Most teams don't need a custom model. The ones who do usually know why: a vocabulary nothing off the shelf understands, data that can't leave the building, or a per-call cost that stops working at their volume.
The expensive mistake in this field is training something before establishing that the simpler option failed. We run that check first, every time.
Before anything is trained we measure what a general-purpose model already does on your task. Often that's the answer, and we'll tell you so rather than sell you a training run.
Where general models genuinely fall short (unusual vocabulary, proprietary formats, judgment specific to your industry), we build one that doesn't.
Taking the quality of a large model and compressing it into something small enough to run cheaply, often on your own hardware, at a fraction of the per-call cost.
A test set drawn from your real cases, scored the same way every time, so "it feels better" is replaced by a number you can defend in a review.
Running on infrastructure you control, for the cases where data genuinely can't leave: regulated industries, confidential records, contractual limits.
Models decay as your data shifts. We set the monitoring and the retraining trigger before that becomes a slow, invisible quality problem.
Every one of these is a project we've been asked to run and, in some cases, talked a client out of.
If the goal is adapting an existing frontier model rather than building one, that's LLM fine-tuning. If it's grounding answers in your documents, see RAG development.
Describe the task and send a sample of the data. We'll measure what a general model already achieves on it, and tell you honestly whether a custom one is worth building.