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Prompt

What is the current MRR (most recent snapshot) for account CRN 8647576055?

Outcome
success
Cost
$30.5349
excludes embeddings
Latency
168.9 s
Query attempts
4
Rows returned
1
Tokens
3,656,709 / 22,467
in / out

Request

request_id
de44e446-2396-4aba-92fd-9fb257afaafa
When
2026-09-24 18:27
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
lookup
Caveat raised
degenerate_column
Sharper question offered
—
Shown to user
Basis: Prepare Monthly ACV for Partner account CRN 8647576055, the governed equivalent of the requested cur, snapshot of 23 Sep 2026. Governed filters applied.
Every row's «Prepare MRR» came back as 0. That is usually a wrong segment filter rather than a real zero.

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

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_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") = 8647576055
  AND omni_dbt__eds_calendar_day."IS_LATEST_AVAILABLE_DATE" = TRUE
  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)
LIMIT 10

Feedback

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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.