SELECT
  omni_dbt__touchpoints_ai_lost_opportunity_tags."TAG_CLASS" AS "Sentiment Tag Class",
  COUNT(DISTINCT omni_dbt__touchpoints_ai_lost_opportunity_tags."SALESFORCE_ACCOUNT_ID") AS "Number of Distinct Accounts"
FROM PROD."TOUCHPOINTS_AI_LOST_OPPORTUNITY_TAGS" AS omni_dbt__touchpoints_ai_lost_opportunity_tags
WHERE omni_dbt__touchpoints_ai_lost_opportunity_tags."TAG_CLASS" IN ('brand_sentiment_positive', 'brand_sentiment_negative')
GROUP BY 1
ORDER BY 1
LIMIT 100

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.