About

I build AI applications on a foundation of data, cloud, and production systems.

My career has taken me from GIS and network engineering into business intelligence management and principal data and cloud engineering. I have owned systems through design, deployment, and on-call support. That experience shapes how I build AI applications today.

My projects start with work I know directly: training for races, researching job opportunities, running a business, and building learning activities my son wants to use. I design the data, write the services, and work through the failures that appear with repeated use.

01

Keep facts outside the model.

Deterministic stores and services retain canonical state, identity, policy, calculations, and lifecycle status.

02

Keep uncertainty visible.

Evidence labels, output validators, fallbacks, and review gates show what is verified, inferred, blocked, or still unknown.

03

Design for operation from the start.

Retries, stale data, blocked sources, mobile constraints, and recovery paths influence service boundaries, storage choices, and release controls.

Container infrastructure supports several projects, including Home Automation. A full-stack Home Automation case study is coming soon and will document the service boundary, integrations, privacy controls, and recovery design.