Build the missing layer of AI for the physical world.
Language and images are conquered territories. The physical world is the frontier — and it has no foundation model. We're building it.
Systems that cannot fail deserve AI that understands them.
The infrastructure civilization depends on is under more pressure than ever — electrification, AI and data-center load growth, grid instability, rising demand charges. It's being asked to do more with less, and the stakes of getting it wrong are physical: outages, waste, failure.
This is the moment a model that genuinely understands physical systems becomes possible — and necessary. The methods exist. The data exists. What's missing is a foundation model built for the physical world instead of borrowed from language.
That's what we're building.
Thermodynamics and conservation laws are inductive biases, not afterthoughts. A model that can act on a system that can't fail must respect how that system can actually behave.
Every capability is scoped to what we've evaluated — leakage-controlled, against the strongest baselines available, including the free models that would replace us.
Language and images are conquered territories. The physical world — every failure-intolerant system that has ever existed — is where the next decade of AI development happens. We're building the model class that reads it.