You would expect a company built entirely around AI visibility to let AI do the scoring. We deliberately don't — because AI hallucinates, and a score you can't trust is worse than no score at all.
It's a fair question, and technical buyers ask it first: your whole product is about AI — so why isn't AI doing the work? Why not point ChatGPT at a website and ask "how likely is this business to get recommended?" It would be faster to build. The honest answer is that it falls apart on the first principle of building anything you can trust: the measurement has to be repeatable, and it has to be right.
Large language models hallucinate. That isn't an insult — it's how they work. An AI answers from memory and pattern, not from checking the facts in front of it. Ask it the same question twice and you can get two different scores. Ask it about a specific business and it will state things about that business that simply aren't true — confidently, in complete sentences, with no flag that it's guessing. You cannot build a measurement system on a tool that does that.
When an AI "hallucinates," it doesn't sound unsure. It doesn't hedge. It declares a fact — a phone number, a credential, a review count, a conclusion — that is simply wrong, in the exact same confident voice it uses for everything it gets right.
The model isn't lying. It has no concept of true or false. It is predicting the most plausible-sounding next words from patterns it has seen before — without going and checking the actual website, the actual reviews, the actual schema, the actual business. It is working from a memory of what answers like this usually look like, not from the facts.
For a casual chat answer, that's an annoyance. For a score your business decisions depend on, it is disqualifying. If the number can't be trusted, the number is worthless — and worse than worthless, because you would act on it.
There is no algorithm document for AI search. You can't buy a Google-style index, and no AI-SEO standard existed anywhere — so we built the only one that does. Instead of asking an AI what matters, we looked at what AI actually rewards.
We scanned more than 30,000 businesses that AI systems already recommend, and worked backward to the facts: what these businesses have in common, and what an AI needs to see before it will put a business on its short list. That is the difference between opinion and evidence — we didn't ask for a theory, we observed 30,000 real outcomes.
That work produced roughly 140 criteria common to every major AI system. Each one is weighted — not equally, but by how much it genuinely moves a recommendation. And because the AI engines keep shifting, we re-weight the criteria on a regular schedule, so your score always reflects what matters now, not what mattered last year.
Your score is then computed — actually computed, with arithmetic — from how your business measures against those 140 criteria. The same business scanned twice gets the same score. Two different people scanning it get the same score. That is what repeatable means, and it is the one thing an AI's opinion can never give you.
When we hand you a score and a list of what to fix, you are not getting a chatbot's hunch. You are getting concrete, measured evidence: this is where you stand, this is what's missing, this is what the businesses already winning have in place that you don't.
It also means progress is real. Fix something, re-scan, and the number moves for a reason you can point to — not because the model happened to phrase it differently this time. No conjecture. No speculation. No guesswork.
The irony isn't lost on us: a company built around AI visibility that deliberately keeps AI out of its own scoring. But that's exactly the point. AI is brilliant at sounding right. Measuring whether your business will be recommended is a job that has to be right. So we did it the harder way — 30,000 businesses, ~140 weighted criteria, real math — so the number we hand you is evidence you can build on.
The Viizable Score measures your business against roughly 140 trust signals — across 12 categories — that AI assistants weigh when deciding which businesses to recommend. The model is built by reverse-engineering what the AI engines reward, calibrated against nearly 30,000 business scans, and re-weighted on a regular schedule as the AI platforms' requirements shift.
Your score is a baseline — one number that tells you exactly where you stand today. Make improvements, re-scan, and watch the number move. It turns "I think our website is fine" into "we went from 41 to 68, and here's the next step."
Run your business against the ~140 criteria AI weighs before it recommends. One number, computed from real data, with the exact list of what to fix.
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