I spent about a decade behind the bar, which taught me to skillfully
anticipate needs and gaps before they became problems. The pattern
recognition and the work of holding a lot of variables in mind under
pressure show up in how I approach technology and consulting now.
My path wasn't linear. I studied geology at Western Michigan University,
worked in a remote sensing and hydrology lab processing satellite imagery
(including NASA GRACE gravity data used in climate and hydrology research),
presented at the Geological Society of America, then chose mental health
over a PhD. I found my way behind the bar, and it turned out the bar teaches
you something academia doesn't: systems exist to serve people, not the other
way around.
Today I run Reduct, a small AI strategy & ops
consulting firm focused on AI operations for energy and climate organizations.
Most of the work is helping teams build local-first, trustworthy knowledge
systems with LLMs, cutting AI costs, and making tools that work
with people, not instead of them. Reduct also carries a food & beverage
operations track; a decade behind the bar still teaches the hardest
lessons about systems that survive contact with real people.
Alongside Reduct, I build MarsalaCC,
a 25-system personal AI system I use to test my hypotheses about applying
AI more efficiently in practice. It's where the ideas get stress-tested
before they show up in client work.