AI Product Architect
I design the systems and interfaces that turn ambiguous AI ambition into things people actually understand, trust and ship.
Human-in-the-loop AI does not automatically create meaningful oversight. If the interface quietly turns review into queue-clearing, what you have is approval theatre.
Featured Case Study
Solve Axis is a concept for an AI-native enterprise supply-chain modelling product — organised around how analysts actually reason about a solve, with a designed accountability layer that makes AI safe to trust on a high-stakes decision.
Narrated walkthrough — best watched with sound on.
Work
SS-1 Signal Shell is a market intelligence engine for product teams. It monitors competitor changes, filters noise, generates structured strategy briefs and asks a human to approve any update to the product's strategic record. The project explores how AI can support competitive judgement without turning every external signal into a reactive backlog item.
Swarm Lite is an AI product strategy prototype that helps teams test strategic questions before turning them into roadmap work. It generates representative market personas, runs structured huddle sessions, produces decision-ready strategy briefs and routes consequential actions through human approval. The project explores how AI can challenge assumptions earlier without pretending to replace real research.
TX-1 Terminal Explorer is a prototype for AI that earns the right to act. It diagnoses supply chain optimisation failures, validates proposed fixes in a dry run, and asks a human to approve the change before anything is committed. The project explores what trustworthy agentic AI should look like in serious enterprise workflows: visible reasoning, bounded autonomy, human ownership and a full audit trail.
The serious money in AI isn't hiding in the shiniest product. It's sitting in the invoice queue nobody owns, the compliance check that still depends on someone reading three documents, and the procurement exception that delays a supplier payment.
For years, UX fought to get invited earlier. AI may have finally kicked the door open — but it also changed what we are expected to bring into the room.
The question is not 'should we train our own model?' The better question is: what will our product learn that nobody else can see?
The best AI strategy session may start less like a hackathon and more like a good piece of UX research: who is doing the work, what are they trying to decide, and where does the system currently fail them?
