Queries · q224 · Chief Financial Officer · week 2, night 2

Ledger-to-Payment Reconciliation Pattern at Scale, 2025

“Across all of 2025's payments, does the 'ledger credit = payment + refund' pattern seen in the sample hold universally, and how large are the 'no ledger entries' and 'other mismatch' buckets?”

Fictional people at a fictional company, each played by a language-model agent. Who they are and what they are told

1. A business event lands on the Chief Financial Officer's desk

   Week 2

   The external auditors start the 2025 year-end audit in two weeks. They will want to see that the money adds up across the whole chain: what shoppers paid, what was refunded or credited, what merchants received in payouts, what Tidewick kept, and that invoices agree with all of it. They will also trace sample transactions end to end.

   One of the 35 events we wrote for the role play; the manager heard of it at the start of week 2 and had to cover it in that week's note. How events arise · All the events

2. Tobias Lindqvist writes to Kofi Mensah

   The manager, at the start of week 2

   Subject: Good progress — but the audit clock is now the priority

   Kofi,

   Thanks — this is useful, and I appreciate you flagging the caveats rather than smoothing them over. A few reactions and then where I want to focus next week.

   What stood out:

   • The correction on the revenue report (off by roughly half, now fixed and cross-checked) is reassuring that you caught it, but it also makes me want a second pair of eyes before anything like this goes near the auditors.
   • The refund numbers disagreeing by 16–40% depending on which source we use is the kind of thing an auditor will ask about directly. I don't want to walk in with two different refund totals and no explanation.
   • Concentration easing as we add merchants is good context for the budget conversation — no urgent follow-up needed there this week.
   • I also saw the notes from pricing and risk this week — the free-delivery giveaway and the refund pattern on that welcome discount are small in isolation, but they're exactly the kind of "where did the money go" questions auditors and I will both ask, so I'd like a rough sense of scale rather than letting them sit as separate anecdotes.

   The big new driver: external audit starts in two weeks. They'll want to walk the full money trail — what a shopper paid, what was refunded or credited back, what the merchant was paid out, what we kept as our fee, and that it all agrees with the invoices and accounts — and they'll pick sample transactions and trace them start to finish. I'd rather we got ahead of that than react to it.

   Questions for this week, in priority order:

   1. Can we demonstrate, for a sample of transactions, that the money actually adds up end to end — shopper payment through refund/credit, payout to the merchant, and our retained fee — so we have something ready to hand the auditors rather than assembling it under time pressure?
   2. Which refund figure should I trust for the 2025 accounts, and why do the two methods disagree? I don't need the full reconciliation solved, but I need to understand the likely cause well enough to explain it credibly if asked.
   3. Where do things stand on getting our own plan-fee revenue into the picture, even approximately, so I'm not presenting half of 2025 revenue as the whole story?
   4. If there's room: roughly how much revenue or margin might be leaking through the control gaps your peers flagged this week (free deliveries to canceled customers, uncapped coupon reuse) — just enough for me to judge whether it's noise or something that belongs in front of the auditors too.

   Audit readiness (#1) is the one I most need — please make that the lead request even if it means FX and B2B receivables slip another week.

   Thanks, Tobias

   Show the whole note

3. Kofi Mensah plans the night

   The analyst · week 2, night 2, request 2 of 3

   Report 1's ledger columns are the big finding. For every refunded payment, ledger_credit_total exactly equals payment_amount + amount_refunded, not net_amount: e.g. OfficeHub Nordic SO-100363 (265,275.15 paid, fully refunded) shows ledger_credit_total = 530,550.30 = 2x the payment, and Fell & Fjell #1179 shows 69,408.75 = 34,885.85 + 34,522.90 exactly. My ledger_debit_credit_diff column is useless for catching this (it's 0 everywhere by construction, since any balanced journal entry nets to zero regardless of which accounts are touched) — I designed that check too naively. Before I tell Tobias "the ledger balances," I need to see the actual account-level entries for a few of these to confirm this is just a normal (and explainable) refund-accounting pattern — e.g. a reversing entry on a different account — rather than revenue being double-booked.

   Two more things in Report 1 don't fit any pattern and need chasing at scale rather than left as anecdotes: Castell Fasteners' SO-100233 (no refund, not yet paid out) has ledger_credit_total of 805.56 against a payment_amount of 752.91 — an unexplained ~7% gap; and Marrowby & Vane's #3307 (a tiny $20 payment) has NULL ledger_credit_total, meaning no ledger entries exist for it at all. Given this week's pattern across other teams (CISO: deletions and role changes with zero audit trail), an "orphan payment with no ledger entry" is exactly the kind of gap auditors will zero in on, so I want to know if that's a one-off or systemic before Friday.

   Tonight's three slots: (1) pull the actual ledger line items for the flagged refunded + anomalous sample payments to explain the credit-total pattern; (2) run the same credit-total-vs-payment reconciliation across ALL of 2025's payments (not just the 25-sample) to see if the "+refund" pattern is universal/explainable and how big the "other mismatch" and "no ledger entries" buckets really are; (3) quantify the no-ledger-entry gap by merchant to see if it's concentrated or spread thin.

   Read the whole plan
   Ledger-to-Payment Reconciliation Pattern at Scale, 2025

   Question

   Across all of 2025's payments, does the 'ledger credit = payment + refund' pattern seen in the sample hold universally, and how large are the 'no ledger entries' and 'other mismatch' buckets?

   One row per

   one reconciliation category, over all payments captured in 2025

   Columns

   • reconciliation_category
   • payment_count
   • pct_of_payments
   • total_payment_amount_eur
   • total_ledger_credit_eur
   • total_ledger_debit_eur

4. Grace writes the SQL

   The engineer · 1 dry run on the empty schema, then the submission

   Dry run 1 · STATUS: COMPILES (dry run on the empty schema, no data read)

   WITH qualifying_payments AS (
     SELECT
       p.id,
       p.currency_code,
       p.amount,
       p.amount_refunded,
       p.captured_at
     FROM payments p
     WHERE p.status IN ('captured', 'refunded', 'partially_refunded')
       AND p.captured_at >= DATE '2025-01-01'
       AND p.captured_at < DATE '2026-01-01'
   ),
   payment_amounts_eur AS (
     SELECT
       qp.id,
       qp.amount / er.rate AS payment_amount_eur,
       qp.amount_refunded / er.rate AS amount_refunded_eur
     FROM qualifying_payments qp
     JOIN exchange_rates er
       ON er.base_code = 'EUR'
      AND er.quote_code = qp.currency_code
      AND er.as_of = date(qp.captured_at)
   ),
   ledger_entries_eur AS (
     SELECT
       le.payment_id,
       le.direction,
       le.amount / er.rate AS amount_eur
     FROM ledger_entries le
     JOIN exchange_rates er
       ON er.base_code = 'EUR'
      AND er.quote_code = le.currency_code
      AND er.as_of = date(le.occurred_at)
     WHERE le.payment_id IN (SELECT id FROM qualifying_payments)
   ),
   ledger_agg AS (
     SELECT
       payment_id,
       SUM(CASE WHEN direction = 'credit' THEN amount_eur ELSE 0 END) AS total_credit_eur,
       SUM(CASE WHEN direction = 'debit' THEN amount_eur ELSE 0 END) AS total_debit_eur,
       COUNT(*) AS entry_count
     FROM ledger_entries_eur
     GROUP BY payment_id
   ),
   classified AS (
     SELECT
       pa.id,
       pa.payment_amount_eur,
       pa.amount_refunded_eur,
       COALESCE(la.total_credit_eur, 0) AS total_credit_eur,
       COALESCE(la.total_debit_eur, 0) AS total_debit_eur,
       COALESCE(la.entry_count, 0) AS entry_count,
       (pa.payment_amount_eur - pa.amount_refunded_eur) AS target_net,
       (pa.payment_amount_eur + pa.amount_refunded_eur) AS target_gross
     FROM payment_amounts_eur pa
     LEFT JOIN ledger_agg la ON la.payment_id = pa.id
   ),
   categorized AS (
     SELECT
       id,
       payment_amount_eur,
       total_credit_eur,
       total_debit_eur,
       CASE
         WHEN entry_count = 0 THEN 'no ledger entries'
         WHEN ABS(total_credit_eur - target_net) <= GREATEST(0.01 * ABS(target_net), 1) THEN 'credit = net amount'
         WHEN ABS(total_credit_eur - target_gross) <= GREATEST(0.01 * ABS(target_gross), 1) THEN 'credit = payment + refund'
         ELSE 'other mismatch'
       END AS reconciliation_category
     FROM classified
   )
   SELECT
     reconciliation_category,
     COUNT(*) AS payment_count,
     ROUND(100.0 * COUNT(*) / SUM(COUNT(*)) OVER (), 2) AS pct_of_payments,
     ROUND(SUM(payment_amount_eur), 2) AS total_payment_amount_eur,
     ROUND(SUM(total_credit_eur), 2) AS total_ledger_credit_eur,
     ROUND(SUM(total_debit_eur), 2) AS total_ledger_debit_eur
   FROM categorized
   GROUP BY reconciliation_category
   ORDER BY payment_count DESC

   “Query compiles and produces the requested grain/columns. Submitting.”

   Submitted · the SQL of the last dry run, unchanged · Submitted to tonight's batch. Your work on this request is done.

   “Submitted. Summary of the approach: - Scoped to payments with status in (captured, refunded, partially_refunded) and captured_at in 2025. - Converted each payment's amount/amount_refunded to EUR using the exchange rate on captured_at's date, and converted each linked ledger_entries.amount to EUR independently using that entry's own currency_code and occurred_at date, as requested. - Classified each payment into no ledger entries, credit = net amount, credit = payment + refund, or other mismatch using a tolerance of max(1%, €1) against the net and gross targets respectively, checking the net-amount pattern first (noted in the submit note since it affects edge cases where refund = 0). - Aggregated to one row per category with count, share, and summed EUR totals, ordered by payment_count descending.”

5. The report comes back overnight

   As Kofi Mensah saw it, from the small dataset the agents worked on

   reconciliation_category   | payment_count | pct_of_payments | total_payment_amount_eur | total_ledger_credit_eur | total_ledger_debit_eur
   --------------------------+---------------+-----------------+--------------------------+-------------------------+-----------------------
   credit = net amount       |          9143 |           78.55 |               2169737.87 |              2169721.91 |             2169721.91
   credit = payment + refund |          2181 |           18.74 |                553394.46 |               829231.76 |              829231.76
   other mismatch            |           249 |            2.14 |                 84443.43 |                135386.7 |               135386.7
   no ledger entries         |            67 |            0.58 |                   3380.6 |                     0.0 |                    0.0
   (4 rows)

6. What came of it

   The memo and the queries that followed; links found by title, so inferred

   Later queries that build on this report:

   • q226Other-Mismatch Payments: Characterization by Payment Method, 2025
   • q2272025 Revenue Reconciliation via Correct Ledger Accounts, by Quarter
   • q231Ledger Reconciliation Outcome by Payment Method, All 2025 Payments

   It builds on:

   • q220Audit Sample: End-to-End Transaction Walkthrough, 2025

7. It becomes benchmark query q224

   Changed only where the benchmark needs the same result on every system

   • spellingdate cast (2×)
   • determinismtie-breaker: the group key
   • numericsdocs/NUMERICS.md: rounding removed; results keep full precision
   • numericsdocs/NUMERICS.md: x / exchange rate cast to NUMERIC(18,6) (3×)
   • made explicita selected t.col gets AS col, its standard name made explicit (11×)
   • determinismthe standard leaves the place of NULLs in a sort to the implementation: NULLS LAST ascending, NULLS FIRST descending, as in the reference results (2×)
   The benchmark text, against the submitted SQL

   WITH qualifying_payments AS (  SELECT    p.id,    p.currency_code,    p.amount,    p.amount_refunded,    p.captured_at    p.id AS id,    p.currency_code AS currency_code,    p.amount AS amount,    p.amount_refunded AS amount_refunded,    p.captured_at AS captured_at  FROM payments p  WHERE p.status IN ('captured', 'refunded', 'partially_refunded')    AND p.captured_at >= DATE '2025-01-01'    AND p.captured_at < DATE '2026-01-01'),payment_amounts_eur AS (  SELECT    qp.id,    qp.amount / er.rate AS payment_amount_eur,    qp.amount_refunded / er.rate AS amount_refunded_eur    qp.id AS id,    CAST(qp.amount / er.rate AS NUMERIC(18,6)) AS payment_amount_eur,    CAST(qp.amount_refunded / er.rate AS NUMERIC(18,6)) AS amount_refunded_eur  FROM qualifying_payments qp  JOIN exchange_rates er    ON er.base_code = 'EUR'   AND er.quote_code = qp.currency_code   AND er.as_of = date(qp.captured_at)   AND er.as_of = CAST(qp.captured_at AS DATE)),ledger_entries_eur AS (  SELECT    le.payment_id,    le.direction,    le.amount / er.rate AS amount_eur    le.payment_id AS payment_id,    le.direction AS direction,    CAST(le.amount / er.rate AS NUMERIC(18,6)) AS amount_eur  FROM ledger_entries le  JOIN exchange_rates er    ON er.base_code = 'EUR'   AND er.quote_code = le.currency_code   AND er.as_of = date(le.occurred_at)   AND er.as_of = CAST(le.occurred_at AS DATE)  WHERE le.payment_id IN (SELECT id FROM qualifying_payments)),ledger_agg AS (  SELECT    payment_id,    SUM(CASE WHEN direction = 'credit' THEN amount_eur ELSE 0 END) AS total_credit_eur,    SUM(CASE WHEN direction = 'debit' THEN amount_eur ELSE 0 END) AS total_debit_eur,    COUNT(*) AS entry_count  FROM ledger_entries_eur  GROUP BY payment_id),classified AS (  SELECT    pa.id,    pa.payment_amount_eur,    pa.amount_refunded_eur,    pa.id AS id,    pa.payment_amount_eur AS payment_amount_eur,    pa.amount_refunded_eur AS amount_refunded_eur,    COALESCE(la.total_credit_eur, 0) AS total_credit_eur,    COALESCE(la.total_debit_eur, 0) AS total_debit_eur,    COALESCE(la.entry_count, 0) AS entry_count,    (pa.payment_amount_eur - pa.amount_refunded_eur) AS target_net,    (pa.payment_amount_eur + pa.amount_refunded_eur) AS target_gross  FROM payment_amounts_eur pa  LEFT JOIN ledger_agg la ON la.payment_id = pa.id),categorized AS (  SELECT    id,    payment_amount_eur,    total_credit_eur,    total_debit_eur,    CASE      WHEN entry_count = 0 THEN 'no ledger entries'      WHEN ABS(total_credit_eur - target_net) <= GREATEST(0.01 * ABS(target_net), 1) THEN 'credit = net amount'      WHEN ABS(total_credit_eur - target_gross) <= GREATEST(0.01 * ABS(target_gross), 1) THEN 'credit = payment + refund'      ELSE 'other mismatch'    END AS reconciliation_category  FROM classified)SELECT  reconciliation_category,  COUNT(*) AS payment_count,  ROUND(100.0 * COUNT(*) / SUM(COUNT(*)) OVER (), 2) AS pct_of_payments,  ROUND(SUM(payment_amount_eur), 2) AS total_payment_amount_eur,  ROUND(SUM(total_credit_eur), 2) AS total_ledger_credit_eur,  ROUND(SUM(total_debit_eur), 2) AS total_ledger_debit_eur  100.0 * COUNT(*) / SUM(COUNT(*)) OVER () AS pct_of_payments,  SUM(payment_amount_eur) AS total_payment_amount_eur,  SUM(total_credit_eur) AS total_ledger_credit_eur,  SUM(total_debit_eur) AS total_ledger_debit_eurFROM categorizedGROUP BY reconciliation_categoryORDER BY payment_count DESCORDER BY payment_count DESC NULLS FIRST, reconciliation_category NULLS LAST

   As each system received it; highlighted, the parts spelled for that system

   DuckDB · Hyper · GizmoSQL · CedarDB · pg_duckdb · PostgreSQL · DataFusion · Firebolt · ClickHouse · chDB · clickhouse_brew

   WITH qualifying_payments AS (
     SELECT
       p.id AS id,
       p.currency_code AS currency_code,
       p.amount AS amount,
       p.amount_refunded AS amount_refunded,
       p.captured_at AS captured_at
     FROM payments p
     WHERE p.status IN ('captured', 'refunded', 'partially_refunded')
       AND p.captured_at >= DATE '2025-01-01'
       AND p.captured_at < DATE '2026-01-01'
   ),
   payment_amounts_eur AS (
     SELECT
       qp.id AS id,
       CAST(qp.amount / er.rate AS NUMERIC(18,6)) AS payment_amount_eur,
       CAST(qp.amount_refunded / er.rate AS NUMERIC(18,6)) AS amount_refunded_eur
     FROM qualifying_payments qp
     JOIN exchange_rates er
       ON er.base_code = 'EUR'
      AND er.quote_code = qp.currency_code
      AND er.as_of = CAST(qp.captured_at AS DATE)
   ),
   ledger_entries_eur AS (
     SELECT
       le.payment_id AS payment_id,
       le.direction AS direction,
       CAST(le.amount / er.rate AS NUMERIC(18,6)) AS amount_eur
     FROM ledger_entries le
     JOIN exchange_rates er
       ON er.base_code = 'EUR'
      AND er.quote_code = le.currency_code
      AND er.as_of = CAST(le.occurred_at AS DATE)
     WHERE le.payment_id IN (SELECT id FROM qualifying_payments)
   ),
   ledger_agg AS (
     SELECT
       payment_id,
       SUM(CASE WHEN direction = 'credit' THEN amount_eur ELSE 0 END) AS total_credit_eur,
       SUM(CASE WHEN direction = 'debit' THEN amount_eur ELSE 0 END) AS total_debit_eur,
       COUNT(*) AS entry_count
     FROM ledger_entries_eur
     GROUP BY payment_id
   ),
   classified AS (
     SELECT
       pa.id AS id,
       pa.payment_amount_eur AS payment_amount_eur,
       pa.amount_refunded_eur AS amount_refunded_eur,
       COALESCE(la.total_credit_eur, 0) AS total_credit_eur,
       COALESCE(la.total_debit_eur, 0) AS total_debit_eur,
       COALESCE(la.entry_count, 0) AS entry_count,
       (pa.payment_amount_eur - pa.amount_refunded_eur) AS target_net,
       (pa.payment_amount_eur + pa.amount_refunded_eur) AS target_gross
     FROM payment_amounts_eur pa
     LEFT JOIN ledger_agg la ON la.payment_id = pa.id
   ),
   categorized AS (
     SELECT
       id,
       payment_amount_eur,
       total_credit_eur,
       total_debit_eur,
       CASE
         WHEN entry_count = 0 THEN 'no ledger entries'
         WHEN ABS(total_credit_eur - target_net) <= GREATEST(0.01 * ABS(target_net), 1) THEN 'credit = net amount'
         WHEN ABS(total_credit_eur - target_gross) <= GREATEST(0.01 * ABS(target_gross), 1) THEN 'credit = payment + refund'
         ELSE 'other mismatch'
       END AS reconciliation_category
     FROM classified
   )
   SELECT
     reconciliation_category,
     COUNT(*) AS payment_count,
     100.0 * COUNT(*) / SUM(COUNT(*)) OVER () AS pct_of_payments,
     SUM(payment_amount_eur) AS total_payment_amount_eur,
     SUM(total_credit_eur) AS total_ledger_credit_eur,
     SUM(total_debit_eur) AS total_ledger_debit_eur
   FROM categorized
   GROUP BY reconciliation_category
   ORDER BY payment_count DESC NULLS FIRST, reconciliation_category NULLS LAST

   • Firebolt{ "errors": [ { "description": "Line 16, Column 20: Operations between decimals with different precision and scale is not supported at the moment. Explicitly cast one of the decimals to other decimal precision and scale." } ], "query": { "query_id": "43078e0f-5891-44ac-a3c9-b0ec5938239d", "query_lab
   • ClickHouseCode: 69. DB::Exception: Decimal result's scale is less than argument's one: while executing function divide on arguments __table4.amount Decimal(14, 4) Decimal64(size = 0), __table6.rate Decimal(12, 6) Decimal64(size = 0). (ARGUMENT_OUT_OF_BOUND)

   Scale factor 1000, c8a.24xlarge, after the last load. q224 on the start page, with every configuration.

8. Everything, as recorded

   The agents' own records, as the role play wrote them; only the personal data of the person who ran it is redacted

   Grace's whole session, 7 turns

   • The request, as Grace received it

     Report request from Kofi Mensah (analyst for the Chief Financial Officer):
     
     Title: Ledger-to-Payment Reconciliation Pattern at Scale, 2025
     Question: Across all of 2025's payments, does the 'ledger credit = payment + refund' pattern seen in the sample hold universally, and how large are the 'no ledger entries' and 'other mismatch' buckets?
     Grain: one reconciliation category, over all payments captured in 2025
     Columns:
     - reconciliation_category
     - payment_count
     - pct_of_payments
     - total_payment_amount_eur
     - total_ledger_credit_eur
     - total_ledger_debit_eur
     Details: Source: payments with status in a captured/refunded/partially_refunded state and captured_at in 2025. For each payment, aggregate linked ledger_entries (via payment_id) into total credit amount and total debit amount, converting each ledger_entries.amount to EUR via exchange_rates matched on ledger_entries.currency_code and date(ledger_entries.occurred_at) — do not assume the ledger entry's currency matches the payment's currency, convert each independently. Also convert payments.amount and amount_refunded to EUR via exchange_rates on captured_at. Classify each payment into exactly one reconciliation_category: 'no ledger entries' (zero linked ledger_entries rows); else 'credit = net amount' if total_ledger_credit_eur is within 1% (or €1, whichever is larger) of (payment_amount_eur - amount_refunded_eur); else 'credit = payment + refund' if total_ledger_credit_eur is within the same tolerance of (payment_amount_eur + amount_refunded_eur); else 'other mismatch'. Count payments and sum amounts per category; pct_of_payments is each category's share of all qualifying 2025 payments. Order by payment_count descending.
     
     Check your query with the warehouse's dry_run tool, then hand it in with submit.

   • Grace calls dry_run

     WITH qualifying_payments AS (
       SELECT
         p.id,
         p.currency_code,
         p.amount,
         p.amount_refunded,
         p.captured_at
       FROM payments p
       WHERE p.status IN ('captured', 'refunded', 'partially_refunded')
         AND p.captured_at >= DATE '2025-01-01'
         AND p.captured_at < DATE '2026-01-01'
     ),
     payment_amounts_eur AS (
       SELECT
         qp.id,
         qp.amount / er.rate AS payment_amount_eur,
         qp.amount_refunded / er.rate AS amount_refunded_eur
       FROM qualifying_payments qp
       JOIN exchange_rates er
         ON er.base_code = 'EUR'
        AND er.quote_code = qp.currency_code
        AND er.as_of = date(qp.captured_at)
     ),
     ledger_entries_eur AS (
       SELECT
         le.payment_id,
         le.direction,
         le.amount / er.rate AS amount_eur
       FROM ledger_entries le
       JOIN exchange_rates er
         ON er.base_code = 'EUR'
        AND er.quote_code = le.currency_code
        AND er.as_of = date(le.occurred_at)
       WHERE le.payment_id IN (SELECT id FROM qualifying_payments)
     ),
     ledger_agg AS (
       SELECT
         payment_id,
         SUM(CASE WHEN direction = 'credit' THEN amount_eur ELSE 0 END) AS total_credit_eur,
         SUM(CASE WHEN direction = 'debit' THEN amount_eur ELSE 0 END) AS total_debit_eur,
         COUNT(*) AS entry_count
       FROM ledger_entries_eur
       GROUP BY payment_id
     ),
     classified AS (
       SELECT
         pa.id,
         pa.payment_amount_eur,
         pa.amount_refunded_eur,
         COALESCE(la.total_credit_eur, 0) AS total_credit_eur,
         COALESCE(la.total_debit_eur, 0) AS total_debit_eur,
         COALESCE(la.entry_count, 0) AS entry_count,
         (pa.payment_amount_eur - pa.amount_refunded_eur) AS target_net,
         (pa.payment_amount_eur + pa.amount_refunded_eur) AS target_gross
       FROM payment_amounts_eur pa
       LEFT JOIN ledger_agg la ON la.payment_id = pa.id
     ),
     categorized AS (
       SELECT
         id,
         payment_amount_eur,
         total_credit_eur,
         total_debit_eur,
         CASE
           WHEN entry_count = 0 THEN 'no ledger entries'
           WHEN ABS(total_credit_eur - target_net) <= GREATEST(0.01 * ABS(target_net), 1) THEN 'credit = net amount'
           WHEN ABS(total_credit_eur - target_gross) <= GREATEST(0.01 * ABS(target_gross), 1) THEN 'credit = payment + refund'
           ELSE 'other mismatch'
         END AS reconciliation_category
       FROM classified
     )
     SELECT
       reconciliation_category,
       COUNT(*) AS payment_count,
       ROUND(100.0 * COUNT(*) / SUM(COUNT(*)) OVER (), 2) AS pct_of_payments,
       ROUND(SUM(payment_amount_eur), 2) AS total_payment_amount_eur,
       ROUND(SUM(total_credit_eur), 2) AS total_ledger_credit_eur,
       ROUND(SUM(total_debit_eur), 2) AS total_ledger_debit_eur
     FROM categorized
     GROUP BY reconciliation_category
     ORDER BY payment_count DESC

   • The warehouse answers

     STATUS: COMPILES (dry run on the empty schema, no data read)
     COLUMNS: reconciliation_category, payment_count, pct_of_payments, total_payment_amount_eur, total_ledger_credit_eur, total_ledger_debit_eur

   • GraceQuery compiles and produces the requested grain/columns. Submitting.
   • Grace calls submit

     WITH qualifying_payments AS (
       SELECT
         p.id,
         p.currency_code,
         p.amount,
         p.amount_refunded,
         p.captured_at
       FROM payments p
       WHERE p.status IN ('captured', 'refunded', 'partially_refunded')
         AND p.captured_at >= DATE '2025-01-01'
         AND p.captured_at < DATE '2026-01-01'
     ),
     payment_amounts_eur AS (
       SELECT
         qp.id,
         qp.amount / er.rate AS payment_amount_eur,
         qp.amount_refunded / er.rate AS amount_refunded_eur
       FROM qualifying_payments qp
       JOIN exchange_rates er
         ON er.base_code = 'EUR'
        AND er.quote_code = qp.currency_code
        AND er.as_of = date(qp.captured_at)
     ),
     ledger_entries_eur AS (
       SELECT
         le.payment_id,
         le.direction,
         le.amount / er.rate AS amount_eur
       FROM ledger_entries le
       JOIN exchange_rates er
         ON er.base_code = 'EUR'
        AND er.quote_code = le.currency_code
        AND er.as_of = date(le.occurred_at)
       WHERE le.payment_id IN (SELECT id FROM qualifying_payments)
     ),
     ledger_agg AS (
       SELECT
         payment_id,
         SUM(CASE WHEN direction = 'credit' THEN amount_eur ELSE 0 END) AS total_credit_eur,
         SUM(CASE WHEN direction = 'debit' THEN amount_eur ELSE 0 END) AS total_debit_eur,
         COUNT(*) AS entry_count
       FROM ledger_entries_eur
       GROUP BY payment_id
     ),
     classified AS (
       SELECT
         pa.id,
         pa.payment_amount_eur,
         pa.amount_refunded_eur,
         COALESCE(la.total_credit_eur, 0) AS total_credit_eur,
         COALESCE(la.total_debit_eur, 0) AS total_debit_eur,
         COALESCE(la.entry_count, 0) AS entry_count,
         (pa.payment_amount_eur - pa.amount_refunded_eur) AS target_net,
         (pa.payment_amount_eur + pa.amount_refunded_eur) AS target_gross
       FROM payment_amounts_eur pa
       LEFT JOIN ledger_agg la ON la.payment_id = pa.id
     ),
     categorized AS (
       SELECT
         id,
         payment_amount_eur,
         total_credit_eur,
         total_debit_eur,
         CASE
           WHEN entry_count = 0 THEN 'no ledger entries'
           WHEN ABS(total_credit_eur - target_net) <= GREATEST(0.01 * ABS(target_net), 1) THEN 'credit = net amount'
           WHEN ABS(total_credit_eur - target_gross) <= GREATEST(0.01 * ABS(target_gross), 1) THEN 'credit = payment + refund'
           ELSE 'other mismatch'
         END AS reconciliation_category
       FROM classified
     )
     SELECT
       reconciliation_category,
       COUNT(*) AS payment_count,
       ROUND(100.0 * COUNT(*) / SUM(COUNT(*)) OVER (), 2) AS pct_of_payments,
       ROUND(SUM(payment_amount_eur), 2) AS total_payment_amount_eur,
       ROUND(SUM(total_credit_eur), 2) AS total_ledger_credit_eur,
       ROUND(SUM(total_debit_eur), 2) AS total_ledger_debit_eur
     FROM categorized
     GROUP BY reconciliation_category
     ORDER BY payment_count DESC

   • The warehouse answers

     Submitted to tonight's batch. Your work on this request is done.

   • GraceSubmitted. Summary of the approach: - Scoped to payments with status in (captured, refunded, partially_refunded) and `captured_at` in 2025. - Converted each payment's `amount`/`amount_refunded` to EUR using the exchange rate on `captured_at`'s date, and converted each linked `ledger_entries.amount` to EUR independently using that entry's own `currency_code` and `occurred_at` date, as requested. - Classified each payment into `no ledger entries`, `credit = net amount`, `credit = payment + refund`, or `other mismatch` using a tolerance of max(1%, €1) against the net and gross targets respectively, checking the net-amount pattern first (noted in the submit note since it affects edge cases where refund = 0). - Aggregated to one row per category with count, share, and summed EUR totals, ordered by payment_count descending.

   Not shown here: 239 KB of context the agent was given, its instructions and the schema documentation; it is in the raw transcript.

   Grace's transcript (273 KB) · Kofi Mensah's transcript (906 KB) · Tobias Lindqvist's transcript (269 KB) · the night's plan (6 KB) · the night's reports (19 KB) · the week's memo (3 KB)