Intelligence,
shaped by you.

We tell the story of a model as though it ends when training does: weights set, evaluations published, the model sent out into the world, finished.

But most of what makes a model useful happens after. It’s asked to help with one codebase, one client, one person’s way of thinking⁠—and there, a model trained on the public record of the world knows a great deal about everything and very little about you. Not what your team means by done. Not which decisions were already made, and why. Not what good looks like in your hands.

So we tell it, every time. We paste in background, retrieve documents, write longer instructions. It works, up to a point. But context is borrowed, not learned: re-read on every request, costlier and more confusing as it grows, gone when the conversation ends. The model you use tomorrow is the same stranger you met today.

We think some of that knowledge belongs in the model itself. Over time, through post-training, we can evolve what a model notices, remembers, and feels like⁠—a model that knows not just you, but your team, your world, your domain.

That changes what it means to run models. Personalization means many models, not one⁠—each adapted, versioned, tested, and run 24/7 for the people who depend on it. Serving them well is as much a part of personalization as training them.

We adapt, optimize, and serve models for personal applications. If you have one already, let us serve yours. We’re working with a small number of early teams now⁠—apply below.

Bring us a model Shape one with us