Models Trained with Own Data.
When RAG isn't enough, fine-tuning. Your own model with your voice, knowledge, and patterns.
For very specific cases (technical language, proprietary format, high consistency), prompt engineering and RAG are not enough. Fine-tuning an open-source or closed model delivers results that no prompt can match.
What we do
- Assessment if fine-tuning is the right path
- Dataset curation and cleaning
- Fine-tune Llama, Mistral or OpenAI
- Rigorous evaluation
- Deploy on controlled infrastructure
- Private API for your app
Clean fine-tune pipeline
Your data → clean → base model → fine-tune → eval → deploy → private API.
What we build in this area
Domain-specific
Medical, legal, technical language.
Brand voice
Model that writes in your exact voice.
Format-specific
Outputs in a very particular format.
Cost optimization
Small, fine-tuned model that replaces GPT-4 for a specific task.
Tools and technology
We're not married to any. We choose the best one for the problem.
Frequently asked questions
How much data is needed?+
Viable minimum: 500-2000 quality examples.
Cost?+
Setup + compute cost (typically a few hundreds to thousands of €).
Where does it run?+
AWS, Modal, Replicate, or your infrastructure.
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