Test algorithms with simulated users before deployment
The Problem
Recommendation models and ranking changes go live with zero behavioral coverage. A/B tests take weeks. By then, the damage is done.
They validate output metrics, not behavior. The metrics may look good while real user journeys quietly degrade.
Search, recommendations, and agents evolve over sessions through queries, clicks, and feedback loops. Static evaluation misses how systems actually behave over time.
The Solution
Distill real user sessions into representative behavioral patterns that capture how people search, click, and adapt.
Test how experiences evolve across sessions. Surface degradation in ranking quality, navigation paths, and feedback loops before release.
See how changes influence engagement and outcomes without exposing real users.
Enter experiments with stronger confidence and fewer unknowns.
Built For
Product Leaders
ML Engineers