A model that already knows your domain.
Fine-tuned on your own data, so you stop paying for a page of instructions on every single request.
You are here if…
- You paste the same long instructions into every prompt.
- The output is close, but never quite in your format.
- A general model does not know your products, codes or terminology.
- Your per-token bill scales with prompt length, not with value.
What we deliver.
Dataset preparation
Collect, clean and label what the model learns from.
Fine-tuning
Full or LoRA, depending on your data volume and budget.
Evaluation
Measured against the base model on your own examples.
Domain vocabulary
Your product codes, abbreviations and house style.
Smaller, cheaper models
A tuned small model often beats a large general one.
Retraining
Refreshed as your data and products change.
How it runs.
Check first whether prompting or retrieval already solves it — often it does.
Assemble and clean a dataset that represents the real task.
Fine-tune, starting with the smallest model that could work.
Score it against the base model on examples you have held back.
Deploy, monitor drift, and retrain on a schedule.
Hugging Face, LoRA / QLoRA, PyTorch, Llama, Mistral, OpenAI fine-tuning, Weights & Biases
Questions.
Often not. Good prompting and retrieval solve most problems more cheaply, and we will tell you when that is the case. Fine-tuning earns its cost when you need a consistent format, a specialist vocabulary or a smaller model at high volume.
Need this built properly?
A free 30-minute technical call. We tell you what it takes and what it costs.