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Prompt

For account CRN 9925868674 (GKP Accounts), what was its MRR on 2026-04-25 versus its current MRR?

Outcome
success
Cost
$24.0199
excludes embeddings
Latency
205.5 s
Query attempts
4
Rows returned
14
Tokens
2,831,461 / 24,718
in / out

Request

request_id
9007c66c-634b-46b8-8660-bb004ff58223
When
2026-09-24 18:19
Tool
ask
User
chris.scott@dext.com · Chris Scott
Identity
google
Session
67cf914d-9500-4868-80a9-322430d351e9
Model
gpt-5.6-sol

Route taken

Topic
Bookings
Omni model
e407bba6-4fae-4079-ac4e-cd6cf8fdf6f8
Match method
agent loop
Nudge shown
—
Question shape
decision_shaped
Caveat raised
—
Sharper question offered
—
Shown to user
Basis: Prepare Monthly ACV for GKP Accounts, because it is an Accountant/Partner account and the model says, daily snapshots from 1 Apr through 31 May 2026 to diagnose the missing 25 Apr value. Governed filters applied.
Showing the top 10 of 14 rows.

Per-step detail — each LLM call, each Omni request, each rejected draft — is traced to stdout only and is not stored, so it cannot be shown here. Query attempts counts run_query executions, not model turns.

SQL that ran

WITH booking_snapshot_grain AS (
  SELECT
    omni_dbt__eds_calendar_day."DATE_DAY" AS "Snapshot Date",
    omni_dbt__eds_all_accounts."ACCOUNT_CRN" AS "Account CRN",
    omni_dbt__eds_all_accounts."ACCOUNT_NAME" AS "Account Name",
    omni_dbt__eds_bookings."PRIMARY_KEY" AS "Booking Primary Key",
    MAX(omni_dbt__eds_bookings."PREPARE_BOOKINGS_MONTHLY_ACV") AS "Prepare Monthly ACV",
    MAX(omni_dbt__eds_bookings."HAS_PAID_SUBSCRIPTION") AS "Has Paid Subscription"
  FROM PROD."EDS_ALL_ACCOUNTS" AS omni_dbt__eds_all_accounts
INNER JOIN PROD."EDS_CALENDAR_DAY" AS omni_dbt__eds_calendar_day
  ON omni_dbt__eds_calendar_day."DATE_DAY" >= omni_dbt__eds_all_accounts."VALID_FROM" AND omni_dbt__eds_calendar_day."DATE_DAY" <= omni_dbt__eds_all_accounts."VALID_TO"
LEFT JOIN PROD."EDS_BOOKINGS" AS omni_dbt__eds_bookings
  ON omni_dbt__eds_all_accounts."DERIVED_ACCOUNT_ID" = omni_dbt__eds_bookings."DERIVED_ACCOUNT_ID" AND omni_dbt__eds_calendar_day."DATE_DAY" >= omni_dbt__eds_bookings."VALID_FROM" AND omni_dbt__eds_calendar_day."DATE_DAY" <= omni_dbt__eds_bookings."VALID_TO"
  WHERE TRY_TO_NUMBER(omni_dbt__eds_all_accounts."ACCOUNT_CRN") = 9925868674
    AND omni_dbt__eds_calendar_day."DATE_DAY" >= DATE_FROM_PARTS(2026, 4, 1)
    AND omni_dbt__eds_calendar_day."DATE_DAY" < DATE_FROM_PARTS(2026, 6, 1)
    AND omni_dbt__eds_all_accounts."FINANCE_ACCOUNT_TYPE" = 'Accountant'
    AND COALESCE(omni_dbt__eds_all_accounts."IS_DEXT_DEMO", FALSE) = FALSE
    AND omni_dbt__eds_all_accounts."FRANCHISE_NAME" IS NULL
    AND (omni_dbt__eds_all_accounts."SALESFORCE_ACCOUNT_TYPE" != 'Reseller'
      OR omni_dbt__eds_all_accounts."SALESFORCE_ACCOUNT_TYPE" IS NULL)
  GROUP BY
    omni_dbt__eds_calendar_day."DATE_DAY",
    omni_dbt__eds_all_accounts."ACCOUNT_CRN",
    omni_dbt__eds_all_accounts."ACCOUNT_NAME",
    omni_dbt__eds_bookings."PRIMARY_KEY"
)
SELECT
  "Snapshot Date" AS "Snapshot Date",
  MAX("Account Name") AS "Account Name",
  SUM("Prepare Monthly ACV") AS "Prepare Monthly ACV",
  COUNT(DISTINCT "Booking Primary Key") AS "Booking Records",
  MAX("Has Paid Subscription") AS "Has Paid Subscription"
FROM booking_snapshot_grain
GROUP BY "Snapshot Date"
ORDER BY "Snapshot Date"
LIMIT 100

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Cost is the LLM completion spend LiteLLM priced for each call, summed per request. Chandler is running on the Codex CLI, which draws ChatGPT plan quota rather than per-token API billing, so this figure is not money paid — it is what the same traffic would have cost on the API, priced from the token counts Codex reports. Read it as the size of the bill avoided. Two known limits: embedding spend is not recorded, so retrieval and matching cost is missing, and because the figure is one sum per request it cannot be split by model within a request — a request's classifier and agent calls can use different models while llm_model holds only one name. Only authenticated calls are logged, USAGE_LOG_ENABLED can switch logging off, and log writes are fail-soft — this is not a complete record of traffic. Questions are grouped into sessions: a session is exact when the caller echoed its id back to us and otherwise inferred from a 30-minute gap in that user’s activity, so a grouping is only as good as the source shown on the session itself. A call with no attributable user gets no session at all; those questions are listed separately rather than dropped.