Engagements
These cards describe typical work patterns and outcomes without naming clients or disclosing confidential details. The emphasis is on confidence, clarity, and risk reduction.
Systems delivery governance across instrument + software
Context: Diagnostics platform with cross-disciplinary teams and evolving requirements.
Role: Systems/engineering leadership; requirements quality and integration planning.
Delivered: Architecture narrative, interface definitions, acceptance criteria, and verification alignment.
Result: Improved integration clarity and stronger evidence linkage supporting regulated delivery discussions.
Traceability and V&V strategy for regulated software
Context: Regulated software with inconsistent linkage between risk controls and verification evidence.
Role: Verification strategy and evidence packaging lead.
Delivered: Traceability model, verification levels, quality gates, and release readiness criteria.
Result: Faster review cycles and reduced uncertainty in release decision-making.
Architecture authority for safety-critical workflows
Context: Complex workflows spanning clinical stakeholders, data teams, and engineering.
Role: Architecture and delivery authority across system boundaries.
Delivered: System boundary definition, interface contracts, governance for change and decisions.
Result: Clearer responsibilities, fewer integration misunderstandings, improved delivery confidence.
Secure connectivity patterns and evidence-led delivery
Context: Connected devices requiring security-conscious architecture and operational controls.
Role: Systems and security-informed design lead.
Delivered: Data flow mapping, security requirements framing, verification hooks for controls.
Result: Stronger security posture articulation and more defensible operational control narrative.
Evidence-driven pipeline design (regulated expectations)
Context: CI/CD present but not producing consistent audit-useful artefacts.
Role: DevSecOps and quality gate design authority.
Delivered: Pipeline architecture, quality gates, evidence capture and packaging approach.
Result: Less manual evidence assembly and clearer release readiness signals.
Pragmatic guardrails for AI/ML integration
Context: AI component introduced into regulated workflows with unclear validation expectations.
Role: Integration and governance lead across clinical/data/engineering stakeholders.
Delivered: Intended use framing, risk/control linkage, integration architecture, validation considerations.
Result: Reduced ambiguity and improved cross-team alignment on responsibilities and evidence needs.
See how these outcomes are produced in the Regulated Delivery Approach. For engagement options, use Contact to request the capability statement.