AI Product Engineering
From AI experimentation to reliable production.
I help turn AI from scattered experimentation into reliable product capabilities and measurable improvements in delivery.
Services
How we can work together.
Consultation
Short engagements to help your team use AI better: workflow design, tooling choices, eval strategy, and honest assessment of what's worth building vs. what's theater.
Project with deliverables
Hands-on engagement to get an AI product or feature production-ready: architecture, implementation partnership, evals, and operational hardening so it holds up with real users.
About & now
A practitioner's focus.
The biggest problem with AI isn't what it seems on the surface. AI makes developers faster — but many engineering teams aren't actually shipping faster. Prototypes for AI-powered features appear quickly but struggle to evolve into reliable production systems, and remain fragile once real users start interacting with them.
The gap between AI experimentation and real operational impact is much larger than most organizations expect. That's where I focus my work — changing how engineering teams think about features from idea through to production, and building eval frameworks that ensure AI behaves as expected in production.
Writing
- The eval harness is the featureIf your eval suite doesn't run on every PR, it isn't part of your product. A practical pattern for golden sets that survive contact with real users.
- Why your AI prototype fell apart in week threeDemos are easy; production is a different sport. A short field guide to the failure modes that show up once real users start poking at an LLM feature.
- Shipping faster, not just typing fasterAI makes individual developers more productive, yet team throughput barely moves. The bottleneck is rarely the code — it's the path from idea to prod.
- Golden sets that don't rotA golden set is a promise to your future self. How to keep eval fixtures honest as the model, the prompts, and the product all drift over time.
- Observability for things that talk backLLM features need more than logs. A small set of signals that tell you, in production, whether the model is still behaving the way you intended.
- From demo to decision: scoping an AI featureThe first conversation about an AI feature decides whether it ships. A worksheet for turning a fuzzy idea into something a team can actually build and measure.
Get in touch
Let's talk.
If you're shipping an AI feature that needs to hold up in production, I'd like to hear about it.