ABOUT
I didn't set out to build an AI video system.
I set out to fix what was broken.
If you've ever felt like video should be working better than it is — you're not wrong.
For most of my career, I worked inside the systems that shaped modern advertising. And eventually, I saw where they stopped making sense.
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Where I Started
I began my advertising career in 1998, after moving from Vancouver to Miami.
I worked inside a large U.S. Hispanic advertising agency, supporting national and enterprise brands like Capital One, T.J. Maxx, Marshalls, and BellSouth. This was traditional, high-level advertising — TV, radio, print — where messaging, strategy, and execution had to work under real pressure.
From there, I moved into corporate campaign work, helping create and supervise advertising for brands like Volkswagen, Porsche, and Wendy's, primarily across Latin America. These were multi-channel campaigns — TV, radio, print, and early digital — built inside strict brand systems with little room for error.
That experience mattered. It taught me how real brands communicate at scale — and what breaks when systems slow down.
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The Problem I Couldn't Ignore
When I eventually started my own company, my work shifted into websites, SEO, and performance-driven digital strategy, and then naturally into video production.
And that's where the frustration became impossible to ignore.
Research — the most important part of any campaign — took days or weeks. Competitive insights were often superficial. Most messaging decisions were based on demographics instead of real behavior. Even when the research existed, synthesizing it into something clear and actionable was another bottleneck.
Production was no better.
Shoots required half days or full days on location. Content had to be refreshed every few months. Editing couldn't begin until everything was filmed. For larger projects, timelines stretched into weeks or months. For e-commerce and UGC, even simple videos could take 2–4 weeks due to creators, shipping, and endless back-and-forth.
The issue wasn't effort or quality.
It was this:
The entire research → production → iteration model was completely misaligned with how fast platforms and markets were moving.
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The Turning Point
About two years ago, I started working seriously with AI.
The real shift happened with our first true AI-driven production, roughly a year and a half ago.
A client — a successful home improvement coach — was hosting a live event in Orlando. He wanted something bold: Walt Disney as the narrator, both visually (cartoon-style) and vocally, integrated into live event sequences.
Under traditional production rules, it would have been dismissed immediately:
- Weeks of illustration and animation
- Nearly impossible voice work
- Tens of thousands of dollars in cost
Instead, we tested what was now possible.
The entire project — from concept to execution — took about five days.
That moment reframed everything.
It wasn't just faster.
It was structurally different.
We weren't accelerating production — we were unlocking scenarios that were previously too expensive, too slow, or simply impossible.
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What Changed After That
The biggest shift wasn't visual. It was research.
With the emergence of advanced language models — ChatGPT, Claude, Perplexity, Grok, and others — research stopped being a bottleneck and became an advantage.
What once took weeks could now be done in hours — and done better.
Instead of surface-level assumptions, we could:
- Analyze competitors at scale
- Extract real messaging patterns
- Understand how audiences actually speak and respond
Most people fail here because they rely on a single tool.
We don't.
We use multiple LLMs together, especially during research, because each model excels at different tasks. From there, we generate multiple scripts, hooks, and bodies, and I apply judgment — selecting, refining, and shaping what aligns best with the client's goal.
AI handles range. Experience handles decisions.
That's the difference.
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Experience Still Matters
Over the past 25+ years, I've created or overseen thousands of videos — conservatively over 5,000 — across every format imaginable:
- Short-form clips
- Commercials
- Campaign videos
- Long-form presentations
- Internal, sales, and training content
That volume matters. Pattern recognition only comes from repetition.
And while my strength has always been strategy and campaign architecture, execution matters just as much.
That's why I've built a team specifically for this environment.
AI editing is not traditional editing. It requires people who understand prompting, context, variation, and big-picture strategy — not task-based technicians.
We don't hire for tools. We hire for thinking.
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Why This Exists
The Research-Driven Creative Engine™ exists because the old model no longer works.
AI didn't give me taste. It didn't give me strategy. It didn't give me judgment.
It gave me leverage.
And when leverage is applied correctly, it removes friction, reduces risk, and opens creative possibilities that were once reserved for the biggest budgets.
This isn't about replacing production. It's about building a system that finally matches how modern platforms reward visibility, speed, and iteration.