Fine-tuning is easy to start and hard to know you've got right. Without a fixed evaluation set you can't tell improvement from noise, and most teams find that out after they've shipped.
The training run is a few hours. Assembling examples that genuinely represent the work, and agreeing what a good answer looks like, is the part that takes weeks.
The dataset is the project. We help you assemble examples that represent the hard cases rather than the easy ones, and label them consistently enough to train on.
Teaching format, tone, and domain conventions, which prompting can approximate but never quite pin down across thousands of calls.
Where two answers are both correct but one is right for your business, preference methods encode that judgment instead of describing it in a prompt.
A held-out set scored automatically on every run, with the cases you can't afford to regress on pinned as a gate rather than a hope.
A tuned smaller model against a prompted larger one, compared on quality, latency, and cost at your actual volume. Sometimes the tuning doesn't pay for itself.
Base models get replaced. We document the dataset and pipeline so moving to a newer foundation is a rerun rather than a rediscovery.
Without a fixed test set, every tuning run feels like progress. Half of them aren't, and there's no way to tell which half.
Fine-tuning teaches behaviour, not facts. If the model needs to know things it wasn't trained on, you want RAG development instead, and often both together.
Send us the task and where the current model falls short. We'll build the eval set first, then tell you whether tuning is the right lever at all.