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Session

User
suzannah.weinfass@dext.com · Suzannah Weinfass
Started
2026-09-22 08:48
Last activity
2026-09-22 10:55
Questions
4
Grouping
client — exact — the caller echoed the session id back to us
session_id
131c9741-1aaf-4757-b194-699d018c62dd

Transcript

  1. 2026-09-22 08:54 ask needs_input $2.7524 39.9 s full detail →
    AI Assist paid licences sold by region as of 18 September 2026
    Should ‘paid licences sold’ mean the contracted AI Assist licence units (AI Assist Client Limit), or the count of paid AI Assist product-licence records?
    Bookings
  2. 2026-09-22 08:59 ask success $58.2535 646.7 s full detail →
    AI Assist paid licence sales: total net paid AI Assist licences sold as of 18 September 2026, broken down by region (UK, NA including Canada and US, APAC, France, South Africa), split by partner vs direct, plus licences ended since 23 April
    Data answer — see full detail.
    5 rows Bookings
  3. 2026-09-22 08:59 ask success $30.2353 194.1 s full detail →
    AI Assist paid licences sold by region as of 18 September 2026
    Data answer — see full detail.
    5 rows Bookings
  4. 2026-09-22 10:58 ask success $12.5819 133.6 s full detail →
    AI Assist paid licences by region as of 18 September 2026, using contracted AI Assist Client Limit, but WITHOUT excluding franchise accounts or Reseller-type accounts — include resellers and franchises
    Data answer — see full detail.
    5 rows Bookings

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.