The short version: ten years turning messy customer workflows into products people actually adopt, and lately working nearly every seat in the business — product, solutions, support engineering, partnerships, and the commercial model. Equally comfortable writing the spec on day one and owning the P&L on day 365. I've lived inside these integrations, not managed them from a slide.
→ These days I run it AI-augmented — agentic tools let me deliver the daily impact of a small team as a single operator.
The headline numbers as a route profile — the waypoints where the climb actually gained ground.
Four things I bring to a growth-stage team — the rare combination is the operator who can do all four.
Discovery through roadmap, engineering handoff, launch, and iteration — products that get adopted and paid for, not just shipped.
Pricing, margin, portfolio rationalization, profitability. The GM-track differentiator that's rare in a PM.
HubSpot, NetSuite, ConnectWise, ERP/CRM — messy enterprise workflows turned into integrations that run like a silent helper, not a fire drill.
Daily, advanced use of AI and agentic tooling to expand what one operator can deliver. The 2026 edge.
Two chapters of professional work — the products I built and owned, and how I operate across the business now — plus two I built on my own time.
Chapter I — I built it
Led it 0→1: mothership-to-child-portal sync, lead & workflow distribution, cross-portal reporting — product, pricing, and launch.
Repricing, contract renegotiation, and portfolio cuts that flipped the economics — with a profitable core reinvesting into the next build.
Chapter II — Across the business, AI-augmented
Claude wired across CRM, support, the product database, engineering tickets and comms — I run the whole customer operation from one hub.
A partner-funded SmartRec–HubSpot build, launched with a flagship YMCA and armed for sale — then reset into a bounded, paid operating model.
On my own time
A from-scratch NFL auction-pricing system — public-data ETL, a backtested model, and a Monte-Carlo decision tool. Built solo, with AI, across 40+ iterations.
No full-time hours for a full-time search, so I built a system: city-records screening, our own neighborhood grade, a Chrome extension, and a shared pipeline for a two-city hunt.
Why they're here: the clearest proof of how I think and build — real users, end-to-end data work, honest validation, and shipping discipline — done on my own time, with AI.
Working AI-native isn't just a workday thing; it's how I keep the skills sharp. Here's what I build on my own time.
The fastest way I know to understand a new technology is to build something real with it. So on my own time, with AI as the pair I learn alongside, I've been teaching myself the things that aren't in the job description — statistical modeling, data pipelines, front-end craft.
None of it was assigned. The projects on the right are what I reach for when the workday ends; each one started as a way to push a new skill until it stuck.
A little of what's playing, screening, and hanging on the wall while I work. Guitarist of 20 years, cinephile, sports diehard, mountain-town transplant.
Film history is a lens I think in. Four that never leave the top shelf.
The pennants on the wall — teams I've given too much of my heart to.
What’s running while I work — indie rock, roots country, experimental pop.
Product & operations leadership roles at growth-stage B2B SaaS — PM through Director of Product, and on the operations side, BizOps through GM. If you're building something in that space and want an operator who's lived inside the work, let's talk.