Getting good results from a model means showing it how your company works. We help you battle the reverse-information paradox with the 5Cs, so that knowledge builds up in your systems and every token you pay for compounds.
Your evals, traces and decisions stay in your systems
Training on environments built from your workflows
Switch models without losing what you’ve built
Each task goes to the cheapest model that passes your evals
Learnings from every frontier-model attempt feed your own evals and models
Private evals built around claims tasks, and open-weight models fine-tuned where the evals show a gap. In a study on public legal decisions, a fine-tuned 27B model beat a frontier model at a ninth of the inference cost.
How we develop modelsClinical-history environments for post-training and evaluation, built from partner records, with rubrics shaped by clinicians. Each cohort is qualified for rights and provenance before any training.
Explore healthcare dataSpecialist agents inside the product, with company context, tools, approvals and budgets, running on Stablehand in the product’s own infrastructure.
How the runtime worksIn a completed study on structured extraction from public legal decisions, a fine-tuned 27B open-weight model answered 85% of sealed test tasks completely, against 67% for a frontier model, at a ninth of the inference cost.
See the studyWe write the evals with the people who do the work, then run agents on Stablehand inside your infrastructure. When the evals show a gap a smaller model can close, we fine-tune an open-weight model for it.
Training and serving run on dedicated GPUs from our neocloud partners or in your own cloud.
Tell us what the work is, what data it touches and what constraints apply. We’ll work out what to build and how to measure it.