Model Fine-Tuning & Customization
When prompting and retrieval aren't enough, fine-tuning bakes your domain knowledge, tone, and formats directly into a model you own. We handle the full loop: dataset curation, training, evaluation, and deployment back into your stack.
What we deliver
- Use-case analysis: fine-tuning vs RAG vs better prompting — honest recommendation first
- Dataset preparation and synthesis from your docs, tickets, transcripts, and style guides
- LoRA / QoRA adapter training on local or rented compute, with experiment tracking
- Structured evaluation: task-specific benchmarks before and after, so improvement is measurable
- Deployment of adapters into your existing serving stack (vLLM, llama.cpp, Ollama)
Common questions
Should we fine-tune or use RAG?
Different tools for different problems. RAG injects fresh facts at query time; fine-tuning changes how a model behaves and sounds. Most businesses need RAG first. Fine-tuning earns its keep for specialized formats, classification, tone, and narrow high-volume tasks. We'll tell you honestly which one your use case needs.
How much training data does fine-tuning require?
Far less than most teams assume — often a few hundred to a few thousand high-quality examples with LoRA methods. Data quality matters much more than quantity, and part of our service is curating and synthesizing that dataset with you.
Who owns the fine-tuned model?
You do. Adapters, datasets, and evaluation results are delivered to your infrastructure. No ongoing royalties, no platform dependency.