A local AI visibility scan tests how your business actually appears when AI systems answer the kinds of queries your customers ask. Understanding what a scan measures — and how to set one up well — determines whether the results are useful or misleading.
A local scan is not a ranking check. It does not tell you where you appear in a sorted list or how your position compares to last month. It tests something more fundamental: when AI systems receive queries that match your business type and location, do they recommend you?
The scan works by running a structured set of prompts against AI systems — prompts that represent the way real users ask for recommendations in your category and area. For each prompt, the scan records whether your business appears in the AI response, what competitors appear, and how consistently your business is recommended across the range of intent types you should be capturing. The output is a recommendation share picture: not rank, but inclusion — and the gaps between where you appear and where you should.
The quality of a scan depends directly on the quality of its inputs. A scan run with a single generic prompt produces a single data point — not a picture of your visibility. To get useful results, you need inputs that reflect the range of queries your actual customers use.
The scan needs to know exactly what to look for in AI responses. Provide your business name as it appears on your GBP listing and in your most common external references. If you operate under a name that differs from your legal entity or parent brand, use the name customers know you by — that is the name AI systems are most likely to include in a recommendation.
Location specificity matters too. A city-level scan gives you a broad picture. A neighborhood- or district-level scan is more useful for businesses where proximity signals are strong — and more honest about the geographic footprint you realistically compete in.
A useful prompt set covers multiple intent types in your category, not just the most obvious one. Think about the range of occasions and use cases your customers come to you for. A restaurant should test prompts that cover business dining, celebration occasions, family visits, and quick meals separately — because these are different intent filters, and a business that appears for one may not appear for others.
Include prompts with explicit location markers ("near [neighborhood]," "in [city]") alongside prompts that rely on the AI system's location inference. This reveals whether your visibility depends on explicit geographic matching or holds up when location is implicit — a meaningful distinction for businesses whose customers arrive from different contexts.
A scan that only tests whether you appear is half the picture. Knowing which competitors are consistently appearing in your place — and for which prompts — is what makes the results actionable. Provide a list of the competitors you consider your true AI competitors: businesses serving the same customer, in the same area, for the same use cases. For more on identifying who these competitors actually are, see Identify Your True AI Competitors.
The first scan you run is a baseline, not a verdict. It establishes where your recommendation share stands before any optimization work — and identifies which prompt types and intent categories represent your largest gaps. It should not trigger an immediate reaction to any single result.
Run the scan across at least ten to fifteen prompts to get a pattern, not a single data point. Look for the prompts where you appear consistently — these are your signal strengths. Look for the prompts where you never appear, despite being a legitimate recommendation — these are your gap priorities. And note which competitors appear across both categories: they are providing a signal you are not.
The output of an initial scan is a prioritized gap list: the intent types and query contexts where your visibility is weakest relative to competitors and relative to the quality of your actual business. That list is the input to your optimization work — not a cause for alarm, but a specific starting point.
AI visibility is not static. It shifts as your review profile grows, as competitors update their signals, as AI systems are updated, and as your own GBP and content evolve. A scan run once is a snapshot. A scan run consistently over time is a trend line — and the trend line is what lets you evaluate whether your optimization work is having the intended effect.
A monthly cadence is a reasonable baseline for most local businesses. After a significant change — a content update, a review push, a GBP revision — running a scan within two to four weeks gives you a signal check on whether the change moved the needle. The goal is not to scan obsessively but to have enough data points to see movement and connect it to causes.
For businesses with multiple locations, each location needs its own scan. Aggregate results across locations can hide strong performance in one area masking weak performance in another — and the optimization work for each location is distinct.
A scan answers the question most local businesses cannot currently answer: which AI systems recommend me, for which queries, and how does that compare to my competitors? That visibility gap — knowing where you stand — is the starting point for everything else.
Local Visibility
AI recommendations change by location, intent, and competition. Most businesses do not know when — or why — they are being excluded.
Run a Local Scan to see how your business actually appears across AI systems.
Run a Local Scan