AIHumanity
Use Case

See How Every Customer Actually Felt — Not Just What They Said

Live emotion detection changes what happens in the moment. But most support calls, interviews, and usability tests still end with nothing more than a transcript and a CSAT score. A structured emotion report — generated automatically after the session — surfaces which moments actually mattered, without a QA lead re-listening to every call.

See Session Insights
Post-session
structured report, not a raw recording
Automatic
no manual review or tagging

What you get after the session ends

Try it yourself: every call in the live Voice Agent demo ends with exactly this kind of report.

Emotional arc

How the session opened, moved, and ended

Not a single sentiment score — a timeline of how the person's emotional state moved across the whole interaction.

  • ›Dominant emotion with a confidence score
  • ›Opening vs. closing state, side by side
  • ›Flags for moments that needed a strategy change
No manual review

Skips the re-listen

QA leads and researchers don't have to re-listen to every call to find the ones that went sideways — the report flags them.

  • ›Automatic key-moment detection
  • ›Works across voice, video, or text sessions
  • ›Aggregate trends across many sessions, not just one

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Session Insights

Structured post-session emotion reports for any recorded interaction.

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Emotion Models

Face · voice · text recognition behind every report.

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Multimodal Fusion

Combines every signal captured during the session.

See a real session report

Run the live voice agent demo end to end, then talk to us about wiring this into your own support, research, or CX workflow.

Try the live demo →
Talk to David