AI Development for Regulated Industries

What we do

Production AI systems where behaviour is constrained, tested and explainable: retrieval architectures that cite their sources, agent workflows with deterministic control paths, model evaluation harnesses that run before every release, and the documentation trail regulators expect. We work inside your compliance frame, whether that is HIPAA, GxP, APRA CPS 230, DPDP or the emerging AI governance regimes.

What audit-ready AI actually means

It means three things are true at once. The system’s behaviour is bounded: you can state what it will and will not do, and the architecture enforces it rather than a prompt requesting it. The system’s performance is measured: an evaluation suite with real cases runs before every release, so "the model got better" is a number, not an impression. And the system’s decisions are traceable: when an output matters, you can show what informed it.

Most AI deployments have none of the three, which is fine for a chatbot and disqualifying for a clinical, financial or safety context.

Why we lead with architecture over fine-tuning

We rebuilt a healthcare AI platform that had plateaued at 60 percent recall despite months of fine-tuning. The fix was not a better model; it was a better architecture, and recall went to 92 percent. We published the lesson because it generalizes: when an AI system is unreliable, teams reach for training when they should be reaching for structure.

Retrieval that cites sources, decomposed pipelines where each stage is testable, and deterministic control flow around the model do more for reliability than any amount of tuning, and unlike tuning, they produce evidence.

Determinism matters more than accuracy

Ask an agent the same question twice. If the answers differ, no amount of fine-tuning fixes that, and we have written publicly about why. For regulated use, reproducibility is a compliance property: an auditor re-running last quarter’s decision needs last quarter’s answer. We engineer for it with pinned models and prompts, versioned retrieval corpora, sampling discipline, and control paths that route high-stakes branches through deterministic code instead of model judgment.

Questions we hear before engagements

Should we build or buy?

If your differentiation lives in the workflow, buy the model and build the system around it. We will tell you plainly if a vendor product covers your case, because a build recommendation we cannot defend costs us more than it earns.

Can you fix hallucinations?

You constrain them structurally: retrieval with citation, bounded output formats, and verification stages. Anyone promising elimination through prompting is selling you their optimism.

Which models do you use?

The one the evaluation harness says wins on your cases, under your data-residency constraints. Model choice is an output of the process, not a loyalty.

What happens to our data?

It stays inside your boundary. Architectures run in your cloud or on sovereign infrastructure, and data flow is documented as part of the compliance trail.