Close the control gap.

A model alone solves part of the work.

A frontier model does its best work when a capable person supervises it. Alone, it handles some of a real job well and makes mistakes elsewhere that nobody notices.

Point at or tap a marked task on the map to see what happens to it.

Bigger models move the edge a little.

Each new generation solves somewhat more. Waiting for scale closes only a small part of the distance.

Supervision moves it much further.

With a capable person who checks its work, the same model solves far more. We call the difference the control gap.

Adherence closes it.

We orchestrate several agents, check each action against your rules outside the model, test each change on the agent's own benchmark and learn from your team's corrections. People step in where the system asks them to.

The drawing and its tasks are examples of the idea, not measured data.

Your employees build the agents. Adherence runs them.

Anyone in the company can build an agent for their own work and register it with the company runtime. Each agent follows the rules the company sets, is scored on its own benchmark at each new version and stays supervised. You see all of them in one place.

an agentan agent that just passed its tests

The drawing shows the idea.

Your data, your models, under your control.

Your organisation decides which model providers its agents may use, model by model. A private use case runs only on models you host yourself. When a task is too hard for a general model, we train a specialised one for it on GPU servers your organisation adds as its own.

The drawing shows the idea.