Train a custom AI model on your company's data

The problem a custom model solves

General-purpose models are good at almost everything and precise at very little. When your operation has its own vocabulary, categories, and rules, an ever-longer prompt stops being enough: answers drift, the cost per call grows with volume, and your data travels to an outside provider with every request.

A model trained on your examples learns how your team classifies, writes, or decides. It is usually smaller, faster, and more predictable on that specific task. And because the weights stay with you, you can deploy it in your cloud or on your own servers, as we explain in private AI on-premise.

Who should train their own model

Not every company needs to train a model. It makes sense when some of these conditions apply. For example, a collections company could train a classifier that reads each debtor reply and tags it as a payment promise, a dispute, or a wrong number, without paying for an LLM call on every message. That is an illustration, not a client case.

• You repeat the same language task thousands of times: classifying requests, tagging documents, extracting fields, or answering in a fixed format.
• You have historical examples of your team doing it well, even if they live in spreadsheets, emails, or tickets.
• Per-call API cost, latency, or dependence on one provider is already a problem.
• Your data should not leave the company because of internal policy or regulation.

How we work, step by step

Every project starts small and is measured before it grows:

1. We define the task and the metric: what the model must answer, which error is expensive, and what we will compare it against.
2. We review and prepare the data: clean the examples, label the ambiguous cases, and set aside a test set the model never sees.
3. We choose the technique: fine-tuning an open LLM, distilling a large model into a small one, or a classical classifier if that is enough.
4. We train and evaluate against the baseline — for example, the prompted LLM you use today — on your own cases.
5. We deploy the model behind an API, connect it to your systems, and leave a retraining procedure in place.

What we deliver

You do not end up with a notebook, but with something your team can run:

• The trained model weights, under your control.
• The evaluation set and a results report against the baseline.
• An API or service ready to deploy on your cloud or your servers.
• Documentation of the data pipeline and a retraining procedure.
• Support for the first production rollout.

Technologies and techniques

We work with open-weight models and the technique that fits each case: full or parameter-efficient fine-tuning with LoRA and QLoRA, knowledge distillation from a large LLM, calibrated classifiers, and embeddings for search. The usual stack includes Python, PyTorch, and the Hugging Face ecosystem, served through an API in containers.

A public example is Falco, our open source project: an LLM labels your texts once and Falco uses those labels to train a small, calibrated classifier that answers in milliseconds without paying per call. It is the same idea we apply in custom projects. If the problem is prediction over tabular data rather than language, we approach it through machine learning models.