Texa AI in your product

Transcripts, quality and reports

Read a conversation's transcript and everything that happened in it, its quality scores, and your support reports.

One conversation

Route	Gives
`GET /v1/conversations/{id}/transcript`	what was said, in order, by the customer, Texa AI and people
`GET /v1/conversations/{id}/export`	everything: the transcript, every action with its inputs and result, handovers, and the procedure followed. For audits and disputes
`GET /v1/conversations/{id}/quality`	the quality scores of a closed conversation

Shell

curl https://api.texadesk.com/v1/conversations/cnv_8a7f0c.../export -H "Authorization: Bearer $TEXA_API_KEY"

Fields Texa AI marks as private (an account number, a balance) are masked in what you read here, as they are in Texa Desk.

Quality

Every closed conversation is scored: whether it was resolved, whether Texa AI followed the procedure, whether its actions were right, and whether a handover was appropriate. A score arrives as the quality.scored webhook. The same scores drive Texa AI → Quality in Texa Desk.

Reports

Shell

curl "https://api.texadesk.com/v1/reports/support?since=2026-10-01&until=2026-10-19" \
  -H "Authorization: Bearer $TEXA_API_KEY"

Report	Carries
`GET /v1/reports/support`	`conversations`, `handover_rate`, `contact_reasons` (what customers wrote about, grouped into themes with quotes), `procedures` (conversations per procedure), `handovers`, `actions` with their `failure_rate`, and `knowledge_gaps`: what customers asked that your knowledge doesn't answer
`GET /v1/reports/quality`	`conversations`, `resolved_rate`, `handover_rate`, `procedure_followed`, `actions_correct`, `handover_appropriate`, `first_reply_s`, `turns_to_resolution`

Both take since and until (ISO dates) and give back the period. Conversations in your own shadow mode (people answer, Texa AI drafts) are counted apart as shadow_conversations.

For Texa Desk's own reports (your team's response times, conversations by inbox, agent and team), use the Reports API. For questions in plain words ("what are customers complaining about this week?"), connect your AI tools through MCP.