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Raw session replays tell you what one user did. The state engine turns thousands of them into a single structural map of your product: which screens exist, and how users move between them.

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From replay to screens

Every recorded session is replayed server-side in a real browser. As the replay runs, the engine captures the screen at meaningful moments: significant DOM changes, clicks, and scroll dwell points. Each captured screen is a candidate for becoming, or matching, a state.

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Canonicalization

Two users on the same screen never see identical DOM. Usernames, timestamps, feed contents, and A/B variants all differ. Before comparison, each captured screen is canonicalized: dynamic content is ignored so that what remains describes the screen’s structure rather than one user’s data on it.

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Deduplication

Canonicalized screens are deduplicated across all sessions in the project. The engine combines several signals:
  • Perceptual hashing of the rendered screen, to catch visually identical screens cheaply.
  • Visual and text embeddings, to measure similarity when hashes alone are not conclusive.
  • An AI judge for ambiguous cases, deciding whether two screens are really the same screen of the product.
The result is a state: one canonical screen of your product, with every session’s traffic mapped onto it. A checkout page visited by 10,000 users in 10,000 slightly different renderings becomes one state with 10,000 visits.

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The state graph

States are connected by transitions: user A went from the pricing state to the signup state, and so did 4,000 others. Each transition is weighted by the real traffic that traversed it, giving you a graph of your product where edge weights are actual user behavior, not instrumentation you had to define up front. Each state keeps representative screenshots, which the dashboard shows and the agents use for analysis.

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Why this matters

  • No manual instrumentation. You do not tag screens or define funnels by hand; the map is built from what users actually rendered.
  • Aggregate, not anecdote. Questions like “where do users go after onboarding?” are answered from the whole graph, not a sample of replays.
  • A foundation for mining. The path engine mines this graph to find journeys and goals with real drop-off numbers.