Fine-tune frontier models on compliance-sensitive data

What we actually do

Generic foundation models underperform in regulated domains because they have no idea what your terminology means, what your constraints are, or what a defensible output looks like. We close that gap with supervised fine-tuning, instruction tuning and preference alignment run on your data, inside your perimeter.

• Domain-adaptive fine-tuning of open-source (Llama-family, Mistral-family, Qwen-family) and frontier models on your proprietary corpus
• Instruction and preference alignment (DPO/IPO/RLHF-style) tuned to your review standards and acceptance criteria
• Retrieval-augmented training so long-tail factual recall improves without bloating context windows
• Evaluation harnesses with domain-specific pass/fail criteria, not generic benchmarks
• On-client-environment training paths for data that cannot egress under HIPAA, GDPR, MiFID II or attorney-client privilege

Where this earns its keep

The highest-leverage use cases are the ones where a generic prompt gets it dangerously wrong: clinical document synthesis, regulatory disclosure drafting, financial ontologies, and legal research where a hallucinated citation is a liability, not an inconvenience.

How an engagement runs

We start with a one-page brief: the modelled task, the success metrics, the hard constraints, and the single most expensive assumption to test first. Then a fixed-price training plan you can take to your board the same day. Senior-only execution - no ramp-up billing, no status theatre.