Chandler · usage
← Back to sessions

Session

User
suzannah.weinfass@dext.com · Suzannah Weinfass
Started
2026-09-10 14:54
Last activity
2026-09-10 15:29
Questions
4 (7 calls recorded — the difference is calls whose log write did not land)
Grouping
client — exact — the caller echoed the session id back to us
session_id
4956b00b-4cfd-4f56-9e4d-9bc442267270

Transcript

  1. 2026-09-10 15:03 ask success $0.8842 161.0 s full detail →
    Is there a field that captures actual active usage of Payments (e.g. processed a transaction), distinct from HAS_PAYMENTS_ACCESS (access/entitlement)? Also, what was total active account count for each month March-August 2026, to compute an adoption rate?
    Data answer — see full detail.
    1 row Product Usage - Accounts
  2. 2026-09-10 15:21 ask success $4.0633 161.5 s full detail →
    How many accounts have improving sentiment?
    Data answer — see full detail.
    1 row NPS
  3. 2026-09-10 15:27 ask success $1.6589 133.0 s full detail →
    How many accounts have improving sentiment based on the touchpoints_ai_sentiment_extraction_vectors table?
    Data answer — see full detail.
    2 rows [EXPERIMENTAL] Lost Opportunities
  4. 2026-09-10 15:32 ask success $1.6841 174.7 s full detail →
    Using the ai_sentiment_trend field in the touchpoints_ai_sentiment_extraction_vectors table, how many distinct accounts have an improving sentiment trend?
    Data answer — see full detail.
    3 rows Customer Support SLA Metrics

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.