Skip to content
Operating Automation
01 — About

I came to AI consulting from a strange direction.

Two decades of operating before I called myself a consultant. Three industries. The same lesson learned in each.

Out of college I went to work at a kitchen-oil recycling company. I led operations and ran hands-on service through a 6× growth spurt — 800 square feet and one truck to 6,000 square feet and four trucks. I built the physical and mechanical systems that made that growth possible, and I was the person who solved the operational problems that came with that scale. Service quality was the wedge — and service is an operations problem, not a marketing problem.

Then I farmed. Built one of the largest certified-organic operations by acreage in my county in under five years. Highly mechanized. I wasn't the engineer — I was the farmer. I designed everything from planting seeds to harvesting to selling and marketing to record-keeping. Farming teaches you, faster than anything else, that no plan survives bad inputs. You can have the best strategy in the world — it doesn't matter if the soil isn't ready.

Then I co-founded the manufacturing company I run now. A $50 starting investment. Today: a multi-million-dollar business with elite operating leverage — small team, high revenue per head. I designed all the mechanical and physical machines, handled every major equipment purchase, and built the entire tech stack myself — Apps Script automations, integrations, a custom CRM, a knowledge system. Most of it was built because the off-the-shelf option didn't fit, was too expensive, or required us to remold our operation around someone else's data model.

Across all three: the hard part wasn't the technology. The hard part was the work underneath — service systems, mechanized processes, clean data, captured knowledge.

That's why I'm not selling AI agents. I'm selling the capture-and-organize work that has to come first. I've done it in three industries already. I do it every day inside my own company. And I'll do it inside yours.

Right now I'm building a content engine inside my own company — turning operator knowledge into articles that actually rank. Making what lives in one person's head findable by everyone. It's the same kind of work I do for clients.


02 — By the numbers

20+

Years operating

3

Industries

Growth managed

$50

Starting investment → multi-million


03 — What I believe

Five things I'm willing to lose work over.

  1. 01

    Capture before you automate.

    You can't deploy agents on knowledge that isn't written down. Every engagement I take starts with getting what's in your head into a system your team can actually reach — because everything you want AI to do depends on that work.

  2. 02

    MVP, then peel back.

    I've seen too many over-architected systems collapse under their own weight. We ship the simplest version that works, then add layers only when reality demands them.

  3. 03

    Build before buy when the gap is small.

    I once nearly bought an enterprise CPQ for my own company. Then I built what we actually needed in a few weeks. I'll make the same call inside yours.

  4. 04

    Human-in-the-loop on what matters.

    Managers curate. The AI doesn't go autonomous on customer-facing work or institutional knowledge until a human has signed off. The systems I build assume someone is still watching.

  5. 05

    No retainer without an outcome.

    Every engagement ties to something measurable. If we can't define what success looks like, we shouldn't be working together yet.

04

Want to see if there's a fit?

A 30-minute call costs you nothing. The worst-case outcome is you leave with a clearer sense of what to do next, with or without me.

Book a call →