Monthly trend of total Partner/accountancy practice revenue for the last 6 full months, using the same definition as the ARPA report: sum of Prepare Bookings Monthly ACV per customer account, filtered to accounts with a paid subscription (Finance Account Type = Accountant, Has Paid Subscription = TRUE), at month-end snapshot
Basis: Total Partner/accountancy practice revenue, using the ARPA definition: Prepare Bookings Monthly ACV, Month-end snapshots for the six full months from March through August 2026. 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.
WITH booking_snapshot_grain AS (
SELECT
omni_dbt__eds_calendar_day."DATE_DAY" AS "Snapshot Date",
omni_dbt__eds_all_accounts."DERIVED_ACCOUNT_ID" AS "Customer Account ID",
omni_dbt__eds_bookings."PRIMARY_KEY" AS "Booking Primary Key",
MAX(omni_dbt__eds_bookings."HAS_PAID_SUBSCRIPTION") AS "Has Paid Subscription",
MAX(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" >= DATE_FROM_PARTS(2026, 3, 1)
AND omni_dbt__eds_calendar_day."DATE_DAY" < DATE_FROM_PARTS(2026, 9, 1)
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 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)
GROUP BY
omni_dbt__eds_calendar_day."DATE_DAY",
omni_dbt__eds_all_accounts."DERIVED_ACCOUNT_ID",
omni_dbt__eds_bookings."PRIMARY_KEY"
),
customer_monthly_snapshot AS (
SELECT
"Snapshot Date" AS "Snapshot Date",
"Customer Account ID" AS "Customer Account ID",
SUM("Prepare Monthly ACV") AS "Prepare Monthly ACV"
FROM booking_snapshot_grain
WHERE "Has Paid Subscription" = TRUE
GROUP BY
"Snapshot Date",
"Customer Account ID"
)
SELECT
"Snapshot Date" AS "Month End",
SUM("Prepare Monthly ACV") AS "Total Partner Revenue"
FROM customer_monthly_snapshot
GROUP BY "Snapshot Date"
ORDER BY "Month End" DESC
LIMIT 6
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.