Founder and Principal, Grayduck Partners
I help teams build decision-grade AI: systems that know where they can be trusted to act and where they can't, so the people who rely on them can decide with confidence.
My career started as a military weather forecaster and atmospheric scientist. It is work where you develop a situational relationship with uncertainty, or missions fail. Some calls you make with confidence. Others you hedge, watch closely, and lean on human expertise. I've spent the years since building production ML and AI systems that bring that same discipline to consequential decisions.
In proprietary trading, I built machine learning and rigorous applied statistics to understand when the firm should deploy capital and when it should hold back. Resource allocation under uncertainty, with real money on the line.
At an AI-enabled SaaS company, I saw the cost of getting it wrong. The product shipped numbers with no read on how sure they were. When they were wrong, and nothing had flagged that they might be, customers churned. Uncharacterized confidence is not a technical nicety. It is a retention problem.
Most people who can build production AI can't design the study that proves it works. Most people who can design that study can't build. I do both. Every recommendation I make is something I could sit down and build myself. The architecture and the evidence, together, is what it takes to deploy AI when results matter.
If your team is putting AI in front of decisions where being wrong is expensive, or a capable tool is sitting unused because no one is sure when to trust it, that is what I do. A 30-minute call, no pitch.
info@grayduckpartners.com