AIHumanity
Use Case

Cut Support Escalations Before They Start

The same scripted response to a calm question and a furious one is how tickets turn into escalations. Reading frustration, confusion, or distress in real time and adapting tone and strategy on the spot is what keeps a hard moment from becoming a bad review.

See it in action
-32% *
escalation risk (billing scenario)
+54% *
trust lift (policy scenario)

* Measured from the Agent EQ demo scenarios below — run them yourself to see the comparison.

What changes in the interaction

Try it live: the Demo Lab's Agent EQ tab runs this exact comparison.

Real-time detection

Reads frustration before it escalates

Voice tone, word choice, and pacing are scored live so a rising-frustration moment gets flagged while there's still time to change course.

  • ›Frustration, confusion, and distress scored live
  • ›Works across voice and text channels
  • ›No manual tagging required — scored turn by turn
Adaptive response

Shifts strategy, not just tone of voice

Detected emotion changes what the agent (human or AI) does next — ownership language, pacing, and prioritization all adapt.

  • ›Repair-mode responses for high-frustration moments
  • ›Guided pacing for confused or new users
  • ›Care-first framing for distressed customers

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Emotion Models

Face · voice · text recognition, tuned for support conversations.

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Multimodal Fusion

Combine voice and text signal for a more reliable read.

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Emotion API & SDK

REST and client SDKs to wire this into your existing stack.

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Session Insights

Aggregate every call into a post-session report for coaching and QA.

See the before/after on real support scenarios

Try the live Agent EQ demo, then talk to us about integrating it into your support stack.

See it in action →
Talk to David