AI Engineering
Artificial intelligence applied to practical problems and real-world workflows — engineered, supervised and measured, not just demoed.
Engineering first, tools second
Useful AI systems are designed, not prompted into existence. My process starts with the workflow: where does judgement live, what should be automated, where must a human stay in the loop, and what does failure look like?
From there, the engineering is conventional and disciplined — model and API integration, orchestration between steps, structured inputs and outputs, testing against real cases, and iteration once the system meets reality.
Working areas
- Generative AI & large language models
- AI agents, assistants & agentic workflows
- Conversational and voice AI
- Prompt engineering & AI orchestration
- API and model integrations
- AI-powered business processes & intelligent automation
- Human-supervised AI & AI adoption
- Experimentation & prototyping
How a practical AI system gets built
Map the workflow
Understand the real process, its decision points, and where intelligence genuinely adds value.
Design the system
Choose models, define structured inputs and outputs, and place human supervision deliberately.
Build & integrate
Connect APIs, orchestrate steps, and wire the system into the tools people already use.
Test & iterate
Evaluate against real cases, correct failure modes, and improve the system in production.
Where the work supports it, this extends into more specialised capability — retrieval-augmented generation, structured data extraction and multi-step orchestration — always in service of the workflow, never as a feature list.
Have a workflow AI could transform?
Let's assess it honestly — including where AI isn't the answer.