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Prompt

Partners MRR by month, last 6 months

Outcome
success
Cost
$34.3821
excludes embeddings
Latency
139.6 s
Query attempts
2
Rows returned
1
Tokens
1,949,621 / 8,790
in / out

Request

request_id
44a73dae-03f7-4ca6-977b-b826efc00118
When
2026-09-15 13:26
Tool
ask
User
yordan@dext.com · Yordan Todorov Ivanov
Identity
google
Session
78571fcb-eb3d-498e-b6d7-90c0ab8b69fb
Model
gpt-6-astra

Route taken

Topic
Bookings
Omni model
e407bba6-4fae-4079-ac4e-cd6cf8fdf6f8
Match method
agent loop
Nudge shown
—
Question shape
not classified
Caveat raised
—
Sharper question offered
—

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_month_grain AS (
  SELECT DISTINCT
    DATE_TRUNC('MONTH', omni_dbt__eds_calendar_day."DATE_DAY") AS "Month",
    omni_dbt__eds_bookings."PRIMARY_KEY" AS "Booking Primary Key",
    omni_dbt__eds_bookings."PREPARE_BOOKINGS_MONTHLY_ACV" AS "Prepare Monthly ACV"
  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 omni_dbt__eds_calendar_day."DATE_DAY" >= '2026-03-01'
    AND omni_dbt__eds_calendar_day."DATE_DAY" < '2026-09-01'
    AND (omni_dbt__eds_calendar_day."DATE_DAY" = date_trunc('month', omni_dbt__eds_calendar_day."DATE_DAY" + interval '1 month') - interval '1 day') = TRUE
    AND omni_dbt__eds_all_accounts."FINANCE_ACCOUNT_TYPE" = 'Accountant'
    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)
), monthly_partner_acv AS (
  SELECT "Month" AS "Month", SUM("Prepare Monthly ACV") AS "Prepare Monthly ACV"
  FROM booking_month_grain
  GROUP BY "Month"
)
SELECT LISTAGG(TO_CHAR("Month", 'YYYY-MM') || ': ' || TO_CHAR(ROUND("Prepare Monthly ACV", 2), 'FM9999999990.00'), '\n') WITHIN GROUP (ORDER BY "Month") AS "Prepare Monthly ACV by Month"
FROM monthly_partner_acv
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.