Compliance-aligned architecture
We design system architectures with HIPAA, GDPR, HITRUST, and SOC 2 controls embedded as architectural properties — not bolted on later. This includes:
What we do
Healthcare Software Consulting
Build clinical-grade software with a team that understands both code and compliance.
Healthcare software is unforgiving.
The patterns that work in B2C SaaS — fast-and-loose iteration, "we'll bolt on compliance later," generic identity flows — fail under HIPAA, HITRUST, GDPR, and the scrutiny of enterprise health systems. At the same time, the pressure to ship is no less intense than anywhere else: clinical workflows need to improve, integrations need to land, and AI/ML features need to reach production without violating any of the privacy rules they sit close to.
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Who we work with
We've helped product teams in several healthcare contexts:
Clinical decision-support platforms
That integrate with EHRs and handle Protected Health Information (PHI) at scale
Telehealth and virtual care
Companies with stringent uptime and privacy requirements
Digital therapeutics (DTx)
Seeking FDA Class II clearance and EU MDR conformity alongside their engineering work
Medical imaging and AI/ML diagnostic platforms
Balancing model performance, latency, and clinical traceability
Health-system internal platforms
Modernising provider workflows under HITRUST or NIST 800-53 mandates
Healthcare payor and benefits platforms
Dealing with claims, prior authorisation, and FHIR integrations
If you operate at the intersection of clinical workflows and software engineering, you'll likely recognise the patterns we describe below.
Capabilities we bring
Each of these is a full engagement area on its own. In healthcare, they almost always combine.
PHI domain boundaries with explicit classification at every interface
Encryption-in-transit and at-rest as default, with key management aligned to compliance requirements
Audit logging as a first-class architectural concern (tamper-evident where required)
TIdentity and access models that scale beyond ad-hoc role tables
Data residency patterns for US/EU dual-deployment
DevSecOps for regulated software
In healthcare, you can't separate security engineering from delivery engineering. We build pipelines and practices that satisfy both:
Schema mapping and transformation rules
documented and reviewed
Migration scripts
with idempotency, restart-ability, and observability built in
Reconciliation reports
automated comparison of old and new, every run
Cutover plans
staged, with rollback paths, and rehearsed
EHR integration architecture
Almost every healthcare product touches EHRs, and the integration story is rarely simple:
HL7 v2 and FHIR R4 integration patterns
Epic, Cerner / Oracle Health, Athena, Meditech, and EU-specific systems
Bulk FHIR for population-level workflows
SMART on FHIR authorization patterns for clinician-facing apps
Eventing and reconciliation between EHR data and product data
High-availability clinical platforms
Clinical software has uptime expectations closer to financial infrastructure than to consumer apps. We architect platforms for:
Multi-region deployment with active-active or active-standby patterns
Graceful degradation strategies (a critical workflow must keep functioning even when peripheral services are down)
Defensible SLAs/SLOs for hospital and health-system contracts
Disaster recovery with RPO/RTO calibrated to clinical risk
AI/ML in healthcare
If your product uses ML or LLMs in clinical workflows, the engineering challenges multiply: model traceability, explainability, drift monitoring, PHI handling in training and inference pipelines, and a clear story for regulators.
ML/LLM ops aligned to FDA "Good Machine Learning Practice" principles
Inference pipelines that respect PHI boundaries
Evaluation and monitoring frameworks that satisfy clinical review
Capability transfer for healthcare teams
The point of working with us is not to make us indispensable. It is to leave you with:
A documented architecture and engineering handbook your team owns
Trained internal "compliance champions" who can review code for HIPAA/GDPR implications
Runbooks for incident response, audit preparation, and operational continuity
A Skills Matrix mapping each capability to internal owners
What's distinctive about healthcare
Three things make healthcare engagements different from generic SaaS work:
1.
Compliance is a property, not a project.
HIPAA is not something you "complete." It is a continuous state of the system, the team, and the process. Architectures that treat compliance as a one-time effort end up rebuilt the next time an audit changes scope. We design for the long game.
2.
Stakes are higher.
Downtime, data exposure, and incorrect outputs in healthcare have consequences that don't show up in a SaaS dashboard. We bring patterns and practices from contexts where this is taken seriously.
3.
Talent is scarce.
Engineers with both modern cloud-native skills and healthcare-compliance experience are rare. Most teams can hire one or the other, not both. We help bridge that gap and train your team along the way.
How we work
Our engagements follow a three-phase model:
Phase 1
Co-Execution
Co-execution on critical scope
We embed alongside your engineers and lead the technical work for the first few months. Your team pairs on every significant decision.
Phase 2
Transition
Codification of domain & technical knowledge
Your team leads new work. We mentor, review, and codify standards.
Phase 3
Self-sufficiency
Transition toward your CoE
Your internal team owns the practice. We step back to ad-hoc strategic counsel.
A typical healthcare engagement runs 9–12 months end-to-end, depending on scope and starting state. We don't run 3-year retainers — that's what we mean by "exit by design."
Why teams choose us for healthcare work
Solution-Specific Expertise doesn’t exist in a vacuum. Every initiative we support in these domains is also:
Senior-only
Every engineer on your project has prior healthcare-compliance experience. We don't learn HIPAA on your time.
Embedded, not outsourced
We work alongside your team, in your repos, on your real problems.
Documentation-first
Everything we build lives in your systems — not in a private vendor knowledge base.
Exit-aware from kickoff
Transition timelines and self-sufficiency milestones are defined in week one.
When to bring us in
A healthcare engagement is especially valuable when you are:
Preparing for HIPAA, HITRUST, or SOC 2 and want controls baked into engineering, not added under audit pressure
Selling to enterprise health systems or payors with procurement requirements you don't yet meet
Building an AI/ML or LLM-powered clinical product and need a defensible engineering story for regulators
Modernising a legacy clinical platform and want to do it without freezing the product roadmap
Building a US/EU dual product with data residency, GDPR, and state-level US requirements to satisfy
F. A. Q.
We’re early-stage and not yet HIPAA-bound. Should we still architect for it?
If healthcare is your trajectory, yes — but pragmatically. We help early-stage teams identify the small, high-leverage decisions that preserve HIPAA-readiness without overbuilding. Retrofitting HIPAA into an architecture that ignored it is significantly more expensive than designing with it in mind from week one.
Do you work with US, EU, or both?
Both. We have engineers experienced with HIPAA (US), GDPR (EU), and dual-residency patterns. Most modern healthcare products eventually need both.
Can you help us prepare for an FDA submission?
We are not a regulatory affairs firm and we don’t write FDA submissions. But we work alongside your regulatory team to ensure the engineering practices, documentation, and traceability your submission depends on are in place and defensible.
We use a third-party EHR integration platform (Redox, Particle, Health Gorilla). Do we still need integration architecture work?
Often, yes. These platforms simplify the wire-level integration but don’t solve the deeper questions: how integration data flows through your product domain, where your boundaries are, how you handle reconciliation, how you handle EHR-specific edge cases. We’ve worked with all the major aggregators and direct integration patterns.
Our team is small. Will an embedded model work?
Embedded models work well from team sizes of about 8 engineers and up. For smaller teams, we sometimes recommend starting with a Capability Blueprint Workshop to identify the highest-leverage areas first.
Get an honest read on your modernization options
In a 4-week Blueprint Sprint, we assess your legacy, surface the real constraints, and produce a modernization roadmap calibrated to your business reality.
Start your Blueprint Sprint