Every tool worth using was built by someone who got burned by not having it. That's the short version of this story.
The longer version starts in Houston, Texas, in the early 1980s — and it explains exactly why I believe local businesses have somewhere between 12 and 36 months to get positioned in front of AI assistants before the window closes on a lot of them for good.
I started in business at 21 working in insurance — sold my own agency before I was 23, and was written up as the fastest-growing agency in Houston at the time. What I learned early wasn't insurance. It was process. How to look at something that works, strip it down to its parts, and rebuild it better.
That skill took me into banking systems, then to a federal agency that was liquidating the assets of roughly 900 failed banks. Within two years I'd gone from managing systems at a single bank to carrying joint responsibility for 150 banks at a time. From there I moved into Washington D.C. and spent more than a decade as a business process engineering consultant — the kind of work where large organizations paid serious money to have someone map exactly what they did, cut what didn't need to exist, and redesign everything else. Typical result: 60% reduction in staffing costs, 50% reduction in systems cost, development timelines that shrank from years to months.
Pattern recognition under pressure. That's what the work was.
In the late 1990s a group of investors — including Dave Thomas of Wendy's and a former Kentucky governor — raised over $100 million to build the world's largest college bookstore online. I joined as Chief Technology Officer. We built everything from scratch: data centers, payment processing, warehouse communication, customer service systems, a social platform for college students to connect and trade books. There were no APIs, no plug-and-play infrastructure. Every piece was engineered by hand.
When the CEO needed someone to drive marketing, he turned to me because I understood both sides of the equation. We built outbound outreach systems, seeded what became Amazon's marketplace with our 2.6 million book catalog, and helped launch eBay's Half.com. Hockey-stick growth followed — until it didn't. The company eventually filed for bankruptcy after the incoming Harvard-pedigreed marketing executive redirected the budget toward Rolling Stone ads and cinema spots.
The buyers kept me on. I pulled out the original marketing plan, started executing it with a fraction of the original budget, and took the company from $4 million to $21 million in revenue over 21 months.
Here is what made that possible: we knew how to rank. This was the era of Yahoo, Lycos, and AltaVista — before Google had established dominance. There was no course to take, no authority to consult. The only method we had was to take the top-ranked businesses and reverse engineer them. It's the same approach I'd used on business processes for fifteen years. It translated perfectly.
Later, as the primary internet marketing executive at Colorado's largest direct marketing company, I took sales from $11 million to $66 million in 13 months. I co-founded a performance marketing company called Convertis that hit an $18 million run rate in its first year. I ran my own agency, Ad Leverage, and worked with thousands of small businesses through a company called National Gift.
After all of that, I semi-retired. I opened an outsourcing operation in Peru that grew to 150 employees across six Latin American countries. I spent years traveling — Nome, Alaska to the southern tip of South America — looking for a place where the water was clean enough to drink from the ground, the beef was genuinely grass-fed, and the pace of life didn't grind people down. I found it in Colombia's coffee region, and I've lived there most of the time since. I'm a father and grandfather, and I'll tell you honestly: the coffee here, sitting a few miles from where it was grown, is worth the trip alone.
But I kept watching what was happening to the small businesses I'd worked with over the years. And what I saw concerned me enough to come back.
AI assistants — ChatGPT, Gemini, Perplexity, and the others that now handle roughly 85% of local business queries — are not Google. They don't return ten blue links and let the user decide. They return a short list of businesses they trust, drawn from a very specific set of signals. Most local business owners don't know what those signals are. Most marketing agencies are still debating them.
I wasn't willing to debate. I wanted data.
My team and I pulled in over 35,000 local businesses that AI assistants had already recommended as best-in-area across more than 100 business types. We ran every one of them through our analysis and identified approximately 150 measurable factors that these recommended businesses had in common — factors that one or more of the major AI systems consistently used to validate trust, relevance, and authority.
A few things we confirmed quickly:
That research became the AI Ranking Factors framework. And the framework needed a workbench — a tool that didn't just show a business where it stood, but let an SEO or AEO professional actually dig in and fix what needed fixing, with competitive context built in.
That tool is Viizable.
Viizable is not built for national retailers. It's built for the local plumber, the independent restaurant, the family dentist, the bakery that's been on the same corner for twenty years — and for the agencies and SEO professionals who serve them.
Pull up any local business. See exactly how it scores against the AI Ranking Factors. Then pull up the competitors that are already getting recommended ahead of it and compare them side by side. The gap between where a business is and where it needs to be becomes concrete and actionable — not a theory, not a debate topic.
I've published the framework openly because I'd rather see thousands of small businesses helped than protect a methodology. These businesses are running out of time to adapt, and the marketing industry is still arguing about whether AI recommendations are real yet.
They're real. The race has started. The businesses that move now — with clear data and a workbench to act on it — are the ones that will still have a pipeline of new customers five years from now.
That's why I built this. That's why I came back.