Custom language models, built for your domain.
Fine-tuning, distillation, retrieval, and evals: end to end. We deliver models that actually move your metrics.
- Pilot to production
- 0-8wk
- Eval-set lift
- 0-70%
- P95 inference latency
- <0ms
- LLM projects shipped
- 0+
From open-weight base to a model that earns its keep.
We start with the right base model for your task, fine-tune on your data, wire in retrieval where it pays off, and ship behind your latency and cost budget. The result is a model your team can defend in front of an auditor.
- LoRA, QLoRA, and full-parameter fine-tuning pipelines
- Distillation for cost-sensitive workloads
- Reproducible training with versioned datasets
From discovery to production.
- 01
Discover
We audit your data, the use case, and the constraints. Then we pick base model, training strategy, and serving path.
- 02
Prototype with evals
Build the eval suite first. The model passes when it lifts your business metrics, not when it sounds smart.
- 03
Deploy
Shipped to your cloud with cost controls, latency budgets, and PII handling locked down from day one.
- 04
Operate
Ongoing eval against live traffic, drift detection, and continuous re-tuning as your data evolves.
Have a half-built LLM project that needs a senior team to ship it?
Book a 30-min consultWhat you get.
Latency, cost, and quality, all measurable.
We deploy to your AWS, GCP, or Azure account with bring-your-own-VPC routing. Inference costs are tracked per-tenant. Drift gets detected in hours, not weeks.
- Bring-your-own-VPC inference; data stays in your perimeter
- Per-tenant cost dashboards and SLO budgets
- Drift alerts and automated re-evaluation on new traffic
Common questions.
Ship an LLM that lifts the metric you actually care about.
Free 30-minute consultation. Bring a problem, leave with a plan.
Schedule consultation