Fine-tuning is the right tool less often than people expect. It helps when you need a model to follow a format every time, use your domain language, or match a big model's quality with a smaller, cheaper one.
Tell us what you want to build. A senior engineer sends back a scope and a fixed price within two business days, no obligation.
In short
What we build
Models that return the exact structure, tone or classification scheme you need without long, fragile prompts.
Fine-tuned open models such as Llama or Mistral that handle a narrow task well enough to replace an expensive general model.
Models that understand your industry's abbreviations and jargon, trained on examples you already have.
LoRA and full fine-tunes run on your infrastructure when training data is sensitive.
Fit
Process
We measure what prompting and retrieval achieve on your task before training anything.
Clean, de-duplicate and split the examples, keeping a held-out set the model never sees.
Usually LoRA on an open model, compared against the baseline on accuracy, speed and cost.
Serving set up on your cloud or hardware, with drift checks so you know when to retrain.
Pitfalls
We have seen these enough times to plan around them from the start.
Next step
Want to see similar work? Browse our case studies or tell us what you are working on.
FAQ
For narrow format or classification tasks, a few hundred to a few thousand good examples is often enough to see a difference. Quality matters more than volume.
OpenAI offers fine-tuning on some of its models, and we use it when it fits. More often we fine-tune open models, which you can then host and own.
They solve different problems. RAG supplies facts. Fine-tuning shapes behaviour. Many good systems use RAG with an untuned model.
You do. Weights, training data and scripts are handed over.
Keep reading
Indicative ranges only get you so far. Tell us the specifics and get a scope and a fixed price in two business days.