To get an AI assistant to recommend your business in its area, you have to look like the obvious best-fit answer everywhere the model reads — not just the nearest pin on a map. That means name, address and phone details that match across the web, LocalBusiness structured data a crawler can parse, review text that describes what you're genuinely good at, and mentions on the third-party sites (Yelp, local "best of" lists, community forums) that engines like ChatGPT and Perplexity cite when someone asks "where should I go?" The shift is blunt: local search used to be about ranking for "near me," and Local GEO is about being named as the recommendation.
Key takeaways
Local recommendations now come from assistants that name one or two businesses, not a three-pack of pins — Local GEO is about being the best-fit answer, not the closest one.
AI models triangulate across your website, directories and review platforms; businesses with reviews on three or more sites are far more likely to be recommended than those relying on a single profile.
LocalBusiness schema, specific service descriptions and mentions in "best of [city]" lists are the raw material engines quote back to users.
Star ratings matter less than the words inside reviews — sentiment and specific keywords decide which query you match.
You can't verify this by searching yourself once a day; recommendation share has to be measured across all seven major engines.
From "near me" to "best for me"
Traditional local SEO optimizes for proximity and keywords — you win "italian restaurant near me" by being close and using the right terms. A generative engine works differently. When someone asks ChatGPT for "a quiet coffee shop downtown with fast Wi-Fi and oat milk," the model isn't sorting pins by distance. It assembles a shortlist of businesses whose descriptions, reviews and citations match every attribute in the request, then recommends one or two by name.
A few practical contrasts:
The primary signal moves from proximity and keyword match to entity clarity and contextual fit.
The query gets longer and more specific: not "plumber near me" but "who's the most reliable plumber for old cast-iron pipes in my neighborhood?"
The output changes from a list of links and map pins to a conversational recommendation that often names a single winner.
The source of truth widens from your Google Business Profile to a blend of your site, review platforms, directories and community mentions the model has read.
That last shift is the one that stings. In a map three-pack, roughly a third of nearby businesses get some exposure. In an AI answer the model names very few, so the gap between "recommended" and "invisible" is much wider than it ever was in maps. This is the same "get cited, not just clicked" logic behind as a whole.
How an AI decides which local business to recommend
Engines don't keep one tidy "local" database — they triangulate. ChatGPT's search layer is crawled by OAI-SearchBot and leans heavily on Bing-indexed pages, your own website, and third-party platforms such as Yelp, Foursquare, the BBB and Google Maps (OpenAI's crawler docs spell out which bot does what). Perplexity and Google's AI experiences pull from an overlapping but not identical set.
Reviews are the tiebreaker, and diversity beats volume. Whitespark's 2026 research found businesses with reviews on three or more platforms were roughly 2.7x more likely to be recommended by ChatGPT than those with reviews on a single site — the spread of trust signals mattered more than raw review count (Whitespark study).
Brand mention monitoring in AI search: per-prompt visibility, citation rate and tracking status across AI engines — Source: GEOly AI (app.geoly.ai)
The lesson for a local owner: you're not optimizing one profile, you're making sure a dozen sources tell the same specific story about who you serve and what you do best.
A five-step Local GEO playbook
1. Lock down your entity data (NAP plus context)
Models lower their confidence when your Name, Address and Phone number disagree across sources.
Keep NAP identical on your site, Google Business Profile, Yelp, Facebook, Apple Business Connect and any industry directories that matter in your category.
Replace generic blurbs with specific ones. "We sell pizza" tells a model nothing; "wood-fired Neapolitan pizza with imported San Marzano tomatoes and a gluten-free crust" matches dozens of long-tail requests.
2. Ship LocalBusiness structured data
Schema markup translates your details into a format crawlers resolve instantly, and LocalBusiness schema is the baseline for anyone with a physical location or service area (Schema.org LocalBusiness).
Use a specific @type — Dentist, Restaurant, Locksmith — rather than the generic parent.
Include priceRange, areaServed, openingHours, hasMap and aggregateRating so the model can match budget, radius and timing constraints.
3. Shape review sentiment, not just your star rating
AI reads the words, not only the average. Fifty reviews mentioning "slow service" can keep you out of "quick lunch" answers even at 4.5 stars.
Nudge happy customers to name the specific thing they loved — "the truffle fries," "same-day repair" — not just "great service."
Reply to reviews; engagement adds context and signals an active business.
Spread reviews across platforms rather than concentrating them on Google alone.
4. Earn local citations and digital word of mouth
A slot in a "Best [service] in [city]" roundup is worth more than its traffic suggests, because models weight these aggregators when forming a shortlist.
Local press, neighborhood blogs and active forums act as citations that build authority over time.
Keep social profiles current; agents cross-check them to confirm you're still open and operating.
A quick audit tip: run GEOly's 29-point GEO audit before you rewrite anything. It flags the schema and entity gaps that quietly cost recommendations, so you fix the highest-leverage issues first.
5. Measure across every engine
You can't confirm any of this by searching yourself once a day, and each engine answers differently. Track your recommendation share across all seven major assistants rather than trusting a single spot check.
Measuring Local GEO with GEOly AI
GEOly AI monitors how the engines actually talk about your business across ChatGPT, Gemini, Perplexity, Copilot, Grok, Google AI Mode and AI Overviews. Instead of guessing, you get concrete signals:
An AIGVR score from 0 to 100 that summarizes visibility, plus mention and citation rates per engine.
Citation-source analysis that shows which Yelp pages, listicles and directories the models pull from — so you know where to earn the next mention.
Competitor benchmarking: when the AI recommends someone else, you see who and what they're doing differently.
A 29-point GEO audit that flags schema and entity issues before they cost you a recommendation.
Citation source analysis: source type distribution and the domains AI engines cite most — Source: GEOly AI (app.geoly.ai)
Does Local GEO replace Google Business Profile and local SEO?
No — it extends them. A complete, accurate Google Business Profile still feeds both the map pack and AI answers, so it stays foundational. Local GEO adds the layers maps ignore: schema depth, the language inside your reviews, and the off-site citations engines quote when they recommend a single business.
How is Local GEO different from regular GEO?
It's the same discipline aimed at local intent. Everything about entity clarity, structured data and citations still applies, but geographic signals — areaServed, city-specific reviews, and "best in [city]" listicles — carry extra weight, and the winning answer is usually one nearby business rather than a national brand.
How long until AI starts recommending my business?
Usually weeks, not days, because engines have to re-crawl your updated citations and rebuild confidence in your entity. Fixing NAP consistency and shipping LocalBusiness schema tend to move the needle fastest, since they raise the model's certainty immediately rather than waiting on new reviews to accumulate.
Which engine should a local business focus on first?
Start where your customers already ask. ChatGPT holds the largest share of assistant queries and leans on Bing plus Yelp and the BBB, so it's a sensible first target — but measure all seven, because a plumber and a natural-wine bar can end up with very different engine mixes.
