When off-the-shelf models hit a ceiling on quality, cost or latency, we train models on your domain — fine-tuned, distilled and evaluated against your targets, then deployed in your cloud, your VPC or at the edge.
Adapt open-weight or hosted models to your tone, format and domain knowledge with curated data.
Compress a large teacher into a small, fast student that hits your latency and cost budget.
RLHF / DPO to align outputs with human judgment and your quality bar.
Domain-tuned embedding models for retrieval, clustering and recommendation that actually fit your data.
A rigorous, versioned evaluation suite so every model change is measured, not guessed.
Versioning, autoscaling inference, canary rollouts and drift monitoring in production.
We assemble, clean and label a dataset that reflects your real distribution.
Fine-tune or distill with sweeps to find the quality/cost frontier.
Score against a golden set and guard against regressions before release.
Serve with autoscaling, canaries and drift alerts — wherever you need it.
We’re model-agnostic. We pick the foundation that fits your constraints — licensing, hardware, languages and the accuracy you need — and benchmark before we commit.
Your weights, your infrastructure, your data — no third-party lock-in or surprise policy changes.
A small specialized model can beat a giant general one on your task — for a fraction of the cost.
A model trained on your proprietary data is something competitors simply can’t buy.
Share your task and constraints. We’ll benchmark options and return a projected accuracy, cost and latency picture — before you commit a dollar to training.