Structured Data for AI-Readable Local Pages

Structured data for local businesses: making your website AI-readable

Structured data is how a website explicitly tells Google, and increasingly AI systems, what a business is, where it sits, and what it does — in a machine-readable format rather than prose Google has to interpret. Without it, Google infers these facts from unstructured page content. With it, Google extracts entity information directly and confidently, without guessing.

For local SEO and AI-overview citation both, structured data matters more with every passing quarter.

Why structured data matters more in the AI search era

Traditional SEO benefits from structured data are well established: rich snippets with star ratings, FAQ blocks, breadcrumbs in search results, and better crawl efficiency generally. The AI search era adds a layer on top of that. LocalBusiness schema gives AI systems the precise, structured entity data they extract for local recommendation citations. When an AI Overview or an LLM response says "Sharma Skin Clinic, located at [address], with a 4.8-star rating," that fact came from schema or GBP data, not from an AI model reading a paragraph of marketing copy and inferring the details.

Core schema types for Indian local businesses

LocalBusiness and its subtypes are the starting point for any business with a physical location. A hospital or clinic uses MedicalOrganization, Physician, or MedicalClinic depending on structure. A pharmacy uses Pharmacy. A restaurant or café uses Restaurant or FoodEstablishment. A hotel uses LodgingBusiness or Hotel. A school or coaching centre uses EducationalOrganization or School. A bank branch uses BankOrCreditUnion. A real estate business uses RealEstateAgent. A law firm uses LegalService, a CA firm AccountingService, and a retail store uses Store or one of its more specific subtypes like ClothingStore or ElectronicsStore.

A working LocalBusiness schema template

{
  "@context": "https://schema.org",
  "@type": "MedicalClinic",
  "name": "Sharma Skin Clinic",
  "url": "https://sharmaskin.in",
  "telephone": "+91-98XXX-XXXXX",
  "email": "info@sharmaskin.in",
  "description": "Board-certified dermatology clinic in Koramangala, Bengaluru. Specialising in acne, laser hair removal, and cosmetic dermatology. Est. 2016.",
  "foundingDate": "2016",
  "address": {
    "@type": "PostalAddress",
    "streetAddress": "42, 5th Cross, 5th Block",
    "addressLocality": "Koramangala",
    "addressRegion": "Bengaluru",
    "addressCountry": "IN",
    "postalCode": "560095"
  },
  "geo": {
    "@type": "GeoCoordinates",
    "latitude": "12.9352",
    "longitude": "77.6245"
  },
  "openingHoursSpecification": [
    {
      "@type": "OpeningHoursSpecification",
      "dayOfWeek": ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday"],
      "opens": "09:00",
      "closes": "20:00"
    },
    {
      "@type": "OpeningHoursSpecification",
      "dayOfWeek": "Saturday",
      "opens": "10:00",
      "closes": "18:00"
    }
  ],
  "aggregateRating": {
    "@type": "AggregateRating",
    "ratingValue": "4.8",
    "reviewCount": "312",
    "bestRating": "5"
  },
  "medicalSpecialty": "Dermatology",
  "availableService": [
    {"@type": "MedicalProcedure", "name": "Acne Treatment"},
    {"@type": "MedicalProcedure", "name": "Laser Hair Removal"},
    {"@type": "MedicalProcedure", "name": "Chemical Peel"}
  ],
  "sameAs": [
    "https://www.practo.com/bengaluru/clinic/sharma-skin-clinic",
    "https://www.facebook.com/SharmaSkinclinic",
    "https://www.linkedin.com/company/sharma-skin-clinic"
  ]
}

A few notes on the fields that trip people up. sameAs matters for entity disambiguation — links to your profiles on other platforms tell Google that all these profiles refer to the same underlying business. aggregateRating should only be included if the reviews it reflects are actually displayed on the page — schema referencing ratings the page doesn't show violates Google's structured data guidelines and can get the markup ignored or penalised. geo coordinates enable precise geographic mapping, which matters more for AI systems doing distance-based reasoning than it does for a human reading the page.

Schema for multiple locations

A business with several locations needs a separate LocalBusiness schema instance on every location page — not one schema shared across all of them. Each instance carries that specific location's address, phone number, hours, and aggregate rating if displayed, plus a parentOrganization attribute linking back to the brand entity:

{
  "@type": "MedicalClinic",
  "name": "Sharma Skin Clinic — Koramangala",
  "parentOrganization": {
    "@type": "MedicalOrganization",
    "name": "Sharma Skin Clinic"
  },
  "address": {
    ... Koramangala-specific address ...
  }
}

This structure is what lets Google, and AI systems reading the same markup, tell a ten-branch chain's individual locations apart while still recognising them as one brand.

FAQ schema: the most directly AI-beneficial type

FAQ schema is arguably the single most useful schema type for local businesses trying to get cited in AI Overviews. Pages carrying it are among the most commonly cited content types Google's AI Overview feature draws from.

{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "mainEntity": [
    {
      "@type": "Question",
      "name": "How many sessions of laser hair removal are needed?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Most patients need 6–8 sessions spaced 4–6 weeks apart for 80–90% permanent hair reduction. The exact number depends on hair type, skin tone, and treatment area."
      }
    }
  ]
}

Validating and monitoring schema

Three tools cover this end to end. Google's Rich Results Test (search.google.com/test/rich-results) validates the schema and shows which rich result types it's eligible for. The Schema.org Validator (validator.schema.org) catches syntax errors before Google ever sees the page. And Google Search Console's Enhancements report shows the schema errors and warnings Google actually found once it crawled the page — the closest thing to ground truth on whether the markup is doing its job.

Common schema mistakes

Copying a competitor's schema template wholesale and forgetting to update the address, phone number, or sameAs links to the new business's actual profiles. Marking up an aggregateRating for reviews that aren't visible anywhere on the page. Using a single shared schema block across ten location pages instead of one instance per page. And publishing FAQ schema for questions that don't actually appear as visible text on the page — Google expects the markup to reflect real, visible content, not a hidden shortcut.

Does adding schema guarantee an AI Overview citation? No — schema makes extraction easier and more reliable, but it doesn't guarantee inclusion. It's a necessary input, not a guarantee of output.

Can I use the same schema across every page of my website? Not for LocalBusiness data on a multi-location site — each location needs its own instance. A single sitewide Organization schema for brand-level facts is fine alongside that.

Do I need a developer to add schema, or can I do it manually? Many CMS platforms and plugins support schema without custom development, but for anything beyond a single-location business, a developer or a managed provider handling it correctly at scale is usually worth it.

What happens if my schema has an error? Google typically ignores the specific field with the error rather than penalising the whole page, but a repeated pattern of schema errors can affect how much Google trusts the markup going forward.

Related reading: LLM citations for local business and AI overviews and local SEO cover the citation side this schema feeds into. Structured data and schema goes deeper on the fundamentals. For healthcare-specific schema considerations, see local SEO for dentists and clinics. The glossary entries on LocalBusiness schema, FAQPage schema, and structured data cover the terminology in more depth.

Angryturtle implements LocalBusiness schema for all managed clients' websites →