Adaptive AI · Scientific Intelligence
Pyramidal builds infrastructure for AI that integrates new evidence continuously — keeping world models current without costly retraining, and without forgetting what was already learned.
Pyramidal works on the foundations of adaptive AI to enable systems that update their world models continuously — as evidence arrives, not only at training time. From the rapid experimental loops of scientific discovery to the moment-to-moment context of embodied agents and personal assistants, we close the gap between a model and the reality it must reason about: integrating evidence accurately, reconciling conflicting observations, and adapting in real time without forgetting what was already learned.
Our focus
Systems that integrate new evidence continuously, updating their beliefs without waiting for the next training run.
Closing the loop between experiments, observations, and evolving world models — for science that moves at the speed of discovery.
World model updates that require minimal new data — no full dataset replay, no catastrophic forgetting.
Lightweight integration that scales to real-world deployment without the cost of retraining from scratch.