Scaling alone won't get
us to general intelligence
Current frontier AI depends on ever-larger models, ever-larger datasets, and ever-larger compute budgets. This path concentrates capability in a handful of organizations with billions in capital.
It also hits fundamental limits. More parameters don't mean more knowledge — they mean more cost, more energy, and more brittleness. Adding a new domain means retraining from scratch.
We believe there's a better architecture. One where capability grows with knowledge, not with compute.
Modular Expert
Architecture
We separate what a model knows from how it reasons. A core model learns to think, compose, and generate. Independently-trained expert modules encode domain knowledge. A learned adapter integrates any expert — even ones created after training.
The result: add new capabilities in hours, not months. No retraining. Data stays local. Expertise remains attributable.
Built in Berlin,
designed for sovereignty
Our architecture isn't European for political reasons — it's European because it solves European problems. Data that can't leave its jurisdiction. Expertise that must remain attributable. Compliance that's structural, not bolted on.
We're based in Berlin, building with European talent, for a global future.
Built by researchers and engineers from leading AI labs
Our team combines frontier ML research with production systems experience and startup execution.
Core Team
We're building the team. Interested in joining? See opportunities →