Feature tour
Call QA that starts with the call record, not a generic review form.
MeetIndi Call QA keeps the public promise honest: playback, transcript, summary, and audit context are the foundation; scorecard and coaching workflows are staged on top of that foundation.
01 · Call QA
Every review starts from a source call with consent, role, and audit context.
Available now covers the operational call record: recording playback state, transcript, summary, extracted fields, and playback audit posture. Coming next adds scorecard structure, coaching notes, and agent notification workflows.
recording playback
Permissioned playback state and call context
transcript
Summary, extracted fields, and next-action context
scorecard
Review categories, coaching prompts, and agent follow-up
QA surface
- Playback contextSigned URL state, role, consent
- Transcript summaryWhat happened and what changed
- Missed-field reviewWhat the call did not capture
- coaching noteManager feedback once scorecards ship
02 · Truthful state
A useful QA page needs to show what is live and what is staged.
The first version explains the operating record without implying every brokerage scorecard workflow is already available for every customer. Long-term QA reporting stays separate from the available call-review substrate.
truthful product visual
Available nowPlayback and transcript
Call stateConsent and role checked
Coming nextScorecard workflow
CoachingManager note staged
Long-termTrend reporting
Illustrative product model, not customer data. No fake customers, no fake logos, and no unsupported production screenshots.
Illustrative product model, not customer data
- Available nowPlayback, transcript, summary, audit context
- Coming nextScorecard categories and coaching workflow
- Long-termOwner QA programs and trend reporting
Available now: call context, playback state, transcript, summary, and audit posture.
Coming next: scorecard categories, coaching note creation, and agent notification workflow.
Long-term: owner QA programs, trend reporting, and training loops after review data is stable.