Human-centered evaluation for voice and conversational AI

Conversational AI is advancing fast. LLM-powered chat and voice agents are more fluent and more capable than ever, and they often score well on automated metrics. But strong scores don't tell you whether a conversation felt clear, whether a refusal landed right, or whether a delay broke the flow.

The qualities that decide whether people trust a conversation are perceptual. Clarity, tone, timing, and whether someone feels understood rather than managed all depend on human judgment and context. They surface when real users interrupt, change their minds, and react to what they hear, not in scripted tests.

That's why modern evaluation combines automated metrics with structured human validation. Not as a final check, but as a signal you can apply throughout the evaluation lifecycle.