Fine-tuning adjusts a model on your examples so it adopts a consistent style, format or narrow skill. It is the right tool for behaviour, not for adding facts, which retrieval handles better. We prepare data, train or fine-tune open and hosted models, evaluate against a baseline, and deploy with monitoring.
Measure the improvement.
A simplified view of a training run: loss falling while quality on a held-out test set rises.
Numbers shown are for illustration. Real projects report scores on your own test set.
Built to hold up.
Data preparation
Collect, clean and label training examples, with privacy controls and a held-out test set.
Fine-tuning and distillation
LoRA and full fine-tuning of open models, hosted fine-tuning APIs, and distilling large models into smaller, cheaper ones.
Evaluation
Side-by-side scoring against the base model so you can see whether the customisation actually helped.
Deployment
Serve on your own GPUs or managed endpoints with batching, caching and monitoring.
Questions we often get.
When you have hundreds of high-quality examples of a stable task and need consistent style, format or a cheaper small model. For private knowledge, retrieval is usually better.
Have something that should run itself?
Tell us what is slowing your team down. We will come back within one working day with an honest view on whether it is worth building and what it would take.