Specialist services for regulated healthcare systems.
Each service is designed to reduce technical and regulatory risk, increase delivery confidence, and produce artefacts that withstand audit scrutiny.
1) Systems Engineering & Architecture (Regulated Products)
Client problem
Architecture and requirements drift cause integration failures, unclear responsibilities, and late-stage regulatory risk.
Approach
Define boundaries, interfaces, and acceptance criteria; build an architecture narrative aligned to design controls and cross-disciplinary delivery.
Tangible deliverables
• System decomposition & interface definitions
• Requirements quality improvements & acceptance criteria
• Architecture decision records (ADRs) and design rationale
• Integration plan and verification strategy alignment
Outcomes
Reduced integration uncertainty, clearer accountability, fewer late-stage surprises, stronger evidence chain.
2) Risk Management & Regulatory Compliance
Client problem
Audit anxiety, inconsistent risk controls, and weak linkage between risk, requirements, and verification evidence.
Approach
Integrate ISO 14971 risk thinking into the lifecycle; align software delivery to IEC 62304 and design controls expectations under ISO 13485.
Tangible deliverables
• Risk control integration into requirements and design
• Traceability strategy and governance
• Remediation plan for gaps in evidence and process
• Security/compliance alignment (incl. ISO/IEC 27001 intent where relevant)
Outcomes
Clearer compliance narrative, defensible artefacts, reduced audit uncertainty, practical governance teams can follow.
3) Verification & Validation Strategy
Client problem
Testing is present, but not structured: weak coverage, unclear traceability, and evidence that doesn’t support release decisions.
Approach
Define a V&V strategy that maps directly to risk controls and acceptance criteria, with pragmatic automation where it adds evidence value.
Tangible deliverables
• Verification strategy, levels, and coverage model
• Traceability mapping: requirements ↔ risk ↔ tests
• CI quality gates and release readiness criteria
• Test framework improvements and evidence packaging
Outcomes
Higher confidence releases, better test value, and a clean evidence story that can be reviewed quickly.
4) DevSecOps for Regulated Software (Evidence-driven)
Client problem
Pipelines ship builds, but don’t generate trustworthy evidence. Security posture is unclear and traceability is fragmented.
Approach
Design pipelines to produce audit-useful artefacts: controlled builds, quality gates, security checks, and traceability outputs.
Tangible deliverables
• Pipeline design aligned to QMS expectations
• Quality gates (tests, linting, coverage, review controls)
• Evidence artefact strategy (build provenance, test evidence, SBOM where appropriate)
• Team enablement and governance templates
Outcomes
Faster, safer releases with clearer evidence, fewer manual “audit scramble” activities, and stronger security hygiene.
5) Embedded IoT, Secure Connectivity & Cloud Platforms
Client problem
Device-to-cloud systems introduce cybersecurity and data integrity risk, often without a coherent, regulated architecture story.
Approach
Threat-informed architecture, secure connectivity patterns, and evidence-led engineering that supports regulated operation and maintenance.
Tangible deliverables
• Device-to-cloud reference architecture and data flow mapping
• Security requirements and threat modelling outputs (scope-appropriate)
• Operational controls and logging strategy
• Verification hooks for connectivity and security controls
Outcomes
Reduced cybersecurity risk, clearer operational control, and a system narrative consistent with regulated expectations.
6) AI/ML Integration in Healthcare Systems
Client problem
AI/ML adds uncertainty: safety, explainability, validation strategy, data governance, and clinical workflow integration.
Approach
Define guardrails: intended use, validation approach, monitoring expectations, and integration architecture that supports control and evidence.
Tangible deliverables
• AI/ML integration architecture and governance approach
• Requirements and risk control framing for AI behaviours
• Validation and monitoring considerations (scope-dependent)
• Interface definitions between clinical, data, and engineering teams
Outcomes
Safer, more defensible AI integration with clearer responsibilities, fewer unknowns, and improved delivery confidence.
Want to see how these services fit together in practice? Review the Regulated Delivery Approach or Engagement Highlights.