We train and build the models behind your AI.
Custom predictive and ML models built from your data, and frontier models post-trained on your domain, voice, and judgment — every gain proven on held-out evals before it ships.
Build from scratch, or post-train the frontier.
The right approach depends on the problem. We do both — and tell you honestly which one fits.
Build custom models
When the problem is prediction — risk, demand, propensity, churn — we build and validate models on your data from the ground up, with the explainability your risk team accepts.
Post-train frontier models
When the problem is language and judgment, we take a frontier model and post-train it on your domain, voice, and format — supervised fine-tuning and preference optimization, proven on evals.
From data to a model that earns.
Custom predictive models
Risk, forecasting, propensity, and churn models built from your data and validated with explainability and reason codes — models your risk team can defend, not black boxes.
Fine-tuning & post-training
We adapt frontier models to your domain, voice, and format with supervised fine-tuning and preference optimization, and prove the gain on held-out evals — not vibes.
Training data & synthetic generation
The data training needs — curated, labeled, and where privacy or coverage demands it, synthetically generated and validated against real distributions.
Evaluation & benchmarking
Task benchmarks and held-out evals that prove a trained model actually beats the baseline — factuality, task success, safety, and bias — run in CI.
RL environments & verifiers
For agentic tasks we build environments, tasks, and verifiers that reward the right behavior and catch failure — reliability engineered, not hoped for.
Deploy & retrain
Trained models shipped behind an API with monitoring, drift detection, and scheduled or triggered retraining — plus routing and distillation to control cost.
A trained model ships only when it wins.
No eval, no ship. Every model is held to a labeled benchmark and tracked in CI for quality, safety, and cost.
Every trained model is measured against the incumbent on a labeled benchmark before it ships.
For generative systems, answers stay traceable to a source, with citation coverage scored each release.
The right refusals under adversarial and jailbreak pressure, tested continuously.
Outcome testing on consequential decisions — especially lending and eligibility.
Held-out evaluation proves the model generalized, rather than just fitting the training set.
Per-task cost and latency tracked and routed, so quality doesn't blow the budget.
We fine-tune only when it genuinely wins. For most knowledge problems, retrieval is cheaper, more current, and easier to audit than training — and we'll tell you when that's the right call instead of selling you a training project you don't need.
Bring us a hypothesis. Leave with a system.
Tell us what's eating your team's time. We'll give you an honest read on whether AI is the right tool — and if it is, a scoped v1 with a timeline and cost.