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When someone in Berlin asks ChatGPT 'Which plumber can come this evening?' - that is no longer a theoretical scenario. Local AI search is reality. Users submit location queries to ChatGPT with browsing, to Perplexity, and to Gemini - and expect concrete recommendations, not a list of ten links. For service providers in all industries, a new question arises: how do I become visible in this form of search?
Classic local search on Google works via the Google Maps algorithm: proximity, number of reviews, and Google Business Profile completeness determine who appears in the Local Pack. AI-based local search works differently. ChatGPT with browsing, Perplexity, and similar systems combine multiple information sources: indexed web pages, structured data (Schema.org), business directories, and - where available - their own real-time search. An AI answer to 'Tax advisor Frankfurt freelancers' is not based solely on Google rankings. It is based on the sum of all machine-readable information available online about that service provider. The critical difference: AI systems cannot 'guess' poorly structured information. If your address on the website is written differently from the one in the trade directory, if your opening hours are missing, or if your service offering only exists as plain text - then the AI has little basis on which to reliably recommend you. Structural deficiency leads directly to visibility deficiency. Additionally: AI systems tend to point to service providers about whom more structured, verifiable information is available. A trade business with complete LocalBusiness schema, verified reviews, a clear service description, and consistent NAP data (Name, Address, Phone) has a significantly higher chance of AI mentions than a business with an outdated website and inconsistent directory listings.
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LocalBusiness schema is the most important technical lever for local AI visibility. Schema.org offers over 80 LocalBusiness subtypes - from Plumber to Dentist, Lawyer, Attorney, Bakery, and FinancialPlanningService. The choice of the correct subtype is relevant: an AI searching for 'dentist Munich' evaluates Dentist schema differently from a generic LocalBusiness schema. The minimum fields for a complete LocalBusiness schema: name (exactly as in the company name), address (streetAddress, postalCode, addressLocality, addressCountry), telephone, url, openingHoursSpecification (structured, not as free text), geo (latitude/longitude), and priceRange. NAP consistency is even more important in the AI era than in classic Local SEO. Name, address, and telephone number must be identical on your website, in your Google Business Profile, in local directories (Yellow Pages, Yelp, industry-specific), and on social profiles - not 'similar', but character-for-character identical. An AI that aggregates multiple sources and finds contradictory address information will either output your listing with lower confidence or leave it out in cases of doubt. Service Area schema allows service providers to define their geographic reach in a machine-readable way. An electrician operating in Hamburg and the surrounding area can state this explicitly via areaServed fields in the schema. This significantly improves relevance for location queries.
Reviews are doubly relevant for local AI visibility: as a direct signal and as machine-readable content. Review schema on your website enables AI systems to read your customer reviews in a structured way. The most important fields: ratingValue, reviewCount, reviewBody (the actual review text), reviewRating, and - where available - author details. AI systems read review texts substantively. Reviews that name specific services ('repaired our heating system, arrived within 2 hours') signal to the AI which services a business actually provides. This is more valuable than keywords in self-authored descriptions. Google Business Profile remains relevant in the AI era: several AI systems can evaluate structured business data and public profile information. Complete, consistent profile information with current opening hours, photos, and service categories improves interpretability for AI systems. Multi-location setups require particular care. Each location needs its own page, its own LocalBusiness schema with location-specific data, its own Google Business Profile, and - ideally - location-specific reviews. Generic texts that apply to all locations are of little help for AI visibility. Location-specific content significantly increases AI relevance for location-based queries.
For local service providers, the same basic principle applies in the AI era as it did previously in Local SEO - only more consistently: the more complete, consistent, and machine-readable your information is, the higher the chance of AI mentions. LocalBusiness schema, NAP consistency, Review schema, and a well-maintained Google Business Profile are not optional extras. They are the foundation on which AI systems build trust. Those who lay this foundation can remain visible in a world where more and more people use AI rather than search engines for local recommendations.
Check GEO Score for freeMarvin Malessa
Founder, Beconova
Founded Beconova in Germany in 2025 to help shops and service businesses become visible in AI search engines. Writes about GEO, AI visibility, and the future of search.
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