SELECT
  MIN(omni_dbt__eds_prepare_account_user_event_activity."OCCURRED_DATE") AS "First Sales Invoice Date",
  MAX(omni_dbt__eds_prepare_account_user_event_activity."OCCURRED_DATE") AS "Last Sales Invoice Date",
  SUM(omni_dbt__eds_prepare_account_user_event_activity."SALES_INVOICE_CREATED_VOLUME") AS "Volume of Sales Invoices Created"
FROM PROD."EDS_PREPARE_ACCOUNT_USER_EVENT_ACTIVITY" AS omni_dbt__eds_prepare_account_user_event_activity
WHERE omni_dbt__eds_prepare_account_user_event_activity."SALES_INVOICE_CREATED_VOLUME" > 0
LIMIT 1

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.