AI engineering for life sciences and healthcare

Clinical AI, imaging AI, and regulated healthcare workflows live or die on the evidence around the model, not the model itself. Validation packs, audit trails, readiness scoring against published rubrics, and workflow integration that preserves traceability are what unblock the pilot, the approval committee, and the regulated rollout. We work the engineering side of that boundary: the evidence your clinical, regulatory, and compliance roles can read, challenge, and sign against. We do not diagnose, certify, or give regulatory advice.

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Where the Engineering Bottleneck Lives

A clinical or imaging pilot moves toward broader use and needs validation evidence that holds up under engineering review, not a benchmark slide, or an imaging workflow hits an edge case (a rare condition, drift across scanners, a defect class the test set never covered) that production monitoring is not catching.

Or a regulated rollout needs a readiness scorecard against a published rubric (HIPAA, GxP-aligned references, NIST AI RMF) with an evidence trail per scored item that the committee can re-score in twelve months on the same map.

Imaging & Clinical-AI Validation

Most clinical-AI regressions are not accuracy problems on the headline metric: they are silent failures on a slice the test set never covered, drift across acquisition hardware, or a workflow that hides the regression until a clinician notices. We build the eval harness, regression suites, slice-level monitoring, and release-readiness reviews that surface the regression first. The validation evidence is the engineering pre-requisite for clinical and regulatory decisions, not a substitute for them.

Lands in the Production AI Monitoring Harness: 4–10 weeks, milestone or fixed-price.

HIPAA / GxP Readiness Scoring

Approval committees and audit functions ask the same thing: where is the evidence, scored against a named external rubric? We build the scorecard against NIST AI RMF, ML Test Score, or a HIPAA / GxP-aligned checklist, with an evidence map that ties every score to an artefact: test logs, eval outputs, runbooks, lineage notes. Another expert can replay the scoring against the same rubric using the same evidence map.

Lands in the AI Readiness Scorecard: 2–5 weeks, fixed-price.

How We Work With Life-Sciences Teams

Each pack has a fixed scope and a price tied to the outcome, and ends in something your team keeps and can re-run: the eval harness, the re-run script, the rubric-and-evidence map. We work alongside your clinical, regulatory, and compliance functions; we do not substitute for them, and we do not diagnose, certify, or interpret regulation.

Heading into a validation gate, a readiness review, or a regulated rollout? The named pack page is the entry point, or contact us with the question itself and we will route you to the right one.

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