I design governed, AI-augmented systems that operate across multiple domains while preserving lifecycle integrity, authority boundaries, and auditability.
My background spans over two decades in regulated environments (pharmaceutical engineering, CQV, and large-scale project delivery), where correctness, traceability, and governance are non-negotiable. Today, I apply the same architectural discipline to human–AI interaction systems.
- AI-augmented workflow architecture
- Lifecycle-driven system design
- Governance, rules, and authority boundaries
- Structured knowledge systems
- Deterministic interaction logic over probabilistic models
I focus on systems, not prompts — designing how AI is allowed to operate, when it must stop, and how humans remain in control.
A CQV-focused system that models regulated engineering workflows:
- Work Package lifecycle management
- Task gating and sequencing
- Document generation with governance rules
- Deterministic export logic
An IGCSE-oriented educational system designed for:
- Structured lesson orchestration
- Board-agnostic knowledge routing
- Controlled canvas usage
- Revision and assessment workflows
These systems share the same architectural fingerprint despite operating in different domains.
- Governance before capability
- Explicit states over implicit assumptions
- Boundaries are features
- Determinism where it matters
- Human authority is never delegated silently
- Model training
- ML research
- Chatbot demos
- Prompt tinkering without system context
I’m expanding my architectural practice into domain-agnostic AI systems that support decision-making, execution, and learning — without compromising control or accountability.
This profile reflects active system design work, not experiments.