Chandler · usage
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Prompt

Direct MRR by month from March 2026 to August 2026, including all commitment terms (annual and monthly paying customers, not just Annual Paid Annually/Annual Paid Monthly)

Outcome
success
Cost
$0.6743
excludes embeddings
Latency
125.7 s
Query attempts
2
Rows returned
1
Tokens
634,405 / 4,841
in / out

Request

request_id
5b7c2554-6b31-4511-ab9b-a1365c2fa46b
When
2026-09-10 08:56
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
feedback_nudge
Question shape
not classified
Caveat raised
—
Sharper question offered
—
Shown to user
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_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_MRR" AS "Direct MRR"
  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" = '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)
), monthly_direct_mrr AS (
  SELECT
    "Month" AS "Month",
    SUM("Direct MRR") AS "Direct MRR"
  FROM booking_month_grain
  GROUP BY "Month"
)
SELECT
  LISTAGG(TO_CHAR("Month", 'YYYY-MM') || ': ' || TO_CHAR(ROUND("Direct MRR", 2), 'FM9999999990.00'), '\n') WITHIN GROUP (ORDER BY "Month") AS "Direct MRR by Month"
FROM monthly_direct_mrr
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.