AI Automation
Most failed AI projects are failures of understanding, not of technology. So that's where it starts.
How a business runs is rarely how the org chart says it runs. I map where the time and the handoffs actually go before proposing anything. You cannot automate a process you haven't understood.
Intake, filed and routed
Follow-up, sent on time
Billing, reconciled
Systems I've built against, in production.
I have always needed to know how a thing works. Not what it does — how it works. That instinct is most of my career.
I built a mortgage business and scaled it past seven figures. I was automating my own operations long before I called it that, and later doing it for other people through Kinetic Conversions. What I kept noticing was how much of the result came from the efficiency work rather than from working harder: a small, bootstrapped team could carry volume that should have required a much larger one.
In 2021 I started watching AI move quickly and turned deliberately toward business process automation — not the buzzword, the actual discipline of understanding a workflow well enough to rebuild it. I began with billing departments at healthcare clinics, where the gap between what people were doing by hand and what a system could do was impossible to unsee.
Then 2022 arrived. The housing market shifted, and it became clear it was not a blip — it was foundational. GPT-3 landed around the same time. Both of those things pointed the same direction, so I went at it full force: helping businesses find where the work is actually going, and getting it back.
AI is not an employee replacement. It is a business force multiplier. The companies that get value from it are the ones that understood their own operations first.
If the answer you need isn't here, ask me directly.
No, and I'd be sceptical of anyone selling you that. AI is a force multiplier, not an employee replacement. The businesses that get real value from it are the ones that understood their own operations first — the technology amplifies whatever process you already have, including a bad one.
n8n for orchestration, Claude and OpenAI models for the reasoning layer, and whatever systems the business already lives in — EHRs, practice management, CRMs, Microsoft and Google's stacks. The goal is to fit the tools to the operation, not to move the operation onto a tool.
In the work you do hundreds of times a month without thinking about it. Billing, intake, routing, follow-up, reconciliation, reporting. It rarely pays off in the interesting, judgement-heavy work people assume it will — that's where you want your humans.
No. Most of the leverage I've seen is at mid-size operators — big enough that the repetitive work is genuinely expensive, small enough to change how they work without a committee.
By understanding how the business actually runs, which is usually not how the org chart says it runs. I map where the time and the handoffs are going before proposing anything. You cannot automate a process you haven't understood, and most failed AI projects are failures of that step, not of the technology.
Yes. Reach out through the form and pick 'Speaking' — it routes straight to me.
Four questions, about a minute. It comes straight to me — no CRM sequence, no drip campaign, no one else reading it.
Step 1 of 4. What brings you here?