\n=== Report 1: Support Tickets: Affected vs Other Tenants, Baseline vs Incident Period (written by Ines) ===\nEngineer's note: Affected tenants = distinct tenant_ids with a failed job_runs row (via jobs.handler='carts.remind') queued in Nov 2025, per prior reports; this should be exactly the 10 tenants referenced. Periods use created_at on tickets, both exactly 61 days as specified.\nREPORT: Support Tickets: Affected vs Other Tenants, Baseline vs Incident Period\nQUESTION: Was the Nov-Dec ticket rise for the 10 carts.remind-affected tenants a real spike against their own baseline, or just their normal (or seasonal) level?\ngroup_name | tenant_count | avg_weekly_tickets_per_tenant_baseline | avg_weekly_tickets_per_tenant_incident | pct_change\n-----------+--------------+----------------------------------------+----------------------------------------+-----------\nAffected   |           10 |                                   1.89 |                                   5.19 |      173.9\nOther      |          110 |                                   0.15 |                                   0.36 |      147.5\n(2 rows)\n\n=== Report 2: 2025 Webhook Delivery Reliability by Endpoint (written by Felix) ===\nEngineer's note: Webhook deliveries are not multi-currency so no FX conversion was needed; rates are computed per webhook over all 2025 deliveries with at least 20 total, limited to the worst 20 by stuck-retry rate then failure rate.\nREPORT: 2025 Webhook Delivery Reliability by Endpoint\nQUESTION: Which individual webhook endpoints are chronically troublesome - low success rates or deliveries stuck retrying rather than resolving?\nwebhook_name    | tenant_name           | total_deliveries_2025 | success_rate_pct | failed_rate_pct | pending_or_retrying_rate_pct | avg_duration_ms\n----------------+-----------------------+-----------------------+------------------+-----------------+------------------------------+----------------\nERP sync        | Hautnah               |                   120 |             80.0 |             0.0 |                         20.0 |          2655.0\nERP sync        | Lumen Collective      |                    89 |            82.02 |             0.0 |                        17.98 |          5116.0\nERP sync        | Hanse Industriebedarf |                    97 |            83.51 |             0.0 |                        16.49 |          3833.0\nERP sync        | Seilschaft            |                    25 |             84.0 |             0.0 |                         16.0 |          4053.0\nWMS sync        | Linden & Loom         |                   299 |            84.28 |             0.0 |                        15.72 |          2713.0\nERP sync        | Thistlecombe          |                   140 |            84.29 |             0.0 |                        15.71 |          3203.0\nACCOUNTING sync | Brennquell Kaffee     |                  4961 |            84.52 |             0.0 |                        15.48 |          2763.0\nWMS sync        | Ampfeld Elektronik    |                   512 |            84.57 |             0.0 |                        15.43 |          2803.0\nWMS sync        | Brennquell Kaffee     |                  4875 |            84.62 |            0.04 |                        15.34 |          2744.0\nWMS sync        | Cragsley Outfitters   |                  4073 |            85.51 |             0.0 |                        14.49 |          2642.0\nERP sync        | Folio & Fern          |                    58 |            86.21 |             0.0 |                        13.79 |          2957.0\nERP sync        | Wohnglück             |                   197 |            86.29 |             0.0 |                        13.71 |          2357.0\nERP sync        | Ampfeld Elektronik    |                   581 |            86.57 |             0.0 |                        13.43 |          2174.0\nERP sync        | Basecamp Nord         |                    49 |            95.92 |             0.0 |                         4.08 |          1074.0\nERP sync        | Purrfect Pantry       |                    81 |             96.3 |             0.0 |                          3.7 |          1162.0\nWMS sync        | Wiesgrund Wohnen      |                   295 |            96.95 |             0.0 |                         3.05 |           943.0\nERP sync        | Kettle & Key          |                    68 |            97.06 |             0.0 |                         2.94 |          1363.0\nERP sync        | Velo Noord            |                    39 |            97.44 |             0.0 |                         2.56 |          1176.0\nWMS sync        | Marrowby & Vane       |                  1713 |            97.55 |             0.0 |                         2.45 |           762.0\nERP sync        | Noordzee Surfwear     |                    88 |            97.73 |             0.0 |                         2.27 |           816.0\n(20 rows)\n\n=== Report 3: Webhook Delivery Reliability: Carts.remind-Affected Tenants vs Others, 2025 (written by Viktor) ===\nEngineer's note: The \"Affected\" cohort is derived directly in this query as the distinct tenant_ids with a failed job_runs row for a job named 'carts.remind' in Nov 2025 (since I don't have the earlier report's exact tenant list to join against) — if that prior report used a different job name/handler key or a slightly different definition of \"affected,\" the two numbers may not line up exactly with 10 tenants.\nREPORT: Webhook Delivery Reliability: Carts.remind-Affected Tenants vs Others, 2025\nQUESTION: Do the 10 tenants hit by the Nov carts.remind job failures also show worse webhook delivery reliability than other tenants, suggesting clustered reliability problems on the same accounts?\ngroup_name | tenant_count | total_deliveries_2025 | success_rate_pct | failed_rate_pct | pending_or_retrying_rate_pct\n-----------+--------------+-----------------------+------------------+-----------------+-----------------------------\nOther      |           55 |                 49752 |            94.31 |             0.0 |                         5.68\n(1 rows)\n\n\nIt is Friday morning. Write the memo to Diego Morales:\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":"5cb7d91f-5916-4745-85a3-d3f07e9234f9","isSidechain":false,"promptId":"9dd69cfe-d13c-4b5f-93cd-d079b6db051e","type":"user","message":{"role":"user","content":"The printed reports of night 4:\n\n=== Report 1: Support Tickets: Affected vs Other Tenants, Baseline vs Incident Period (written by Ines) ===\nEngineer's note: Affected tenants = distinct tenant_ids with a failed job_runs row (via jobs.handler='carts.remind') queued in Nov 2025, per prior reports; this should be exactly the 10 tenants referenced. Periods use created_at on tickets, both exactly 61 days as specified.\nREPORT: Support Tickets: Affected vs Other Tenants, Baseline vs Incident Period\nQUESTION: Was the Nov-Dec ticket rise for the 10 carts.remind-affected tenants a real spike against their own baseline, or just their normal (or seasonal) level?\ngroup_name | tenant_count | avg_weekly_tickets_per_tenant_baseline | avg_weekly_tickets_per_tenant_incident | pct_change\n-----------+--------------+----------------------------------------+----------------------------------------+-----------\nAffected   |           10 |                                   1.89 |                                   5.19 |      173.9\nOther      |          110 |                                   0.15 |                                   0.36 |      147.5\n(2 rows)\n\n=== Report 2: 2025 Webhook Delivery Reliability by Endpoint (written by Felix) ===\nEngineer's note: Webhook deliveries are not multi-currency so no FX conversion was needed; rates are computed per webhook over all 2025 deliveries with at least 20 total, limited to the worst 20 by stuck-retry rate then failure rate.\nREPORT: 2025 Webhook Delivery Reliability by Endpoint\nQUESTION: Which individual webhook endpoints are chronically troublesome - low success rates or deliveries stuck retrying rather than resolving?\nwebhook_name    | tenant_name           | total_deliveries_2025 | success_rate_pct | failed_rate_pct | pending_or_retrying_rate_pct | avg_duration_ms\n----------------+-----------------------+-----------------------+------------------+-----------------+------------------------------+----------------\nERP sync        | Hautnah               |                   120 |             80.0 |             0.0 |                         20.0 |          2655.0\nERP sync        | Lumen Collective      |                    89 |            82.02 |             0.0 |                        17.98 |          5116.0\nERP sync        | Hanse Industriebedarf |                    97 |            83.51 |             0.0 |                        16.49 |          3833.0\nERP sync        | Seilschaft            |                    25 |             84.0 |             0.0 |                         16.0 |          4053.0\nWMS sync        | Linden & Loom         |                   299 |            84.28 |             0.0 |                        15.72 |          2713.0\nERP sync        | Thistlecombe          |                   140 |            84.29 |             0.0 |                        15.71 |          3203.0\nACCOUNTING sync | Brennquell Kaffee     |                  4961 |            84.52 |             0.0 |                        15.48 |          2763.0\nWMS sync        | Ampfeld Elektronik    |                   512 |            84.57 |             0.0 |                        15.43 |          2803.0\nWMS sync        | Brennquell Kaffee     |                  4875 |            84.62 |            0.04 |                        15.34 |          2744.0\nWMS sync        | Cragsley Outfitters   |                  4073 |            85.51 |             0.0 |                        14.49 |          2642.0\nERP sync        | Folio & Fern          |                    58 |            86.21 |             0.0 |                        13.79 |          2957.0\nERP sync        | Wohnglück             |                   197 |            86.29 |             0.0 |                        13.71 |          2357.0\nERP sync        | Ampfeld Elektronik    |                   581 |            86.57 |             0.0 |                        13.43 |          2174.0\nERP sync        | Basecamp Nord         |                    49 |            95.92 |             0.0 |                         4.08 |          1074.0\nERP sync        | Purrfect Pantry       |                    81 |             96.3 |             0.0 |                          3.7 |          1162.0\nWMS sync        | Wiesgrund Wohnen      |                   295 |            96.95 |             0.0 |                         3.05 |           943.0\nERP sync        | Kettle & Key          |                    68 |            97.06 |             0.0 |                         2.94 |          1363.0\nERP sync        | Velo Noord            |                    39 |            97.44 |             0.0 |                         2.56 |          1176.0\nWMS sync        | Marrowby & Vane       |                  1713 |            97.55 |             0.0 |                         2.45 |           762.0\nERP sync        | Noordzee Surfwear     |                    88 |            97.73 |             0.0 |                         2.27 |           816.0\n(20 rows)\n\n=== Report 3: Webhook Delivery Reliability: Carts.remind-Affected Tenants vs Others, 2025 (written by Viktor) ===\nEngineer's note: The \"Affected\" cohort is derived directly in this query as the distinct tenant_ids with a failed job_runs row for a job named 'carts.remind' in Nov 2025 (since I don't have the earlier report's exact tenant list to join against) — if that prior report used a different job name/handler key or a slightly different definition of \"affected,\" the two numbers may not line up exactly with 10 tenants.\nREPORT: Webhook Delivery Reliability: Carts.remind-Affected Tenants vs Others, 2025\nQUESTION: Do the 10 tenants hit by the Nov carts.remind job failures also show worse webhook delivery reliability than other tenants, suggesting clustered reliability problems on the same accounts?\ngroup_name | tenant_count | total_deliveries_2025 | success_rate_pct | failed_rate_pct | pending_or_retrying_rate_pct\n-----------+--------------+-----------------------+------------------+-----------------+-----------------------------\nOther      |           55 |                 49752 |            94.31 |             0.0 |                         5.68\n(1 rows)\n\n\nIt is Friday morning. Write the memo to Diego Morales:\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":"4d7b5dd6-1189-4d71-a424-578c856270c5","timestamp":"2026-10-04T00:29:37.455Z","permissionMode":"default","promptSource":"sdk","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460","version":"2.1.275","gitBranch":"HEAD"}
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"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":91941,"cache_read_input_tokens":0,"output_tokens":5155,"output_tokens_details":{"thinking_tokens":3365},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":91941,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":5155,"cache_read_input_tokens":0,"cache_creation_input_tokens":91941,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":91941},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":{"cache_miss_reason":{"type":"tools_changed","cache_missed_input_tokens":72588}},"context_management":null},"apiBlockIndex":0,"requestId":"req_011CfgFZvpYrnDU3S6sgJ51L","type":"assistant","uuid":"5a4d8834-4dab-4e9f-9e8c-6f96f4463182","timestamp":"2026-10-04T00:30:14.047Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: 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{"parentUuid":"5a4d8834-4dab-4e9f-9e8c-6f96f4463182","isSidechain":false,"message":{"model":"claude-sonnet-5","id":"msg_011CfgFZwEr5Htoxech4vedM","type":"message","role":"assistant","content":[{"type":"text","text":"**Memo: 2025 Platform Reliability Review**\nTo: Diego Morales (cc Samuel Whitcombe) — From: Lucas Moreau — Fri 2026-01-09\n\n**1. Background jobs were essentially reliable all quarter — one isolated incident, not a trend.**\n`job_runs` only retains Oct 1–Dec 31 2025 (confirmed by row counts, not an error — see caveat below), so I can't show a true year trend, but within that window: failure rate was 0.0% every single week except one, and queue delay/run duration were flat all quarter (~2.5s avg delay, ~5s avg duration, no drift). *Source: \"2025 Q4 Weekly Job Reliability and Lateness.\"* \"Late\" jobs are not a problem — delay never moved.\n\n**2. The one incident: the `carts.remind` job, week of Nov 24, hit exactly 10 tenants.**\nAll 20 failures in the quarter (0.09% of that handler's 21,867 runs) happened in a single week, in a single job type, affecting 10 of ~120 tenants — every other of the 8 job handlers had zero failures. *Source: \"2025 Job Failures by Job Type (Handler).\"* This is a contained, narrow incident, not a systemic job-reliability problem.\n\n**3. It did reach top-tier accounts, not just the long tail.**\nOf the 10 affected tenants, 6 had meaningful 2025 volume, including **Cragsley Outfitters — our #1 tenant by 2025 EUR revenue** — plus tenants ranked #4, #6, #7, #8. The other 4 affected tenants had zero 2025 orders (likely inactive/test accounts). *Source: \"Tenant Size vs Carts.remind Job Failure Exposure.\"* I'm confident this wasn't volume-driven — small and large affected tenants had identical failure counts (2 each); it looks tied to a feature flag/config, not load.\n\n**4. Monitoring caught it — barely, and maybe too late.**\nHealth-check unhealthy rate jumped to 2.44% the week of the incident vs. ~0.95–1.04% baseline in the surrounding weeks. *Source: \"2025 Q4 Weekly Job Failures vs Health Check Alerts.\"* But ticket volume for the 10 affected tenants was already climbing 3–4 weeks before the job failures appeared in logs (1.0/tenant/week on Oct 27 → 5.8 by Nov 24). I'm **not confident** this is a clean \"we caught it early\" story — merchants may have felt it before our systems flagged it.\n\n**5. Ticket-volume spike is real but mostly seasonal, not clearly caused by the incident.**\nAffected tenants' ticket rate rose 174% from an Aug–Sep baseline (1.89→5.19/tenant/week); other tenants rose 148% over the same two periods (0.15→0.36). *Source: \"Support Tickets: Affected vs Other Tenants.\"* Since both groups roughly doubled-to-tripled, most of this looks like a platform-wide holiday-season ticket bump. The extra ~26pp lift for affected tenants is suggestive but not strong evidence with only 10 tenants — I'd call this correlation, not proven cause.\n\n**6. Webhook deliveries are stable overall but a chronic ~15% \"never resolves\" rate sits in ERP/WMS/Accounting sync integrations.**\nPlatform-wide success rate held 93–95% all 12 months, no trend. *Source: \"2025 Webhook Delivery Reliability Trend.\"* But the worst 20 individual endpoints are dominated by ERP/WMS/Accounting sync webhooks stuck in pending/retrying 13–20% of the time, at real volume (e.g., Brennquell Kaffee's accounting sync: ~770 stuck deliveries). Several of these tenants (Brennquell Kaffee, Cragsley Outfitters, Ampfeld Elektronik, Marrowby & Vane) also appear in the carts.remind-affected list. *Source: \"2025 Webhook Delivery Reliability by Endpoint.\"* This looks like a bug in our own retry logic for sync job classes, not a third-party provider problem.\n\n**Caveats:**\n- `job_runs` and `system_health_checks` appear to be retained only ~90 and ~60 days respectively — a full-year trend isn't possible from these tables as-is.\n- My last request (webhook reliability, affected vs. other tenants) used an inconsistent tenant-matching definition and returned no \"Affected\" row — **don't cite that comparison**; the ERP/WMS overlap above is from eyeballing, not a validated cross-tab.\n- Rate limiting (#4) is **not answered**: the warehouse has configured limits (`rate_limits`) but no log of actual throttle/429 events, so this can't be assessed from current data.\n\n**Next steps:** (1) re-run the webhook-clustering comparison with the correct tenant join; (2) root-cause the ERP/WMS/Accounting sync stuck-retry rate — broad, fixable, affects many merchants; (3) push for longer retention on `job_runs`/`system_health_checks` so 2026's review isn't windowed to a quarter; (4) instrument rate-limit hit/block events if we want to answer Diego's #4 next year."}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":91941,"cache_read_input_tokens":0,"output_tokens":5155,"output_tokens_details":{"thinking_tokens":3365},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":91941,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":5155,"cache_read_input_tokens":0,"cache_creation_input_tokens":91941,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":91941},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":{"cache_miss_reason":{"type":"tools_changed","cache_missed_input_tokens":72588}},"context_management":null},"apiBlockIndex":1,"requestId":"req_011CfgFZvpYrnDU3S6sgJ51L","type":"assistant","uuid":"3da83005-9081-4293-aca7-2b79bc39b142","timestamp":"2026-10-04T00:30:30.840Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460","version":"2.1.275","gitBranch":"HEAD"}
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{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-04T03:32:25.573Z","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460","content":"Monday, week 2. Diego Morales replied to your memo:\n\nSubject: Re: 2025 Platform Reliability Review — good start, a few things I need tightened up\n\nLucas,\n\nThanks — this is useful and more honest than I expected on the limits of what we can see. A few things stood out and need more work before Friday's memo goes to leadership.\n\nFirst, the retention gap is itself a finding, not just a caveat. We're flying with 90 days of job history and 60 days of health-check history — that means we genuinely don't know how 2025 looked before October. I want that stated plainly and separately in the memo as a reliability/observability gap in its own right, not buried as a footnote under the carts.remind story.\n\nSecond, the ERP/WMS/Accounting webhook stuck-retry pattern is, to me, the most important and actionable thing in here — more than the one-week carts.remind incident. I want that properly validated and sized, not eyeballed, before we say anything about it to leadership.\n\nQuestions for this week:\n\n1. Can you redo the webhook comparison correctly and tell me: how many merchants are affected by this stuck-retry pattern, and what's the revenue or order volume behind them? I want to know if this is a handful of our biggest accounts or a long tail.\n\n2. Has the ~15% stuck-retry rate on those sync webhooks been happening all year, or is it new/recent? Even without full job history, is there any way to tell whether this is a longstanding bug we've lived with or something that regressed recently?\n\n3. On carts.remind — you said it looks tied to a feature flag or config rather than load. Can we get more confidence on that? I want to know if this is a one-off we already fixed, or a latent risk that could recur with other job types.\n\n4. Setting aside strict proof of causation, can you give me your best read on whether the ticket-volume climb for the 10 affected tenants in the weeks *before* the failures showed up in our logs is something merchants were telling us about directly (i.e., check if their tickets mention sync/reminders/missing carts) — even a rough read would tell us if our support channel was effectively an early warning system we ignored.\n\nOn rate limiting: agreed, drop it for this cycle — don't spend a slot trying to force an answer out of config data alone. Just flag it as a 2026 instrumentation ask, as you already have.\n\nPriority for your remaining slots: #1 and #2 above. The webhook/sync issue is the one I can take into a fix-it conversation with engineering next week if the numbers hold up — I'd rather have that nailed down than more polish on the carts.remind narrative.\n\nDiego\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":"637e2aa5-510d-439e-b1fd-d9c5731e7ee8","isSidechain":false,"promptId":"0efce0ca-670c-43aa-93ed-7f14152880da","type":"user","message":{"role":"user","content":"Monday, week 2. Diego Morales replied to your memo:\n\nSubject: Re: 2025 Platform Reliability Review — good start, a few things I need tightened up\n\nLucas,\n\nThanks — this is useful and more honest than I expected on the limits of what we can see. A few things stood out and need more work before Friday's memo goes to leadership.\n\nFirst, the retention gap is itself a finding, not just a caveat. We're flying with 90 days of job history and 60 days of health-check history — that means we genuinely don't know how 2025 looked before October. I want that stated plainly and separately in the memo as a reliability/observability gap in its own right, not buried as a footnote under the carts.remind story.\n\nSecond, the ERP/WMS/Accounting webhook stuck-retry pattern is, to me, the most important and actionable thing in here — more than the one-week carts.remind incident. I want that properly validated and sized, not eyeballed, before we say anything about it to leadership.\n\nQuestions for this week:\n\n1. Can you redo the webhook comparison correctly and tell me: how many merchants are affected by this stuck-retry pattern, and what's the revenue or order volume behind them? I want to know if this is a handful of our biggest accounts or a long tail.\n\n2. Has the ~15% stuck-retry rate on those sync webhooks been happening all year, or is it new/recent? Even without full job history, is there any way to tell whether this is a longstanding bug we've lived with or something that regressed recently?\n\n3. On carts.remind — you said it looks tied to a feature flag or config rather than load. Can we get more confidence on that? I want to know if this is a one-off we already fixed, or a latent risk that could recur with other job types.\n\n4. Setting aside strict proof of causation, can you give me your best read on whether the ticket-volume climb for the 10 affected tenants in the weeks *before* the failures showed up in our logs is something merchants were telling us about directly (i.e., check if their tickets mention sync/reminders/missing carts) — even a rough read would tell us if our support channel was effectively an early warning system we ignored.\n\nOn rate limiting: agreed, drop it for this cycle — don't spend a slot trying to force an answer out of config data alone. Just flag it as a 2026 instrumentation ask, as you already have.\n\nPriority for your remaining slots: #1 and #2 above. The webhook/sync issue is the one I can take into a fix-it conversation with engineering next week if the numbers hold up — I'd rather have that nailed down than more polish on the carts.remind narrative.\n\nDiego\n\nLast week in the company's analytics channel:\n- Chief Executive Officer team (Hannah Brooks): Revenue concentration: our top 5 merchants generate 72% of Tidewick's processing-fee revenue, and one merchant alone (Cragsley Outfitters) accounts for 42% — we're exposed if a top account leaves.\n- Chief Executive Officer team (Hannah Brooks): Growth is healthy but comes from existing merchants: active merchants grew from 76 to 111 and orders roughly doubled over 2 years, yet new merchants contribute under 1% of revenue in their first quarter after joining.\n- Chief Executive Officer team (Hannah Brooks): Refund rate is creeping up: from ~9-11% in 2024 to 12-14% in late 2025, without a matching rise in payment failures — worth a root-cause look.\n- Chief Financial Officer team (Kofi Mensah): 2025 processing-fee revenue: Tidewick earned €93,583 in payment-processing fees on €3.97M of merchant payment volume, growing to a €14.3k December peak — full platform-fee revenue isn't in this data yet, to follow.\n- Chief Financial Officer team (Kofi Mensah): Merchant concentration: our top 10 merchants still drive ~65% of payment volume (led by Cragsley Outfitters at ~18%), but that share is edging down as the merchant base grows (92 → 113 active merchants year over year).\n- Chief Financial Officer team (Kofi Mensah): Refunds: Apparel merchants run a steady ~20% refund rate (normal for fashion), but a handful of smaller non-Apparel merchants (e.g. OfficeHub Nordic, Sports & Outdoor sellers) drove a refund-rate spike to ~13% company-wide in Q3 — still being double-checked against return records.\n- Chief Operating Officer team (Lena Hartmann): Delivery speed held up over the past two years (~85-90% of orders arrive within 5 days in a normal month), but it took a real hit during peak season: only 56% of orders arrived within 5 days in the worst week (early December), before recovering within about 2 weeks.\n- Chief Operating Officer team (Lena Hartmann): One of our carrier partners, Albion Parcel, broke down starting in November: on-time delivery (within 3 days) fell from 76% in October to 28% in November and December, and hasn't recovered yet.\n- Chief Operating Officer team (Lena Hartmann): Which merchant has 'good' or 'bad' delivery isn't really about the merchant or the warehouse - it's almost entirely about which carrier handles their shipments: the exact same merchant sees 89% of parcels arrive within 3 days on one carrier vs. just 19% on another.\n- VP Customer Experience team (Mateo Ruiz): Slower support replies mean unhappier customers: satisfaction drops from 4.4/5 when resolved in under 4 hours to just 1.7/5 when it takes more than 3 days.\n- VP Customer Experience team (Mateo Ruiz): A big chunk of support tickets (~65% recently) are shoppers asking \"where's my order\" - and this spikes every November/December, matching a rise in late deliveries from ~4% to ~10%+ of shipments.\n- VP Customer Experience team (Mateo Ruiz): One merchant's May 2025 delivery breakdown (70% of shipments took over a week, vs. under 5% normally) explains an unusual mid-year spike in slow support - it was a local incident, not a platform-wide issue.\n- Head of Growth Marketing team (Sofie Nilsen): Discount codes are costing more than they earn in our core retail categories: discounted orders return 5-7 points more often than full-price ones (e.g. apparel 39% vs 35%, electronics 17% vs 11%), with no real boost in order size.\n- Head of Growth Marketing team (Sofie Nilsen): Referral customers are our most engaged shoppers by far — people who refer others place ~8 orders/year vs ~3 for everyone else — while our loyalty program shows no such lift despite similar effort to run.\n- Head of Growth Marketing team (Sofie Nilsen): Growth is concentrated: just 2 of our ~120 merchants generate 31% of all \"direct\" site traffic, and only 1 in 7 orders can be traced back to a marketing channel at all.\n- Head of Merchant Success team (Arjun Patel): Our biggest merchant, Cragsley Outfitters, grew sales +19.8% year-over-year this holiday quarter (vs. the same Oct-Dec period last year) - real growth, not just seasonal bump.\n- Head of Merchant Success team (Arjun Patel): Castell Fasteners, one of our top 4 merchants, is flashing a warning sign: shopper traffic on its site dropped 44% this quarter and it's sitting on €5,080 in unpaid invoices.\n- Head of Merchant Success team (Arjun Patel): Merchants with naturally recurring purchases (coffee/food subscriptions, B2B restocking) keep 83-94% of customers coming back, vs. ~58% for general retail - a strong case for building reorder habits into any merchant's playbook.\n- Head of Risk and Payments team (Claire Dubois): Access control: we can show who has access today, but we found zero audit records of role/permission changes over 3 years — so we can't yet prove when access was granted or revoked.\n- Head of Risk and Payments team (Claire Dubois): Data privacy requests: all healthy — ~48 requests since 2023, with a consistent 30-day response window and no real backlog.\n- Head of Risk and Payments team (Claire Dubois): Login security: one sharp anomaly — in Feb 2025, 61% of login attempts failed (7,429 of 12,211), a 10x jump from the normal ~6% baseline, and it wasn't flagged by our suspicious-access alerts at the time.\n- VP Product team (Emeka Obi): 🛒 Most lost sales leak at the shopping cart, not at payment: only ~1 in 4 carts convert within an hour, and the rest mostly stall for 1-7 days before fizzling out (just 1-19% of those convert) — payment itself works fine once an order is actually placed (97.6% succeed instantly).\n- VP Product team (Emeka Obi): 🔁 Most first-time buyers do come back: 40-62% of new customers place a second order within 90 days, typically within 1-2.5 months — a healthy, stable repeat-purchase baseline across the last 3 years.\n- VP Product team (Emeka Obi): 📦 A return modestly cools the relationship but doesn't end it: customers with a returned order come back 49.7% of the time within 90 days, vs 57.7% for customers with no return — about an 8-point dip, not a cliff.\n- Head of Data team (Yuki Tanaka): Revenue checks out: once you count refunded payments correctly, orders match actual money collected to within 1-5% (not the 20-40% gap it looked like at first).\n- Head of Data team (Yuki Tanaka): Refunds are real and not lost or double-counted, but most (85%) land ~3 weeks after the original payout already went out, so we currently can't point to exactly which payout absorbed a given refund.\n- Head of Data team (Yuki Tanaka): \"How many customers do we have\" depends entirely on definition: 11,258 total accounts, but only 6,159 bought in the last 12 months and just 411 have an active subscription — we recommend standardizing on the 6,159 figure as \"active customers.\"\n- Chief Information Security Officer team (Noah Fischer): 🔐 Shopper accounts have 0% multi-factor authentication (MFA) enabled — literally none of our ~11,200 shopper logins use a second factor, vs. ~55% of staff.\n- Chief Information Security Officer team (Noah Fischer): 🕵️ We can't currently prove who deleted things: of 615 products deleted across 91 merchants, 0 show up in our audit log as a logged deletion.\n- Chief Information Security Officer team (Noah Fischer): 👋 When a merchant churns, their staff logins don't get shut off: 16 Owner/Admin accounts on churned merchants are still marked \"active,\" some unused for 500+ days.\n- Data Protection Officer team (Priyanka Desai): 🔒 Privacy audit: our email-retention rule (delete after 730 days) is configured for all 120 merchants but has never actually run — we're still holding ~44,000+ customer emails that are over 2 years old.\n- Data Protection Officer team (Priyanka Desai): ⚖️ 3 shoppers' erasure requests were correctly put on hold for payment disputes, but those holds are now 289–954 days old (up to 2.6 years) and need review before we can say the requests are truly resolved.\n- Data Protection Officer team (Priyanka Desai): 📉 Marketing-email opt-in rate has slipped from 99.4% to ~90% since 2023, with a sharp spike in opt-outs in July–August 2025 — worth watching if we rely on consent for marketing.\n- VP Platform Engineering team (Lucas Moreau): Background jobs ran reliably almost all of Q4 2025, except one bug in our cart-reminder emails that failed for a single week (Nov 24) and hit 10 merchants — including our #1 merchant by revenue, Cragsley Outfitters.\n- VP Platform Engineering team (Lucas Moreau): About 1 in 7 webhook notifications we send to merchants' inventory/accounting systems (ERP, WMS, and Accounting syncs) never go through — they get stuck retrying instead of succeeding or failing cleanly, all year round.\n- VP Platform Engineering team (Lucas Moreau): Support tickets roughly doubled for everyone over the holidays, but the 10 merchants hit by the cart-reminder bug saw tickets rise even more (+174% vs +148% for everyone else) — a real but modest extra hit on top of the seasonal spike.\n- Head of Merchandising team (Zara Ahmed): Quality tail at a top merchant: 1 in 5 reviewed products (23 of 105) at Cragsley Outfitters, our #2 merchant by revenue, average below 3 stars — some still sell hundreds of units, so it's a brewing risk, not yet a sales problem.\n- Head of Merchandising team (Zara Ahmed): Bundles are a missed opportunity: none of our top 6 merchants by revenue use product bundles at all (0% of their catalog), while curated collections are already used by 65-100% of merchants' products.\n- Head of Merchandising team (Zara Ahmed): December 2025 saw the worst customer ratings in 3 years (3.71 avg, 1 in 4 reviews 1-2 stars) — traced mainly to two Home & Living merchants, not a platform-wide quality problem.\n- Head of Pricing and Promotions team (Oliver Grant): Free subscription-box deliveries are going to already-canceled customers: 82% of orphaned 'free delivery' orders belong to customers who canceled an average of 117 days earlier (~€57k given away).\n- Head of Pricing and Promotions team (Oliver Grant): No coupon in our system has any usage or per-customer limit set — repeat use of codes isn't a rule violation because no rule exists.\n- Head of Pricing and Promotions team (Oliver Grant): Welcome10 orders run ~2x bigger than normal, but its refunds (€12.2k, spread across 92 separate returns) now exceed the discount's own cost.\n- Head of Lifecycle Marketing team (Ingrid Solberg): Loyalty programme payout check: points redeemed by members cost us 19.4% of the revenue those members bring in (€177.8k redeemed vs €915k revenue) — a high \"discount rate\" for a rewards programme, with another €669k in issued-but-unredeemed points still owed.\n- Head of Lifecycle Marketing team (Ingrid Solberg): Correcting an early red flag: loyalty members don't actually return items more than other customers once you compare like-for-like by product category — the earlier \"members refund 2x more\" number was mostly because members are concentrated in our highest-return category (apparel, ~38% refund rate for everyone there), not because membership itself drives returns.\n- Head of Lifecycle Marketing team (Ingrid Solberg): Programme reach is small and uneven: only ~16% of customers are enrolled in loyalty (flat for 2 years), under 1% have ever used a referral code, and our B2B wholesale merchants have no loyalty programme at all.\n- Head of B2B Commerce team (Daniel Kim): Lost B2B wholesale deals take ~4x longer to close than won ones (35.5 vs 9.1 days avg) — time-open is our clearest warning sign.\n- Head of B2B Commerce team (Daniel Kim): Deal size doesn't predict win/loss: win rates stay flat at 79-88% across all deal-size bands, from under €3,000 to over €12,000.\n- Head of B2B Commerce team (Daniel Kim): Performance is broadly consistent across our 5 B2B wholesale merchants (75-88% win rate), but one rep at Polder Packaging wins only 57% — a likely coaching opportunity, not a systemic issue.\n- Director of Fulfilment Operations team (Fatima Zahra): Inventory accuracy: ongoing stock miscounts are small but rising — about 850 single-unit discrepancies/month in pick locations, up 24% since January (now ~940/month), worth roughly €20-25k/month.\n- Director of Fulfilment Operations team (Fatima Zahra): It's a picking/process issue, not a tracking-tech issue: mismatches cluster heavily in high-SKU apparel/footwear lines, while items tracked by lot or serial number (food, appliances) actually show fewer discrepancies per unit sold.\n- Director of Fulfilment Operations team (Fatima Zahra): A separate one-time ~€138k year-end stock correction on Dec 31 was ~94% concentrated in just 5 B2B wholesale merchants (office/industrial supplies) — likely an annual stocktake true-up, being confirmed with ops before we treat it as a trend.\n- Support Operations Manager team (Ben Carter): We miss our own support SLA more often than we'd like: in 2025, 38% of tickets got a late first reply and 20% were resolved late — urgent tickets fared worst, missing resolution half the time (50.6%).\n- Support Operations Manager team (Ben Carter): Customer happiness tracks almost perfectly with speed: average satisfaction drops from 4.4/5 on tickets resolved within 4 hours to 1.7/5 on tickets that take over a week — so faster replies are the single biggest lever on CSAT.\n- Support Operations Manager team (Ben Carter): Support pain is concentrated, not spread evenly: just 3 of our ~94 merchants (Cragsley Outfitters, Marrowby & Vane, Studio Vrij) generate 34% of all support tickets, and these same merchants have the worst response times in the whole book.\n\nNight 1 of 4: you have 3 report slots 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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":104407,"cache_read_input_tokens":0,"output_tokens":5410,"output_tokens_details":{"thinking_tokens":3485},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":104407,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":5410,"cache_read_input_tokens":0,"cache_creation_input_tokens":104407,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":104407},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":{"cache_miss_reason":{"type":"previous_message_not_found"}},"context_management":null},"apiBlockIndex":0,"requestId":"req_011CfgVWS8w5nnxQFWg1hsux","type":"assistant","uuid":"c1958049-1b9f-4a39-a0b4-dba4527defc6","timestamp":"2026-10-04T03:33:03.650Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: 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{"parentUuid":"c1958049-1b9f-4a39-a0b4-dba4527defc6","isSidechain":false,"message":{"model":"claude-sonnet-5","id":"msg_011CfgVWSYjxLiLVjkJWpMUD","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01CkciFpLfWRRK3t2vi5D4ad","name":"StructuredOutput","input":{"notes":"Diego wants the webhook stuck-retry finding properly validated before it goes to leadership, and that's explicitly his #1 and #2 priority this week, so two of tonight's three slots go there: first, a correct tenant-level view (fixing last week's broken join) showing exactly how many merchants have ERP/WMS/Accounting sync webhooks stuck above a real threshold and what revenue/order volume sits behind them, so we can say concentrated-in-big-accounts vs long-tail; second, a full-year monthly trend of that same stuck-rate (webhook_deliveries has clean 12-month coverage, unlike job_runs/health_checks) to tell if this is longstanding or a recent regression. I'm using my third slot on Diego's #3 (carts.remind confidence) since it's a cheap, high-value pull: the actual error_message on the 20 failed runs will tell us in one query whether this was one shared root cause (supports his 'latent risk that could recur' concern being containable) or scattered causes (supports a load/capacity read). I'm deferring Diego's #4 (checking if tickets from the 10 affected tenants actually mention sync/reminders/carts) to night 2 since it needs careful text-search scoping and isn't his top priority this week.","requests":[{"title":"Merchants Affected by the Sync-Webhook Stuck-Retry Pattern and Their Size","question":"How many merchants have ERP/WMS/Accounting sync webhooks stuck in retry, and is this concentrated in our biggest accounts or spread across the long tail?","grain":"one row per tenant that has at least one ERP sync, WMS sync, or ACCOUNTING sync webhook","columns":["tenant_name","total_sync_deliveries_2025","success_rate_pct","pending_or_retrying_rate_pct","total_orders_2025","total_revenue_eur_2025","revenue_rank"],"details":"Tables: webhook_deliveries joined to webhooks (match webhooks.name in the sync-integration set actually present in the data - 'ERP sync', 'WMS sync', 'ACCOUNTING sync', confirm exact distinct values and include any close variants) joined to tenants. Filter created_at in 2025. Aggregate all of a tenant's sync-type webhooks together: total_sync_deliveries_2025 = count of deliveries; success_rate_pct = share with status='success'; pending_or_retrying_rate_pct = share with status in ('pending','retrying'), both rounded 2dp. total_orders_2025 = count of orders with placed_at in 2025; total_revenue_eur_2025 = sum of orders.grand_total converted to EUR via exchange_rates (base_code='EUR', quote_code=orders.currency_code, as_of=date(placed_at)), rounded to 2dp. revenue_rank = dense rank of ALL tenants (not just this subset) by total_revenue_eur_2025 descending, so we can see where these merchants sit platform-wide. Order by pending_or_retrying_rate_pct descending, tie-break tenant_name ascending. Return all qualifying tenants (note total count in the report even if under 50).","builds_on":["2025 Webhook Delivery Reliability by Endpoint","Webhook Delivery Reliability: Carts.remind-Affected Tenants vs Others, 2025"]},{"title":"2025 Monthly Stuck-Retry Rate for Sync Webhooks (ERP/WMS/Accounting)","question":"Has the ~15% stuck-in-retry rate on sync webhooks been happening all year, or did it start/worsen recently?","grain":"one row per calendar month of 2025","columns":["month","total_sync_deliveries","success_rate_pct","failed_rate_pct","pending_or_retrying_rate_pct"],"details":"Table webhook_deliveries joined to webhooks, filtered to webhooks.name in the same sync-integration set as the companion merchant-level request ('ERP sync', 'WMS sync', 'ACCOUNTING sync'). Filter created_at in 2025 (full year - this table has complete 2025 coverage per last week's '2025 Webhook Delivery Reliability Trend'). Group by month of created_at, platform-wide across all affected tenants combined. Rates as shares of total_sync_deliveries for that month, rounded 2dp. Order by month ascending.","builds_on":["2025 Webhook Delivery Reliability Trend"]},{"title":"Carts.remind Failure Root Cause: Error Messages","question":"Did the 20 carts.remind failures in Nov 2025 share a common root cause (pointing to one shared bug/config issue) or look like scattered, tenant-specific problems (pointing to load/capacity)?","grain":"one row per failed carts.remind job run (20 rows expected)","columns":["tenant_name","queued_at","started_at","finished_at","error_message","attempt"],"details":"Table job_runs joined to jobs (handler='carts.remind') and tenants, filtered to status='failed' and queued_at in November 2025 (the same 20 rows identified in last week's handler breakdown). Show the full error_message text and attempt number for each failed run so we can see if they cluster around one error string/cause or vary. Order by queued_at ascending, tie-break tenant_name ascending.","builds_on":["2025 Job Failures by Job Type (Handler)"]}]},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":104407,"cache_read_input_tokens":0,"output_tokens":5410,"output_tokens_details":{"thinking_tokens":3485},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":104407,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":5410,"cache_read_input_tokens":0,"cache_creation_input_tokens":104407,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":104407},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":{"cache_miss_reason":{"type":"previous_message_not_found"}},"context_management":null},"wireToolInputs":{"toolu_01CkciFpLfWRRK3t2vi5D4ad":{"notes":"Diego wants the webhook stuck-retry finding properly validated before it goes to leadership, and that's explicitly his #1 and #2 priority this week, so two of tonight's three slots go there: first, a correct tenant-level view (fixing last week's broken join) showing exactly how many merchants have ERP/WMS/Accounting sync webhooks stuck above a real threshold and what revenue/order volume sits behind them, so we can say concentrated-in-big-accounts vs long-tail; second, a full-year monthly trend of that same stuck-rate (webhook_deliveries has clean 12-month coverage, unlike job_runs/health_checks) to tell if this is longstanding or a recent regression. I'm using my third slot on Diego's #3 (carts.remind confidence) since it's a cheap, high-value pull: the actual error_message on the 20 failed runs will tell us in one query whether this was one shared root cause (supports his 'latent risk that could recur' concern being containable) or scattered causes (supports a load/capacity read). I'm deferring Diego's #4 (checking if tickets from the 10 affected tenants actually mention sync/reminders/carts) to night 2 since it needs careful text-search scoping and isn't his top priority this week.","requests":[{"title":"Merchants Affected by the Sync-Webhook Stuck-Retry Pattern and Their Size","question":"How many merchants have ERP/WMS/Accounting sync webhooks stuck in retry, and is this concentrated in our biggest accounts or spread across the long tail?","grain":"one row per tenant that has at least one ERP sync, WMS sync, or ACCOUNTING sync webhook","columns":["tenant_name","total_sync_deliveries_2025","success_rate_pct","pending_or_retrying_rate_pct","total_orders_2025","total_revenue_eur_2025","revenue_rank"],"details":"Tables: webhook_deliveries joined to webhooks (match webhooks.name in the sync-integration set actually present in the data - 'ERP sync', 'WMS sync', 'ACCOUNTING sync', confirm exact distinct values and include any close variants) joined to tenants. Filter created_at in 2025. Aggregate all of a tenant's sync-type webhooks together: total_sync_deliveries_2025 = count of deliveries; success_rate_pct = share with status='success'; pending_or_retrying_rate_pct = share with status in ('pending','retrying'), both rounded 2dp. total_orders_2025 = count of orders with placed_at in 2025; total_revenue_eur_2025 = sum of orders.grand_total converted to EUR via exchange_rates (base_code='EUR', quote_code=orders.currency_code, as_of=date(placed_at)), rounded to 2dp. revenue_rank = dense rank of ALL tenants (not just this subset) by total_revenue_eur_2025 descending, so we can see where these merchants sit platform-wide. Order by pending_or_retrying_rate_pct descending, tie-break tenant_name ascending. Return all qualifying tenants (note total count in the report even if under 50).","builds_on":["2025 Webhook Delivery Reliability by Endpoint","Webhook Delivery Reliability: Carts.remind-Affected Tenants vs Others, 2025"]},{"title":"2025 Monthly Stuck-Retry Rate for Sync Webhooks (ERP/WMS/Accounting)","question":"Has the ~15% stuck-in-retry rate on sync webhooks been happening all year, or did it start/worsen recently?","grain":"one row per calendar month of 2025","columns":["month","total_sync_deliveries","success_rate_pct","failed_rate_pct","pending_or_retrying_rate_pct"],"details":"Table webhook_deliveries joined to webhooks, filtered to webhooks.name in the same sync-integration set as the companion merchant-level request ('ERP sync', 'WMS sync', 'ACCOUNTING sync'). Filter created_at in 2025 (full year - this table has complete 2025 coverage per last week's '2025 Webhook Delivery Reliability Trend'). Group by month of created_at, platform-wide across all affected tenants combined. Rates as shares of total_sync_deliveries for that month, rounded 2dp. Order by month ascending.","builds_on":["2025 Webhook Delivery Reliability Trend"]},{"title":"Carts.remind Failure Root Cause: Error Messages","question":"Did the 20 carts.remind failures in Nov 2025 share a common root cause (pointing to one shared bug/config issue) or look like scattered, tenant-specific problems (pointing to load/capacity)?","grain":"one row per failed carts.remind job run (20 rows expected)","columns":["tenant_name","queued_at","started_at","finished_at","error_message","attempt"],"details":"Table job_runs joined to jobs (handler='carts.remind') and tenants, filtered to status='failed' and queued_at in November 2025 (the same 20 rows identified in last week's handler breakdown). Show the full error_message text and attempt number for each failed run so we can see if they cluster around one error string/cause or vary. 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{"parentUuid":"09662427-f121-44ec-b3b8-108dc09bdfba","isSidechain":false,"attachment":{"type":"structured_output","data":{"notes":"Diego wants the webhook stuck-retry finding properly validated before it goes to leadership, and that's explicitly his #1 and #2 priority this week, so two of tonight's three slots go there: first, a correct tenant-level view (fixing last week's broken join) showing exactly how many merchants have ERP/WMS/Accounting sync webhooks stuck above a real threshold and what revenue/order volume sits behind them, so we can say concentrated-in-big-accounts vs long-tail; second, a full-year monthly trend of that same stuck-rate (webhook_deliveries has clean 12-month coverage, unlike job_runs/health_checks) to tell if this is longstanding or a recent regression. 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Filter created_at in 2025. Aggregate all of a tenant's sync-type webhooks together: total_sync_deliveries_2025 = count of deliveries; success_rate_pct = share with status='success'; pending_or_retrying_rate_pct = share with status in ('pending','retrying'), both rounded 2dp. total_orders_2025 = count of orders with placed_at in 2025; total_revenue_eur_2025 = sum of orders.grand_total converted to EUR via exchange_rates (base_code='EUR', quote_code=orders.currency_code, as_of=date(placed_at)), rounded to 2dp. revenue_rank = dense rank of ALL tenants (not just this subset) by total_revenue_eur_2025 descending, so we can see where these merchants sit platform-wide. Order by pending_or_retrying_rate_pct descending, tie-break tenant_name ascending. Return all qualifying tenants (note total count in the report even if under 50).","builds_on":["2025 Webhook Delivery Reliability by Endpoint","Webhook Delivery Reliability: Carts.remind-Affected Tenants vs Others, 2025"]},{"title":"2025 Monthly Stuck-Retry Rate for Sync Webhooks (ERP/WMS/Accounting)","question":"Has the ~15% stuck-in-retry rate on sync webhooks been happening all year, or did it start/worsen recently?","grain":"one row per calendar month of 2025","columns":["month","total_sync_deliveries","success_rate_pct","failed_rate_pct","pending_or_retrying_rate_pct"],"details":"Table webhook_deliveries joined to webhooks, filtered to webhooks.name in the same sync-integration set as the companion merchant-level request ('ERP sync', 'WMS sync', 'ACCOUNTING sync'). Filter created_at in 2025 (full year - this table has complete 2025 coverage per last week's '2025 Webhook Delivery Reliability Trend'). Group by month of created_at, platform-wide across all affected tenants combined. 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{"type":"last-prompt","lastPrompt":"Monday, week 2. Diego Morales replied to your memo:  Subject: Re: 2025 Platform Reliability Review — good start, a few things I need tightened up  Lucas,  Thanks — this is useful and more honest than…","leafUuid":"0555d038-eec0-4585-bf68-3451386b031b","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460"}
{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-04T03:34:26.021Z","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460","content":"The printed reports of night 1:\n\n=== Report 1: Merchants Affected by the Sync-Webhook Stuck-Retry Pattern and Their Size (written by Grace) ===\nEngineer's note: Dry runs never expose data, so I could not inspect the exact distinct webhooks.name values; I matched any webhook whose name contains 'erp'+'sync', 'wms'+'sync', or 'accounting'+'sync' (case-insensitive) to capture the three sync types and close variants. Tenants with a qualifying webhook but no 2025 deliveries, orders, or EUR rate match show 0/NULL rather than being dropped; revenue_rank is NULL if the tenant had no 2025 orders. No row limit was applied since the analyst asked for all qualifying tenants.\nREPORT: Merchants Affected by the Sync-Webhook Stuck-Retry Pattern and Their Size\nQUESTION: How many merchants have ERP/WMS/Accounting sync webhooks stuck in retry, and is this concentrated in our biggest accounts or spread across the long tail?\ntenant_name                   | total_sync_deliveries_2025 | success_rate_pct | pending_or_retrying_rate_pct | total_orders_2025 | total_revenue_eur_2025 | revenue_rank\n------------------------------+----------------------------+------------------+------------------------------+-------------------+------------------------+-------------\nTeapot Society                |                          2 |             50.0 |                         50.0 |                 1 |                  78.81 |          112\nKaffeekontor Nord             |                         13 |            61.54 |                        38.46 |                 8 |                 405.82 |          105\nHautnah                       |                        120 |             80.0 |                         20.0 |                92 |                9190.62 |           51\nLumen Collective              |                         89 |            82.02 |                        17.98 |                52 |                6391.23 |           62\nHanse Industriebedarf         |                         97 |            83.51 |                        16.49 |                72 |              113320.38 |           13\nSeilschaft                    |                         25 |             84.0 |                         16.0 |                17 |                6759.28 |           60\nThistlecombe                  |                        140 |            84.29 |                        15.71 |                92 |               12570.49 |           44\nFolio & Fern                  |                         58 |            86.21 |                        13.79 |                46 |                2148.33 |           88\nBrennquell Kaffee             |                      14149 |            88.87 |                        11.12 |              4195 |              114554.65 |           11\nAmpfeld Elektronik            |                       1583 |            89.83 |                        10.17 |               449 |              512006.37 |            4\nLinden & Loom                 |                        614 |            91.21 |                         8.79 |               263 |               38475.39 |           20\nWohnglück                     |                        341 |            91.79 |                         8.21 |               151 |                22859.8 |           32\nKibble Crate                  |                         19 |            94.74 |                         5.26 |                18 |                1211.91 |           95\nCragsley Outfitters           |                      14104 |            95.04 |                         4.96 |              3635 |              724835.93 |            2\nBasecamp Nord                 |                         49 |            95.92 |                         4.08 |                43 |               10657.51 |           46\nPurrfect Pantry               |                         81 |             96.3 |                          3.7 |                74 |                3583.86 |           74\nKettle & Key                  |                         68 |            97.06 |                         2.94 |                60 |                7581.57 |           54\nVelo Noord                    |                         39 |            97.44 |                         2.56 |                31 |                9780.02 |           49\nNoordzee Surfwear             |                         88 |            97.73 |                         2.27 |                65 |               10894.01 |           45\nWiesgrund Wohnen              |                        642 |            97.98 |                         2.02 |               296 |               38433.09 |           21\nSorella Skin                  |                        105 |             98.1 |                          1.9 |                99 |               10495.53 |           47\nMirelle                       |                         68 |            98.53 |                         1.47 |                63 |                2920.13 |           78\nStudio Vrij                   |                       2396 |            98.62 |                         1.38 |               631 |              113540.14 |           12\nGratlinie Outdoor             |                        291 |            98.63 |                         1.37 |               232 |               96277.59 |           14\nMarrowby & Vane               |                       6425 |            98.63 |                         1.37 |              1758 |              288909.93 |            8\nKibblewick                    |                       1349 |            98.74 |                         1.26 |               672 |               23801.41 |           30\nFjordline Wear                |                         81 |            98.77 |                         1.23 |                57 |                8615.11 |           52\nNordic Bloom Cosmetics        |                        163 |            98.77 |                         1.23 |               151 |                19074.7 |           36\nOstwind Mode                  |                        162 |            98.77 |                         1.23 |               122 |                21621.0 |           33\nAtelier Nouveau               |                         87 |            98.85 |                         1.15 |                64 |                9406.81 |           50\nGrind Collective              |                       1136 |            98.94 |                         1.06 |              1111 |               25610.52 |           27\nKontor24 Bürobedarf           |                        534 |            99.06 |                         0.94 |               264 |              840409.53 |            1\nChocolaterie Mireille         |                        108 |            99.07 |                         0.93 |               104 |                4566.99 |           67\nSpice Route Box               |                        796 |            99.12 |                         0.88 |               780 |               17113.71 |           39\nBallycorran Industrial Supply |                        380 |            99.21 |                         0.79 |               344 |              559149.73 |            3\nCinder Peak Sports Co.        |                        748 |            99.33 |                         0.67 |               338 |              124756.84 |           10\nCoastline Interiors           |                        179 |            99.44 |                         0.56 |               155 |               21468.31 |           34\nFennel & Flint                |                        814 |            99.51 |                         0.49 |               389 |               65764.13 |           16\nOfficeHub Nordic              |                        350 |            99.71 |                         0.29 |               170 |              428472.93 |            5\nPetalcourt Beauty             |                        775 |            99.74 |                         0.26 |               706 |               63508.22 |           17\nBijoux Lune                   |                         23 |            100.0 |                          0.0 |                21 |                3794.91 |           72\nCopper Lane                   |                          8 |            100.0 |                          0.0 |                 6 |                1003.56 |           98\nCourtside Collective          |                         23 |            100.0 |                          0.0 |                20 |                8514.48 |           53\nDewdrop Studio                |                          5 |            100.0 |                          0.0 |                 5 |                 271.34 |          108\nFern & Fable                  |                         48 |            100.0 |                          0.0 |                42 |                 5564.1 |           64\nGlasshouse Grooming           |                         10 |            100.0 |                          0.0 |                10 |                 1455.2 |           93\nNorthstar Watches             |                         14 |            100.0 |                          0.0 |                11 |                2655.08 |           85\nOlive & Oak Pantry            |                          1 |            100.0 |                          0.0 |                 1 |                 118.55 |          111\nOndine Botanicals             |                         27 |            100.0 |                          0.0 |                25 |                1694.77 |           90\nPapeterie Grossiste           |                         20 |            100.0 |                          0.0 |                18 |               41189.21 |           18\n(56 rows, first 50 printed)\n\n=== Report 2: 2025 Monthly Stuck-Retry Rate for Sync Webhooks (ERP/WMS/Accounting) (written by Bea) ===\nEngineer's note: Platform-wide monthly rates for webhooks named 'ERP sync', 'WMS sync', 'ACCOUNTING sync' across all tenants, using webhook_deliveries.created_at for 2025. \"Pending or retrying\" combines the pending and retrying status codes (the only non-success/non-failed states in this table).\nREPORT: 2025 Monthly Stuck-Retry Rate for Sync Webhooks (ERP/WMS/Accounting)\nQUESTION: Has the ~15% stuck-in-retry rate on sync webhooks been happening all year, or did it start/worsen recently?\nmonth               | total_sync_deliveries | success_rate_pct | failed_rate_pct | pending_or_retrying_rate_pct\n--------------------+-----------------------+------------------+-----------------+-----------------------------\n2025-01-01 00:00:00 |                  4438 |            94.21 |            0.02 |                         5.77\n2025-02-01 00:00:00 |                  3088 |             94.3 |             0.0 |                          5.7\n2025-03-01 00:00:00 |                  3551 |            93.41 |             0.0 |                         6.59\n2025-04-01 00:00:00 |                  3434 |            94.09 |             0.0 |                         5.91\n2025-05-01 00:00:00 |                  3810 |            93.99 |             0.0 |                         6.01\n2025-06-01 00:00:00 |                  3413 |            94.67 |             0.0 |                         5.33\n2025-07-01 00:00:00 |                  3702 |            94.41 |             0.0 |                         5.59\n2025-08-01 00:00:00 |                  3308 |            94.77 |             0.0 |                         5.23\n2025-09-01 00:00:00 |                  3724 |            94.04 |             0.0 |                         5.96\n2025-10-01 00:00:00 |                  4110 |            94.72 |             0.0 |                         5.28\n2025-11-01 00:00:00 |                  5720 |            93.41 |            0.02 |                         6.57\n2025-12-01 00:00:00 |                  7454 |            95.26 |             0.0 |                         4.74\n(12 rows)\n\n=== Report 3: Carts.remind Failure Root Cause: Error Messages (written by Omar) ===\nEngineer's note: One row per failed carts.remind job run in Nov 2025, with tenant, timing and error_message/attempt to spot shared vs. scattered root causes; expect 20 rows.\nREPORT: Carts.remind Failure Root Cause: Error Messages\nQUESTION: Did the 20 carts.remind failures in Nov 2025 share a common root cause (pointing to one shared bug/config issue) or look like scattered, tenant-specific problems (pointing to load/capacity)?\ntenant_name            | queued_at               | started_at              | finished_at             | error_message                           | attempt\n-----------------------+-------------------------+-------------------------+-------------------------+-----------------------------------------+--------\nGratlinie Outdoor      | 2025-11-28 14:40:04.027 | 2025-11-28 14:40:05.004 | 2025-11-28 14:40:06.074 | 503 Service Unavailable: storefront API |       3\nMarrowby & Vane        | 2025-11-28 14:40:05.741 | 2025-11-28 14:40:10.093 | 2025-11-28 14:40:12.292 | 503 Service Unavailable: storefront API |       3\nCinder Peak Sports Co. | 2025-11-28 14:40:06.352 | 2025-11-28 14:40:06.776 | 2025-11-28 14:40:08.558 | 503 Service Unavailable: storefront API |       3\nPetalcourt Beauty      | 2025-11-28 14:40:06.878 | 2025-11-28 14:40:08.079 | 2025-11-28 14:40:09.171 | 503 Service Unavailable: storefront API |       3\nBrennquell Kaffee      | 2025-11-28 14:40:06.939 | 2025-11-28 14:40:07.282 | 2025-11-28 14:40:08.716 | 503 Service Unavailable: storefront API |       3\nKibblewick             | 2025-11-28 14:40:07.143 | 2025-11-28 14:40:07.534 | 2025-11-28 14:40:08.362 | 503 Service Unavailable: storefront API |       3\nFennel & Flint         | 2025-11-28 14:40:08.753 | 2025-11-28 14:40:12.312 | 2025-11-28 14:40:13.509 | 503 Service Unavailable: storefront API |       3\nStudio Vrij            | 2025-11-28 14:40:09.151 | 2025-11-28 14:40:12.35  | 2025-11-28 14:40:14.566 | 503 Service Unavailable: storefront API |       3\nAmpfeld Elektronik     | 2025-11-28 14:40:09.362 | 2025-11-28 14:40:11.517 | 2025-11-28 14:40:12.429 | 503 Service Unavailable: storefront API |       3\nCragsley Outfitters    | 2025-11-28 14:40:09.758 | 2025-11-28 14:40:11.022 | 2025-11-28 14:40:13.348 | 503 Service Unavailable: storefront API |       3\nGratlinie Outdoor      | 2025-11-28 15:40:03.673 | 2025-11-28 15:40:06.151 | 2025-11-28 15:40:07.4   | 503 Service Unavailable: storefront API |       3\nKibblewick             | 2025-11-28 15:40:05.175 | 2025-11-28 15:40:07.89  | 2025-11-28 15:40:11.728 | 503 Service Unavailable: storefront API |       3\nCragsley Outfitters    | 2025-11-28 15:40:05.504 | 2025-11-28 15:40:06.246 | 2025-11-28 15:40:09.483 | 503 Service Unavailable: storefront API |       3\nFennel & Flint         | 2025-11-28 15:40:05.605 | 2025-11-28 15:40:10.525 | 2025-11-28 15:40:11.663 | 503 Service Unavailable: storefront API |       3\nStudio Vrij            | 2025-11-28 15:40:05.75  | 2025-11-28 15:40:06.179 | 2025-11-28 15:40:07.151 | 503 Service Unavailable: storefront API |       3\nAmpfeld Elektronik     | 2025-11-28 15:40:05.814 | 2025-11-28 15:40:06.925 | 2025-11-28 15:40:08.173 | 503 Service Unavailable: storefront API |       3\nMarrowby & Vane        | 2025-11-28 15:40:06.447 | 2025-11-28 15:40:10.262 | 2025-11-28 15:40:11.83  | 503 Service Unavailable: storefront API |       3\nCinder Peak Sports Co. | 2025-11-28 15:40:07.655 | 2025-11-28 15:40:08.369 | 2025-11-28 15:40:09.655 | 503 Service Unavailable: storefront API |       3\nPetalcourt Beauty      | 2025-11-28 15:40:09.473 | 2025-11-28 15:40:14.407 | 2025-11-28 15:40:15.307 | 503 Service Unavailable: storefront API |       3\nBrennquell Kaffee      | 2025-11-28 15:40:10.038 | 2025-11-28 15:40:11.938 | 2025-11-28 15:40:15.191 | 503 Service Unavailable: storefront API |       3\n(20 rows)\n\n\nNight 2 of 4: you have 3 report slots tonight."}
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{"parentUuid":"2e7d271c-ca42-42a9-a836-bc83d31b2896","isSidechain":false,"promptId":"14d5ec65-0737-4232-8184-abbc24bf6d4e","type":"user","message":{"role":"user","content":"The printed reports of night 1:\n\n=== Report 1: Merchants Affected by the Sync-Webhook Stuck-Retry Pattern and Their Size (written by Grace) ===\nEngineer's note: Dry runs never expose data, so I could not inspect the exact distinct webhooks.name values; I matched any webhook whose name contains 'erp'+'sync', 'wms'+'sync', or 'accounting'+'sync' (case-insensitive) to capture the three sync types and close variants. Tenants with a qualifying webhook but no 2025 deliveries, orders, or EUR rate match show 0/NULL rather than being dropped; revenue_rank is NULL if the tenant had no 2025 orders. No row limit was applied since the analyst asked for all qualifying tenants.\nREPORT: Merchants Affected by the Sync-Webhook Stuck-Retry Pattern and Their Size\nQUESTION: How many merchants have ERP/WMS/Accounting sync webhooks stuck in retry, and is this concentrated in our biggest accounts or spread across the long tail?\ntenant_name                   | total_sync_deliveries_2025 | success_rate_pct | pending_or_retrying_rate_pct | total_orders_2025 | total_revenue_eur_2025 | revenue_rank\n------------------------------+----------------------------+------------------+------------------------------+-------------------+------------------------+-------------\nTeapot Society                |                          2 |             50.0 |                         50.0 |                 1 |                  78.81 |          112\nKaffeekontor Nord             |                         13 |            61.54 |                        38.46 |                 8 |                 405.82 |          105\nHautnah                       |                        120 |             80.0 |                         20.0 |                92 |                9190.62 |           51\nLumen Collective              |                         89 |            82.02 |                        17.98 |                52 |                6391.23 |           62\nHanse Industriebedarf         |                         97 |            83.51 |                        16.49 |                72 |              113320.38 |           13\nSeilschaft                    |                         25 |             84.0 |                         16.0 |                17 |                6759.28 |           60\nThistlecombe                  |                        140 |            84.29 |                        15.71 |                92 |               12570.49 |           44\nFolio & Fern                  |                         58 |            86.21 |                        13.79 |                46 |                2148.33 |           88\nBrennquell Kaffee             |                      14149 |            88.87 |                        11.12 |              4195 |              114554.65 |           11\nAmpfeld Elektronik            |                       1583 |            89.83 |                        10.17 |               449 |              512006.37 |            4\nLinden & Loom                 |                        614 |            91.21 |                         8.79 |               263 |               38475.39 |           20\nWohnglück                     |                        341 |            91.79 |                         8.21 |               151 |                22859.8 |           32\nKibble Crate                  |                         19 |            94.74 |                         5.26 |                18 |                1211.91 |           95\nCragsley Outfitters           |                      14104 |            95.04 |                         4.96 |              3635 |              724835.93 |            2\nBasecamp Nord                 |                         49 |            95.92 |                         4.08 |                43 |               10657.51 |           46\nPurrfect Pantry               |                         81 |             96.3 |                          3.7 |                74 |                3583.86 |           74\nKettle & Key                  |                         68 |            97.06 |                         2.94 |                60 |                7581.57 |           54\nVelo Noord                    |                         39 |            97.44 |                         2.56 |                31 |                9780.02 |           49\nNoordzee Surfwear             |                         88 |            97.73 |                         2.27 |                65 |               10894.01 |           45\nWiesgrund Wohnen              |                        642 |            97.98 |                         2.02 |               296 |               38433.09 |           21\nSorella Skin                  |                        105 |             98.1 |                          1.9 |                99 |               10495.53 |           47\nMirelle                       |                         68 |            98.53 |                         1.47 |                63 |                2920.13 |           78\nStudio Vrij                   |                       2396 |            98.62 |                         1.38 |               631 |              113540.14 |           12\nGratlinie Outdoor             |                        291 |            98.63 |                         1.37 |               232 |               96277.59 |           14\nMarrowby & Vane               |                       6425 |            98.63 |                         1.37 |              1758 |              288909.93 |            8\nKibblewick                    |                       1349 |            98.74 |                         1.26 |               672 |               23801.41 |           30\nFjordline Wear                |                         81 |            98.77 |                         1.23 |                57 |                8615.11 |           52\nNordic Bloom Cosmetics        |                        163 |            98.77 |                         1.23 |               151 |                19074.7 |           36\nOstwind Mode                  |                        162 |            98.77 |                         1.23 |               122 |                21621.0 |           33\nAtelier Nouveau               |                         87 |            98.85 |                         1.15 |                64 |                9406.81 |           50\nGrind Collective              |                       1136 |            98.94 |                         1.06 |              1111 |               25610.52 |           27\nKontor24 Bürobedarf           |                        534 |            99.06 |                         0.94 |               264 |              840409.53 |            1\nChocolaterie Mireille         |                        108 |            99.07 |                         0.93 |               104 |                4566.99 |           67\nSpice Route Box               |                        796 |            99.12 |                         0.88 |               780 |               17113.71 |           39\nBallycorran Industrial Supply |                        380 |            99.21 |                         0.79 |               344 |              559149.73 |            3\nCinder Peak Sports Co.        |                        748 |            99.33 |                         0.67 |               338 |              124756.84 |           10\nCoastline Interiors           |                        179 |            99.44 |                         0.56 |               155 |               21468.31 |           34\nFennel & Flint                |                        814 |            99.51 |                         0.49 |               389 |               65764.13 |           16\nOfficeHub Nordic              |                        350 |            99.71 |                         0.29 |               170 |              428472.93 |            5\nPetalcourt Beauty             |                        775 |            99.74 |                         0.26 |               706 |               63508.22 |           17\nBijoux Lune                   |                         23 |            100.0 |                          0.0 |                21 |                3794.91 |           72\nCopper Lane                   |                          8 |            100.0 |                          0.0 |                 6 |                1003.56 |           98\nCourtside Collective          |                         23 |            100.0 |                          0.0 |                20 |                8514.48 |           53\nDewdrop Studio                |                          5 |            100.0 |                          0.0 |                 5 |                 271.34 |          108\nFern & Fable                  |                         48 |            100.0 |                          0.0 |                42 |                 5564.1 |           64\nGlasshouse Grooming           |                         10 |            100.0 |                          0.0 |                10 |                 1455.2 |           93\nNorthstar Watches             |                         14 |            100.0 |                          0.0 |                11 |                2655.08 |           85\nOlive & Oak Pantry            |                          1 |            100.0 |                          0.0 |                 1 |                 118.55 |          111\nOndine Botanicals             |                         27 |            100.0 |                          0.0 |                25 |                1694.77 |           90\nPapeterie Grossiste           |                         20 |            100.0 |                          0.0 |                18 |               41189.21 |           18\n(56 rows, first 50 printed)\n\n=== Report 2: 2025 Monthly Stuck-Retry Rate for Sync Webhooks (ERP/WMS/Accounting) (written by Bea) ===\nEngineer's note: Platform-wide monthly rates for webhooks named 'ERP sync', 'WMS sync', 'ACCOUNTING sync' across all tenants, using webhook_deliveries.created_at for 2025. \"Pending or retrying\" combines the pending and retrying status codes (the only non-success/non-failed states in this table).\nREPORT: 2025 Monthly Stuck-Retry Rate for Sync Webhooks (ERP/WMS/Accounting)\nQUESTION: Has the ~15% stuck-in-retry rate on sync webhooks been happening all year, or did it start/worsen recently?\nmonth               | total_sync_deliveries | success_rate_pct | failed_rate_pct | pending_or_retrying_rate_pct\n--------------------+-----------------------+------------------+-----------------+-----------------------------\n2025-01-01 00:00:00 |                  4438 |            94.21 |            0.02 |                         5.77\n2025-02-01 00:00:00 |                  3088 |             94.3 |             0.0 |                          5.7\n2025-03-01 00:00:00 |                  3551 |            93.41 |             0.0 |                         6.59\n2025-04-01 00:00:00 |                  3434 |            94.09 |             0.0 |                         5.91\n2025-05-01 00:00:00 |                  3810 |            93.99 |             0.0 |                         6.01\n2025-06-01 00:00:00 |                  3413 |            94.67 |             0.0 |                         5.33\n2025-07-01 00:00:00 |                  3702 |            94.41 |             0.0 |                         5.59\n2025-08-01 00:00:00 |                  3308 |            94.77 |             0.0 |                         5.23\n2025-09-01 00:00:00 |                  3724 |            94.04 |             0.0 |                         5.96\n2025-10-01 00:00:00 |                  4110 |            94.72 |             0.0 |                         5.28\n2025-11-01 00:00:00 |                  5720 |            93.41 |            0.02 |                         6.57\n2025-12-01 00:00:00 |                  7454 |            95.26 |             0.0 |                         4.74\n(12 rows)\n\n=== Report 3: Carts.remind Failure Root Cause: Error Messages (written by Omar) ===\nEngineer's note: One row per failed carts.remind job run in Nov 2025, with tenant, timing and error_message/attempt to spot shared vs. scattered root causes; expect 20 rows.\nREPORT: Carts.remind Failure Root Cause: Error Messages\nQUESTION: Did the 20 carts.remind failures in Nov 2025 share a common root cause (pointing to one shared bug/config issue) or look like scattered, tenant-specific problems (pointing to load/capacity)?\ntenant_name            | queued_at               | started_at              | finished_at             | error_message                           | attempt\n-----------------------+-------------------------+-------------------------+-------------------------+-----------------------------------------+--------\nGratlinie Outdoor      | 2025-11-28 14:40:04.027 | 2025-11-28 14:40:05.004 | 2025-11-28 14:40:06.074 | 503 Service Unavailable: storefront API |       3\nMarrowby & Vane        | 2025-11-28 14:40:05.741 | 2025-11-28 14:40:10.093 | 2025-11-28 14:40:12.292 | 503 Service Unavailable: storefront API |       3\nCinder Peak Sports Co. | 2025-11-28 14:40:06.352 | 2025-11-28 14:40:06.776 | 2025-11-28 14:40:08.558 | 503 Service Unavailable: storefront API |       3\nPetalcourt Beauty      | 2025-11-28 14:40:06.878 | 2025-11-28 14:40:08.079 | 2025-11-28 14:40:09.171 | 503 Service Unavailable: storefront API |       3\nBrennquell Kaffee      | 2025-11-28 14:40:06.939 | 2025-11-28 14:40:07.282 | 2025-11-28 14:40:08.716 | 503 Service Unavailable: storefront API |       3\nKibblewick             | 2025-11-28 14:40:07.143 | 2025-11-28 14:40:07.534 | 2025-11-28 14:40:08.362 | 503 Service Unavailable: storefront API |       3\nFennel & Flint         | 2025-11-28 14:40:08.753 | 2025-11-28 14:40:12.312 | 2025-11-28 14:40:13.509 | 503 Service Unavailable: storefront API |       3\nStudio Vrij            | 2025-11-28 14:40:09.151 | 2025-11-28 14:40:12.35  | 2025-11-28 14:40:14.566 | 503 Service Unavailable: storefront API |       3\nAmpfeld Elektronik     | 2025-11-28 14:40:09.362 | 2025-11-28 14:40:11.517 | 2025-11-28 14:40:12.429 | 503 Service Unavailable: storefront API |       3\nCragsley Outfitters    | 2025-11-28 14:40:09.758 | 2025-11-28 14:40:11.022 | 2025-11-28 14:40:13.348 | 503 Service Unavailable: storefront API |       3\nGratlinie Outdoor      | 2025-11-28 15:40:03.673 | 2025-11-28 15:40:06.151 | 2025-11-28 15:40:07.4   | 503 Service Unavailable: storefront API |       3\nKibblewick             | 2025-11-28 15:40:05.175 | 2025-11-28 15:40:07.89  | 2025-11-28 15:40:11.728 | 503 Service Unavailable: storefront API |       3\nCragsley Outfitters    | 2025-11-28 15:40:05.504 | 2025-11-28 15:40:06.246 | 2025-11-28 15:40:09.483 | 503 Service Unavailable: storefront API |       3\nFennel & Flint         | 2025-11-28 15:40:05.605 | 2025-11-28 15:40:10.525 | 2025-11-28 15:40:11.663 | 503 Service Unavailable: storefront API |       3\nStudio Vrij            | 2025-11-28 15:40:05.75  | 2025-11-28 15:40:06.179 | 2025-11-28 15:40:07.151 | 503 Service Unavailable: storefront API |       3\nAmpfeld Elektronik     | 2025-11-28 15:40:05.814 | 2025-11-28 15:40:06.925 | 2025-11-28 15:40:08.173 | 503 Service Unavailable: storefront API |       3\nMarrowby & Vane        | 2025-11-28 15:40:06.447 | 2025-11-28 15:40:10.262 | 2025-11-28 15:40:11.83  | 503 Service Unavailable: storefront API |       3\nCinder Peak Sports Co. | 2025-11-28 15:40:07.655 | 2025-11-28 15:40:08.369 | 2025-11-28 15:40:09.655 | 503 Service Unavailable: storefront API |       3\nPetalcourt Beauty      | 2025-11-28 15:40:09.473 | 2025-11-28 15:40:14.407 | 2025-11-28 15:40:15.307 | 503 Service Unavailable: storefront API |       3\nBrennquell Kaffee      | 2025-11-28 15:40:10.038 | 2025-11-28 15:40:11.938 | 2025-11-28 15:40:15.191 | 503 Service Unavailable: storefront API |       3\n(20 rows)\n\n\nNight 2 of 4: you have 3 report slots tonight."},"uuid":"8b86c5a0-6029-4641-a5a6-47c190e17f69","timestamp":"2026-10-04T03:34:26.027Z","permissionMode":"default","promptSource":"sdk","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460","version":"2.1.275","gitBranch":"HEAD"}
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{"parentUuid":"209c7915-692e-44b8-a784-777fcc006bd1","isSidechain":false,"message":{"model":"claude-sonnet-5","id":"msg_011CfgVfKXouJcnr5PvFnHXf","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01JzcMLAUgAQEiZMSFf1bD3F","name":"StructuredOutput","input":{"notes":"Night 1 results reshape the story in an important way. Report 1 shows the stuck-retry pattern touches 56 tenants (not a handful) at wildly different % rates, but many of the worst percentages (e.g. 50%, 38%) are tiny-volume tenants (1-2 stuck deliveries) - a small-sample artifact, not a real signal. When I compute absolute stuck-delivery counts (rate x volume) by hand from that report, ~91% of all stuck deliveries sit with just 4 tenants: Brennquell Kaffee (~1,574 stuck), Cragsley Outfitters (~700), Ampfeld Elektronik (~161), and Marrowby & Vane (~88) - and these are the same 4 of our 10 carts.remind-affected tenants with real order volume. That's the concentration story Diego wants, and I don't need another slot to prove it, it's derivable from what we have. Report 2 shows the platform-wide stuck rate has been flat at ~5.2-6.6% every month all year (not the ~15% I quoted last week, which was a selection-biased view of only the worst 20 endpoints) - so no recent regression at the platform level. Report 3 is a clean, high-confidence answer on carts.remind: all 20 failures are the identical error ('503 Service Unavailable: storefront API') in two one-hour-apart batches on Nov 28, across the same 10 tenants both times - this reads as a brief outage of one shared upstream dependency, not a tenant-specific or load issue, and not really a 'config/feature flag' story as I speculated last week. Tonight I'll: (1) check whether the stuck rate for those same 4 high-impact tenants has itself been stable all year or is recent/worsening (refines Diego's #2 to the account level that matters for the fix-it conversation); (2) pull internal health checks around the Nov 28 14:00-16:00 window to see if monitoring caught the storefront-API blip in real time (closes out #3 with hard evidence); (3) do a first lightweight look at ticket subject lines from the 10 affected tenants in the weeks before Nov 24 to give Diego his requested 'rough read' on whether support was an early warning signal (#4).","requests":[{"title":"2025 Monthly Sync-Webhook Stuck Rate for the 4 Highest-Impact Tenants","question":"For the 4 tenants that account for ~91% of all stuck sync-webhook deliveries (Brennquell Kaffee, Cragsley Outfitters, Ampfeld Elektronik, Marrowby & Vane), has their stuck rate been stable all year or is it new/worsening?","grain":"one row per tenant per calendar month of 2025","columns":["tenant_name","month","total_sync_deliveries","pending_or_retrying_rate_pct"],"details":"Table webhook_deliveries joined to webhooks (name in 'ERP sync','WMS sync','ACCOUNTING sync' and close variants, same matching as the prior sync-webhook report) and tenants, restricted to these 4 tenants: Brennquell Kaffee, Cragsley Outfitters, Ampfeld Elektronik, Marrowby & Vane. Filter created_at in 2025. Group by tenant and month of created_at. pending_or_retrying_rate_pct = share of that tenant-month's sync deliveries with status in ('pending','retrying'), rounded 2dp. Order by tenant_name ascending, month ascending.","builds_on":["Merchants Affected by the Sync-Webhook Stuck-Retry Pattern and Their Size","2025 Monthly Stuck-Retry Rate for Sync Webhooks (ERP/WMS/Accounting)"]},{"title":"Internal Health Check Signal Around the Nov 28 2025 Storefront API Outage","question":"Did our internal health checks detect a problem around the two carts.remind failure windows (2025-11-28, roughly 14:40 and 15:40 UTC)?","grain":"one row per hour and component, for 2025-11-28 between 12:00 and 18:00 UTC","columns":["hour","component","total_checks","unhealthy_checks","unhealthy_rate_pct","avg_latency_ms"],"details":"Table system_health_checks. Filter checked_at >= 2025-11-28 12:00:00 and < 2025-11-28 18:00:00. Group by hour of checked_at (truncated to the hour) and component. unhealthy_rate_pct = is_healthy=false count / total_checks * 100, rounded 2dp. avg_latency_ms over non-null latency_ms, rounded to whole number. Order by hour ascending, component ascending.","builds_on":["Carts.remind Failure Root Cause: Error Messages","2025 Q4 Weekly Job Failures vs Health Check Alerts"]},{"title":"Ticket Subject Lines from Affected Merchants Before the Nov 24 Failures","question":"In the weeks before the carts.remind failures showed up in our logs, did the 10 affected merchants' shoppers raise tickets that look related to carts, reminders, or duplicate/missing emails?","grain":"one row per ticket","columns":["tenant_name","created_at","subject","status","priority"],"details":"Table tickets joined to users (requester_user_id) to get tenant_id, joined to tenants for tenant_name. Filter requester's tenant_id in the 10 carts.remind-affected tenants (Ampfeld Elektronik, Brennquell Kaffee, Cinder Peak Sports Co., Cragsley Outfitters, Fennel & Flint, Gratlinie Outdoor, Kibblewick, Marrowby & Vane, Petalcourt Beauty, Studio Vrij) and created_at >= 2025-10-27 and < 2025-11-24. Additionally filter to tickets whose subject matches (case-insensitive) any of: '%cart%', '%remind%', '%duplicate%', '%missing%', '%email%'. Order by created_at ascending. If more than 50 rows match, note the total count; otherwise return all.","builds_on":["Support Ticket Volume Around the Nov 2025 Carts.remind Incident","Support Tickets: Affected vs Other Tenants, Baseline vs Incident Period"]}]},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":10833,"cache_read_input_tokens":104407,"output_tokens":8075,"output_tokens_details":{"thinking_tokens":5846},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":10833,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":8075,"cache_read_input_tokens":104407,"cache_creation_input_tokens":10833,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":10833},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"wireToolInputs":{"toolu_01JzcMLAUgAQEiZMSFf1bD3F":{"notes":"Night 1 results reshape the story in an important way. Report 1 shows the stuck-retry pattern touches 56 tenants (not a handful) at wildly different % rates, but many of the worst percentages (e.g. 50%, 38%) are tiny-volume tenants (1-2 stuck deliveries) - a small-sample artifact, not a real signal. When I compute absolute stuck-delivery counts (rate x volume) by hand from that report, ~91% of all stuck deliveries sit with just 4 tenants: Brennquell Kaffee (~1,574 stuck), Cragsley Outfitters (~700), Ampfeld Elektronik (~161), and Marrowby & Vane (~88) - and these are the same 4 of our 10 carts.remind-affected tenants with real order volume. That's the concentration story Diego wants, and I don't need another slot to prove it, it's derivable from what we have. Report 2 shows the platform-wide stuck rate has been flat at ~5.2-6.6% every month all year (not the ~15% I quoted last week, which was a selection-biased view of only the worst 20 endpoints) - so no recent regression at the platform level. Report 3 is a clean, high-confidence answer on carts.remind: all 20 failures are the identical error ('503 Service Unavailable: storefront API') in two one-hour-apart batches on Nov 28, across the same 10 tenants both times - this reads as a brief outage of one shared upstream dependency, not a tenant-specific or load issue, and not really a 'config/feature flag' story as I speculated last week. Tonight I'll: (1) check whether the stuck rate for those same 4 high-impact tenants has itself been stable all year or is recent/worsening (refines Diego's #2 to the account level that matters for the fix-it conversation); (2) pull internal health checks around the Nov 28 14:00-16:00 window to see if monitoring caught the storefront-API blip in real time (closes out #3 with hard evidence); (3) do a first lightweight look at ticket subject lines from the 10 affected tenants in the weeks before Nov 24 to give Diego his requested 'rough read' on whether support was an early warning signal (#4).","requests":[{"title":"2025 Monthly Sync-Webhook Stuck Rate for the 4 Highest-Impact Tenants","question":"For the 4 tenants that account for ~91% of all stuck sync-webhook deliveries (Brennquell Kaffee, Cragsley Outfitters, Ampfeld Elektronik, Marrowby & Vane), has their stuck rate been stable all year or is it new/worsening?","grain":"one row per tenant per calendar month of 2025","columns":["tenant_name","month","total_sync_deliveries","pending_or_retrying_rate_pct"],"details":"Table webhook_deliveries joined to webhooks (name in 'ERP sync','WMS sync','ACCOUNTING sync' and close variants, same matching as the prior sync-webhook report) and tenants, restricted to these 4 tenants: Brennquell Kaffee, Cragsley Outfitters, Ampfeld Elektronik, Marrowby & Vane. 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Group by hour of checked_at (truncated to the hour) and component. unhealthy_rate_pct = is_healthy=false count / total_checks * 100, rounded 2dp. avg_latency_ms over non-null latency_ms, rounded to whole number. Order by hour ascending, component ascending.","builds_on":["Carts.remind Failure Root Cause: Error Messages","2025 Q4 Weekly Job Failures vs Health Check Alerts"]},{"title":"Ticket Subject Lines from Affected Merchants Before the Nov 24 Failures","question":"In the weeks before the carts.remind failures showed up in our logs, did the 10 affected merchants' shoppers raise tickets that look related to carts, reminders, or duplicate/missing emails?","grain":"one row per ticket","columns":["tenant_name","created_at","subject","status","priority"],"details":"Table tickets joined to users (requester_user_id) to get tenant_id, joined to tenants for tenant_name. Filter requester's tenant_id in the 10 carts.remind-affected tenants (Ampfeld Elektronik, Brennquell Kaffee, Cinder Peak Sports Co., Cragsley Outfitters, Fennel & Flint, Gratlinie Outdoor, Kibblewick, Marrowby & Vane, Petalcourt Beauty, Studio Vrij) and created_at >= 2025-10-27 and < 2025-11-24. Additionally filter to tickets whose subject matches (case-insensitive) any of: '%cart%', '%remind%', '%duplicate%', '%missing%', '%email%'. Order by created_at ascending. If more than 50 rows match, note the total count; otherwise return all.","builds_on":["Support Ticket Volume Around the Nov 2025 Carts.remind Incident","Support Tickets: Affected vs Other Tenants, Baseline vs Incident Period"]}]}},"apiBlockIndex":1,"requestId":"req_011CfgVfK4Y4tEnqvmsY1CZ3","type":"assistant","uuid":"be60753e-5f8f-4935-a599-dfc73f0a38b2","timestamp":"2026-10-04T03:35:44.128Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"be60753e-5f8f-4935-a599-dfc73f0a38b2","isSidechain":false,"attachment":{"type":"structured_output","data":{"notes":"Night 1 results reshape the story in an important way. Report 1 shows the stuck-retry pattern touches 56 tenants (not a handful) at wildly different % rates, but many of the worst percentages (e.g. 50%, 38%) are tiny-volume tenants (1-2 stuck deliveries) - a small-sample artifact, not a real signal. When I compute absolute stuck-delivery counts (rate x volume) by hand from that report, ~91% of all stuck deliveries sit with just 4 tenants: Brennquell Kaffee (~1,574 stuck), Cragsley Outfitters (~700), Ampfeld Elektronik (~161), and Marrowby & Vane (~88) - and these are the same 4 of our 10 carts.remind-affected tenants with real order volume. That's the concentration story Diego wants, and I don't need another slot to prove it, it's derivable from what we have. Report 2 shows the platform-wide stuck rate has been flat at ~5.2-6.6% every month all year (not the ~15% I quoted last week, which was a selection-biased view of only the worst 20 endpoints) - so no recent regression at the platform level. Report 3 is a clean, high-confidence answer on carts.remind: all 20 failures are the identical error ('503 Service Unavailable: storefront API') in two one-hour-apart batches on Nov 28, across the same 10 tenants both times - this reads as a brief outage of one shared upstream dependency, not a tenant-specific or load issue, and not really a 'config/feature flag' story as I speculated last week. Tonight I'll: (1) check whether the stuck rate for those same 4 high-impact tenants has itself been stable all year or is recent/worsening (refines Diego's #2 to the account level that matters for the fix-it conversation); (2) pull internal health checks around the Nov 28 14:00-16:00 window to see if monitoring caught the storefront-API blip in real time (closes out #3 with hard evidence); (3) do a first lightweight look at ticket subject lines from the 10 affected tenants in the weeks before Nov 24 to give Diego his requested 'rough read' on whether support was an early warning signal (#4).","requests":[{"title":"2025 Monthly Sync-Webhook Stuck Rate for the 4 Highest-Impact Tenants","question":"For the 4 tenants that account for ~91% of all stuck sync-webhook deliveries (Brennquell Kaffee, Cragsley Outfitters, Ampfeld Elektronik, Marrowby & Vane), has their stuck rate been stable all year or is it new/worsening?","grain":"one row per tenant per calendar month of 2025","columns":["tenant_name","month","total_sync_deliveries","pending_or_retrying_rate_pct"],"details":"Table webhook_deliveries joined to webhooks (name in 'ERP sync','WMS sync','ACCOUNTING sync' and close variants, same matching as the prior sync-webhook report) and tenants, restricted to these 4 tenants: Brennquell Kaffee, Cragsley Outfitters, Ampfeld Elektronik, Marrowby & Vane. Filter created_at in 2025. Group by tenant and month of created_at. pending_or_retrying_rate_pct = share of that tenant-month's sync deliveries with status in ('pending','retrying'), rounded 2dp. Order by tenant_name ascending, month ascending.","builds_on":["Merchants Affected by the Sync-Webhook Stuck-Retry Pattern and Their Size","2025 Monthly Stuck-Retry Rate for Sync Webhooks (ERP/WMS/Accounting)"]},{"title":"Internal Health Check Signal Around the Nov 28 2025 Storefront API Outage","question":"Did our internal health checks detect a problem around the two carts.remind failure windows (2025-11-28, roughly 14:40 and 15:40 UTC)?","grain":"one row per hour and component, for 2025-11-28 between 12:00 and 18:00 UTC","columns":["hour","component","total_checks","unhealthy_checks","unhealthy_rate_pct","avg_latency_ms"],"details":"Table system_health_checks. Filter checked_at >= 2025-11-28 12:00:00 and < 2025-11-28 18:00:00. Group by hour of checked_at (truncated to the hour) and component. unhealthy_rate_pct = is_healthy=false count / total_checks * 100, rounded 2dp. avg_latency_ms over non-null latency_ms, rounded to whole number. Order by hour ascending, component ascending.","builds_on":["Carts.remind Failure Root Cause: Error Messages","2025 Q4 Weekly Job Failures vs Health Check Alerts"]},{"title":"Ticket Subject Lines from Affected Merchants Before the Nov 24 Failures","question":"In the weeks before the carts.remind failures showed up in our logs, did the 10 affected merchants' shoppers raise tickets that look related to carts, reminders, or duplicate/missing emails?","grain":"one row per ticket","columns":["tenant_name","created_at","subject","status","priority"],"details":"Table tickets joined to users (requester_user_id) to get tenant_id, joined to tenants for tenant_name. Filter requester's tenant_id in the 10 carts.remind-affected tenants (Ampfeld Elektronik, Brennquell Kaffee, Cinder Peak Sports Co., Cragsley Outfitters, Fennel & Flint, Gratlinie Outdoor, Kibblewick, Marrowby & Vane, Petalcourt Beauty, Studio Vrij) and created_at >= 2025-10-27 and < 2025-11-24. Additionally filter to tickets whose subject matches (case-insensitive) any of: '%cart%', '%remind%', '%duplicate%', '%missing%', '%email%'. Order by created_at ascending. If more than 50 rows match, note the total count; otherwise return all.","builds_on":["Support Ticket Volume Around the Nov 2025 Carts.remind Incident","Support Tickets: Affected vs Other Tenants, Baseline vs Incident Period"]}]},"toolUseID":"toolu_01JzcMLAUgAQEiZMSFf1bD3F"},"type":"attachment","uuid":"aadba190-ead5-4a26-896d-654e8c8fa9c6","timestamp":"2026-10-04T03:35:44.137Z","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460","version":"2.1.275","gitBranch":"HEAD"}
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{"type":"last-prompt","lastPrompt":"The printed reports of night 1:  === Report 1: Merchants Affected by the Sync-Webhook Stuck-Retry Pattern and Their Size (written by Grace) === Engineer's note: Dry runs never expose data, so I could…","leafUuid":"c512acf7-a9c7-4916-82b5-1c5ae9f2e93d","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460"}
{"type":"ai-title","aiTitle":"2025 platform reliability review","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460"}
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{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-04T03:36:08.003Z","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460","content":"The printed reports of night 2:\n\n=== Report 1: 2025 Monthly Sync-Webhook Stuck Rate for the 4 Highest-Impact Tenants (written by Omar) ===\nEngineer's note: Month is printed as the first calendar day of that month (date type) since TO_CHAR isn't available in this engine; sync-webhook matching uses ILIKE '%ERP%sync%'/'%WMS%sync%'/'%ACCOUNTING%sync%' on webhooks.name to catch close name variants, consistent with the prior sync-webhook report's approach.\nREPORT: 2025 Monthly Sync-Webhook Stuck Rate for the 4 Highest-Impact Tenants\nQUESTION: For the 4 tenants that account for ~91% of all stuck sync-webhook deliveries (Brennquell Kaffee, Cragsley Outfitters, Ampfeld Elektronik, Marrowby & Vane), has their stuck rate been stable all year or is it new/worsening?\ntenant_name         | month      | total_sync_deliveries | pending_or_retrying_rate_pct\n--------------------+------------+-----------------------+-----------------------------\nAmpfeld Elektronik  | 2025-01-01 |                   185 |                        11.35\nAmpfeld Elektronik  | 2025-02-01 |                    97 |                        11.34\nAmpfeld Elektronik  | 2025-03-01 |                    60 |                         3.33\nAmpfeld Elektronik  | 2025-04-01 |                    80 |                         10.0\nAmpfeld Elektronik  | 2025-05-01 |                   106 |                        11.32\nAmpfeld Elektronik  | 2025-06-01 |                    87 |                          4.6\nAmpfeld Elektronik  | 2025-07-01 |                    98 |                         10.2\nAmpfeld Elektronik  | 2025-08-01 |                    78 |                         7.69\nAmpfeld Elektronik  | 2025-09-01 |                   117 |                        17.09\nAmpfeld Elektronik  | 2025-10-01 |                   106 |                         7.55\nAmpfeld Elektronik  | 2025-11-01 |                   235 |                        11.06\nAmpfeld Elektronik  | 2025-12-01 |                   334 |                         9.88\nBrennquell Kaffee   | 2025-01-01 |                  1309 |                        11.69\nBrennquell Kaffee   | 2025-02-01 |                  1086 |                        10.41\nBrennquell Kaffee   | 2025-03-01 |                  1275 |                        13.73\nBrennquell Kaffee   | 2025-04-01 |                  1139 |                        11.41\nBrennquell Kaffee   | 2025-05-01 |                  1104 |                         12.5\nBrennquell Kaffee   | 2025-06-01 |                  1032 |                        10.27\nBrennquell Kaffee   | 2025-07-01 |                  1090 |                        10.64\nBrennquell Kaffee   | 2025-08-01 |                  1068 |                         9.93\nBrennquell Kaffee   | 2025-09-01 |                  1139 |                        10.97\nBrennquell Kaffee   | 2025-10-01 |                  1175 |                          9.7\nBrennquell Kaffee   | 2025-11-01 |                  1186 |                        11.72\nBrennquell Kaffee   | 2025-12-01 |                  1546 |                        10.22\nCragsley Outfitters | 2025-01-01 |                  1177 |                         3.91\nCragsley Outfitters | 2025-02-01 |                   705 |                         5.11\nCragsley Outfitters | 2025-03-01 |                   858 |                         4.78\nCragsley Outfitters | 2025-04-01 |                   900 |                         4.56\nCragsley Outfitters | 2025-05-01 |                  1045 |                         5.17\nCragsley Outfitters | 2025-06-01 |                   942 |                         4.14\nCragsley Outfitters | 2025-07-01 |                  1088 |                         5.06\nCragsley Outfitters | 2025-08-01 |                   816 |                          4.9\nCragsley Outfitters | 2025-09-01 |                   999 |                          4.8\nCragsley Outfitters | 2025-10-01 |                  1171 |                         5.12\nCragsley Outfitters | 2025-11-01 |                  1861 |                          6.5\nCragsley Outfitters | 2025-12-01 |                  2542 |                         4.68\nMarrowby & Vane     | 2025-01-01 |                   708 |                         1.27\nMarrowby & Vane     | 2025-02-01 |                   362 |                         0.83\nMarrowby & Vane     | 2025-03-01 |                   433 |                         0.23\nMarrowby & Vane     | 2025-04-01 |                   389 |                         1.29\nMarrowby & Vane     | 2025-05-01 |                   430 |                         0.93\nMarrowby & Vane     | 2025-06-01 |                   396 |                         2.02\nMarrowby & Vane     | 2025-07-01 |                   450 |                         0.89\nMarrowby & Vane     | 2025-08-01 |                   386 |                          1.3\nMarrowby & Vane     | 2025-09-01 |                   468 |                         0.21\nMarrowby & Vane     | 2025-10-01 |                   507 |                         1.58\nMarrowby & Vane     | 2025-11-01 |                   823 |                         4.37\nMarrowby & Vane     | 2025-12-01 |                  1073 |                         0.37\n(48 rows)\n\n=== Report 2: Internal Health Check Signal Around the Nov 28 2025 Storefront API Outage (written by Ines) ===\nEngineer's note: system_health_checks is not tenant-scoped in this report (it tracks internal platform components, not merchant data), so results are aggregated across all tenants per hour/component as requested.\nREPORT: Internal Health Check Signal Around the Nov 28 2025 Storefront API Outage\nQUESTION: Did our internal health checks detect a problem around the two carts.remind failure windows (2025-11-28, roughly 14:40 and 15:40 UTC)?\nhour                | component            | total_checks | unhealthy_checks | unhealthy_rate_pct | avg_latency_ms\n--------------------+----------------------+--------------+------------------+--------------------+---------------\n2025-11-28 12:00:00 | accounting_connector |            5 |                1 |               20.0 |         2085.0\n2025-11-28 12:00:00 | checkout             |          111 |                1 |                0.9 |          294.0\n2025-11-28 12:00:00 | erp_connector        |           54 |                2 |                3.7 |          466.0\n2025-11-28 12:00:00 | marketplace_feed     |           15 |                2 |              13.33 |         1422.0\n2025-11-28 12:00:00 | storefront           |          111 |                1 |                0.9 |          194.0\n2025-11-28 12:00:00 | wms_connector        |           13 |                0 |                0.0 |           99.0\n2025-11-28 14:00:00 | accounting_connector |            5 |                0 |                0.0 |           96.0\n2025-11-28 14:00:00 | checkout             |          111 |              111 |              100.0 |        10000.0\n2025-11-28 14:00:00 | erp_connector        |           54 |                2 |                3.7 |          461.0\n2025-11-28 14:00:00 | marketplace_feed     |           15 |                0 |                0.0 |          101.0\n2025-11-28 14:00:00 | storefront           |          111 |              111 |              100.0 |        10000.0\n2025-11-28 14:00:00 | wms_connector        |           13 |                0 |                0.0 |          110.0\n2025-11-28 16:00:00 | accounting_connector |            5 |                0 |                0.0 |           83.0\n2025-11-28 16:00:00 | checkout             |          111 |                3 |                2.7 |          451.0\n2025-11-28 16:00:00 | erp_connector        |           54 |                0 |                0.0 |          102.0\n2025-11-28 16:00:00 | marketplace_feed     |           15 |                1 |               6.67 |          730.0\n2025-11-28 16:00:00 | storefront           |          111 |                0 |                0.0 |          101.0\n2025-11-28 16:00:00 | wms_connector        |           13 |                0 |                0.0 |          119.0\n(18 rows)\n\n=== Report 3: Ticket Subject Lines from Affected Merchants Before the Nov 24 Failures (written by Bea) ===\nEngineer's note: Query returns every matching ticket ordered by created_at (no row cap applied here); if the batch run returns more than 50 rows, only the first 50 will print per house rules and the analyst should check the total row count noted on the report.\nREPORT: Ticket Subject Lines from Affected Merchants Before the Nov 24 Failures\nQUESTION: In the weeks before the carts.remind failures showed up in our logs, did the 10 affected merchants' shoppers raise tickets that look related to carts, reminders, or duplicate/missing emails?\ntenant_name | created_at | subject | status | priority\n------------+------------+---------+--------+---------\n(0 rows)\n\n\nNight 3 of 4: you have 3 report slots tonight."}
{"type":"queue-operation","operation":"dequeue","timestamp":"2026-10-04T03:36:08.003Z","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460"}
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{"parentUuid":"7370f91c-e027-4e7b-b0cc-cc1713527cf8","isSidechain":false,"type":"assistant","uuid":"8012ba6b-28e6-4669-9eb3-3fbf1d3c1682","timestamp":"2026-10-04T03:36:07.996Z","message":{"diagnostics":null,"id":"2d83abc7-ae89-4486-aad7-22b587332df1","container":null,"model":"<synthetic>","role":"assistant","stop_details":null,"stop_reason":"stop_sequence","stop_sequence":"","type":"message","usage":{"output_tokens_details":null,"input_tokens":0,"output_tokens":0,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":null,"cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":0},"inference_geo":null,"iterations":null,"speed":null},"content":[{"type":"text","text":"No response requested."}],"context_management":null},"isApiErrorMessage":false,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"8012ba6b-28e6-4669-9eb3-3fbf1d3c1682","isSidechain":false,"promptId":"c0cc600a-c9bb-4d62-826c-cf1a5df88774","type":"user","message":{"role":"user","content":"The printed reports of night 2:\n\n=== Report 1: 2025 Monthly Sync-Webhook Stuck Rate for the 4 Highest-Impact Tenants (written by Omar) ===\nEngineer's note: Month is printed as the first calendar day of that month (date type) since TO_CHAR isn't available in this engine; sync-webhook matching uses ILIKE '%ERP%sync%'/'%WMS%sync%'/'%ACCOUNTING%sync%' on webhooks.name to catch close name variants, consistent with the prior sync-webhook report's approach.\nREPORT: 2025 Monthly Sync-Webhook Stuck Rate for the 4 Highest-Impact Tenants\nQUESTION: For the 4 tenants that account for ~91% of all stuck sync-webhook deliveries (Brennquell Kaffee, Cragsley Outfitters, Ampfeld Elektronik, Marrowby & Vane), has their stuck rate been stable all year or is it new/worsening?\ntenant_name         | month      | total_sync_deliveries | pending_or_retrying_rate_pct\n--------------------+------------+-----------------------+-----------------------------\nAmpfeld Elektronik  | 2025-01-01 |                   185 |                        11.35\nAmpfeld Elektronik  | 2025-02-01 |                    97 |                        11.34\nAmpfeld Elektronik  | 2025-03-01 |                    60 |                         3.33\nAmpfeld Elektronik  | 2025-04-01 |                    80 |                         10.0\nAmpfeld Elektronik  | 2025-05-01 |                   106 |                        11.32\nAmpfeld Elektronik  | 2025-06-01 |                    87 |                          4.6\nAmpfeld Elektronik  | 2025-07-01 |                    98 |                         10.2\nAmpfeld Elektronik  | 2025-08-01 |                    78 |                         7.69\nAmpfeld Elektronik  | 2025-09-01 |                   117 |                        17.09\nAmpfeld Elektronik  | 2025-10-01 |                   106 |                         7.55\nAmpfeld Elektronik  | 2025-11-01 |                   235 |                        11.06\nAmpfeld Elektronik  | 2025-12-01 |                   334 |                         9.88\nBrennquell Kaffee   | 2025-01-01 |                  1309 |                        11.69\nBrennquell Kaffee   | 2025-02-01 |                  1086 |                        10.41\nBrennquell Kaffee   | 2025-03-01 |                  1275 |                        13.73\nBrennquell Kaffee   | 2025-04-01 |                  1139 |                        11.41\nBrennquell Kaffee   | 2025-05-01 |                  1104 |                         12.5\nBrennquell Kaffee   | 2025-06-01 |                  1032 |                        10.27\nBrennquell Kaffee   | 2025-07-01 |                  1090 |                        10.64\nBrennquell Kaffee   | 2025-08-01 |                  1068 |                         9.93\nBrennquell Kaffee   | 2025-09-01 |                  1139 |                        10.97\nBrennquell Kaffee   | 2025-10-01 |                  1175 |                          9.7\nBrennquell Kaffee   | 2025-11-01 |                  1186 |                        11.72\nBrennquell Kaffee   | 2025-12-01 |                  1546 |                        10.22\nCragsley Outfitters | 2025-01-01 |                  1177 |                         3.91\nCragsley Outfitters | 2025-02-01 |                   705 |                         5.11\nCragsley Outfitters | 2025-03-01 |                   858 |                         4.78\nCragsley Outfitters | 2025-04-01 |                   900 |                         4.56\nCragsley Outfitters | 2025-05-01 |                  1045 |                         5.17\nCragsley Outfitters | 2025-06-01 |                   942 |                         4.14\nCragsley Outfitters | 2025-07-01 |                  1088 |                         5.06\nCragsley Outfitters | 2025-08-01 |                   816 |                          4.9\nCragsley Outfitters | 2025-09-01 |                   999 |                          4.8\nCragsley Outfitters | 2025-10-01 |                  1171 |                         5.12\nCragsley Outfitters | 2025-11-01 |                  1861 |                          6.5\nCragsley Outfitters | 2025-12-01 |                  2542 |                         4.68\nMarrowby & Vane     | 2025-01-01 |                   708 |                         1.27\nMarrowby & Vane     | 2025-02-01 |                   362 |                         0.83\nMarrowby & Vane     | 2025-03-01 |                   433 |                         0.23\nMarrowby & Vane     | 2025-04-01 |                   389 |                         1.29\nMarrowby & Vane     | 2025-05-01 |                   430 |                         0.93\nMarrowby & Vane     | 2025-06-01 |                   396 |                         2.02\nMarrowby & Vane     | 2025-07-01 |                   450 |                         0.89\nMarrowby & Vane     | 2025-08-01 |                   386 |                          1.3\nMarrowby & Vane     | 2025-09-01 |                   468 |                         0.21\nMarrowby & Vane     | 2025-10-01 |                   507 |                         1.58\nMarrowby & Vane     | 2025-11-01 |                   823 |                         4.37\nMarrowby & Vane     | 2025-12-01 |                  1073 |                         0.37\n(48 rows)\n\n=== Report 2: Internal Health Check Signal Around the Nov 28 2025 Storefront API Outage (written by Ines) ===\nEngineer's note: system_health_checks is not tenant-scoped in this report (it tracks internal platform components, not merchant data), so results are aggregated across all tenants per hour/component as requested.\nREPORT: Internal Health Check Signal Around the Nov 28 2025 Storefront API Outage\nQUESTION: Did our internal health checks detect a problem around the two carts.remind failure windows (2025-11-28, roughly 14:40 and 15:40 UTC)?\nhour                | component            | total_checks | unhealthy_checks | unhealthy_rate_pct | avg_latency_ms\n--------------------+----------------------+--------------+------------------+--------------------+---------------\n2025-11-28 12:00:00 | accounting_connector |            5 |                1 |               20.0 |         2085.0\n2025-11-28 12:00:00 | checkout             |          111 |                1 |                0.9 |          294.0\n2025-11-28 12:00:00 | erp_connector        |           54 |                2 |                3.7 |          466.0\n2025-11-28 12:00:00 | marketplace_feed     |           15 |                2 |              13.33 |         1422.0\n2025-11-28 12:00:00 | storefront           |          111 |                1 |                0.9 |          194.0\n2025-11-28 12:00:00 | wms_connector        |           13 |                0 |                0.0 |           99.0\n2025-11-28 14:00:00 | accounting_connector |            5 |                0 |                0.0 |           96.0\n2025-11-28 14:00:00 | checkout             |          111 |              111 |              100.0 |        10000.0\n2025-11-28 14:00:00 | erp_connector        |           54 |                2 |                3.7 |          461.0\n2025-11-28 14:00:00 | marketplace_feed     |           15 |                0 |                0.0 |          101.0\n2025-11-28 14:00:00 | storefront           |          111 |              111 |              100.0 |        10000.0\n2025-11-28 14:00:00 | wms_connector        |           13 |                0 |                0.0 |          110.0\n2025-11-28 16:00:00 | accounting_connector |            5 |                0 |                0.0 |           83.0\n2025-11-28 16:00:00 | checkout             |          111 |                3 |                2.7 |          451.0\n2025-11-28 16:00:00 | erp_connector        |           54 |                0 |                0.0 |          102.0\n2025-11-28 16:00:00 | marketplace_feed     |           15 |                1 |               6.67 |          730.0\n2025-11-28 16:00:00 | storefront           |          111 |                0 |                0.0 |          101.0\n2025-11-28 16:00:00 | wms_connector        |           13 |                0 |                0.0 |          119.0\n(18 rows)\n\n=== Report 3: Ticket Subject Lines from Affected Merchants Before the Nov 24 Failures (written by Bea) ===\nEngineer's note: Query returns every matching ticket ordered by created_at (no row cap applied here); if the batch run returns more than 50 rows, only the first 50 will print per house rules and the analyst should check the total row count noted on the report.\nREPORT: Ticket Subject Lines from Affected Merchants Before the Nov 24 Failures\nQUESTION: In the weeks before the carts.remind failures showed up in our logs, did the 10 affected merchants' shoppers raise tickets that look related to carts, reminders, or duplicate/missing emails?\ntenant_name | created_at | subject | status | priority\n------------+------------+---------+--------+---------\n(0 rows)\n\n\nNight 3 of 4: you have 3 report slots tonight."},"uuid":"5c78b7bf-855c-4de1-bc25-b767147bcbc8","timestamp":"2026-10-04T03:36:08.010Z","permissionMode":"default","promptSource":"sdk","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460","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":11352,"cache_read_input_tokens":115240,"output_tokens":7648,"output_tokens_details":{"thinking_tokens":5852},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":11352,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":7648,"cache_read_input_tokens":115240,"cache_creation_input_tokens":11352,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":11352},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CfgVnqECFKA8qaPyDGpCb","type":"assistant","uuid":"33f5437b-f2f6-4b1a-9ce8-a834a86a7448","timestamp":"2026-10-04T03:37:12.010Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: 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{"parentUuid":"33f5437b-f2f6-4b1a-9ce8-a834a86a7448","isSidechain":false,"message":{"model":"claude-sonnet-5","id":"msg_011CfgVnqcWcXxH3jXcJ1PjT","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01PETADAfg71w5rPnySr62qu","name":"StructuredOutput","input":{"notes":"Report 2 from last night changes the scale of this story substantially: at the 14:00 UTC hour check on Nov 28 2025, BOTH 'checkout' and 'storefront' components were 100% unhealthy (111/111 checks failing, latency pegged at a 10,000ms timeout ceiling) - a full, unambiguous outage, not the mild 2.44% weekly blip I reported last week (that number was just this severe hour diluted across a whole week of otherwise-healthy checks). And critically, Nov 28 2025 is Black Friday - so if checkout itself was down for part of the busiest shopping day of the year, that's a much bigger finding than '10 merchants' cart-reminder emails failed,' and it would affect every merchant trying to check out then, not just the 10 using carts.remind. I didn't request a 15:00 bucket so I can't see the second (15:40) failure's health-check status yet, and my hourly buckets are too coarse to know exactly when the outage started/ended. Report 3 (ticket subject search) came back empty - no evidence in ticket subjects that shoppers flagged cart/reminder problems before Nov 24, so I can't support the 'support was an early warning sign' theory from this angle; I'm treating that as a genuine (if limited - subjects only, not message bodies) negative result rather than spending more budget on it, since the checkout-outage/Black-Friday angle is now clearly the more important thread with one night left. Tonight's 3 slots: (1) pin down the exact start/end of the checkout outage at finer time resolution; (2) measure the actual order-volume impact hour-by-hour on Nov 28 itself; (3) put Nov 28 in context against surrounding Fridays and Black Friday 2024 so Diego has a real 'how much did this cost us' number for leadership.","requests":[{"title":"Checkout/Storefront Outage Timeline, Nov 28 2025","question":"Exactly when did the checkout/storefront outage start and end on Nov 28 2025?","grain":"one row per 30-minute interval and component, for checkout and storefront only, 13:00-17:00 UTC on 2025-11-28","columns":["interval_start","component","total_checks","unhealthy_checks","unhealthy_rate_pct","avg_latency_ms"],"details":"Table system_health_checks. Filter checked_at >= 2025-11-28 13:00:00 and < 2025-11-28 17:00:00, component in ('checkout','storefront'). Group by 30-minute interval (floor checked_at to the nearest 30 minutes) and component. unhealthy_rate_pct = is_healthy=false count / total_checks * 100, rounded 2dp. avg_latency_ms over non-null latency_ms, rounded to whole number. Order by interval_start ascending, component ascending.","builds_on":["Internal Health Check Signal Around the Nov 28 2025 Storefront API Outage"]},{"title":"Order Volume by Hour on Black Friday 2025 (Nov 28)","question":"Did order placement actually drop during the checkout outage window on Nov 28 2025, compared to the rest of that same (peak traffic) day?","grain":"one row per hour of the day, for 2025-11-28 (UTC)","columns":["hour","total_orders_placed","distinct_tenants_with_orders","total_revenue_eur"],"details":"Table orders, filtered to placed_at on 2025-11-28 (all 24 hours UTC). Group by hour of placed_at. total_revenue_eur = sum of grand_total converted to EUR via exchange_rates (base_code='EUR', quote_code=orders.currency_code, as_of=2025-11-28), rounded to whole number. distinct_tenants_with_orders = count distinct tenant_id. Order by hour ascending (0-23).","builds_on":["Checkout/Storefront Outage Timeline, Nov 28 2025"]},{"title":"Black Friday 2025 Sales vs Surrounding Fridays and Black Friday 2024","question":"Despite the checkout outage, how did total Black Friday 2025 sales compare to the Fridays immediately before/after it and to Black Friday 2024 - was the day's performance meaningfully dented?","grain":"one row per named reference day","columns":["date","label","total_orders","total_revenue_eur"],"details":"Table orders, filtered to placed_at on each of these four specific dates (full day, UTC): 2025-11-21 (label 'Prior Friday'), 2025-11-28 (label 'Black Friday 2025'), 2025-12-05 (label 'Following Friday'), 2024-11-29 (label 'Black Friday 2024'). total_revenue_eur = sum of grand_total converted to EUR via exchange_rates (base_code='EUR', quote_code=orders.currency_code, as_of=that date), rounded to whole number. Platform-wide, all tenants. Order by date ascending.","builds_on":["Order Volume by Hour on Black Friday 2025 (Nov 28)"]}]},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":11352,"cache_read_input_tokens":115240,"output_tokens":7648,"output_tokens_details":{"thinking_tokens":5852},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":11352,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":7648,"cache_read_input_tokens":115240,"cache_creation_input_tokens":11352,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":11352},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"wireToolInputs":{"toolu_01PETADAfg71w5rPnySr62qu":{"notes":"Report 2 from last night changes the scale of this story substantially: at the 14:00 UTC hour check on Nov 28 2025, BOTH 'checkout' and 'storefront' components were 100% unhealthy (111/111 checks failing, latency pegged at a 10,000ms timeout ceiling) - a full, unambiguous outage, not the mild 2.44% weekly blip I reported last week (that number was just this severe hour diluted across a whole week of otherwise-healthy checks). And critically, Nov 28 2025 is Black Friday - so if checkout itself was down for part of the busiest shopping day of the year, that's a much bigger finding than '10 merchants' cart-reminder emails failed,' and it would affect every merchant trying to check out then, not just the 10 using carts.remind. I didn't request a 15:00 bucket so I can't see the second (15:40) failure's health-check status yet, and my hourly buckets are too coarse to know exactly when the outage started/ended. Report 3 (ticket subject search) came back empty - no evidence in ticket subjects that shoppers flagged cart/reminder problems before Nov 24, so I can't support the 'support was an early warning sign' theory from this angle; I'm treating that as a genuine (if limited - subjects only, not message bodies) negative result rather than spending more budget on it, since the checkout-outage/Black-Friday angle is now clearly the more important thread with one night left. Tonight's 3 slots: (1) pin down the exact start/end of the checkout outage at finer time resolution; (2) measure the actual order-volume impact hour-by-hour on Nov 28 itself; (3) put Nov 28 in context against surrounding Fridays and Black Friday 2024 so Diego has a real 'how much did this cost us' number for leadership.","requests":[{"title":"Checkout/Storefront Outage Timeline, Nov 28 2025","question":"Exactly when did the checkout/storefront outage start and end on Nov 28 2025?","grain":"one row per 30-minute interval and component, for checkout and storefront only, 13:00-17:00 UTC on 2025-11-28","columns":["interval_start","component","total_checks","unhealthy_checks","unhealthy_rate_pct","avg_latency_ms"],"details":"Table system_health_checks. Filter checked_at >= 2025-11-28 13:00:00 and < 2025-11-28 17:00:00, component in ('checkout','storefront'). Group by 30-minute interval (floor checked_at to the nearest 30 minutes) and component. unhealthy_rate_pct = is_healthy=false count / total_checks * 100, rounded 2dp. avg_latency_ms over non-null latency_ms, rounded to whole number. Order by interval_start ascending, component ascending.","builds_on":["Internal Health Check Signal Around the Nov 28 2025 Storefront API Outage"]},{"title":"Order Volume by Hour on Black Friday 2025 (Nov 28)","question":"Did order placement actually drop during the checkout outage window on Nov 28 2025, compared to the rest of that same (peak traffic) day?","grain":"one row per hour of the day, for 2025-11-28 (UTC)","columns":["hour","total_orders_placed","distinct_tenants_with_orders","total_revenue_eur"],"details":"Table orders, filtered to placed_at on 2025-11-28 (all 24 hours UTC). Group by hour of placed_at. total_revenue_eur = sum of grand_total converted to EUR via exchange_rates (base_code='EUR', quote_code=orders.currency_code, as_of=2025-11-28), rounded to whole number. distinct_tenants_with_orders = count distinct tenant_id. Order by hour ascending (0-23).","builds_on":["Checkout/Storefront Outage Timeline, Nov 28 2025"]},{"title":"Black Friday 2025 Sales vs Surrounding Fridays and Black Friday 2024","question":"Despite the checkout outage, how did total Black Friday 2025 sales compare to the Fridays immediately before/after it and to Black Friday 2024 - was the day's performance meaningfully dented?","grain":"one row per named reference day","columns":["date","label","total_orders","total_revenue_eur"],"details":"Table orders, filtered to placed_at on each of these four specific dates (full day, UTC): 2025-11-21 (label 'Prior Friday'), 2025-11-28 (label 'Black Friday 2025'), 2025-12-05 (label 'Following Friday'), 2024-11-29 (label 'Black Friday 2024'). total_revenue_eur = sum of grand_total converted to EUR via exchange_rates (base_code='EUR', quote_code=orders.currency_code, as_of=that date), rounded to whole number. Platform-wide, all tenants. Order by date ascending.","builds_on":["Order Volume by Hour on Black Friday 2025 (Nov 28)"]}]}},"apiBlockIndex":1,"requestId":"req_011CfgVnqECFKA8qaPyDGpCb","type":"assistant","uuid":"8d55f67d-00ae-4fb3-a020-30ac8f396e0d","timestamp":"2026-10-04T03:37:26.200Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"8d55f67d-00ae-4fb3-a020-30ac8f396e0d","isSidechain":false,"attachment":{"type":"structured_output","data":{"notes":"Report 2 from last night changes the scale of this story substantially: at the 14:00 UTC hour check on Nov 28 2025, BOTH 'checkout' and 'storefront' components were 100% unhealthy (111/111 checks failing, latency pegged at a 10,000ms timeout ceiling) - a full, unambiguous outage, not the mild 2.44% weekly blip I reported last week (that number was just this severe hour diluted across a whole week of otherwise-healthy checks). And critically, Nov 28 2025 is Black Friday - so if checkout itself was down for part of the busiest shopping day of the year, that's a much bigger finding than '10 merchants' cart-reminder emails failed,' and it would affect every merchant trying to check out then, not just the 10 using carts.remind. I didn't request a 15:00 bucket so I can't see the second (15:40) failure's health-check status yet, and my hourly buckets are too coarse to know exactly when the outage started/ended. Report 3 (ticket subject search) came back empty - no evidence in ticket subjects that shoppers flagged cart/reminder problems before Nov 24, so I can't support the 'support was an early warning sign' theory from this angle; I'm treating that as a genuine (if limited - subjects only, not message bodies) negative result rather than spending more budget on it, since the checkout-outage/Black-Friday angle is now clearly the more important thread with one night left. Tonight's 3 slots: (1) pin down the exact start/end of the checkout outage at finer time resolution; (2) measure the actual order-volume impact hour-by-hour on Nov 28 itself; (3) put Nov 28 in context against surrounding Fridays and Black Friday 2024 so Diego has a real 'how much did this cost us' number for leadership.","requests":[{"title":"Checkout/Storefront Outage Timeline, Nov 28 2025","question":"Exactly when did the checkout/storefront outage start and end on Nov 28 2025?","grain":"one row per 30-minute interval and component, for checkout and storefront only, 13:00-17:00 UTC on 2025-11-28","columns":["interval_start","component","total_checks","unhealthy_checks","unhealthy_rate_pct","avg_latency_ms"],"details":"Table system_health_checks. Filter checked_at >= 2025-11-28 13:00:00 and < 2025-11-28 17:00:00, component in ('checkout','storefront'). Group by 30-minute interval (floor checked_at to the nearest 30 minutes) and component. unhealthy_rate_pct = is_healthy=false count / total_checks * 100, rounded 2dp. avg_latency_ms over non-null latency_ms, rounded to whole number. Order by interval_start ascending, component ascending.","builds_on":["Internal Health Check Signal Around the Nov 28 2025 Storefront API Outage"]},{"title":"Order Volume by Hour on Black Friday 2025 (Nov 28)","question":"Did order placement actually drop during the checkout outage window on Nov 28 2025, compared to the rest of that same (peak traffic) day?","grain":"one row per hour of the day, for 2025-11-28 (UTC)","columns":["hour","total_orders_placed","distinct_tenants_with_orders","total_revenue_eur"],"details":"Table orders, filtered to placed_at on 2025-11-28 (all 24 hours UTC). Group by hour of placed_at. total_revenue_eur = sum of grand_total converted to EUR via exchange_rates (base_code='EUR', quote_code=orders.currency_code, as_of=2025-11-28), rounded to whole number. distinct_tenants_with_orders = count distinct tenant_id. Order by hour ascending (0-23).","builds_on":["Checkout/Storefront Outage Timeline, Nov 28 2025"]},{"title":"Black Friday 2025 Sales vs Surrounding Fridays and Black Friday 2024","question":"Despite the checkout outage, how did total Black Friday 2025 sales compare to the Fridays immediately before/after it and to Black Friday 2024 - was the day's performance meaningfully dented?","grain":"one row per named reference day","columns":["date","label","total_orders","total_revenue_eur"],"details":"Table orders, filtered to placed_at on each of these four specific dates (full day, UTC): 2025-11-21 (label 'Prior Friday'), 2025-11-28 (label 'Black Friday 2025'), 2025-12-05 (label 'Following Friday'), 2024-11-29 (label 'Black Friday 2024'). total_revenue_eur = sum of grand_total converted to EUR via exchange_rates (base_code='EUR', quote_code=orders.currency_code, as_of=that date), rounded to whole number. Platform-wide, all tenants. Order by date ascending.","builds_on":["Order Volume by Hour on Black Friday 2025 (Nov 28)"]}]},"toolUseID":"toolu_01PETADAfg71w5rPnySr62qu"},"type":"attachment","uuid":"94f2dcba-7833-4bf7-becf-230949539583","timestamp":"2026-10-04T03:37:26.208Z","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460","version":"2.1.275","gitBranch":"HEAD"}
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{"type":"last-prompt","lastPrompt":"The printed reports of night 2:  === Report 1: 2025 Monthly Sync-Webhook Stuck Rate for the 4 Highest-Impact Tenants (written by Omar) === Engineer's note: Month is printed as the first calendar day o…","leafUuid":"cb83cb1b-8b9a-4c8f-8589-09eb23780869","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460"}
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{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-04T03:37:47.482Z","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460","content":"The printed reports of night 3:\n\n=== Report 1: Checkout/Storefront Outage Timeline, Nov 28 2025 (written by Ines) ===\nEngineer's note: system_health_checks has no tenant filter requested, so this aggregates health checks across all tenants for the checkout/storefront components in the given window, as specified.\nREPORT: Checkout/Storefront Outage Timeline, Nov 28 2025\nQUESTION: Exactly when did the checkout/storefront outage start and end on Nov 28 2025?\ninterval_start      | component  | total_checks | unhealthy_checks | unhealthy_rate_pct | avg_latency_ms\n--------------------+------------+--------------+------------------+--------------------+---------------\n2025-11-28 14:00:00 | checkout   |          111 |              111 |              100.0 |        10000.0\n2025-11-28 14:00:00 | storefront |          111 |              111 |              100.0 |        10000.0\n2025-11-28 16:00:00 | checkout   |          111 |                3 |                2.7 |          451.0\n2025-11-28 16:00:00 | storefront |          111 |                0 |                0.0 |          101.0\n(4 rows)\n\n=== Report 2: Order Volume by Hour on Black Friday 2025 (Nov 28) (written by Felix) ===\nEngineer's note: Converted each order's grand_total to EUR by dividing by the EUR→order-currency exchange_rates.rate on 2025-11-28 (since rate is quoted as currency units per 1 EUR), then summed and rounded per hour. Hours with zero orders (if any) will not appear as rows since this is a GROUP BY over actual order placements.\nREPORT: Order Volume by Hour on Black Friday 2025 (Nov 28)\nQUESTION: Did order placement actually drop during the checkout outage window on Nov 28 2025, compared to the rest of that same (peak traffic) day?\nhour | total_orders_placed | distinct_tenants_with_orders | total_revenue_eur\n-----+---------------------+------------------------------+------------------\n   0 |                   3 |                            1 |             542.0\n   1 |                   3 |                            2 |            1076.0\n   2 |                   1 |                            1 |             141.0\n   3 |                   2 |                            1 |             514.0\n   4 |                   4 |                            4 |             406.0\n   5 |                   1 |                            1 |               0.0\n   6 |                   1 |                            1 |             143.0\n   7 |                   2 |                            2 |             189.0\n   8 |                   7 |                            5 |             952.0\n   9 |                   6 |                            5 |            1153.0\n  10 |                   2 |                            2 |             177.0\n  11 |                   5 |                            4 |            3119.0\n  12 |                  11 |                            7 |            1467.0\n  13 |                  10 |                            5 |             994.0\n  14 |                   1 |                            1 |             103.0\n  16 |                  10 |                            7 |            1657.0\n  17 |                   7 |                            6 |             935.0\n  18 |                   3 |                            3 |             281.0\n  19 |                  11 |                            8 |            1400.0\n  20 |                  11 |                            7 |            1545.0\n  21 |                  11 |                            5 |            1383.0\n  22 |                  11 |                            4 |            3144.0\n  23 |                   5 |                            4 |             988.0\n(23 rows)\n\n=== Report 3: Black Friday 2025 Sales vs Surrounding Fridays and Black Friday 2024 (written by Ines) ===\nEngineer's note: Revenue is sum of grand_total across all orders placed that UTC day, converted to EUR via exchange_rates (orders already in EUR use rate=1 since exchange_rates may not carry an EUR/EUR row). No order-status filter was applied since the request said \"total orders/sales\" without specifying a status; all orders placed that day are counted, including cancelled ones if any.\nREPORT: Black Friday 2025 Sales vs Surrounding Fridays and Black Friday 2024\nQUESTION: Despite the checkout outage, how did total Black Friday 2025 sales compare to the Fridays immediately before/after it and to Black Friday 2024 - was the day's performance meaningfully dented?\ndate       | label             | total_orders | total_revenue_eur\n-----------+-------------------+--------------+------------------\n2024-11-29 | Black Friday 2024 |          227 |           54186.0\n2025-11-21 | Prior Friday      |           82 |           22302.0\n2025-11-28 | Black Friday 2025 |          201 |           40166.0\n2025-12-05 | Following Friday  |          119 |           15088.0\n(4 rows)\n\n\nNight 4 of 4: you have 3 report slots tonight."}
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{"parentUuid":"86c15642-f48b-425b-a31c-52f192b9a945","isSidechain":false,"promptId":"3cddf458-0d66-432a-a0b1-9d4880b87277","type":"user","message":{"role":"user","content":"The printed reports of night 3:\n\n=== Report 1: Checkout/Storefront Outage Timeline, Nov 28 2025 (written by Ines) ===\nEngineer's note: system_health_checks has no tenant filter requested, so this aggregates health checks across all tenants for the checkout/storefront components in the given window, as specified.\nREPORT: Checkout/Storefront Outage Timeline, Nov 28 2025\nQUESTION: Exactly when did the checkout/storefront outage start and end on Nov 28 2025?\ninterval_start      | component  | total_checks | unhealthy_checks | unhealthy_rate_pct | avg_latency_ms\n--------------------+------------+--------------+------------------+--------------------+---------------\n2025-11-28 14:00:00 | checkout   |          111 |              111 |              100.0 |        10000.0\n2025-11-28 14:00:00 | storefront |          111 |              111 |              100.0 |        10000.0\n2025-11-28 16:00:00 | checkout   |          111 |                3 |                2.7 |          451.0\n2025-11-28 16:00:00 | storefront |          111 |                0 |                0.0 |          101.0\n(4 rows)\n\n=== Report 2: Order Volume by Hour on Black Friday 2025 (Nov 28) (written by Felix) ===\nEngineer's note: Converted each order's grand_total to EUR by dividing by the EUR→order-currency exchange_rates.rate on 2025-11-28 (since rate is quoted as currency units per 1 EUR), then summed and rounded per hour. Hours with zero orders (if any) will not appear as rows since this is a GROUP BY over actual order placements.\nREPORT: Order Volume by Hour on Black Friday 2025 (Nov 28)\nQUESTION: Did order placement actually drop during the checkout outage window on Nov 28 2025, compared to the rest of that same (peak traffic) day?\nhour | total_orders_placed | distinct_tenants_with_orders | total_revenue_eur\n-----+---------------------+------------------------------+------------------\n   0 |                   3 |                            1 |             542.0\n   1 |                   3 |                            2 |            1076.0\n   2 |                   1 |                            1 |             141.0\n   3 |                   2 |                            1 |             514.0\n   4 |                   4 |                            4 |             406.0\n   5 |                   1 |                            1 |               0.0\n   6 |                   1 |                            1 |             143.0\n   7 |                   2 |                            2 |             189.0\n   8 |                   7 |                            5 |             952.0\n   9 |                   6 |                            5 |            1153.0\n  10 |                   2 |                            2 |             177.0\n  11 |                   5 |                            4 |            3119.0\n  12 |                  11 |                            7 |            1467.0\n  13 |                  10 |                            5 |             994.0\n  14 |                   1 |                            1 |             103.0\n  16 |                  10 |                            7 |            1657.0\n  17 |                   7 |                            6 |             935.0\n  18 |                   3 |                            3 |             281.0\n  19 |                  11 |                            8 |            1400.0\n  20 |                  11 |                            7 |            1545.0\n  21 |                  11 |                            5 |            1383.0\n  22 |                  11 |                            4 |            3144.0\n  23 |                   5 |                            4 |             988.0\n(23 rows)\n\n=== Report 3: Black Friday 2025 Sales vs Surrounding Fridays and Black Friday 2024 (written by Ines) ===\nEngineer's note: Revenue is sum of grand_total across all orders placed that UTC day, converted to EUR via exchange_rates (orders already in EUR use rate=1 since exchange_rates may not carry an EUR/EUR row). No order-status filter was applied since the request said \"total orders/sales\" without specifying a status; all orders placed that day are counted, including cancelled ones if any.\nREPORT: Black Friday 2025 Sales vs Surrounding Fridays and Black Friday 2024\nQUESTION: Despite the checkout outage, how did total Black Friday 2025 sales compare to the Fridays immediately before/after it and to Black Friday 2024 - was the day's performance meaningfully dented?\ndate       | label             | total_orders | total_revenue_eur\n-----------+-------------------+--------------+------------------\n2024-11-29 | Black Friday 2024 |          227 |           54186.0\n2025-11-21 | Prior Friday      |           82 |           22302.0\n2025-11-28 | Black Friday 2025 |          201 |           40166.0\n2025-12-05 | Following Friday  |          119 |           15088.0\n(4 rows)\n\n\nNight 4 of 4: you have 3 report slots tonight."},"uuid":"0dbd6904-ae29-4def-9261-79f6e3a1ff43","timestamp":"2026-10-04T03:37:47.495Z","permissionMode":"default","promptSource":"sdk","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460","version":"2.1.275","gitBranch":"HEAD"}
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{"parentUuid":"2f33e860-d8d2-476a-b0da-af0b30be1ba1","isSidechain":false,"message":{"model":"claude-sonnet-5","id":"msg_011CfgVvAzn9XBi1fDUNQKR7","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_017GYgS3ymtY8a6xqcWnwLrQ","name":"StructuredOutput","input":{"notes":"Report 1 confirms health checks for checkout/storefront run only every 2 hours (checks exist at 12:00, 14:00, 16:00 but nothing in between) - so even though monitoring did catch a 100%-unhealthy reading at 14:00, our detection granularity for this path is coarse (up to 2 hours to notice), which is itself worth flagging for Diego's #3. Report 2 is the smoking gun on business impact: hour 14 had just 1 order and hour 15 has NO row at all (zero orders) on Black Friday, versus 7-11 orders in every neighboring hour that day - a near-total order freeze for roughly 2 hours on the year's biggest shopping day. Report 3 shows Black Friday 2025 total revenue (€40,166) came in 26% below Black Friday 2024 (€54,186) despite the business having grown since then, which is directionally consistent with real lost sales, though other factors could contribute and I haven't isolated the outage's exact share of that gap. This is my last night before the memo, so I'm using the 3 slots to: (1) precisely size the lost-order/revenue window by comparing the same 14:00-16:00 UTC slot across Nov 28 against two baseline Fridays and Black Friday 2024, so I can give Diego an actual number rather than an impression; (2) check whether checkout/storefront ever showed another severe (>=50% unhealthy) outage anywhere else in the Nov-Dec window we have, to answer whether this was a true one-off or a recurring latent risk; (3) check payment attempts during the same window, since a checkout-path outage should show up there too and would confirm this was a full checkout failure, not just a reminder-email glitch.","requests":[{"title":"Orders in the 14:00-16:00 UTC Window: Nov 28 vs Baseline Days","question":"How many orders (and how much revenue) were actually lost in the 14:00-16:00 UTC outage window on Black Friday, compared to the same window on comparable days?","grain":"one row per reference date","columns":["date","label","orders_14_to_16_utc","revenue_eur_14_to_16_utc","total_orders_full_day","pct_of_day_in_window"],"details":"Table orders, placed_at. For each of 2025-11-21 ('Prior Friday'), 2025-11-28 ('Black Friday 2025'), 2025-12-05 ('Following Friday'), 2024-11-29 ('Black Friday 2024'): orders_14_to_16_utc = count of orders with placed_at >= date 14:00:00 and < date 16:00:00 (UTC); revenue_eur_14_to_16_utc = sum of grand_total for those orders converted to EUR via exchange_rates (base_code='EUR', quote_code=orders.currency_code, as_of=that date), rounded to whole number. total_orders_full_day = count of all orders placed that full UTC day (for context). pct_of_day_in_window = orders_14_to_16_utc / total_orders_full_day * 100, rounded 1dp. Order by date ascending.","builds_on":["Order Volume by Hour on Black Friday 2025 (Nov 28)","Black Friday 2025 Sales vs Surrounding Fridays and Black Friday 2024"]},{"title":"Checkout/Storefront Severe Outages, Nov-Dec 2025","question":"Was the Nov 28 checkout/storefront outage a one-off, or did similarly severe (>=50% unhealthy) readings happen at other times in the window we have data for?","grain":"one row per hour and component where unhealthy_rate_pct >= 50","columns":["checked_hour","component","total_checks","unhealthy_checks","unhealthy_rate_pct","avg_latency_ms"],"details":"Table system_health_checks. Filter checked_at >= 2025-11-01 and < 2026-01-01, component in ('checkout','storefront'). Group by hour of checked_at and component. Keep only groups where unhealthy_rate_pct (is_healthy=false count / total_checks * 100) >= 50, rounded 2dp. avg_latency_ms over non-null latency_ms, rounded to whole number. Order by checked_hour ascending, component ascending.","builds_on":["Checkout/Storefront Outage Timeline, Nov 28 2025"]},{"title":"Payment Attempts During the Nov 28 2025 Outage Window","question":"Did payment processing itself fail or stall during the same 14:00-16:00 UTC window, confirming a full checkout-path outage rather than just the reminder-email job?","grain":"one row per hour of the day, for 2025-11-28 (UTC)","columns":["hour","total_payment_attempts","captured_or_authorized_count","failed_count","failure_rate_pct"],"details":"Table payment_attempts, filtered to attempted_at on 2025-11-28 (all 24 hours UTC). Group by hour of attempted_at. captured_or_authorized_count = count where status in ('authorized','captured'); failed_count = count where status='failed'; failure_rate_pct = failed_count / total_payment_attempts * 100, rounded 2dp. Order by hour ascending (0-23); hours with zero attempts simply won't appear, note the gap if any in the report.","builds_on":["Order Volume by Hour on Black Friday 2025 (Nov 28)"]}]},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":9302,"cache_read_input_tokens":126592,"output_tokens":6412,"output_tokens_details":{"thinking_tokens":4498},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":9302,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":6412,"cache_read_input_tokens":126592,"cache_creation_input_tokens":9302,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":9302},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"wireToolInputs":{"toolu_017GYgS3ymtY8a6xqcWnwLrQ":{"notes":"Report 1 confirms health checks for checkout/storefront run only every 2 hours (checks exist at 12:00, 14:00, 16:00 but nothing in between) - so even though monitoring did catch a 100%-unhealthy reading at 14:00, our detection granularity for this path is coarse (up to 2 hours to notice), which is itself worth flagging for Diego's #3. Report 2 is the smoking gun on business impact: hour 14 had just 1 order and hour 15 has NO row at all (zero orders) on Black Friday, versus 7-11 orders in every neighboring hour that day - a near-total order freeze for roughly 2 hours on the year's biggest shopping day. Report 3 shows Black Friday 2025 total revenue (€40,166) came in 26% below Black Friday 2024 (€54,186) despite the business having grown since then, which is directionally consistent with real lost sales, though other factors could contribute and I haven't isolated the outage's exact share of that gap. This is my last night before the memo, so I'm using the 3 slots to: (1) precisely size the lost-order/revenue window by comparing the same 14:00-16:00 UTC slot across Nov 28 against two baseline Fridays and Black Friday 2024, so I can give Diego an actual number rather than an impression; (2) check whether checkout/storefront ever showed another severe (>=50% unhealthy) outage anywhere else in the Nov-Dec window we have, to answer whether this was a true one-off or a recurring latent risk; (3) check payment attempts during the same window, since a checkout-path outage should show up there too and would confirm this was a full checkout failure, not just a reminder-email glitch.","requests":[{"title":"Orders in the 14:00-16:00 UTC Window: Nov 28 vs Baseline Days","question":"How many orders (and how much revenue) were actually lost in the 14:00-16:00 UTC outage window on Black Friday, compared to the same window on comparable days?","grain":"one row per reference date","columns":["date","label","orders_14_to_16_utc","revenue_eur_14_to_16_utc","total_orders_full_day","pct_of_day_in_window"],"details":"Table orders, placed_at. For each of 2025-11-21 ('Prior Friday'), 2025-11-28 ('Black Friday 2025'), 2025-12-05 ('Following Friday'), 2024-11-29 ('Black Friday 2024'): orders_14_to_16_utc = count of orders with placed_at >= date 14:00:00 and < date 16:00:00 (UTC); revenue_eur_14_to_16_utc = sum of grand_total for those orders converted to EUR via exchange_rates (base_code='EUR', quote_code=orders.currency_code, as_of=that date), rounded to whole number. total_orders_full_day = count of all orders placed that full UTC day (for context). pct_of_day_in_window = orders_14_to_16_utc / total_orders_full_day * 100, rounded 1dp. Order by date ascending.","builds_on":["Order Volume by Hour on Black Friday 2025 (Nov 28)","Black Friday 2025 Sales vs Surrounding Fridays and Black Friday 2024"]},{"title":"Checkout/Storefront Severe Outages, Nov-Dec 2025","question":"Was the Nov 28 checkout/storefront outage a one-off, or did similarly severe (>=50% unhealthy) readings happen at other times in the window we have data for?","grain":"one row per hour and component where unhealthy_rate_pct >= 50","columns":["checked_hour","component","total_checks","unhealthy_checks","unhealthy_rate_pct","avg_latency_ms"],"details":"Table system_health_checks. Filter checked_at >= 2025-11-01 and < 2026-01-01, component in ('checkout','storefront'). Group by hour of checked_at and component. Keep only groups where unhealthy_rate_pct (is_healthy=false count / total_checks * 100) >= 50, rounded 2dp. avg_latency_ms over non-null latency_ms, rounded to whole number. Order by checked_hour ascending, component ascending.","builds_on":["Checkout/Storefront Outage Timeline, Nov 28 2025"]},{"title":"Payment Attempts During the Nov 28 2025 Outage Window","question":"Did payment processing itself fail or stall during the same 14:00-16:00 UTC window, confirming a full checkout-path outage rather than just the reminder-email job?","grain":"one row per hour of the day, for 2025-11-28 (UTC)","columns":["hour","total_payment_attempts","captured_or_authorized_count","failed_count","failure_rate_pct"],"details":"Table payment_attempts, filtered to attempted_at on 2025-11-28 (all 24 hours UTC). Group by hour of attempted_at. captured_or_authorized_count = count where status in ('authorized','captured'); failed_count = count where status='failed'; failure_rate_pct = failed_count / total_payment_attempts * 100, rounded 2dp. Order by hour ascending (0-23); hours with zero attempts simply won't appear, note the gap if any in the report.","builds_on":["Order Volume by Hour on Black Friday 2025 (Nov 28)"]}]}},"apiBlockIndex":1,"requestId":"req_011CfgVvAdhLrCm7vZxcryLD","type":"assistant","uuid":"36197746-e8ef-470c-8cd2-f0d88c91a4bb","timestamp":"2026-10-04T03:38:51.567Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460","version":"2.1.275","gitBranch":"HEAD"}
{"parentUuid":"36197746-e8ef-470c-8cd2-f0d88c91a4bb","isSidechain":false,"attachment":{"type":"structured_output","data":{"notes":"Report 1 confirms health checks for checkout/storefront run only every 2 hours (checks exist at 12:00, 14:00, 16:00 but nothing in between) - so even though monitoring did catch a 100%-unhealthy reading at 14:00, our detection granularity for this path is coarse (up to 2 hours to notice), which is itself worth flagging for Diego's #3. Report 2 is the smoking gun on business impact: hour 14 had just 1 order and hour 15 has NO row at all (zero orders) on Black Friday, versus 7-11 orders in every neighboring hour that day - a near-total order freeze for roughly 2 hours on the year's biggest shopping day. Report 3 shows Black Friday 2025 total revenue (€40,166) came in 26% below Black Friday 2024 (€54,186) despite the business having grown since then, which is directionally consistent with real lost sales, though other factors could contribute and I haven't isolated the outage's exact share of that gap. This is my last night before the memo, so I'm using the 3 slots to: (1) precisely size the lost-order/revenue window by comparing the same 14:00-16:00 UTC slot across Nov 28 against two baseline Fridays and Black Friday 2024, so I can give Diego an actual number rather than an impression; (2) check whether checkout/storefront ever showed another severe (>=50% unhealthy) outage anywhere else in the Nov-Dec window we have, to answer whether this was a true one-off or a recurring latent risk; (3) check payment attempts during the same window, since a checkout-path outage should show up there too and would confirm this was a full checkout failure, not just a reminder-email glitch.","requests":[{"title":"Orders in the 14:00-16:00 UTC Window: Nov 28 vs Baseline Days","question":"How many orders (and how much revenue) were actually lost in the 14:00-16:00 UTC outage window on Black Friday, compared to the same window on comparable days?","grain":"one row per reference date","columns":["date","label","orders_14_to_16_utc","revenue_eur_14_to_16_utc","total_orders_full_day","pct_of_day_in_window"],"details":"Table orders, placed_at. For each of 2025-11-21 ('Prior Friday'), 2025-11-28 ('Black Friday 2025'), 2025-12-05 ('Following Friday'), 2024-11-29 ('Black Friday 2024'): orders_14_to_16_utc = count of orders with placed_at >= date 14:00:00 and < date 16:00:00 (UTC); revenue_eur_14_to_16_utc = sum of grand_total for those orders converted to EUR via exchange_rates (base_code='EUR', quote_code=orders.currency_code, as_of=that date), rounded to whole number. total_orders_full_day = count of all orders placed that full UTC day (for context). pct_of_day_in_window = orders_14_to_16_utc / total_orders_full_day * 100, rounded 1dp. Order by date ascending.","builds_on":["Order Volume by Hour on Black Friday 2025 (Nov 28)","Black Friday 2025 Sales vs Surrounding Fridays and Black Friday 2024"]},{"title":"Checkout/Storefront Severe Outages, Nov-Dec 2025","question":"Was the Nov 28 checkout/storefront outage a one-off, or did similarly severe (>=50% unhealthy) readings happen at other times in the window we have data for?","grain":"one row per hour and component where unhealthy_rate_pct >= 50","columns":["checked_hour","component","total_checks","unhealthy_checks","unhealthy_rate_pct","avg_latency_ms"],"details":"Table system_health_checks. Filter checked_at >= 2025-11-01 and < 2026-01-01, component in ('checkout','storefront'). Group by hour of checked_at and component. Keep only groups where unhealthy_rate_pct (is_healthy=false count / total_checks * 100) >= 50, rounded 2dp. avg_latency_ms over non-null latency_ms, rounded to whole number. Order by checked_hour ascending, component ascending.","builds_on":["Checkout/Storefront Outage Timeline, Nov 28 2025"]},{"title":"Payment Attempts During the Nov 28 2025 Outage Window","question":"Did payment processing itself fail or stall during the same 14:00-16:00 UTC window, confirming a full checkout-path outage rather than just the reminder-email job?","grain":"one row per hour of the day, for 2025-11-28 (UTC)","columns":["hour","total_payment_attempts","captured_or_authorized_count","failed_count","failure_rate_pct"],"details":"Table payment_attempts, filtered to attempted_at on 2025-11-28 (all 24 hours UTC). Group by hour of attempted_at. captured_or_authorized_count = count where status in ('authorized','captured'); failed_count = count where status='failed'; failure_rate_pct = failed_count / total_payment_attempts * 100, rounded 2dp. Order by hour ascending (0-23); hours with zero attempts simply won't appear, note the gap if any in the report.","builds_on":["Order Volume by Hour on Black Friday 2025 (Nov 28)"]}]},"toolUseID":"toolu_017GYgS3ymtY8a6xqcWnwLrQ"},"type":"attachment","uuid":"bb93f219-0edb-424c-8c70-ab0c887ad1f5","timestamp":"2026-10-04T03:38:51.575Z","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460","version":"2.1.275","gitBranch":"HEAD"}
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{"type":"last-prompt","lastPrompt":"The printed reports of night 3:  === Report 1: Checkout/Storefront Outage Timeline, Nov 28 2025 (written by Ines) === Engineer's note: system_health_checks has no tenant filter requested, so this aggr…","leafUuid":"7b7ff86d-7dbb-4440-98bc-6d674b8190d7","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460"}
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{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-04T03:39:20.472Z","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460","content":"The printed reports of night 4:\n\n=== Report 1: Orders in the 14:00-16:00 UTC Window: Nov 28 vs Baseline Days (written by Viktor) ===\nEngineer's note: This aggregates across all tenants/merchants combined (no tenant filter was specified); revenue is converted to EUR per-order using that order's own currency and the exchange rate as_of its placed date. If the VP wants this broken out per merchant or filtered to specific tenants, let me know and I'll rerun.\nREPORT: Orders in the 14:00-16:00 UTC Window: Nov 28 vs Baseline Days\nQUESTION: How many orders (and how much revenue) were actually lost in the 14:00-16:00 UTC outage window on Black Friday, compared to the same window on comparable days?\ndate       | label             | orders_14_to_16_utc | revenue_eur_14_to_16_utc | total_orders_full_day | pct_of_day_in_window\n-----------+-------------------+---------------------+--------------------------+-----------------------+---------------------\n2024-11-29 | Black Friday 2024 |                  22 |                   4012.0 |                   227 |                  9.7\n2025-11-21 | Prior Friday      |                   6 |                    699.0 |                    82 |                  7.3\n2025-11-28 | Black Friday 2025 |                   1 |                    103.0 |                   201 |                  0.5\n2025-12-05 | Following Friday  |                   6 |                   1574.0 |                   119 |                  5.0\n(4 rows)\n\n=== Report 2: Checkout/Storefront Severe Outages, Nov-Dec 2025 (written by Ines) ===\nEngineer's note: system_health_checks carries tenant_id, but the request did not ask for a per-tenant breakdown, so checks are aggregated across all tenants per hour/component as specified. If this table is tenant-scoped and Nov 28 outage was specific to one or few tenants, a tenant-level cut may be worth a follow-up report.\nREPORT: Checkout/Storefront Severe Outages, Nov-Dec 2025\nQUESTION: Was the Nov 28 checkout/storefront outage a one-off, or did similarly severe (>=50% unhealthy) readings happen at other times in the window we have data for?\nchecked_hour        | component  | total_checks | unhealthy_checks | unhealthy_rate_pct | avg_latency_ms\n--------------------+------------+--------------+------------------+--------------------+---------------\n2025-11-28 14:00:00 | checkout   |          111 |              111 |              100.0 |        10000.0\n2025-11-28 14:00:00 | storefront |          111 |              111 |              100.0 |        10000.0\n(2 rows)\n\n=== Report 3: Payment Attempts During the Nov 28 2025 Outage Window (written by Felix) ===\nEngineer's note: Query is platform-wide (all tenants) since the outage question is about the checkout path generally, not one merchant; hours with zero attempts will simply be absent from the 24-row output, as the request anticipated.\nREPORT: Payment Attempts During the Nov 28 2025 Outage Window\nQUESTION: Did payment processing itself fail or stall during the same 14:00-16:00 UTC window, confirming a full checkout-path outage rather than just the reminder-email job?\nhour | total_payment_attempts | captured_or_authorized_count | failed_count | failure_rate_pct\n-----+------------------------+------------------------------+--------------+-----------------\n   0 |                      4 |                            4 |            0 |              0.0\n   1 |                      5 |                            5 |            0 |              0.0\n   2 |                      2 |                            2 |            0 |              0.0\n   3 |                      2 |                            1 |            1 |             50.0\n   4 |                      4 |                            4 |            0 |              0.0\n   5 |                      2 |                            2 |            0 |              0.0\n   6 |                      6 |                            5 |            1 |            16.67\n   7 |                      4 |                            3 |            1 |             25.0\n   8 |                      9 |                            8 |            1 |            11.11\n   9 |                     13 |                           12 |            1 |             7.69\n  10 |                      3 |                            3 |            0 |              0.0\n  11 |                      7 |                            7 |            0 |              0.0\n  12 |                     15 |                           15 |            0 |              0.0\n  13 |                     14 |                           14 |            0 |              0.0\n  14 |                      3 |                            3 |            0 |              0.0\n  15 |                      2 |                            2 |            0 |              0.0\n  16 |                     17 |                           13 |            4 |            23.53\n  17 |                     11 |                           11 |            0 |              0.0\n  18 |                      9 |                            8 |            1 |            11.11\n  19 |                     18 |                           17 |            1 |             5.56\n  20 |                     17 |                           17 |            0 |              0.0\n  21 |                     18 |                           18 |            0 |              0.0\n  22 |                     13 |                           12 |            1 |             7.69\n  23 |                      6 |                            6 |            0 |              0.0\n(24 rows)\n\n\nIt is Friday morning. Write the memo to Diego Morales:\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":"67f3d5e0-e472-47cb-a9ef-6aabb0a8b49c","isSidechain":false,"promptId":"85e83841-86af-424c-870b-4eeb684076b2","type":"user","message":{"role":"user","content":"The printed reports of night 4:\n\n=== Report 1: Orders in the 14:00-16:00 UTC Window: Nov 28 vs Baseline Days (written by Viktor) ===\nEngineer's note: This aggregates across all tenants/merchants combined (no tenant filter was specified); revenue is converted to EUR per-order using that order's own currency and the exchange rate as_of its placed date. If the VP wants this broken out per merchant or filtered to specific tenants, let me know and I'll rerun.\nREPORT: Orders in the 14:00-16:00 UTC Window: Nov 28 vs Baseline Days\nQUESTION: How many orders (and how much revenue) were actually lost in the 14:00-16:00 UTC outage window on Black Friday, compared to the same window on comparable days?\ndate       | label             | orders_14_to_16_utc | revenue_eur_14_to_16_utc | total_orders_full_day | pct_of_day_in_window\n-----------+-------------------+---------------------+--------------------------+-----------------------+---------------------\n2024-11-29 | Black Friday 2024 |                  22 |                   4012.0 |                   227 |                  9.7\n2025-11-21 | Prior Friday      |                   6 |                    699.0 |                    82 |                  7.3\n2025-11-28 | Black Friday 2025 |                   1 |                    103.0 |                   201 |                  0.5\n2025-12-05 | Following Friday  |                   6 |                   1574.0 |                   119 |                  5.0\n(4 rows)\n\n=== Report 2: Checkout/Storefront Severe Outages, Nov-Dec 2025 (written by Ines) ===\nEngineer's note: system_health_checks carries tenant_id, but the request did not ask for a per-tenant breakdown, so checks are aggregated across all tenants per hour/component as specified. If this table is tenant-scoped and Nov 28 outage was specific to one or few tenants, a tenant-level cut may be worth a follow-up report.\nREPORT: Checkout/Storefront Severe Outages, Nov-Dec 2025\nQUESTION: Was the Nov 28 checkout/storefront outage a one-off, or did similarly severe (>=50% unhealthy) readings happen at other times in the window we have data for?\nchecked_hour        | component  | total_checks | unhealthy_checks | unhealthy_rate_pct | avg_latency_ms\n--------------------+------------+--------------+------------------+--------------------+---------------\n2025-11-28 14:00:00 | checkout   |          111 |              111 |              100.0 |        10000.0\n2025-11-28 14:00:00 | storefront |          111 |              111 |              100.0 |        10000.0\n(2 rows)\n\n=== Report 3: Payment Attempts During the Nov 28 2025 Outage Window (written by Felix) ===\nEngineer's note: Query is platform-wide (all tenants) since the outage question is about the checkout path generally, not one merchant; hours with zero attempts will simply be absent from the 24-row output, as the request anticipated.\nREPORT: Payment Attempts During the Nov 28 2025 Outage Window\nQUESTION: Did payment processing itself fail or stall during the same 14:00-16:00 UTC window, confirming a full checkout-path outage rather than just the reminder-email job?\nhour | total_payment_attempts | captured_or_authorized_count | failed_count | failure_rate_pct\n-----+------------------------+------------------------------+--------------+-----------------\n   0 |                      4 |                            4 |            0 |              0.0\n   1 |                      5 |                            5 |            0 |              0.0\n   2 |                      2 |                            2 |            0 |              0.0\n   3 |                      2 |                            1 |            1 |             50.0\n   4 |                      4 |                            4 |            0 |              0.0\n   5 |                      2 |                            2 |            0 |              0.0\n   6 |                      6 |                            5 |            1 |            16.67\n   7 |                      4 |                            3 |            1 |             25.0\n   8 |                      9 |                            8 |            1 |            11.11\n   9 |                     13 |                           12 |            1 |             7.69\n  10 |                      3 |                            3 |            0 |              0.0\n  11 |                      7 |                            7 |            0 |              0.0\n  12 |                     15 |                           15 |            0 |              0.0\n  13 |                     14 |                           14 |            0 |              0.0\n  14 |                      3 |                            3 |            0 |              0.0\n  15 |                      2 |                            2 |            0 |              0.0\n  16 |                     17 |                           13 |            4 |            23.53\n  17 |                     11 |                           11 |            0 |              0.0\n  18 |                      9 |                            8 |            1 |            11.11\n  19 |                     18 |                           17 |            1 |             5.56\n  20 |                     17 |                           17 |            0 |              0.0\n  21 |                     18 |                           18 |            0 |              0.0\n  22 |                     13 |                           12 |            1 |             7.69\n  23 |                      6 |                            6 |            0 |              0.0\n(24 rows)\n\n\nIt is Friday morning. Write the memo to Diego Morales:\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":"14d94494-90f8-40e7-89a1-7921316d8e86","timestamp":"2026-10-04T03:39:20.479Z","permissionMode":"default","promptSource":"sdk","userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460","version":"2.1.275","gitBranch":"HEAD"}
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BI1HMDeKjm7rqhxOoSueSmT/oF03pW+1fCmRzlFS8Zj97qNt5WASYjkWKY7RcYVXwSw4qiAL2/wR2pYkJuACitPR9xwHHFwfk3TNYdv4H43p43mSe0aG++TREjL/CgQfmkCww5hiuF96okbG7xXCg1Coj7amJ80tOkgPSxiYOKFdsWbpwQXy6mAkkALONQPL4a9C6KvRNrWffxp8tp/6RVSZvVwLHooBMT2cuhHCh2FCBZtGpd5r6zQFmYuNlYjbk4YEznwiLzpv1eZYCFsZpMAPb1R9/NiENL1e+/cj2JWQiXBVYXIVyEZEX5+PTjuGl4zNuDWBbxTX8rpSi7sMlastnVQQBqaCPUm3yhxdNzrgMHaBShV6Kye86GqTZQsqZzmqfgVtGzvIhx5Sd1m3EgqfzqNFNql6bEaeBP3esFbj9c5Dwo8qSWgb58w/whLyWM4mBdG93Hs7VqbztkPZIoJ6H5hvhVJuZjS/FICYV8vG/6L3/ClBf6ToEIe1DCFl+bQ9AEbJpf6idwfZiNwmf3gurVHqyCGbJ+361Q9RNTDbEZjsHTDmNOFLhchljUCuJGrYAnVdRT7Zz6mRqePNXkhSqaKDduxFzQB7ggES5qzSm2i7S05YRWrOtsBrR4uFMBHCWT4ejne+xKjXhSVNUJzUuxCRwpfv0S2I+4bln211bPtB/ZQGsWzduI3eYq6K1B+AblGmOhmj6Z5/1TXiGYxMdaEiPsJZs6dMcrRKQQr52ENsHSu2ebfsg9gZC2V6dx/Wj0uAzbTsWPGxSWlUeZAhahgl9Azzhmd3QtSMRyaET2YVfBT+2o35uRtMwxIV91FxhWODx+xU/t+IwT16N8gXQS+1rJaZRtKd4640vUw9F4H3sUkDiHfz3IQ7gXqG/kvnlNsudacWEK0RA3V1Q1U7k7Mrkd0nVboO4sululwFKqLX0GW5mjYlvIlXfllJa0Hhd2cbkra24qitrGo1XVQbU5umxFLrXUjndlmwvSH2vGxD+PpjvKSOlGrZI4ntVX/iWHtIy43EP2xj8fIo6c86C3FRCNhSj9LheQBhvGUqzc3PWzPWBM1KxBNEaovLePzMECqMBjjpNM1U7B0ZuGX2rBD6QKNd0XvGHjwOUnVZx9BeAaSoJ/fwgW6aWwV4UsayqoypqK+34yHiAxqTJbUBhqolO6KTPTSWzt8OpHe81SiyQqyhng8I6QV8a5dwwiar5onOw5kIRmO83rEMzeCdaD0Ns7BPvlngCKy/UVCTHK4leGVPWGwcZPcBFerOXQTKSK9xr5rw6PYexvbcsL/Y4dzNv2noJUNVgS9UL3Zg/UU/DgFDgP7WBT8Bo9z76VbS5Kz1qnpIoERB42wHifqTgzhlRcmQxJtYw/0ggG+rTjiJ9rVeqJUVYW4PsuzLXLdG468FDV6f2rHwE9J3RpqekX+SYqoops1XOfVzhfLhvjdJp/ZoKheSK9ntx6uEZM5mSvRlC+GhuMxmfcQ9jJxQiVs645B/pcS6ZiQVq5Lw9io1WQKFMQbphqwl4qP8wmJ6N5HsgEV0a1jkK5Z2r1XIo2QR/clBYQmBwPpkI/Q4L+DZW6EDMTpbjI0wCCQfBXxAyi/dlAPpKHAZs4VPqjAU3jOHxbPj5ocBYlVv0otOA+I6Dosr7MARIosO0tTW51BOU9XzWxD/lsmM1Rg+gLkIBk8Nfkl+R008+TZQVX6kPoXEyiSQG+ulFpUMKyC4X181D3ezBv0Fl3NXBAWF+OjhshfiBMmoDh9rJ2z7SFDKiLG5YfkygU2EZpg+sKLJwBiTeNFmaOXW4NLjbi0MoV0IKD5Ij9aVtQ29GTHyXEe0YtpwoobTPm2Om1i/CVp2hgSDt29OLzsulk2VKoBJJ+TpbHImEM3WHBPavYRDaVBJAJ7c8F/Jfp270hMHqgxkdKs44jPII5xBnzucV1cFmjqXbUS5MJtk9GWqFJ0ruiB0jT6/PMDpsGxhuHsUA+Ig+nTaQypuEel+QyYMWr12sIPD1J+VaK9VhaBmSTaY7IapM4J8bG5YFdw/R8NchlTe9mAfCT9MdxMIcxlSpnpNcG11t+el+FDSNpCyx0aq+XLbA951IkjSznBTePWwN+DVTTXqAYCnXewONBzRvUO+GZZT4zDWveD2uXq8ZoVkgdJP0qQXeEAtaB3X5VBhTr7wUk70emJBxsF/AjKQHsHYwdQBjYAjGva2EdM7DNzRmRc9SjUPaZkOfucTq0EGXO0V/1OeYkTH7AkS0mRvYEDmfDGKtdDFN+k4nA63cVXDJUhZ0Au5zVY90aKadlCYiDL8g4x2utoKYF8KiCYExXb6Tg/+AtX8mlUvesnXhCkh2XRWQvdEOmuPvgmxAFMp1QN8Hj4Q2GPKQ5C/KwaevqmXIhAplFlS+ldOHAw2fE6dt1W81muBGDR8DV9otHBheK5WKM4hAAfeum/EomUKQ9RfWg2SdsPeBXSLlGym0sobNl/wfugwK9DuHQNorKjc5dYEWZmEGC2z+qaUPpPAIEBwIBn/dLeJbhGYC/uc6qzvXZU50n8QXCWuHgwz9i2lxKV+Y8/eiaZKlYPkzP6ofxP1wKwdWM5HPUEtsV4Vfavy9pkTvrYJ3GFDR3aZuKqWL2beLgQwfY96LEcHBVaeGFN6RBjAfdxDeo5kPvRaO+rdSHz9IxHf+XwMqDTrKskq7LgCHq+un/NBt2pWbslewQnhKPu2sESs/ea5kb5VX+Ze90lQYF/3udf+rEQU3UEHRYeqqldAnoPrzFKN24VpS0asrOQ2Mejitzs2TCUjPSMxUm3pe0oF9AES65Xs7dQP63d+WFOBTg2RX+sZDQSM4HVb8P0yryaKx30eoNZ1TUtacfobeCAlfCTN9cqrZLjYkazgYxwz0cKncmyEGEkUCOhFVuZ0eeRTGr8SUVVuc7XX/yGH7W8/1akZNMYdvhwAAqf7GpS28TOxQQQKdbme7ONr2aIHdex8A6eVVx/FGS2tjDq0t6YWUgWw5sRAZQgzsMtdij5xberN8wBERiWOpXyCZeQH/PeM1sJ7Ik1TLtHIRiviSaxjxgbOhhbvUBlLSjq2U+PQv/YHw6MZGChb/Fzm4GzzoTAO76qMhqgd+C9Cc2w78P5zkMXkfxDVxobWhX3597JGeaNLXNNEPb5WlYfWsag7sVXb1uY1FEkjd1+vbDFsVyCN1HmKoVYR0g8I42i6WKu4ZCJx94A49eq5ssQtr1Z/+8jY+1Mh5g2AdNAM8eW149yxXIQ+u9hpI+ZhIwcu/3lQmIsPNqTy1lB3FQ7CcH2EW3VXUW8W7iW2u1h/holkZeFy/mQcD2yqVPd8d7XwP9d28EavTyZUqLZBqbxIDKcHPhuFzRLrULNOSLgopurur5/Jf9BwBmHMNzVTyRATpJ/Tn5Ups1RZJ8d+8zIrgTenDbGsUlGg9Vc8KIiuOYr90X7tEfWjymjPYlnLh1A5wfccMuMJV57WT8nDov6zxjqGdfEFeHhyTqJEZbQSEWsOyFVFAWUBgG5Ow1u+9i1FHgDCQGXLl1/GnEyRhw5RAsVqiQBUGBg14UAUhMpUU5ee0f4rmRrNg17m3HSZGokxYFZE7wj5SoddXmDZho8cT9zDk9YjslDjk9kCbT4efas+YxISqGTtZ/+EHKR2yJwDhO/DXWa4RDRKgmdYGsIoqCJJHiJ1S6h5d7eaXpqt+bnP5jZRNf3nWaTb5Un4ttEKs31/6VG88kEJuX3HlvqRdfLFeTPTWlqVowRzfIwbfea1qMJlxZXjQk/Jo5B6YzYp3BwrgzNF8Ws6AKS2sy8lunZ9OZv61vbVNhj+Xzogl/461pM9+FiSl5N1Td4fkyU178meKONrz0YjXGmZJdLJaSqCbVbXLI47YkuNhpW1zjxk09GqAIYVt4Zt5JEN6/8rXlX2oQcNshbyr6ApE8j3EIvEc/djeXJL2QWef+LUPFvbnC6fDlxwWoZN6VWjY7EiyHokQYA1BjaXoV8TMc/S5+eduVj6Uyz74UABuO3Jf+QTo2V18dbKM/SQi2ouQw+tDPgfhwPxveMO0OchLZq2YTOB/Zp48LgUMcaGhxeSVpQoPD2sOJVd34tBqBN3hsjD/cTvcPZ9/ziivxOjgO6ghz7QGgY9tE2+wWcNGmN8dRX3zJRP3x/Ty171cXcUVYtGBiTUlw2Gv2jIEAYwiofsQAE+ey5waVZsN6SwzVxReIR9w3kTdytgxUfFe4neOTBylYOgNWf15j71qNpNHpS8cWhexGRMSocP67BBGkf0W9cmTyQ7nfdYYoD1RJZl7Je24pHpCiM+JyTzDaTGLKfcRrhFdhwP8ASS9iMEhopQKoeG9397KZFa6cycTlGrrtp7FAzP2IvCZGhk5yX90xziWSZsY1fdh8vaLrXDhj4ufunIWqeHLggiDMNYgOfizoWbX1SPGlH3MCUtlA1WNeWZS4oZallli5dcV/8jPYpGpFj4k8BDxZ4yWhmnKzPzxMBdAi5VDsQYH0firG4QovdBc44hnc9Mp8MzNCFl0GjoqYEFqA3LKwBlnOzgDsaydmoF7X/vZRU8f+cwIYKyZ6TfrLURDayRi4kXt4qUZGYmjS0qdL+dAKCimKV2P+6gWxV03WXnM89+HbjUfo5nfGhdEBUyiRsoJGxiRJtNTlWSXNRNGu7vf63gEL2d6QiIbrWGSRTRyFqMv7WENH2PaZ+n1dmgops9N1We+lsrnniRcR7u+m71IB8AwNoAGNeYifo1iawVyB9PgQaOAgy+jwUeJ76+AItAhvzxB43Qj4Bii0ACYqwiyHqG7rLDMFILCTLzLoXA8gHSMJJ1V7Dy3JZIFVltfW6icJTEuj3GV0Qt31YBvggvNW4KCopI4RPlnCi+h6Hbl1GOkMcvp2A7xp6lLvDsGi+IYAT/lIJXUC+SGT2koT37qjmxcRjnGD6xGP76NGyMoV7HfoNChHuQWg2z4KbMR7Cm4+/XdHERsA1G68b1/AgbxSu5ki9rRuF6/OyKsDxWSPIk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{"parentUuid":"088da64a-7ceb-4215-a780-6372235c639a","isSidechain":false,"message":{"model":"claude-sonnet-5","id":"msg_011CfgW32WEnSSr8AUFVXATy","type":"message","role":"assistant","content":[{"type":"text","text":"**Memo: Platform Reliability Review — Week 2 Update**\nTo: Diego Morales (cc Samuel Whitcombe) — From: Lucas Moreau — Fri (Night 4 wrap-up)\n\n**1. The carts.remind \"incident\" was a symptom of something much bigger: checkout itself went down for ~2 hours on Black Friday.**\nHealth checks show `checkout` and `storefront` both at **100% unhealthy, latency pegged at a 10,000ms timeout** at the 14:00 UTC check on Nov 28 2025 — fully recovered by 16:00 (2.7%/0% unhealthy). *Source: \"Checkout/Storefront Outage Timeline.\"* The 20 carts.remind job failures (same week, same error: \"503 Service Unavailable: storefront API,\" two batches an hour apart, same 10 tenants both times) were a side effect of this outage, not a separate bug — those 10 tenants just happened to be the only ones with a cart-reminder job scheduled in that window. I'm now confident this was a shared-dependency outage, not a tenant-specific or load issue.\n\n**2. The business cost was real and measurable: orders nearly stopped for 2 hours on the year's biggest shopping day.**\nIn the 14:00-16:00 UTC window on Black Friday, we placed just **1 order (€103)**, versus 22 orders (€4,012) in the same window on Black Friday 2024, and 6 orders (€699-1,574) on the Fridays immediately before and after. That window normally carries 5-10% of a day's orders; on Nov 28 it carried **0.5%**. *Source: \"Orders in the 14:00-16:00 UTC Window.\"* Full-day Black Friday 2025 revenue (€40,166) came in 26% below Black Friday 2024 (€54,186) despite platform growth since then — directionally consistent with real lost sales, though I haven't isolated the outage's exact share of that gap from other factors.\n\n**3. The block was in checkout/storefront, not payment processing.** Payment attempts during the outage hours were low in volume (5 total across both hours) but had **zero failures** — they simply couldn't be initiated, rather than failing once submitted. *Source: \"Payment Attempts During the Nov 28 Outage Window.\"* This narrows where engineering should look: the fix is in the checkout/storefront serving layer, not the payment gateway.\n\n**4. This looks like a genuine one-off, not a recurring risk — within the window we can see.** Across all of Nov-Dec 2025, the Nov 28 14:00 reading is the *only* time checkout/storefront hit ≥50% unhealthy. *Source: \"Checkout/Storefront Severe Outages, Nov-Dec 2025.\"* I can't check before November (health-check retention stops there), so I can't fully rule out a prior occurrence.\n\n**5. Monitoring caught it, but detection is coarse.** Checks for checkout/storefront only run roughly every 2 hours (data exists at 12:00/14:00/16:00, nothing between) — we got a clean signal, but an outage could run up to 2 hours before any automated check would flag it.\n\n**6. Webhook sync stuck-retries (your top priority): confirmed concentrated and longstanding, not new.** ~91% of all stuck ERP/WMS/Accounting sync deliveries sit with 4 tenants (Brennquell Kaffee ~1,574, Cragsley Outfitters ~700, Ampfeld Elektronik ~161, Marrowby & Vane ~88 of ~2,774 total). Their monthly stuck rates have been stable all year (Brennquell Kaffee 9.7-13.7%, Cragsley Outfitters 3.9-6.5%, Ampfeld Elektronik noisy 3-17% around ~9%) — this is a lived-with condition, not a recent regression. *Sources: \"Merchants Affected by the Sync-Webhook Stuck-Retry Pattern,\" \"Monthly Sync-Webhook Stuck Rate for the 4 Highest-Impact Tenants.\"* Last week's \"~15%\" figure was a selection-biased view of only the worst endpoints — the true platform average is a flat ~5.5%/month all year.\n\n**Caveats:** `job_runs`/`system_health_checks` retention is ~90/60 days, so no pre-October visibility. The ticket-subject keyword search for early-warning signs came back empty (0 rows) — inconclusive, since I only searched subjects, not message bodies.\n\n**Next steps:** get engineering confirmation of root cause/fix for the Nov 28 checkout outage; push to tighten health-check frequency on checkout/storefront; schedule a fix-it conversation on the ERP/WMS/Accounting sync retry logic using the 4-tenant sizing above."}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":143432,"cache_read_input_tokens":0,"output_tokens":4221,"output_tokens_details":{"thinking_tokens":2608},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":143432,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":4221,"cache_read_input_tokens":0,"cache_creation_input_tokens":143432,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":143432},"type":"message"}],"speed":"standard"},"input_transformations":[],"diagnostics":{"cache_miss_reason":{"type":"tools_changed","cache_missed_input_tokens":121701}},"context_management":null},"apiBlockIndex":1,"requestId":"req_011CfgW32CdxGN2wkwRSmmpF","type":"assistant","uuid":"c37e3272-e5fb-49b3-ac58-083bc7938469","timestamp":"2026-10-04T03:40:04.620Z","effort":"high","perTurnEffort":null,"userType":"external","entrypoint":"sdk-cli","cwd":"/private/var/folders/[redacted: per-user temporary folder]/T/analytics-k38ajyh1","sessionId":"6b5c22fe-f888-4838-8044-6c1be28e9460","version":"2.1.275","gitBranch":"HEAD"}
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