Our Story

viizable didn’t start as a software idea. It started as a problem worth coming out of retirement for.

The moment

The day it became obvious where this was going

A little over a year ago I was semi-retired, living in South America, and watching what AI was about to do to local businesses. It was stepping in front of their prospect flow — quietly intercepting the customers a business had always counted on and redirecting them to whoever the AI decided was the better fit.

For most owners it was invisible. The phone just rang a little less. The new patients, the new clients, the walk-ins — a slice of them were being routed somewhere else before the business ever knew there was a race.

Why us

I had seen this happen before — and this time Google was the underdog

This is the same fight we had more than twenty years ago — back when Yahoo was king of search and AltaVista was right behind it, and then Google arrived. There was no certification. No classes. No school. No experts who’d been doing it for twenty years that you could ask. We had to reverse-engineer the top businesses to find out why they were ranking.

My background made that a natural next step. I spent fifteen years in business process reengineering for some of the largest corporate entities in the United States — taking complex, opaque systems apart to find the few things that actually drive the outcome.

And I know why many in the marketing and SEO industry in particular are struggling to get their clients ranked. It’s almost like coming from a classroom with a super-strict teacher who, for many years, gave good grades for following the rules — then being forced into a different class with a schizophrenic teacher who has six different personalities and six different ways of grading you. It has been a shock to a lot of good people.

Built on evidence, not opinion

AI won’t tell you how it ranks businesses — but it can’t hide the ones it does. We reverse-engineered over 30,000 top-mentioned local businesses to identify over 150 weighted ranking factors.

30,000+
AI-recommended businesses reverse-engineered
100+
industries analyzed across the dataset
~200 → 150
candidate factors narrowed to the ones that matter
0%
AI guesswork in how we score

Getting to the answer wasn’t fast. It took tens of thousands of individual analyses — iteration after iteration, reverse-engineering more than 30,000 different local businesses that AI actively recommends — to surface what they all had in common.

What came out was a list of roughly 200 factors AI appeared to be evaluating. Over time we eliminated about fifty of them that turned out to carry no real weight. What’s left is 150 concrete, system-derived, mathematically calculated factors — things that need to be present on the website, off the website, or through external sources — for a business to get recommended into the list.

And because the AI engines keep shifting, we re-weight those factors on a regular cycle, so you always know which ones are gaining or losing importance.

See it on a real domain in about ten minutes.

Drop in a local business, watch it scan and score against all 150 factors, and walk away with the short list of exactly what to fix.