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.
20+
Years operating
3
Industries
6×
Growth managed
$50
Starting investment → multi-million
See the systems behind the numbers:
Five things I'm willing to lose work over.
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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.
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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.
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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.
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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.
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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.
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.
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