The classic "near me" vs the AI version.
These are two different mechanisms responding to the same customer intent.
When someone types "physiotherapist near me" into Google, the search engine uses their device location to pull nearby Google Business Profile listings. It ranks them by the combination of proximity (how close the business is), prominence (review count and rating, backlinks, brand strength), and relevance (how well the listing and website match the query). The top three appear in the map pack. A click to call or get directions follows without the user ever reading a website.
When someone asks an AI assistant — ChatGPT, Gemini, Perplexity — "what's a good physiotherapist near me in Scarborough?", the mechanism is completely different. The model has no real-time access to device location. Instead, it uses what it knows about businesses from its training data and, in models with web search, from recent crawls. It matches the query to businesses it has seen described as physiotherapy providers in Scarborough and picks the ones with the strongest evidence of credibility — consistent mentions, detailed descriptions, real reviews. There's no ranked list; there's a named recommendation.
The practical implication: you can rank in Google's map pack and still be invisible to AI assistants, because the model may not have encountered clear, structured evidence about your business. Conversely, a business with strong content and schema can earn AI mentions even before it dominates the map pack. The fastest path to both is strengthening the shared foundation: a complete Google Business Profile, genuine reviews, and consistent information across the web.
Signals that move the needle on each surface.
Some signals are universal. Others are specific to one channel.
Proximity, recency, and completeness.
Google weights physical proximity heavily for near-me queries. It also rewards GBP profiles that are complete — accurate categories, up-to-date hours, recent posts, and photos. Review recency matters: a business with 80 reviews from last year is not as strong as one with 80 reviews that include several from the past month.
Entity clarity and content depth.
AI assistants can't check your hours in real time, so they rely on entity signals: does your website clearly state what you do, where you are, and what types of patients or customers you serve? Well-structured content and Organization schema make a business readable to the model. Sparse or vague websites leave the model with nothing to cite.
Reviews and NAP consistency.
Review volume and rating feed both surfaces. A business with a strong Google rating and a steady flow of reviews is more likely to rank in the map pack and more likely to be named by AI models that treat review signals as proxy for quality. Consistent NAP — your name, address, and phone number matching everywhere — is foundational to both.
The steps to capture near-me intent across Google and AI.
None of these steps are dramatic, but skipping any of them leaves a gap on one surface or the other.
Complete your GBP fully
Categories, service list, business description, hours, photos and responses to reviews. A thin profile doesn't compete for the map pack and gives AI models nothing to quote.
Add your city and neighborhood explicitly on your website
AI models can't infer your location from a map embed. Write it in plain text: your neighborhood, your city, the areas you serve. The model reads what's on the page.
Run LocalBusiness and Service schema
Structured data gives both Google and AI crawlers a machine-readable summary of who you are and what you offer. It's one of the fastest signals to add.
Build review recency
Fresh reviews signal to Google that your business is active, and they give AI models recent evidence of quality. A steady trickle of reviews beats a burst followed by silence.
Ensure NAP consistency across directories
If your name, address, or phone number varies across Yelp, Yellow Pages, and your website, the model sees conflicting signals and trusts none of them. Clean it up once; it compounds.
Related, and what to do next.
Questions, answered.
What are near me searches?
"Near me" searches are local-intent queries where the buyer wants a business close to their current location — for example, "plumber near me" or "coffee shop near me." Google handles them with the map pack, using GPS proximity. AI assistants handle them with named recommendations based on entity signals and reviews.
Do AI assistants handle "near me" queries the same as Google?
No. Google uses your device location and GBP data to rank nearby results in real time. AI assistants like ChatGPT and Gemini have no access to your live location — they use training data and web crawls to name businesses they associate with a given area and service. The ranking criteria are fundamentally different.
How do I get my business to show up for near me searches on AI?
State your location clearly in plain text on your website, complete your Google Business Profile, run LocalBusiness and Service schema, maintain a steady flow of genuine reviews, and keep your NAP consistent across directories. These signals build the entity picture an AI model needs to name you with confidence.
Does proximity matter for AI near me results?
AI models don't have access to real-time proximity data, so physical distance matters less than it does for Google Maps. What matters is whether the model has learned that your business operates in a specific neighborhood or city, which comes from explicit location mentions on your site and consistent GBP data.
Can I rank for near me without a physical address?
Service-area businesses — plumbers, landscapers, mobile services — can appear in both Google and AI near-me results without a public storefront. For Google, you set a service area in GBP. For AI, you list the specific neighborhoods and cities you serve on your website in plain text.