AI generates more data than we can learn from.
Agents, software, machines, and robots continuously produce observations, actions, and outcomes across text, structured data, images, audio, and video.
As software development accelerates, it generates questions we can't foresee. We can't build new pipelines and train models for every question.
We believe this requires a new frontier of data intelligence.
One where software can predict possible futures and compare tradeoffs to find paths toward desired outcomes.
It starts with two complementary foundation models:
Fold is a small model designed to run close to your data. It continuously folds observations, actions, and outcomes into compact learned trajectories.
↓
learned trajectory↓
Navigate is a large model that reasons over learned state. Developers and agents can ask predictive questions and compare possible actions to explore paths toward their objectives.
○ now ● predicted future× dominated─ frontier✦ objective
Our two models form a continuous learning loop that tracks your world state and learns from measured outcomes to improve future predictions.
Prediction becomes a new primitive.
Software and agents make predictions nobody would train a dedicated model for. They predict before acting, then measure what happens to learn from the outcomes.
As predictions need evidence, Spacefolder preserves history, traces sources and compares predictions with actual outcomes, so developers know when to trust them.
Spacefolder enables developers to build a new generation of predictive software and agents:
Coding agents predict the impact of changes before writing or releasing them.
Physical AI understands the consequences of actions before moving.
Personal superintelligence anticipates what matters to each person.
You set the destination, taste, and limits. Your software and agents explore how to get there.
Spacefolder’s name is a nod to Dune, where Navigators use prescience to see possible futures and find paths through them.