AI World Model
Beyond the context window.
An LLM forgets. Recapt's World Model remembers every session, every interaction, every data source, so when you troubleshoot, the full history is already in scope.
How it compares to a traditional LLM.
Traditional LLM
Bounded by a context window
- Holds a limited number of sessions in memory at once.
- Forgets prior conversations once the window fills.
- Reasons over the text you happen to paste in.
- No native link between behavior and business data.
Recapt World Model
Learns from every session, forever
- Every single session stays in memory and informs the next answer.
- Troubleshoot with the full history of your product in scope.
- Ingests any data source: sales, support, analytics, logs.
- Surfaces correlations like sales drops tied to a UX pattern.
What makes a world model different.
Infinite memory
No context window. Every session your users have ever run is part of the model, searchable, comparable, queryable.
Multi-source
Behavioral data sits next to sales, support tickets, revenue, and product telemetry. One model, one view.
Correlation, not just recall
Ask why MRR dipped last week and get the friction pattern that caused it, not a summary of what happened.
Example
"Why did checkout conversion drop 8% last Thursday?"
LLM answer
"I don't have access to your conversion data. Could you paste the metrics?"
Recapt answer
"1,243 sessions hit a validation delay on the card field after Thursday's deploy. 71% of drop-off correlates with that pattern. Suggested fix attached."