I design and build production-grade LLM-powered and agentic AI applications — helping organisations move confidently from AI experimentation to real, business-value-generating systems, built on sound architectural foundations.
Most organisations have explored AI in pilots or proofs of concept. The hard part is getting to production — with the reliability, observability, cost control, and governance that enterprise systems demand. I bridge that gap.
Architecture and development of production-grade LLM-powered applications. I design systems that are robust, observable, and cost-efficient — selecting the right models, embedding strategies, and retrieval patterns for each specific use case.
Design and build multi-agent systems and autonomous AI workflows — where AI agents plan, use tools, delegate, and collaborate to complete complex, multi-step tasks. We architect systems with the reliability and observability production environments demand.
Integrating LLM capabilities into existing products and platforms — via well-designed APIs, event-driven architectures, and cloud-native infrastructure. We ensure AI features are performant, scalable, and cost-controlled in production.
For organisations earlier in their AI journey, I help identify where AI can genuinely create value, assess your readiness to deliver it, and produce a prioritised roadmap for adoption — grounded in your specific business context, not generic AI hype.
Production AI is harder than it looks. I've developed a set of principles that guide every engagement — from the first design session to post-launch monitoring.
I design for production from day one — with observability, error handling, cost controls, and fallback paths built in from the start, not bolted on after a demo.
I establish evaluation frameworks early and measure quality continuously — because AI systems that aren't measured don't improve and can't be trusted.
LLM applications are software systems and deserve the same architectural rigour. I apply proven patterns — RAG, agents, streaming, caching — with clear rationale and documented trade-offs.
Safety, explainability, and governance aren't optional extras. I embed them from requirements through to deployment — building systems your organisation can stand behind.
I don't favour any single model provider. I select the right model for each task — considering capability, cost, latency, data privacy, and your existing relationships.
My goal is to leave your teams more capable, not more dependent. I document decisions, share rationale, and build your team's understanding alongside every delivery.