AI is changing how software is built. But adding AI to a product because it’s popular rarely creates value. The real question is where AI genuinely helps — and where careful engineering matters more.
I help businesses put AI to work inside the products and workflows they already use: assistants trained on your own content, document analysis, and automation that takes repetitive work off your team’s plate. The goal is always practical — reduce manual effort, improve decisions, and keep the results reliable.
Where AI adds real value
- Knowledge assistants trained on your documentation and policies
- Customer support that answers common questions and escalates the rest
- Document analysis — extracting structured data from contracts, invoices and reports
- Intelligent search using natural language
- Workflow automation for repetitive administrative tasks
Engineering, not demos
Anyone can build a demo with AI. Production systems need more: context management, retrieval, data privacy, model selection, caching, monitoring, evaluation and sensible fallback behaviour. I build AI features that hold up when real users arrive — and I’m honest about where a traditional solution would be more reliable.
My experience
I’ve spent the last couple of years using modern language models daily in real development work, and building with approaches like retrieval-augmented generation, function calling and the Model Context Protocol. That hands-on use is what lets me tell you where AI helps and where it gets in the way.
[NEEDS: a shipped client AI feature to reference here, if available]
Thinking about adding AI to your product? Get in touch.