Chandler · usage
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Prompt

Are there any other accounts (any account type, including non-paying, demo, franchise or reseller accounts) with an account name similar to "Lubbock Fine" — for example a separate Prepare-only account for the same firm? Show account ID (CRN), account name, finance account type, country, Salesforce account ID, platform, and which products they pay for (Prepare, Precision, Commerce) as of the 2026-09-30 snapshot.

Outcome
success
Cost
$59.4025
excludes embeddings
Latency
259.7 s
Query attempts
4
Rows returned
9
Tokens
7,165,981 / 48,908
in / out

Request

request_id
7b3b16dc-8f27-4bbb-9bd0-91814673a4dc
When
2026-10-01 09:32
Tool
ask
User
mihail.iliev@dext.com · Mihail Iliev
Identity
google
Session
f625c317-5c7d-4e6e-8148-303004f8af81
Model
gpt-5.6-sol

Route taken

Topic
Bookings
Omni model
e407bba6-4fae-4079-ac4e-cd6cf8fdf6f8
Match method
agent loop
Nudge shown
—
Question shape
lookup
Caveat raised
degenerate_column
Sharper question offered
—
Shown to user
Basis: Accounts with names containing both ‘Lubbock’ and ‘Fine’, with paid-product flags, 2026-09-30 snapshot.
Every row's «Pays for Prepare» came back as 0. That is usually a wrong segment filter rather than a real zero.

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
    omni_dbt__eds_all_accounts."DERIVED_ACCOUNT_ID" AS "Account ID Internal",
    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_all_accounts."FINANCE_COUNTRY_GROUPING" AS "Country",
    omni_dbt__eds_all_accounts."SALESFORCE_ACCOUNT_ID" AS "Salesforce Account ID",
    omni_dbt__eds_all_accounts."ACCOUNT_PLATFORM" AS "Platform",
    omni_dbt__eds_bookings."PRIMARY_KEY" AS "Booking Primary Key",
    MAX(omni_dbt__eds_bookings."HAS_PREPARE_PAID_SUBSCRIPTION") AS "Pays for Prepare",
    MAX(omni_dbt__eds_bookings."HAS_PRECISION_PAID_SUBSCRIPTION") AS "Pays for Precision",
    MAX(omni_dbt__eds_bookings."HAS_COMMERCE_PAID_SUBSCRIPTION") AS "Pays for Commerce"
  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" = DATE_FROM_PARTS(2026, 9, 30)
    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."ACCOUNT_NAME" ILIKE '%Lubbock%Fine%'
      OR omni_dbt__eds_all_accounts."ACCOUNT_NAME" ILIKE '%Fine%Lubbock%'
    )
  GROUP BY
    omni_dbt__eds_all_accounts."DERIVED_ACCOUNT_ID",
    omni_dbt__eds_all_accounts."ACCOUNT_CRN",
    omni_dbt__eds_all_accounts."ACCOUNT_NAME",
    omni_dbt__eds_all_accounts."FINANCE_ACCOUNT_TYPE",
    omni_dbt__eds_all_accounts."FINANCE_COUNTRY_GROUPING",
    omni_dbt__eds_all_accounts."SALESFORCE_ACCOUNT_ID",
    omni_dbt__eds_all_accounts."ACCOUNT_PLATFORM",
    omni_dbt__eds_bookings."PRIMARY_KEY"
),
account_snapshot AS (
  SELECT
    "Account ID Internal" AS "Account ID Internal",
    "Account CRN" AS "Account CRN",
    "Account Name" AS "Account Name",
    "Finance Account Type" AS "Finance Account Type",
    "Country" AS "Country",
    "Salesforce Account ID" AS "Salesforce Account ID",
    "Platform" AS "Platform",
    MAX(CASE WHEN "Pays for Prepare" = TRUE THEN 1 ELSE 0 END) AS "Pays for Prepare",
    MAX(CASE WHEN "Pays for Precision" = TRUE THEN 1 ELSE 0 END) AS "Pays for Precision",
    MAX(CASE WHEN "Pays for Commerce" = TRUE THEN 1 ELSE 0 END) AS "Pays for Commerce"
  FROM booking_snapshot_grain
  GROUP BY
    "Account ID Internal",
    "Account CRN",
    "Account Name",
    "Finance Account Type",
    "Country",
    "Salesforce Account ID",
    "Platform"
)
SELECT
  "Account CRN" AS "Account CRN",
  "Account Name" AS "Account Name",
  "Finance Account Type" AS "Finance Account Type",
  "Country" AS "Country",
  "Salesforce Account ID" AS "Salesforce Account ID",
  "Platform" AS "Platform",
  "Pays for Prepare" AS "Pays for Prepare",
  "Pays for Precision" AS "Pays for Precision",
  "Pays for Commerce" AS "Pays for Commerce"
FROM account_snapshot
ORDER BY "Account Name" DESC, "Account CRN" DESC
LIMIT 100

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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.