What this actually involves
Most teams reach for fine-tuning too early. We start by proving that retrieval and prompting cannot close the gap, then adapt a model only where the task is narrow, the data exists, and the economics work. Often the honest answer is that you do not need this.
- A build-versus-prompt assessment before any training runs
- Dataset construction, cleaning, and held-out evaluation splits
- LoRA and QLoRA adaptation with reproducible training configs
- Serving setup with a documented rollback to the base model
Ready to move from AI experiments to AI in production?
Thirty minutes with a senior engineer. We assess the use case, name the risks, and tell you honestly whether we are the right fit.