How I implement AI in a business — from strategy to shipped product
Most AI projects stall between an exciting demo and a system a business can actually rely on. My focus is closing that gap: I help companies decide where AI genuinely pays off, design the architecture around it, and then build it end-to-end. Not a slide deck — a running system.
Start with the value, not the model
The first job of an AI consultant is to say no to the wrong ideas. I map your workflows, find the one or two places where AI removes real cost or unlocks real revenue, and pressure-test them against data availability, latency, accuracy needs and budget. That means you invest in the use case that ships and pays back — not the one that demos well.
Design an architecture that survives production
A prototype and a production system are different animals. I design the whole pipeline — retrieval-augmented generation over your data (vector databases like Weaviate or Pinecone), model routing across OpenAI, Claude, Gemini and self-hosted models on Ollama, evaluation, guardrails, caching, cost controls and observability — so quality and spend stay predictable as usage grows.
Build it end-to-end, then hand it over
I have shipped real GenAI in production: AI-search visibility pipelines, document-trained assistants, clinical decision support, AI product scoring and multi-model chat used by millions. Because I own the architecture and the code, the strategy actually gets built — and your team gets a system they can extend, not a dependency on me.
- Prioritise the AI use case that ships and pays back, not the flashiest demo.
- RAG, model routing, evals, guardrails and cost controls designed for production from day one.
- End-to-end delivery — advice and engineering from the same person.