About
Strategy that ships.
There is a gap in enterprise AI between the people who write the strategy and the people who build the systems. I have spent my career on both sides of it. That is the only place real change actually happens.
The arc
I am a degreed engineer who started out in technical recruiting in 1999. Everyone said computers could not help you recruit. The internet was quietly proving them wrong. That was my first real lesson in what happens when the received wisdom runs into a paradigm shift.
From there I moved into sales engineering and spent two decades selling complex technology. I built a digital agency, ran it for fourteen years, and sold it in 2017 so I could get inside the enterprise and see these problems from the other side of the table.
Since then: an international SaaS company, then a top-20 global law firm doing $2.4 billion in revenue, where I was Global Director of Digital Marketing. Today I am inside a Fortune 100, building real AI applications from the ground up. Production systems with learning loops that prove the compounding idea week after week.
Outside the day job, I am building on my own. Lyrn is the AI-native ecosystem I am putting together independently — an operating environment and a persistent memory layer — and Digivisory is a live experiment in how AI answer engines discover and cite content. It all lives in what I am building.
The through-line is not a job title. It is a way of working: understand the economics of the problem, decide the strategy, decompose it, architect the technology, and build enough of it myself to know whether the strategy survives contact with reality. I am an executive who builds — not a builder trying to become an executive. Building is part of the thinking, not a step that happens after it. Most people pick one altitude or the other. I have always worked at both. If you want to work together, here is how engagements run.
Philosophy
The idea I keep coming back to is Organizational Intelligence Infrastructure. Buying tools is not transformation. The real work is building the connective tissue that lets a company think, remember, and act as one.
So every engagement starts with the same question: can your organization actually learn from its own operations? If it cannot remember what it does, no amount of tooling is going to add up to much.
Getting from experiments to trusted autonomy follows a sequence I have watched hold up every time: Deliver → Measure → Learn → Remember → Expand. Skip a step and the whole thing falls over.
First-principles thinking, applied without exception. Break the problem into its real parts. Put the people with genuine authority over each part in the room, and no one else. Then build, measure, learn, remember, and expand.
Proof of Work
What I have built
None of that is theory. Everything here is running inside a Fortune 100 — not demos or proofs of concept, but production systems doing real work.
ABM Engine
An account-based marketing system that runs about 90% of the workflow on its own — research, discovery, content at scale, and outreach. It learns as it goes.
Intelligent Analysis
A data analysis app where people ask questions in plain language, get recommendations instead of dashboards, and the system remembers what it figures out.
Content Engine
An enterprise copywriting system grounded in the company’s own context and voice. Consistent and on-brand, without generic-prompt output.
Data Integrity
An app that keeps CRM and operational data clean and to standard without anyone having to babysit it.
In progress
All apps are interconnected via API — they speak to one another and share context across the system.
How Engagements Work
Deliberate, premium, built to matter.
Seeing what the approach produces is one thing. Here is how an engagement actually runs.
Engagements are advisory — focused commitments long enough to move an organization onto a compounding path, not hourly billing that rewards slow progress. The work centers on outcomes: where your intelligence infrastructure should go, what to build first, and how to sequence it so each step makes the next one cheaper.
That means sitting inside the real decision-making — understanding the business model, the data, the org dynamics — and mapping a path that accounts for all of it. Not a slide deck. A working blueprint you can actually execute against, with clear priorities and a logic that holds up under pressure.
The goal is to get your team onto a compounding trajectory as fast as possible — where every investment in AI makes the next one more valuable. Once you are on that path, the gap between you and organizations still restarting from scratch widens on its own.