From pioneer-era modeling to multimodal AI.
The last mile is the only one that matters.
I started building predictive models when the industry was still figuring out whether data could drive decisions at all. The tools were primitive. The frameworks had to be invented. The work was slow and the validation was manual. That era taught me something most people practicing analytics today have never had to learn: the model is not the answer. The decision the model enables is the answer.
Thirty years later, the compute has changed beyond recognition. The principles have not. Prediction, validation, framework design, and the discipline of testing your work against reality — those fundamentals are as true in a multimodal AI pipeline as they were in a regression on punch card data.
What I bring to any engagement is the full arc: the rigor of an era when analytical frameworks had to be built from first principles, the fluency of someone who has shipped a production AI platform from concept to deployment, and the judgment that comes from decades of sitting across from C-suite executives who needed to act on what the data was telling them.
I take on a small number of engagements — short-scoped projects and selective advisory work — where the analytical challenge is genuinely interesting and the client is serious about using the output to make better decisions.
Co-founded and served as Chief Strategy Officer for a multimodal AI SaaS platform for video brand safety classification and contextual ad placement. Conceived the product thesis, designed the Core Taxonomy (92 emotions × 189 contexts × 28 media genres), architected the IE Score methodology, and participated in full-stack production deployment — V1 through V8 on Railway, Celery, PostgreSQL, and Gemini multimodal AI.
The IE Score Engine applies behavioral science principles to a weighted scoring model (Emotion ×2.5, Context ×1.75, Genre ×1.0) that enables premium inventory identification at scale. GARM/IAB brand safety standards are integrated across 80+ categories. The system is live and production-deployed.
This is the most complete proof point of what it means to translate a behavioral science framework into a commercially deployable AI product — from taxonomy whiteboard to Railway container to revenue conversation.
A validated decision framework for predicting NFL game outcomes — not a prediction tool, but a systematic methodology. Includes the Fragility Index (defensive depth, OL sustainability, coaching conservatism, explosive play vulnerability), the Reversibility Index, and a Red Flag Analysis with quantitative score adjustments.
Validated retrospectively with an 86% improvement metric. The model illustrates how analytical frameworks built for one domain apply universally: the same framework thinking that predicts fourth-quarter lead collapses applies to organizational risk and market positioning.
Request methodology overview →Designed and built an AI-powered analytics reporting application integrating large language model APIs with a custom data pipeline. Demonstrates the implementation side of AI strategy: not just the recommendation, but the working system.
Built independently, from architecture to deployment. The application automates analytical narrative generation from structured data outputs — turning model results into executive-ready reports without manual interpretation.
Learn more →The current AI conversation treats 2024 as year zero. It is not. The principles that make an AI system useful — rigorous problem framing, validation against reality, the discipline of knowing what your model cannot tell you — have been true since the first regression ran on a mainframe. The delivery mechanism changed. The hard part did not.
This is a practitioner's perspective on what has actually changed, what hasn't, and what most organizations are still getting wrong.
Read ArticleAdvertising has always known that emotion drives purchasing behavior. The industry has spent decades trying to engineer around the fact that emotion was too expensive and too variable to measure at scale. Contextual targeting was a proxy. Brand safety scoring is a proxy.
Multimodal AI makes direct measurement possible for the first time. This is what that actually means commercially — and why most of the current "AI in advertising" conversation is focused on the wrong problem.
Publishing SoonFor most of my career, work came to me by reputation. When I decided to wind down my active practice, companies still called — explained their challenge — and then told me they hoped I found it interesting. That dynamic has shaped how I work.
I am not available for every engagement. I am available for problems where the analytical challenge is real, the organization is serious about acting on the output, and the work has a defined beginning and end.
The right engagement is one where, six weeks in, you have a decision framework you didn't have before — one you can explain, defend, and build on. Not a dashboard. Not a report. A framework.
The Calendly link below takes fifteen seconds. It asks for your name, your company, and the challenge you're trying to solve. That's the vetting step — for both of us.
If it looks like a fit, we schedule thirty minutes. If thirty minutes confirms the fit, we discuss an engagement. If not, you'll leave with a clearer picture of the problem than you came in with.
Note: I do not respond to vendor outreach, software sales, or SEO inquiries through this form.