⚙ Capability
Fine-tuning & Model Customization
Smaller, faster, cheaper models that know your domain.
When prompting isn’t enough, we fine-tune. We build training datasets, run supervised fine-tuning, LoRA/QLoRA and preference tuning (DPO), and distil large-model behaviour into small open models you can host yourself, always benchmarked against the prompted baseline.
Typical use cases
Challenge. A large hosted model works but is too slow, costly or can’t see sensitive data.
What we build. Distillation into a fine-tuned 3–14B open model running in your own cloud, matched to the original on your evals.
Challenge. General models misread your terminology, codes or languages.
What we build. Fine-tuning on curated domain data (e.g. Polish legal text, medical coding, internal jargon) with measurable accuracy gains.
Challenge. Millions of messages need labelling; LLM calls per item are too expensive.
What we build. A fine-tuned compact classifier or embedding model delivering LLM-level quality at a fraction of the cost.
Examples
Use cases with Fine-tuning.
Next step
Let's talk about your AI system.
A free 30-minute call with an Engagement Lead or AI Architect. You'll leave with a clearer view of options, risks and cost, whether or not we work together. Your case doesn't need to fit any box on this site; just tell us what you're facing.
Keep exploring