The Geography of AI Recommendations: Why Location Matters More Than You Think
How AI models interpret location data — and why geographic accuracy is one of the most critical (and overlooked) visibility factors.
The Phoenix Problem
A dental practice owner in Phoenix asked ChatGPT: "Who are the top dentists near me?" She expected to see herself in the results. Instead, the AI confidently recommended dentists in Scottsdale and Tempe — cities where her practice has zero presence. The kicker? One of the recommended offices was located 2 miles from her own clinic.
This isn't an edge case. It's one of the most common and least understood problems in AI visibility: geographic confusion. And it affects every local and regional business trying to appear in AI recommendations.
How AI Models Actually Interpret Location
AI models aren't using real-time GPS data. They don't have access to your business coordinates unless you explicitly provide them in machine-readable format. Instead, AI systems learn geographic associations from the training data they were built on.
Your business gets associated with a location based on four fundamental signals:
- Consistency of geographic mentions — how often your business name appears alongside a specific city or region across the web
- Geographic modifiers in content — whether your content explicitly states "serving San Francisco" versus just "serving Bay Area professionals"
- Local directory listings — which cities your business is registered in on Google Business Profile, Yelp, industry directories, and local sites
- Structured data signals — location information embedded in schema markup (latitude, longitude, address fields, area served)
If these signals are inconsistent — if some directories list your address one way, others list it differently, and your website mentions three different variations — the AI either ignores location entirely or makes a confident guess that's completely wrong.
The Four Geographic Signal Types That Matter
Not all geographic signals are created equal. Here are the ones that actually influence AI recommendations:
NAP Consistency (Name, Address, Phone)
Your business name, address, and phone number must appear in exactly the same format across every online listing. This means identical handling of street abbreviations: "Street" vs "St.", "Avenue" vs "Ave.", postal abbreviations, even spacing and punctuation. When NAP varies across directories, search engines and AI models treat them as potentially different businesses, creating signal noise and geographic ambiguity.
LocalBusiness Schema & Geo Coordinates
Machine-readable location signals are far more reliable than prose mentions. Schema markup with properties like latitude/longitude, addressLocality, addressRegion, and areaServed gives AI systems explicit, unambiguous location data. When you include geo coordinates in your schema, you're essentially drawing a digital pin on a map — far more precise than any text description.
Local Citation Density
How many times does "[your city] + [your business name]" appear together on external websites? Local newspaper features, city business directories, chamber of commerce listings, local event sponsorships, and community mentions all build geographic association. Higher citation density in a specific location makes that geographic connection stronger and harder for AI to misinterpret.
Geographic Content Signals
Content on your website that explicitly addresses your local market creates clearer location signals. "Serving commercial real estate investors in the Sacramento Valley" gives AI a much cleaner geographic context than simply "serving commercial real estate investors." The more specific your geographic language, the more confidently AI can associate your business with that location.
The City vs. Metro Area Problem
AI models often conflate cities and their surrounding metro areas — and this ambiguity causes real visibility problems. A business in Roseville, CA might be confidently described as being in "Sacramento" by an AI system. Or a business in Brooklyn gets associated with "Manhattan." These are close, but wrong. And they matter because users ask AI for recommendations in specific cities.
How to address this:
- Be explicit in schema — include both the specific city (addressLocality) and the broader metro area or county in your areaServed property
- Clarify in content — use language like "Located in Roseville, serving the greater Sacramento region" rather than just "Sacramento-area business"
- Verify directory accuracy — regularly audit that business directories list your specific city, not the nearest major city
Why this matters for AI: AI models use statistical patterns to infer meaning. When they see "Roseville" and "Sacramento" used interchangeably for your business, they assign probability to both. To users searching for recommendations in Roseville specifically, you become less relevant. Clear geographic distinction increases relevance in more search contexts.
Multi-Location Businesses: A Special Challenge
If your business has multiple locations, geographic accuracy becomes exponentially more important — and more complex.
The core problem: consolidated pages that cover all locations create geographic ambiguity. An AI system doesn't know whether to associate your brand with Location A, Location B, Location C, or all of them equally. When the signals are muddled, the AI often either defaults to the most prominent location or avoids making location-specific recommendations for any of your locations.
The solution is strict geographic separation:
- Each location needs its own entity signals — distinct Google Business Profile, dedicated schema markup, and ideally a dedicated web page
- Each location should have distinct citations — local directory listings, sponsorships, and press mentions that specifically mention that location
- Avoid geographic overlap in content — if you have a page for "Location 1" and a page for "Locations 1-3", you're creating signal noise
The areaServed Advantage
One of the most underutilized schema properties is areaServed. When you explicitly define your service area using this property, you tell AI systems exactly where you operate — and you appear in more location variants as a result.
Here's the difference:
- Without areaServed: AI must infer your service area from indirect signals — mentions, citations, directory listings. It might assign you to one location or be uncertain.
- With areaServed: A commercial lender in Sacramento who specifies areaServed: ["Sacramento", "Northern California", "Central Valley"] gets associated with all three geographic contexts, not just one.
This is particularly powerful for service businesses (contractors, consultants, professionals) that operate across multiple cities or regions. Instead of confusing AI about where you actually serve, you make it clear.
Testing Your Geographic Accuracy
Before you assume your geographic signals are clean, test them. Here's a practical approach:
- Run your baseline query — ask ChatGPT or another AI: "[Your industry] in [your city]" and see if your business appears
- Test neighborhood names — if you're in a specific neighborhood or district, ask for recommendations using that name instead of the city name
- Test the broader metro area — ask for recommendations in your county or region and track where you appear
- Map your presence — where does your appearance rate drop? That's your geographic signal gap
Real example: A tax consultant in Denver ran this test and discovered they appeared in "Colorado" and "Denver metro" recommendations, but disappeared entirely when users asked for recommendations in "Downtown Denver" or "LoDo" — neighborhoods where they actually have office space. The gap? Missing neighborhood-specific citations and content.
Quick Fixes That Actually Work
If you're concerned about geographic accuracy, these practical fixes deliver results:
- Standardize your address format everywhere — decide on one format for street abbreviations, spacing, and punctuation. Use it consistently across your website, Google Business Profile, local directories, and anywhere else your address appears online
- Add geo coordinates to schema — include latitude and longitude in your LocalBusiness schema markup. You can find your exact coordinates on Google Maps
- Add the areaServed property — explicitly list the cities, counties, or regions you serve in your schema
- Create a "markets we serve" page — dedicated web page with geographic copy that clearly states which regions you operate in, why you serve them, and what you offer in each area
- Get listed in your city's business association directory — chamber of commerce listings, local business leagues, and municipal directories all build geographic citation density
Ready to fix your geographic visibility? SurfAI's AI Visibility Audit includes a dedicated geographic accuracy test that identifies gaps in your location signals and provides specific recommendations for improvement. Get started with a free audit.
The Bottom Line
Geographic accuracy isn't a nice-to-have for AI visibility — it's foundational. Every inconsistency in how you represent your location creates noise that makes AI systems less confident about recommending you. Every explicit signal you add makes those recommendations more likely.
The dentist in Phoenix isn't competing against dentists in Scottsdale. She's competing against the confusion in her own geographic signals. Fix that, and AI recommendations follow.