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Prompt

What is the current MRR (most recent snapshot) for account CRN 8307087859?

Outcome
success
Cost
$17.4488
excludes embeddings
Latency
121.5 s
Query attempts
2
Rows returned
1
Tokens
2,062,154 / 13,151
in / out

Request

request_id
a1c8148a-3218-4553-a0c2-aaf06d44a096
When
2026-09-24 18:16
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
feedback_nudge
Question shape
lookup
Caveat raised
—
Sharper question offered
—
Shown to user
Basis: Current monthly revenue for account CRN 8307087859, using the governed Partner or Direct revenue fie, most recent available snapshot. Governed filters applied.
How did I do with this report?

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",
    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") = 8307087859
    AND omni_dbt__eds_calendar_day."IS_LATEST_AVAILABLE_DATE" = TRUE
    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
  MAX("Snapshot Date") AS "Snapshot Date",
  MAX("Account CRN") AS "Account CRN",
  MAX("Account Name") AS "Account Name",
  MAX("Finance Account Type") AS "Finance Account Type",
  SUM("Monthly Revenue") AS "Current Monthly Revenue",
  COUNT(DISTINCT "Booking Primary Key") AS "Booking Records"
FROM booking_snapshot_grain
HAVING COUNT(*) > 0
LIMIT 1

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.