Small AI models trained for one job your team repeats

When your team does the same step hundreds of times a week, a small model trained on your own examples can do it faster and cheaper than a big general one. It runs on your own server or an edge device.

Good fits

• Sorting and routing incoming email or tickets

• Pulling the same fields off invoices and delivery orders

• Checking work against your SOP or house rules before it goes out

• Writing replies in your house format and tone

How we do it

• Fine-tune or distil: we train an open model on examples of the job done right, taken from your own records. When labelled examples are scarce, a large model does the job on your real inputs, we check its answers, and the small model learns from them.

• Quantize: we shrink the trained model so it runs fast on the hardware you have.

What you get

• A before and after score on a slice of your own data held back from training.

• The trained model, which you own, running on your own hardware, with retraining when your work or data changes.

When training is the wrong tool: if the job is answering questions from documents that keep changing, a trained model goes stale and cannot point to its source. We build search over your documents instead.

Models we have trained: a 4B review model that caught 12 of 12 planted mistakes with 0 false blocks in 30 clean cases, and a 0.5B chat model, learned from a 32B model, that runs on players' phones.