Real World. Real Intelligence.

We are enabling the next AI revolution by building a new breed of AI systems that (1) understand the real world, (2) have persistent memory, (3) can reason and plan, (4) are controllable and safe.

Our main goal is to build intelligent systems that understand the physical world. Real-world data is continuous, high-dimensional, and noisy. Generative architectures trained by self-supervised learning have been astonishingly successful for language, but much of real-world sensor data is unpredictable, and generative approaches do not work well.

Action-conditioned world models allow agentic systems to predict the consequences of their actions, and to plan action sequences to accomplish a task, subject to safety guardrails.