Healthcare.

An adverse-event review must reconstruct exactly what the model saw, what it produced, and which human signed off. Most vendor offerings fail this bar. We scope to meet it. In practice, the first deployments cluster around document intelligence, structured extraction, internal copilots, evaluation harnesses: areas where evaluation criteria are objective, data is already in the system, and an evaluation harness can measure quality continuously.

Regulators-of-note for healthcare engagements typically include HHS OCR (HIPAA), TGA, MHRA, PIPEDA. The specific regulators that bind a given engagement depend on jurisdiction and the data classes in scope, so we map this explicitly during discovery rather than assume a global posture.

We do not deploy generative AI as the final decision-maker in any clinical pathway. Every output lands in a clinician's queue with the source documents attached. This is a deliberate trust boundary, not a capability gap. We are equipped to build the systems we decline to build, and we decline to build them because the risk-to-value ratio in this industry does not justify it.

Our healthcare engagements typically sit between Chief Medical Information Officer, Head of Digital Health, Director of Clinical Operations and equivalent senior operators. The decision-maker varies by organisation, but the common thread is a leader accountable for both delivery and downside.

In healthcare, generative AI is most reliable when scoped tightly: clinical-letter drafting, prior-authorisation automation, and structured extraction over EMR notes, all under human review. The same techniques look superficially similar across industries, but the data shapes, evaluation criteria, and adverse-event posture differ enough that copy-pasting an engagement from another industry usually produces a system that fails the first audit it sees.

We spend two weeks with your leadership and operators, rank the use cases that are worth doing, and come back with a clear plan and a clear price. We never start with a six-month strategy exercise. We go after the smallest piece that produces real value first.