Model Engineering

What you get

Smaller, sharper, and yours.

A general model rented by the token is an expensive way to do one narrow job. We build you the model that does that job: small enough to run where you want it, tuned to the work in front of it, and yours to keep.

What comes back runs on your hardware, under your control, at a cost you set rather than one a provider sets for you.

The work

Every lever that changes what a model does.

• Fine-tuning. Trained on your data, with the dataset construction and labeling policy that decides whether it works.
• LoRA modules. One base model serving several jobs, each with its own adapter, instead of several deployments to keep alive.
• Distillation. A large model's behavior on your task carried into a small one you can afford to run at volume.
• Continued pretraining. For domains whose language is far enough from the base model that tuning alone leaves value on the table.
• Quantization and compression. Fitted to the memory, speed, and cost envelope you have to live inside.
• Evaluation. Built from your own work and run against whatever you use today, so the choice is settled by numbers.

Proof

Judged on your work, not a leaderboard.

A public benchmark tells you almost nothing about your task. We build held-out suites from the work you actually do, score the new model against the one you run today, and hand over the harness so you can run it yourself against whatever is released next month.

Your data

It stays where you put it.

Where the data calls for it, the work runs inside your accounts, your storage, and your network, including environments with no route to the outside. The training data, the weights, and the results are yours either way.