The same discipline that makes a great cocktail makes a great system.

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 realized the PhD wasn't the path I wanted to keep pursuing. 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'm developing Reduct's AI practice around practical knowledge and retrieval systems: tools that help people work with their own information more effectively. The focus is on purpose-built systems that are useful, understandable, and grounded in the information they're given. The work grows out of what I've learned building and using my own AI systems, particularly MarsalaCC.

MarsalaCC is where I test those ideas first: an AI platform of interconnected systems that I built and use every day. It coordinates multiple agents, maintains its own living documentation, and serves as my proving ground for persistent context, knowledge retrieval, and AI-assisted workflows.