Generalized

We work on three things a model cannot do on its own: assembling the right context, staying coherent across long-horizon tasks, and running inference efficiently at production scale.

A model can pass every benchmark and still lose the thread ten steps into a real task. What makes it reliable is the system around it: the context it is given, the structure that holds a long task together, and the compute spent reasoning at each step.

We take on a few teams at a time and build with them, hands on, until it holds in production. You bring the hard problem. We bring the context and inference stack.