What I reach for in client engagements.
After 25+ years building and architecting mission-critical payment systems, the kind that can't go down, can't leak data, and can't fail an audit, I now spend most of my time at the intersection of AI and enterprise architecture, turning GenAI from slide-deck promise into governed, production-grade outcomes.
Available for fractional and advisory engagements through Hawk Nest: small, written, senior-led from end to end.
Internal initiative cataloguing and operationalising AI use cases across development, DevOps, documentation, pre-sales, compliance and support.
LLM gateway, audit logging, guardrails and vendor inventory aligned with PSD2, DORA, GDPR and a two-zone security architecture.
Claude Platform, MCP servers and skill-driven workflows for automated technical-documentation generation, with measurable ROI.
Internal RAG learning platform on AWS (ChromaDB, GPT-4o, React) for customer support and onboarding.
Four ways to bring me into your organisation: packaged so you buy outcomes, not days.
From use-case discovery to vendor selection, governance model and roadmap.
Business, Application, Security and Data domains: solution design with traceability from capability to component.
Agents, RAG, internal tooling on Claude, OpenAI, Azure AI, GCP and open-source stacks.
Architecture decisions, build-vs-buy trade-offs and AI roadmaps: senior expertise without a permanent hire.
Anonymised engagements from regulated payments and financial services. Client names stay private; the patterns travel.
Details are anonymised. Client names are never disclosed.
Three sizes. Outcomes, not days.
Independent second opinion
Architecture review, AI use-case triage, build-vs-buy clarification
Governance & roadmap
AI governance framework, vendor selection, 12-month adoption roadmap
Build & graduate to production
First production-grade GenAI use case: agents, RAG or internal tooling
Lightweight on process, heavy on outcomes. Same shape for Light, Medium and Intensive.
30–60 min, free. Understand context, risk posture, regulatory constraints and the one thing that has to work.
Scope, outcomes, KPIs and acceptance criteria written down. No moving goalposts.
Weekly demos. Live decision log. Everything written so the work survives the engagement.
Knowledge transfer to your team. Optional on-call period afterwards.
What clients ask before we work together.
Two-handed work: on one hand, GenAI / agents / RAG patterns and the LLM stack; on the other, the enterprise-architecture discipline (TOGAF, ArchiMate, integration, security) that makes those patterns survive an audit. Most clients hire one and then discover they need the other. I bring both.
A question I haven't covered?
Email me directly →30-minute discovery call, free. Tell me the one thing that has to work and the regulatory constraints around it. I'll tell you whether I can help.
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