About Me
I lead enterprise AI delivery work across strategy, enablement, and implementation. My role combines team leadership, stakeholder training, AI panels, delivery governance, and hands-on architecture thinking.
Background
Most of my practical impact comes from enterprise environments where AI has to work beyond demos: permission-aware systems, secure workflows, reliable retrieval, stakeholder adoption, and responsible implementation.
My path from Aeronautical Engineering through industrial management to AI systems leadership has helped me connect technical architecture with organizational adoption needs. I focus on what helps teams execute consistently in real operating environments.
I focus on the unglamorous parts that make AI actually work: how do you chunk documents so retrieval doesn't suck? How do you handle permissions when users query across multiple systems? How do you build eval harnesses so you know if your changes make things better or worse?
I lead enterprise AI delivery and enablement. My public work translates that experience into practical frameworks for RAG, agents, governance, evaluation, and responsible adoption.
What I Actually Do
Lead AI Delivery
Guide the team responsible for turning AI opportunities into practical enterprise capabilities.
Train Stakeholders
Run AI training sessions for business, technology, and leadership audiences.
Shape Adoption
Help teams understand where AI is useful, risky, overhyped, or ready for implementation.
Participate in Panels
Share practical lessons on AI adoption, governance, and delivery realities.
Define Delivery Patterns
Turn repeated enterprise AI problems into reusable architecture and operating models.
Published Research
SSRN preprints extending my applied enterprise AI work into research-backed frameworks.
AI-Driven Autonomous Enterprises and the Future of Work
Reviews evidence, design patterns, governance standards, and labor-market research for AI-driven autonomous enterprises, arguing for frontier-conditioned autonomy instead of a simplistic copilot-to-autonomy roadmap.
AI-Driven Corporate Climate Risk Decision Systems for Global Enterprises
Introduces the Climate AI Decision System (CADS) architecture for evidence-grounded climate-risk management across disclosure, Scope 3 traceability, governance, auditability, and enterprise decision workflows.
Education
University of Brighton, UK
BEng (Hons), Aeronautical Engineering
Engineering fundamentals: systems design, optimization, failure analysis. Learned to think about how things break under real-world constraints.
University of Texas, USA
MS, Industrial Management
Bridging engineering and operations: how teams adopt technology, how to measure impact, how to build systems that scale beyond prototypes.
How I Work
Design for Production-Grade Systems from Concept
Prototypes are fine for demos, but I architect for what happens after the demo: permissions, error handling, audit logs, eval harnesses. The systems I define need to keep working when they scale and when things go wrong.
Measure everything that matters
If you can't measure it, you can't improve it. I establish eval frameworks, define metrics, and run experiments before making changes. Vibes-based optimization doesn't work at scale.
Security and auditability are not optional
Enterprise AI needs guardrails: least-privilege access, permission passthrough, full audit trails. These aren't features you bolt on later—they're architectural decisions from the start.
Simplicity over cleverness
The simplest solution that works is usually the right one. Add complexity only when you have evidence it's needed—not because it might be useful someday.
Technical Stack
AI & LLM Systems
Backend & APIs
Frontend & Web
Cloud & Infrastructure
Approach
Work with Me
I'm available for strategic consulting, technical advisory roles, thought partnership, and architecture definition. If you're defining your AI strategy or need architectural guidance, let's talk.