LLM fine-tuning that makes a model fluent in your task and your terms.

Cost depends on how much preparation your examples need, how many base models we compare, the model size and where it will be served. Clean, labelled examples cut the work sharply, while messy records or expert labelling add time. GPU hours are billed at cost. We start with a fixed-price baseline, and you'll get a written scope after a free call.

Use retrieval (RAG) when the model needs facts from your documents, especially facts that change, and fine-tuning when it needs to learn a behavior, such as a format, a style or a set of labels. Retrieval is the better way to add knowledge, because answers stay current and cite their sources. Many systems combine both: a tuned model answering from retrieved passages.

Fine-tuning pays off when a task is narrow and repeated, you have a good set of reviewed examples, and prompting and retrieval still miss the bar on format, consistency, cost or speed. It also pays when a smaller tuned model can match a larger one on your task at a lower cost per call. The baseline stage shows which case you're in.

Then we stop after the baseline and hand you the prompts, retrieval setup and test set, which saves you the cost of training. Prompting and retrieval are quicker to change and cheaper to maintain, so they're the right first step for most teams. The test set stays useful if you revisit fine-tuning later.

It depends on the task, and quality matters far more than volume. A narrow task, such as labelling tickets or filling a fixed format, often shows gains from hundreds of carefully reviewed examples rather than millions, while broader behavior needs more. We check your data in the first weeks and tell you plainly whether it's enough.

Fine-tuning changes the model itself so it handles your task better. A knowledge assistant (RAG) keeps the model as it is and feeds it the right passages from your documents at question time, while private AI is about where a model runs: on infrastructure you control. A tuned model can run in a private deployment and power a knowledge assistant too.

We fine-tune open-weight models such as Llama, Mistral, Qwen and Gemma, choosing the smallest size that passes your test at the lowest serving cost. Some providers, such as OpenAI, also offer fine-tuning of their hosted models, and we'll compare that route during the baseline when your data terms allow it. Each model's license is checked before training.

Yes, the adapter weights, training data, scripts and evaluation results stay in accounts you own. With open-weight models, you can run the result wherever the base model's license allows, including your own cloud or hardware.

Yes, training runs in your cloud account or on hardware you control, and personal data is removed or masked before training wherever the task allows. We review examples for names, secrets and other details the model shouldn't learn, because a model can repeat what it was trained on.