Chandler · usage
← Back to sessions ← Back to this session

Prompt

partner MRR by month for the last 6 months

Outcome
success
Cost
$0.6938
excludes embeddings
Latency
105.3 s
Query attempts
2
Rows returned
1
Tokens
627,008 / 5,785
in / out

Request

request_id
6221fe8a-8ec8-4605-831a-c9883e6c088e
When
2026-09-10 08:16
Tool
ask
User
suzannah.weinfass@dext.com · Suzannah Weinfass
Identity
google
Session
5402db02-b4fd-466e-9f49-1828b8f1c6f4
Model
gpt-5.6-sol

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 "Partner 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'
    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("Partner Monthly ACV") AS "Partner Monthly ACV"
  FROM booking_month_grain
  GROUP BY "Month"
)
SELECT
  LISTAGG(
    TO_CHAR("Month", 'YYYY-MM') || ': ' || TO_CHAR(ROUND("Partner Monthly ACV", 2), 'FM9999999990.00'),
    '\n'
  ) WITHIN GROUP (ORDER BY "Month") AS "Partner Monthly ACV by Month"
FROM monthly_partner_acv
LIMIT 1

Feedback

WhenRatingComment
2026-09-10 08:35 negative Metric was mislabeled as MRR when it should be ACV (Partner accounts are tracked on ACV, not MRR, per governed definitions). Also currency was labeled as USD ($) when the underlying values are GBP (£). User also wanted this scoped to the France cohort specifically, not the full Accountant/Partner population.

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.