What is our average customer LTV (total MRR over customer lifetime to date)?
Basis: Coverage check for customer lifetime MRR: count Direct customers whose first paid subscription preda, All available month-end history through 17 September 2026. Governed filters applied.
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_month_grain AS (
SELECT DISTINCT
omni_dbt__eds_calendar_day."DATE_DAY" AS "Snapshot Date",
omni_dbt__eds_bookings."PRIMARY_KEY" AS "Booking Primary Key",
omni_dbt__eds_all_accounts."DERIVED_ACCOUNT_ID" AS "Account ID",
omni_dbt__eds_bookings."FIRST_EVER_PAID_SUBSCRIPTION_STARTED_AT" AS "First Paid Subscription Date"
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" <= '2026-09-17'
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" = '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)
), coverage AS (
SELECT
"Snapshot Date" AS "Snapshot Date",
"Account ID" AS "Account ID",
"First Paid Subscription Date" AS "First Paid Subscription Date",
MIN("Snapshot Date") OVER () AS "Earliest Available Snapshot"
FROM booking_month_grain
)
SELECT
MIN("Snapshot Date") AS "Earliest Available Month-End",
MAX("Snapshot Date") AS "Latest Available Month-End",
MIN("First Paid Subscription Date") AS "Earliest First Paid Subscription Date",
COUNT(DISTINCT "Account ID") AS "Number of Distinct Accounts",
COUNT(DISTINCT CASE WHEN "First Paid Subscription Date" < "Earliest Available Snapshot" THEN "Account ID" END) AS "Customers Starting Before Available History"
FROM coverage
LIMIT 1
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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.