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 was the person who solved the operational problems that came with that scale, working alongside the sales team. Best-in-class service was the wedge. Service is a foundation 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 was the engineer — the person on the ground figuring out how to make the equipment work, how to run the systems, how to solve whatever broke. Farming teaches you, faster than anything else, that the foundation eats the strategy for breakfast. You can have the best plan in the world, and 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 — running ultra-lean on tooling I built 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 foundation underneath. Service systems. Mechanized processes. Clean data and captured knowledge.
That's why I'm not selling AI agents. I'm selling the foundation that has to come first. It's the work I've done in three industries already. It's the work I do every day inside my own company. And it's the work I'll do inside yours.
Five things I'm willing to lose work over.
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01
Foundation before roof.
You can't deploy agents on a swamp. Every engagement I take starts with capturing knowledge and cleaning data — because the AI future you're trying to reach 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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