I spent 15 years attaching dollars to outcomes. Now I attach them to AI.
Before I built AI systems, I spent fifteen-plus years in marketing and data analytics with more than $50M in managed ad spend — a career where every decision was tracked to the dollar and 'it seems to be working' got you laughed out of the room. I brought that discipline to enterprise AI, where it turns out to be rare: every agent, workflow, and system I deploy gets a KPI from day one and a scoreboard everyone can see.
Marketing analytics is an unforgiving teacher. Budgets are real, attribution is contested, and every quarter someone asks what they got for the money — with the numbers on the table. Spend fifteen years in that room and you develop a permanent allergy to unmeasured claims. When I moved into AI consulting, I found an industry running on exactly the kind of claims I'd been trained to distrust: pilots without KPIs, demos without error rates, "transformation" without a baseline.
So I run my practice the way I ran campaigns. Before anything gets built, we agree on the number it has to move — hours saved, cost per task, error rate, revenue impact. During the build, weekly demos against real data. After deployment, a scoreboard that stays public. If the value doesn't clear the cost, you hear it from me first. That's the whole philosophy, and it filters my clients as much as it defines my work: people who want theater hire someone else.
What I actually build
- Agentic AI and workflows — production agents and multi-system orchestration designed around how your organization actually operates.
- RAG and knowledge systems — your documents and data turned into grounded, source-cited answers, with permissions intact.
- Infrastructure and governance — on-prem AI for organizations whose data can't leave, and the security, evals, and compliance work that keeps it defensible.
I work remote-first with enterprises across the United States and internationally. Engagements are fixed-scope with weekly demos — no discovery phases that bill like builds, no decks pretending to be deliverables.
I don't build science projects. I build systems that pay for themselves — and I show you the math.
How I work.
Questions, answered.
What is your background?
Twenty years across strategy and execution, including fifteen-plus in marketing and data analytics with over $50M in managed ad spend — a discipline where every dollar is tracked and argued about. I moved that measurement culture into enterprise AI consulting: agents, RAG, knowledge systems, and the governance around them.
Do you work solo or with a team?
You work with me directly — the person who scopes the engagement builds the system. For larger builds I bring in specialists under my direction, but there is no bait-and-switch to a delivery bench, and accountability never leaves one person.
What does an engagement typically look like?
Fixed scope, fixed price, weekly demos. Assessments run two to four weeks; builds typically run six to twelve. Every engagement — strategy or build — ends with a working system or decision-ready document, plus the KPI baseline to judge it against.
Where do you work?
Remote-first across the United States and internationally. Most enterprise engagements are fully remote, with on-site sessions scheduled where they genuinely help — kickoffs, workshops, and launch milestones.
The first conversation is free math.
Bring the problem. I'll tell you what moving the number is worth, what it would cost, and whether I'd build it — even when the answer is no.