Administrative load is enormous
Documentation, prior authorisation, coding and correspondence consume clinical time that has nothing to do with care.
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Healthcare & life sciences
Clinical and safety work
has no tolerance for
a confident guess.
AI can absorb an enormous amount of administrative burden in healthcare. It can also cause real harm if it is deployed where a plausible-sounding wrong answer is dangerous.
The pressure
The line between burden and risk is the whole design problem.
We are deliberately conservative here: we scope toward documentation, retrieval and evidence work, and away from anything that substitutes for clinical judgement.
Provenance is a requirement
For safety and evidence work, every assertion needs a citation back to a source a reviewer can open.
Validation expectations are formal
Software touching regulated processes carries documentation and change-control obligations that shape the engineering approach.
Use cases
Where we do the work
Six places we are usually brought in. Each one is scoped to a measurable surface rather than a theme.
Clinical documentation support
Draft and structure notes for clinician review and sign-off — always assistive, never autonomous.
Pharmacovigilance case intake
Triage and structure adverse event reports from unstructured sources, with full traceability to the original.
Evidence and literature synthesis
Retrieval across internal and published literature where every claim carries a citation.
Prior authorisation and claims workflow
Assemble and check submissions against payer requirements to cut rework cycles.
Trial operations analytics
Site performance, enrolment forecasting and protocol deviation detection.
Data platform and interoperability work
The pipelines, terminology mapping and quality gates that everything above depends on.
The honest part
What makes this domain genuinely hard
We would rather set expectations here than discover these in month three. Every one of them has changed a delivery plan at some point.
- De-identification has to be verifiable, not assumed, and it belongs in the pipeline rather than in a preprocessing script someone maintains by hand.
- Terminology mapping across coding systems is unglamorous, slow, and the most common reason a promising pilot cannot generalise.
- Retrieval must surface provenance for every claim, because an unsourced assertion is unusable in safety and evidence contexts regardless of accuracy.
- Human-in-the-loop is an architectural commitment with real throughput implications — it cannot be added later without redesigning the workflow.
Engagement shape
How engagements usually start
We start by separating the work into what is safe to automate, what is safe to draft for review, and what should not be touched. That triage is the deliverable of the first phase, and we will recommend against use cases that fall on the wrong side of it. From there the pattern is familiar: one workflow, a measured baseline, provenance built in, and a validation trail from the first commit.
Other industries
Ready when you are
Bring us your hardest workflow in this sector.
We will tell you whether it is ready for AI, what has to change first, and what a realistic first release looks like.