The data and evaluation layer for emotionally intelligent voice AI
Build & measure AI the way people experience it, based on research and grounded in real human judgment
Our decades of research in multimodal emotional intelligence span
50+
Languages
48+
Emotions
600+
Voice Descriptors
The data and evaluation layer for emotionally intelligent voice AI
Build & measure AI the way people experience it, based on research and grounded in real human judgment
Our decades of research in multimodal emotional intelligence span
50+
Languages
48+
Emotions
600+
Voice Descriptors
What it is
Four products. One scientific foundation.
01 · Build
The data your model actually needs.
Custom data collection built around your specific scenarios, use cases, and evaluation requirements.
02 · Simulate + Evaluate
Auto-generate scenarios. Run them. See what breaks.
Kairos lets voice AI teams build evaluation suites from real-world use cases, simulate agent-to-agent and human-to-agent conversations, and track regressions over time, all in record speed.
03 · Measure
Identify emotion in voice, in real time.
Real-time emotion tagging and offline analysis across 48+ emotions and 50+ languages with 600+ output metrics, built for voice-native AI teams who need to know what callers are actually feeling.
04 · Rate
Real participants. At scale.
Pre-screened human raters return per-sample scores, free-response feedback, and aggregated analysis. In hours, not days.
Leaderboards
The standard for voice AI performance
Why it matters
Voice AI quality is built in layers
A reliable, safe foundation is table stakes. Expressivity and natural flow build on top. And at the peak sits emotional intelligence, the dimension that determines whether people actually want to keep talking to voice AI.
Metrics measure accuracy. Expression, emotion, and alignment require human judgment and the right tools to collect it.
What we evaluate
Built for the questions voice teams actually ask
Does the voice fit the use case, persona, and intended role?
Does the model sustain character voices and emote when reading a story?
Are jokes, reassurances, and facts delivered in distinct tones?
Can the model reliably read out assorted web search results?
Key differentiators
What you only get here
Single API call
From study creation to results, one simple request, no operational burden.
Screening & QA
Sophisticated participant screening, fraud detection, and quality monitoring built in.
Simulation at scale
Agent-to-agent and human-to-agent conversation simulation, built for real-world scenarios.
Hours, not days
Ratings back fast enough to close the human eval loop at model-development pace.
Proven on real models
Used internally to evaluate voice AI across simulation, expression measurement, and human studies.
Custom integrations, evaluations, and question types
Collaborate with us to start running human evaluations on your next models. Our research engineering team is ready to help.