Healthcare and life-sciences teams work under validation, privacy and patient-safety obligations that ordinary delivery methods ignore. We help you ship software and AI with the evidence those obligations demand built in from the start.
The question we hear most
Validation was meant to prove that software does what it should. In many organisations it has become a documentation exercise that runs after the engineering is done, and it sets the release cadence for everything it touches.
Regulators have moved towards risk-based assurance, and the tooling has caught up. Plan validation into the work, scale the effort to patient and data risk, and let the evidence accumulate as tests and approvals happen. Releases speed up and the evidence improves, because it is no longer written from memory.
The shifts changing what good delivery looks like here, and what each one asks of the teams doing the work.
The FDA’s Computer Software Assurance guidance and the second edition of GAMP 5 move effort away from scripted documentation and towards critical thinking about risk.
What it means for deliveryTeams that automate their evidence can release faster without weakening control.
Information-blocking rules in the US and national programmes such as India’s Ayushman Bharat Digital Mission push providers towards standard APIs built on HL7 FHIR.
What it means for deliveryIntegration becomes a product with its own life cycle, not a one-off interface project.
Summarisation, coding support and literature review are moving into daily use, where errors carry patient-safety and privacy consequences.
What it means for deliveryKeep a qualified reviewer in the loop, and keep sensitive data inside your boundary.
Ransomware attacks and outages at hospitals have shown that when systems stop, care slows down or stops with them.
What it means for deliveryRecovery has to be tested like any other clinical requirement.
The pressures differ inside an industry too. These are the parts of it our teams work in, and what sets each one apart.
Clinical, administrative and patient-facing systems that must stay available and interoperable.
GxP systems across research, manufacturing and quality, where every change needs evidence.
Device software and connected diagnostics built under IEC 62304 and design-control rules.
Trial systems and data flows between sponsors, CROs and sites, with audit trails throughout.
Claims, eligibility and prior-authorisation processes that run on documents and rules.
Product teams scaling care platforms and apps while taking on the duties of regulated data.
Each offering runs on its own or as part of a wider programme, and draws on the AI services, consulting practices and products listed with it.
Risk-based validation planned in from the first sprint, with automated tests traced to requirements, so the evidence for each release is produced as the work is done.
Interfaces between EHR, laboratory, imaging and back-office systems on HL7 and FHIR standards, with access control designed in rather than added later.
Summarisation, extraction and knowledge assistants that keep patient and trial data inside your environment, with a reviewer signing off every output that matters.
Clinical, laboratory and operational data brought together under access rules an auditor can follow, with de-identification wherever data is reused for research.
We do not certify compliance — your auditors and assessors do. We make sure the evidence each framework asks for is produced by the delivery process itself.
Common first engagements in this sector. Most begin with a two-week, fixed-price discovery that leaves you with a costed plan whatever you decide next.
Yes. We plan for validation from the start: requirements, tests and approvals are linked and versioned, so the evidence a validated environment needs is produced as the work is done.
Yes. Our approach is zero training on your data, with private model routing for hosted models. Where the data cannot leave your control at all, we build the whole stack on your own hardware, air-gapped if required.
No. We integrate with the systems you already run and add the traceability, automation and data layer around them.
Both shape how we work. We scale assurance to the risk a system poses to patients, product quality and data integrity, and keep the evidence those approaches expect — linked, versioned and approved — inside the delivery record.
Usually through the interfaces EHRs already expose — HL7 v2 messages and FHIR APIs — and the integration engine you run today. Access control and audit logging are designed into each interface rather than added afterwards.
GxP software has to be shown fit for its intended use, and the record has to stand up to inspection. Validation does not have to mean a mountain of documents or a waterfall plan.
Running models inside your own boundary solves real problems — data that cannot leave, cost at volume, control over versions — and creates others. A practical way to decide, workload by workload.
A 98% pass rate says nothing about whether the stories you committed to were tested at all. How traceability and recorded quality gates change the release conversation.
Tell us the constraint and the systems behind it. We will put an engineer who has worked with both on the call, not the nearest salesperson.
What the first call covers
