Existing analytics tools are built around discrete interactions — clicks, page views, form submissions. Conversational interfaces don't produce those. They produce text, and most teams have no way to read it at scale.
Intent blindness
A chat interface replaces dozens of discrete UI surfaces. All of that signal collapses into one input. You know users opened it — not what they were trying to accomplish.
Hidden demand
Users ask for things your product can't yet answer. That demand accumulates in logs, unread. It's the most honest product roadmap you have.
Silent failure
A conversational app returns 200 OK whether the answer was useful or not. Misunderstood intent and wrong answers leave no trace in your existing monitoring.
Features
Gap Detection
Surfaces and ranks what your AI consistently fails to answer — grouped by topic, sorted by frequency. Concrete enough to drop into a sprint without interpretation.
Intent Clustering
Conversations are automatically grouped by underlying user goal, not surface phrasing. No categories to define upfront. Clusters emerge from your actual traffic.
One-line SDK
Wrap your existing OpenAI or Anthropic client. Nothing else changes. Backfill historical logs on first login so you start with signal, not an empty state.
Integration
OpenAI and Anthropic. Pass historical logs on first login.
Who it’s for
AI-native startups
Your product is a chat interface. You have traces and cost data from LangSmith and Helicone. What you're missing is intent — what users are actually trying to do.
SaaS teams shipping AI features
Your AI assistant is live. Your existing analytics tells you how many users opened it. It can't tell you what they asked, or whether they got what they needed.
Developer tool companies
Developers ask your docs AI questions every day. The gap between what they ask and what it can answer is a direct signal about documentation coverage and developer friction.