Industries Healthcare & Life Sciences
Where technology meetsreal health impact.
AI Engineering and software development for healthcare companies.
Helping MedTech, biotech, and pharma teams put AI, data, and interconnected systems in the most critical places. Moving faster and safer, with regulatory confidence and compliance built in.
Florence HealthcareClinical Document Intelligence
Document intelligence and AI workflows that keep clinical trials inspection-ready across 65,000+ research sites in 90+ countries, without adding manual overhead.
- 75%
- Less manual review
- 65K+
- Research sites
- 90+
- Countries covered
Why healthcare companiestrust us with delivery.
A mislabeled record or a silent integration failure simply aren't a possibility as every stage of the process impacts the patient. This is why we pay special attention to the most sensitive parts of the system.
Compliance is architecture
If every release ends in a compliance scramble, the problem isn't your team. HIPAA, GDPR, FDA SaMD guidance, and IEC 62304 have to shape the data model, the audit trail, and who signs off from day one, not get attached at the end.
Interoperability decides everything
Integrations drag for months for the same reasons everywhere: HL7 v2 that isn't quite HL7 v2, FHIR with local dialects, interfaces nobody documented. Software that meets the data where it already lives gets adopted. Software that asks the hospital to change does not.
Output a clinician can defend
A model can be right and still sit unused, because the person acting on it holds a licence. Clinicians adopt output they can defend: provenance, confidence, and reasoning in plain sight.
Downtime is a clinical event
An outage here means a ward falling back to paper mid-shift. Availability, graceful degradation, and offline behaviour belong in the first architecture diagram, not in a postmortem.
Validated on reality, not clean data
Real clinical records are incomplete, contradictory, and entered at 3am. A model that only performs on a curated dataset was never validated. The bar that counts is the data your wards actually produce.
What healthcare demandsfrom engineering and AI.
01Earlier disease detection
A detection model earns its place by buying time: the tumour flagged one scan earlier, the sepsis trajectory caught hours before it turns. It has to do that without flooding the ward with false alarms, because an alert clinicians ignore detects nothing.
02Patient records built for care
A patient's history lives scattered across systems, half structured, half free text, and clinicians lose hours a day to it. Records that are consolidated, coded, and summarised give that time back to care and give every model downstream cleaner input.
03A named owner for every decision
Someone has to own the call that a model is fit for use and be able to show why: data provenance and consent, the approved version, who signed off, what changed since. That record separates a clean audit from an inspection finding.
04Proof on demand
A regulator can ask how a model behaved months after it shipped, and the answer has to be exact. Versioned data and weights, automated validation, and drift monitoring are what make a submission survive scrutiny instead of stalling in review.
Words from the front lines of Health Tech.
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Our impact in healthcare shows through 5 enterprise partnerships and companies that decided to put their trust in Nortik, because of our detailed approach to governance, and engineering principles that help us adapt agentic systems into platforms that require the most attention to detail.
Nortik’s Impact
Five enterprise healthcare partnerships and production platforms across the health tech ecosystem.
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Frequently asked questions
Everything teams usually want to know before building clinical software. Not here? Ask us directly.
Yes. Florence Healthcare is the clearest example: document intelligence and AI workflows that keep clinical trials inspection-ready across 65,000+ research sites in 90+ countries.
That work happens inside HIPAA and GDPR from day one. Audit trails, access boundaries, and the release process are part of the architecture, not paperwork we add before launch.
We build to IEC 62304 and the FDA's SaMD guidance, with the traceability, verification, and design history your quality team needs to file.
We are engineers, not your regulatory affairs function. We work alongside your QA and RA people, or a consultancy you appoint, and make sure the software and its evidence trail hold up under their process.
Wherever possible we develop against de-identified or synthetic data. Production PHI reaches only the environments and people that genuinely need it, under signed BAAs and your access controls.
Data residency, retention, and deletion get decided before the first line of code, because retrofitting them later means rebuilding the data model.
That is usually the bulk of the work. We build against HL7 v2, FHIR, and DICOM, and we plan for the per-site variation that comes with them, because conformance on paper rarely means a clean integration in practice.
Epic, Cerner, or a decade of bespoke interfaces: we integrate with what you have. You should not have to standardise your systems before we can start.
Every model ships versioned and reproducible, with automated validation and drift monitoring, so we can show exactly how it behaved months after release.
When a model needs to keep improving, we structure that around a predetermined change control plan, so improvements do not trigger a fresh submission each time.
You do. Code, trained models, pipelines, and documentation are yours, along with the infrastructure access to run them without us.
We hand over a codebase your own team can pick up. Nothing we build is designed to keep you dependent on us.