AI strategy + implementation
From “we should use AI” to a system worth shipping.
I help teams decide where AI can remove real friction, then carry the idea through architecture, implementation, evaluation, and rollout. That includes the product behavior around the model—not just the API call—so uncertainty, latency, cost, privacy, and failure all have an intentional path.
Explore AI implementation →AI inside the product
01Use-case selection, model and provider tradeoffs, structured outputs, retrieval or tool use, human review, fallbacks, and production integration.
AI across the workflow
02Coding-agent systems that speed up research, implementation, refactoring, and testing while preserving review, verification, and code ownership.
- Find the fit
- Map the workflow, data, risk, and success criteria before choosing a model or vendor.
- Prototype the risk
- Test the uncertain part with representative inputs before building the complete feature around it.
- Integrate the system
- Connect model APIs, product state, permissions, observability, fallbacks, and human decisions.
- Evaluate the edges
- Measure quality, latency, cost, and failure cases with a repeatable evaluation and rollout plan.