Platform

One pipeline from archive to owned model

Evaluate, train, deploy, improve. The same four stages for every engagement, so specialization is a product, not a consulting project.

Evaluate

An eval harness built from your archive

Machine-checkable ground truth.Settled claims, delivered shipments, executed contracts — we mine outcomes your organization has already validated and turn them into repeatable benchmarks.

Honest frontier baselines.Every closed frontier model is scored on your eval before training begins. The gap between them and your specialized model is measured, not asserted.

Evals as a living asset.The harness outlives any single model. Every future base model, ours or anyone's, gets scored against the same yardstick.

Train

Reinforcement learning against your reward

Open weights, 1B to 1T+ parameters.We train from the strongest available open base models and re-run your pipeline when stronger ones ship. Your harness and data carry over unchanged.

Rewards calibrated to business KPIs.Loss ratios, on-time rates, clause-level accuracy. The reward function is written with your domain experts, not guessed at.

Rollout-level observability.Every training rollout is traced. Reward hacking, regressions, and edge cases are caught during training, not in production.

Deploy

Production inference inside your boundary

Your VPC, datacenter, or sovereign cloud.Single-tenant deployments in any region and any jurisdiction. Weights never leave infrastructure you control.

Zero train-to-serve mismatch.The model serves in the exact harness it was trained in. Checkpoints promote to production in minutes, with replicas spinning up in seconds.

Sized to your workload.Throughput, latency, and concurrency configured for your product, from asynchronous batch review to real-time operator tooling.

Improve

Models that get better the more they are used

Production traces become training signal.With your approval, real usage is mined for the next training run. Your model compounds while generic APIs stand still.

A/B tested rollouts.Release candidates ship behind controlled experiments, so every model update is a measured improvement, not a leap of faith.

Quarterly retraining cadence.Improvement is scheduled, not aspirational: a standing pipeline run, reviewed with your team each quarter.

One pipeline from archive to owned model

An eval harness built from your archive

Reinforcement learning against your reward

Production inference inside your boundary

Models that get better the more they are used

See the pipeline run on your workload.

Contact us. We'll onboard you.