Product analytics for AI chat and voice agents

AIThis description was written by a model from public signals. The facts behind it are listed under Evidence.

Problem

Teams building chat and voice agents lack product metrics that reveal user frustration and needs. Traditional web analytics (clicks, funnels) are not useful when the interface is language. Users rarely give explicit feedback; instead they curse, repeat requests, correct the agent, or leave silently. Product engineers see technical metrics (latency, errors, traces) but must guess whether users achieved their goals.

Solution concept

A product analytics platform that reads production chat and voice conversations to automatically surface behavioral failures—rageprompting, repeated rephrasing, corrections, and feature requests—and clusters them into actionable insights. Unlike observability tools (which show what happened technically) or evals (which validate known cases), it discovers what users want and where they get frustrated, helping teams identify which new features or fixes matter most.

Who has it

Product engineers and teams building chat and voice-based AI agents, across industries.

How they handle it today

Today, teams send agent conversation data through SDKs or OpenTelemetry, and manually analyze conversations or rely on basic error tracking and latency metrics to understand user behavior and pain points.

Why now

Chat and voice agents are becoming widespread as a product interface, but lack the product analytics infrastructure that web apps have. Teams building agents need better ways to understand user satisfaction and prioritize feature work as agent-first products become mainstream.

How it could charge

Subscription pricing with free tier for low volume, tiered monthly pricing ($499/month stated for Pro tier) based on message volume and retention needs.