Audit-Ready Engineering for Regulated Healthcare Systems

Architecture & Risk Baseline

Time-boxed baseline engagement · Defined scope

Rapid assessment of system architecture, boundaries, and risk posture against IEC 62304 and ISO 14971.

You leave with:

• Architecture & risk gap summary
• Clear remediation priorities
• Audit-ready narrative for leadership

Evidence Chain Recovery

Targeted remediation engagement · Evidence-driven

Restore traceability between requirements, risk controls, and verification where evidence has drifted or weakened.

You leave with:

• Coherent traceability model
• Defensible release position
• Clear verification roadmap

Regulated DevSecOps Foundation

Foundation engagement · Architecture-first

Design a compliance-aware CI/CD framework that generates audit-useful evidence by design.

You leave with:

• DevSecOps reference architecture
• Evidence-ready pipeline blueprint
• Foundation for automation & AI tooling

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.

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.

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.

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.

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.

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.