How NUDGE Measures AI Visibility for a Local Business

The question a local business is really asking

When an owner in Boca Raton asks whether the business is showing up in AI, they usually mean one thing: when a customer asks ChatGPT, Gemini, Perplexity or Google’s AI features who to call, does the business get named? That is a fair question and a hard one to answer honestly, because AI-generated answers vary by platform, model, query wording, time, location and personalization. Two people asking the same question on the same afternoon can get different lists.

So NUDGE does not answer it with a single number. The answer is two separate measurements, kept deliberately apart, plus a plain statement of what neither one can claim.

Measurement one: the AI Visibility Readiness Score

The readiness score looks at the things you control: your website, your local and entity signals, your service and location clarity, the evidence and authority you make available, and whether your content actually answers buyer questions. It is scored out of 100 across five categories:

1. Technical Discoverability (20 points), 9 checks, for example “https available” and “homepage is publicly fetchable”.
2. Entity + Local Clarity (20 points), 8 checks, for example “business name is clear” and “primary city/service area is visible”.
3. Service + Location Relevance (20 points), 7 checks, for example “primary service is clearly explained” and “dedicated service content exists”.
4. Evidence + Authority Readiness (20 points), 8 checks, for example “real proof elements are present” and “case studies/examples are available when appropriate”.
5. Answer-Ready Content (20 points), 8 checks, for example “key services/concepts are defined directly” and “process/how-it-works is clear”.

Every check is explained in the report, and every check lands in one of three states. Pass means the check verified the signal is there. Gap means the check verified it is missing. Not verified means it could not read enough to judge, because a page timed out, a profile could not be associated, or bot protection blocked the request.

A check that could not be verified earns no credit toward the score. That is a deliberate choice, and it is the part of this methodology I would defend hardest. The tempting alternative is to quietly give unverified signals the benefit of the doubt, which produces a friendlier number and a worse assessment: it flatters exactly the sites the check understood least. Instead the report carries a second figure, Assessment Coverage, which says how much of the intended assessment was actually verified. When coverage drops below 80 percent there is no overall score at all: the report becomes a partial assessment that states what could not be seen.

The score is an internal diagnostic methodology. It is not a ranking-factor score from Google, OpenAI, Gemini, Perplexity or any other platform, and every result says so.

Why start here? Because the official guidance from the platforms themselves points at fundamentals. Google’s documentation on AI features says the same SEO practices that help in traditional Search remain relevant to AI Overviews and AI Mode, that there are no special requirements, that important content should be crawlable and textual, and that structured data should match visible content. OpenAI’s publisher guidance says public websites can appear in ChatGPT search and that blocking its search crawler removes that possibility. None of that is a secret; all of it is measurable on your own site today.

Measurement two: the Observed AI Recommendation Sample

The second measurement is what AI-assisted platforms actually said. When a sampling provider is enabled, a fixed set of commercial queries is run, the questions a real buyer in your category and area would ask. Each run records the exact query text, the platform and model where known, the timestamp, whether your brand was mentioned, which competitors were mentioned, and which sources or citations supported the answer where they are available.

It is called a sample on purpose. An API query does not exactly reproduce what every consumer sees, and a result on Tuesday is not a promise about Thursday. What a sample does give you is evidence: which businesses recur, which websites, reviews, directories and publications the answers lean on, and whether the facts used to describe your company are accurate. That is the raw material for deciding what to fix.

Why the two stay apart

Because they answer different questions and fail in different ways. Readiness can be high while observed visibility is low, usually a sign that third-party evidence, not your own site, is the missing piece. Observed visibility can be decent while readiness is weak, usually a sign of reputation carrying a site that is easy to overtake. Blending them into one score would hide exactly the distinction that tells you what to do next.

Tracking change over time

Client work maintains a fixed benchmark prompt set, repeats it on a schedule, records the platform and model each time, compares trends rather than single snapshots, and keeps exploratory prompts separate from the benchmark. Traditional search and local metrics are tracked alongside it, and Google Search Console’s generative-AI performance reporting where it is available, so AI visibility sits next to the numbers the business already trusts.

What none of this claims

• that any score or sample proves your brand is, or is not, recommended in every AI engine;
• that a higher readiness score guarantees inclusion, ranking or recommendation;
• that NUDGE has access to any platform’s ranking system;
• that a tactic is officially endorsed unless the platform’s own documentation says so.

What can be said is narrower and more useful: here is what your site and profiles make available to be retrieved and verified, here is what a fixed set of questions returned on a given date, here is what changed after the work, and here are the sources behind it. The full method, including the official references, is on the methodology page.