Chandler · usage
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Prompt

For account CRN 6148532298, compare its MRR on 2026-07-20 to its current MRR, give me just those two numbers

Outcome
success
Cost
$0.3645
excludes embeddings
Latency
39.2 s
Query attempts
1
Rows returned
1
Tokens
28,869 / 1,517
in / out

Request

request_id
7e0323aa-0463-4d99-9bd5-aeaada79d214
When
2026-09-24 18:33
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: MRR on 2026-06-19, Current MRR. Governed filters applied.
Figures as of 18:33 UTC — reused from an earlier identical question, not recomputed just now.

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_calendar_day."IS_LATEST_AVAILABLE_DATE" AS "Is Latest Available Date",
    omni_dbt__eds_bookings."PRIMARY_KEY" AS "Booking Primary Key",
    CASE
      WHEN omni_dbt__eds_all_accounts."FINANCE_ACCOUNT_TYPE" = 'Accountant'
        THEN omni_dbt__eds_bookings."PREPARE_BOOKINGS_MONTHLY_ACV"
      WHEN omni_dbt__eds_all_accounts."FINANCE_ACCOUNT_TYPE" = 'Corporate'
        THEN omni_dbt__eds_bookings."PREPARE_BOOKINGS_MRR"
      ELSE NULL
    END AS "Monthly Revenue"
  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") = 6148532298
    AND (omni_dbt__eds_calendar_day."DATE_DAY" = DATE_FROM_PARTS(2026, 7, 20)
      OR omni_dbt__eds_calendar_day."IS_LATEST_AVAILABLE_DATE" = TRUE)
    AND omni_dbt__eds_calendar_day."DATE_DAY" >= DATE_FROM_PARTS(2026, 3, 24)
    AND omni_dbt__eds_calendar_day."DATE_DAY" < DATE_FROM_PARTS(2026, 9, 24)
    AND omni_dbt__eds_all_accounts."FINANCE_ACCOUNT_TYPE" IN ('Accountant', 'Corporate')
    AND (omni_dbt__eds_all_accounts."FINANCE_ACCOUNT_TYPE" = 'Accountant'
      OR (omni_dbt__eds_all_accounts."FINANCE_ACCOUNT_TYPE" = 'Corporate'
        AND omni_dbt__eds_bookings."HAS_PAID_SUBSCRIPTION" = TRUE))
    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
  SUM(CASE WHEN "Snapshot Date" = DATE_FROM_PARTS(2026, 7, 20) THEN "Monthly Revenue" END) AS "MRR on 2026-06-19",
  SUM(CASE WHEN "Is Latest Available Date" = TRUE THEN "Monthly Revenue" END) AS "Current MRR"
FROM booking_snapshot_grain
LIMIT 1

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.