I build AI-powered tools and full-stack systems from architecture to deployment, and I build them to last. I spent years in quality engineering before I started building products, and that background shows in everything I ship: real test coverage, clean interfaces, and error handling that accounts for the ways things actually break. Every project started with a real problem I wanted to solve.
Land acquisition intelligence platform. Conversational AI search, 3D LiDAR visualization, ML owner classification, and deep parcel analytics across western North Carolina.
Conversational codebase assistant for Salesforce projects. Ask questions in plain English, get grounded answers with call graph context. Zero external dependencies.
Deterministic-first codebase analysis across six dimensions: architecture, security, AI maturity, docs, portfolio, diagrams. Works without API keys; LLM validation is opt-in.
A Lightning Web Component that runs hybrid semantic + keyword search over Salesforce Knowledge with LLM-synthesized cited answers, at $0.0004-$0.0009 per search instead of Agentforce's $0.10/action floor.
Some of this goes back to where the instincts came from, before any of it was code. Some works out how I earn the right to trust what a dataset or a model is telling me. The rest is field notes from the work itself. Different starting points, one throughline: learning to read a system well enough to know when to trust it. Some ideas run long enough to need more than one part.
All writing →Building something, hiring, or just want to compare notes on a problem? I read every message and reply to the ones worth replying to.