Chandler · usage
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Prompt

For account CRN 6232467381 (Mon Expert-Comptable), what was its MRR on 2026-05-29 versus its current MRR?

Outcome
success
Cost
$0.3663
excludes embeddings
Latency
29.6 s
Query attempts
1
Rows returned
61
Tokens
30,832 / 1,159
in / out

Request

request_id
64087519-4b93-4718-98ed-1f36731ecf1f
When
2026-09-24 18:28
Tool
ask
User
chris.scott@dext.com · Chris Scott
Identity
google
Session
67cf914d-9500-4868-80a9-322430d351e9
Model
not recorded

Cost was recorded but no model name was. This happens when the answer came from an approved query — that path returns before the model name is stamped — so the cost figure is correct while the model behind it is not identified here. Cost was still priced per call at the right rate.

Route taken

Topic
—
Omni model
—
Match method
cache_patched
Nudge shown
—
Question shape
decision_shaped
Caveat raised
—
Sharper question offered
—
Shown to user
Basis: Account Name, Finance Account Type, Prepare Monthly ACV +3 more, 2026-04-01 to 2026-05-31. Governed filters applied.
Figures as of 18:13 UTC — reused from an earlier identical question, not recomputed just now.
Showing the top 10 of 61 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 DISTINCT
    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_all_accounts."FINANCE_ACCOUNT_TYPE" AS "Finance Account Type",
    omni_dbt__eds_bookings."PRIMARY_KEY" AS "Booking Primary Key",
    omni_dbt__eds_bookings."PREPARE_BOOKINGS_MONTHLY_ACV" AS "Prepare Monthly ACV",
    omni_dbt__eds_bookings."PREPARE_BOOKINGS_MRR" AS "Prepare MRR",
    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") = 6232467381
    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)
)
SELECT
  "Snapshot Date" AS "Snapshot Date",
  MAX("Account Name") AS "Account Name",
  MAX("Finance Account Type") AS "Finance Account Type",
  SUM("Prepare Monthly ACV") AS "Prepare Monthly ACV",
  SUM("Prepare MRR") AS "Prepare MRR",
  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

Feedback

No feedback linked to this request.

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.