Own your intelligence.

Your tasks. Your benchmark.

Public evals become targets. As labs optimize against them, leaderboard gains become harder to separate from overfitting. A higher score tells you little about whether a model can complete your company’s tasks.

We start by building private evals from your production traces. They capture the work you need done, the tools it requires, and what a successful result looks like.

Then we train a decision model to route each request to a right-sized model: one that meets your quality bar at a lower cost. Frontier models where they earn their price; smaller models where they’re enough.

The candidate pool can include open-weight models served through inference providers such as Baseten and Fireworks. Your evals determine which models belong in the mix.

A three-week pilot.

The pilot starts with a scoped set of recurring tasks, access to your traces, and an agreed definition of success.

Week one: build the evals.

Turn real tasks into a private benchmark. Test candidate models against it. Establish which models meet the quality bar, how long they take, and what each successful task costs.

Week two: build the router.

Train the decision model and connect the selected model endpoints. Test whether it chooses models that meet your quality bar across the agreed tasks.

Week three: integrate and measure.

Integrate routing into the chosen workflow. Compare routed results with your current setup on quality, latency, and cost, and identify tasks that may justify specialist fine-tuning.

You leave with

• A private task benchmark you can rerun as models change.
• A decision-model router connected to the selected workflow and model providers.
• A measured comparison with your current setup, including where routing falls short.
• A shortlist of tasks worth considering for specialist fine-tuning.

Next: smaller models trained for your work.

Routing finds the best fit among available models. Fine-tuning lets you build a better fit for work you repeat.

After the pilot, you have a task definition, an evaluation set, and a baseline to beat. Successful production trajectories can supply training examples, kept separate from the evaluation set.

That prepares you to fine-tune smaller specialist models and test whether they earn a place in the router. Fine-tuning is the next engagement; the pilot builds the foundation.

Work with us.

We’re looking for a few design partners with recurring AI workloads and production traces to learn from.