LocalBusiness Schema JSON-LD: Examples That Help AI Find You
When AI search engines crawl your website, they look for structured data to extract business information. LocalBusiness schema markup using JSON-LD provides this data in a format that ChatGPT, Google AI Overviews, and Perplexity can parse directly. Without schema markup, AI systems must interpret unstructured HTML, which leads to errors and omissions. With it, your business data is explicitly defined and machine-readable. This guide provides copy-paste JSON-LD examples for common local business types and explains which properties matter most for AI visibility. For the full picture of AI data sources, see how ChatGPT finds local businesses (/blog/how-chatgpt-finds-local-businesses).
What Is LocalBusiness Schema and Why Does It Matter for AI?
Schema.org defines a standardized vocabulary for structured data on the web. LocalBusiness is a specific type within this vocabulary designed for businesses with a physical location. JSON-LD (JavaScript Object Notation for Linked Data) is the recommended format for implementing schema markup. You add it as a script tag in your page's HTML head.
Google, Bing, and AI search engines all read JSON-LD. The benefits for AI visibility are direct. Websites with properly implemented structured data see 20-30% higher click-through rates from Google. More importantly for AI search, structured data gives AI systems explicit, unambiguous business information they can extract with confidence. AI systems heavily favor websites with structured data when selecting sources for generated answers.
What Properties Should Every LocalBusiness Schema Include?
A complete LocalBusiness schema includes these essential properties.
Required for AI visibility:
@type– use your specific business subtype (for example,Restaurant,Dentist,Plumber).name– exact legal business name, matching all directories.address–PostalAddresswithstreetAddress,addressLocality,addressRegion,postalCode,addressCountry.telephone– primary phone number in international format.geo–GeoCoordinateswith latitude and longitude for AI proximity calculations.openingHoursSpecification– structured hours for each day.url– canonical website URL.
Highly recommended properties include: image (business photos AI can reference), priceRange (helps AI match budget queries), aggregateRating (review data from your website), areaServed (for service-area businesses), sameAs (links to social profiles and directory listings for entity verification), description (concise business description), and hasMap (link to Google Maps listing). Each property you add gives AI systems more confidence in your business entity. For more on ensuring your data matches across platforms, see our guide on NAP consistency for AI search (/blog/nap-consistency-ai-search).
What Does a Complete LocalBusiness JSON-LD Example Look Like?
Here is a practical example structure for a local restaurant. The JSON-LD script tag goes in your page's head section. Use the Restaurant subtype. Include your exact business name matching all directory listings. Add PostalAddress with full street address, city, state, zip, and country. Add telephone in international format. Add GeoCoordinates with real latitude and longitude from Google Maps. Add openingHoursSpecification with dayOfWeek, opens, and closes for each day. Add aggregateRating with ratingValue and reviewCount. Add sameAs array linking to your Yelp, Foursquare, Facebook, and Google Business Profile URLs.
Key implementation notes: Place the JSON-LD script tag in your page's head section or at the end of the body. Use the same business name that appears on your Foursquare, Yelp, and Google Business Profile listings. Include real geo coordinates. List actual opening hours for each day. The sameAs array should link to all your claimed directory profiles to help AI systems verify your business entity across sources.
How Does Schema Markup Help with AI Search Specifically?
Traditional search engines use schema primarily for rich snippets and knowledge panels. AI search engines use schema differently and more extensively. ChatGPT and Copilot can access your website through Bing's index, which reads your schema markup and stores structured business data. When ChatGPT needs to verify or supplement Foursquare data, your schema provides authoritative confirmation. Perplexity crawls websites directly and prioritizes structured data for extraction into its cited answers. Google AI Overviews synthesize information from indexed pages, and schema markup makes your business data a preferred extraction source.
The key insight is that AI systems must choose which information to trust when sources conflict. Schema markup on your own website serves as the authoritative, first-party source. When your schema matches your Foursquare, Yelp, and Google Business Profile data, AI systems have maximum confidence in recommending your business. Learn more about improving your overall AI visibility (/blog/ai-visibility-local-businesses).
What Are Common Schema Markup Mistakes to Avoid?
The most common schema markup mistakes that reduce AI visibility include: Using the generic LocalBusiness type instead of a specific subtype, providing an incomplete address missing postal code or country, omitting geo coordinates which are critical for AI proximity calculations, listing different business information in schema than on your directories, hardcoding hours that become outdated, using schema for information not visible on the page, missing the sameAs property which helps AI verify your entity, and not testing your schema after implementation. The most damaging mistake is data inconsistency between your schema and your directory listings, because it creates conflicting signals that reduce AI confidence.