Search engines and AI platforms process billions of web pages every day. Most of those pages are formatted for human readers, with menus, images, and styling that machines must interpret. Schema markup eliminates that guesswork by providing structured, machine-readable data directly in your page’s HTML.<\/p>\n\n\n\n
For local businesses, the stakes are high. When someone asks Google, ChatGPT, or Perplexity a question like “best dentist in Austin,” the AI engine needs accurate business data it can trust. A business with complete schema markup examples implemented in JSON-LD format gives machines exactly what they need: name, address, hours, services, and reviews in a standardized structure.<\/p>\n\n\n\n
This guide provides concrete, copy-ready code for the three schema types that matter most to local businesses. Each example includes an explanation of what the code does, where to place it, and how AI search engines use it to generate answers and citations.<\/p>\n\n\n\n
Schema markup is structured data code added to your website’s HTML that helps search engines and AI platforms understand your page content without guessing. For local businesses, it directly impacts visibility in Google’s local map pack, AI Overviews, and third-party AI search engines like ChatGPT and Perplexity.<\/p>\n\n\n\n
Schema markup uses a vocabulary defined by Schema.org, a project supported by Google, Microsoft, Yahoo, and Yandex. The code describes entities on your page, such as a business, a product, a review, or a question, in a format that any compliant machine can read.<\/p>\n\n\n\n
Google recommends JSON-LD as the preferred format for implementing schema markup. JSON-LD is a JavaScript-based notation that sits in a script tag in your page’s head or body. It does not alter the visible content of the page, making it easier to implement and maintain than older formats like Microdata or RDFa.<\/p>\n\n\n\n
In 2026, the value of schema markup for seo extends beyond Google. AI search engines use structured data to build entity profiles for businesses, which increases the chance of being cited in generated answers. A business with clean, complete schema is significantly more likely to be surfaced correctly in AI-generated local answers.<\/p>\n\n\n\n
LocalBusiness schema<\/a> tells search engines and AI platforms the essential facts about your physical business: its name, address, phone number, operating hours, and geographic coordinates. It is the single most important schema type for any business that serves customers at a specific location.<\/p>\n\n\n\n A complete local business schema markup block includes your business name, street address, city, state, postal code, country, phone number, website URL, operating hours, and geographic coordinates. Omitting any of these fields weakens the signal you send to machines.<\/p>\n\n\n\n Here is an example for a Miami-based plumbing company:<\/p>\n\n\n\n {<\/p>\n\n\n\n “@context”: “https:\/\/schema.org”,<\/p>\n\n\n\n “@type”: “Plumber”,<\/p>\n\n\n\n “name”: “Coral Gables Plumbing Pros”,<\/p>\n\n\n\n “address”: {<\/p>\n\n\n\n “@type”: “PostalAddress”,<\/p>\n\n\n\n “streetAddress”: “4521 Ponce de Leon Blvd”,<\/p>\n\n\n\n “addressLocality”: “Coral Gables”,<\/p>\n\n\n\n “addressRegion”: “FL”,<\/p>\n\n\n\n “postalCode”: “33146”,<\/p>\n\n\n\n “addressCountry”: “US”<\/p>\n\n\n\n },<\/p>\n\n\n\n “telephone”: “+1-305-555-0172”,<\/p>\n\n\n\n “url”: “https:\/\/www.coralgablesplumbing.com”,<\/p>\n\n\n\n “openingHoursSpecification”: [<\/p>\n\n\n\n {<\/p>\n\n\n\n “@type”: “OpeningHoursSpecification”,<\/p>\n\n\n\n “dayOfWeek”: [“Monday”,”Tuesday”,”Wednesday”,”Thursday”,”Friday”],<\/p>\n\n\n\n “opens”: “07:00”,<\/p>\n\n\n\n “closes”: “18:00”<\/p>\n\n\n\n },<\/p>\n\n\n\n {<\/p>\n\n\n\n “@type”: “OpeningHoursSpecification”,<\/p>\n\n\n\n “dayOfWeek”: “Saturday”,<\/p>\n\n\n\n “opens”: “08:00”,<\/p>\n\n\n\n “closes”: “14:00”<\/p>\n\n\n\n }<\/p>\n\n\n\n ],<\/p>\n\n\n\n “geo”: {<\/p>\n\n\n\n “@type”: “GeoCoordinates”,<\/p>\n\n\n\n “latitude”: “25.7212”,<\/p>\n\n\n\n “longitude”: “-80.2684”<\/p>\n\n\n\n },<\/p>\n\n\n\n “priceRange”: “$$”<\/p>\n\n\n\n }<\/p>\n\n\n\n Notice that the @type field uses “Plumber” instead of the generic “LocalBusiness.” Schema.org provides specific subtypes for dozens of business categories, including Dentist, Restaurant, LegalService, RealEstateAgent, and AutoRepair. Using the most specific subtype available gives AI engines a clearer signal about what your business does.<\/p>\n\n\n\n Place this code inside a <script type=”application\/ld+json”> tag on your homepage and contact page. Keep the data consistent with your Google Business Profile. Mismatches between your schema markup and your GBP listing can confuse AI engines and reduce trust signals.<\/p>\n\n\n\n FAQ schema marks up question-and-answer pairs on your page in a format that AI search engines can extract directly. While Google retired FAQ rich results in 2026, AI platforms like ChatGPT, Gemini, and Perplexity still parse FAQ-structured data when selecting content to cite in generated answers.<\/p>\n\n\n\n FAQ schema is one of the simplest “schema markup examples” to implement and one of the most effective for AI visibility. It wraps your existing FAQ content in a standardized format that any AI engine can read without parsing the surrounding HTML.<\/p>\n\n\n\n Here is an example for a local accounting firm:<\/p>\n\n\n\n {<\/p>\n\n\n\n “@context”: “https:\/\/schema.org”,<\/p>\n\n\n\n “@type”: “FAQPage”,<\/p>\n\n\n\n “mainEntity”: [<\/p>\n\n\n\n {<\/p>\n\n\n\n “@type”: “Question”,<\/p>\n\n\n\n “name”: “How much does tax preparation cost for a small business?”,<\/p>\n\n\n\n “acceptedAnswer”: {<\/p>\n\n\n\n “@type”: “Answer”,<\/p>\n\n\n\n “text”: “Tax preparation for a small business typically costs between $500 and $2,500, depending on the complexity of your returns, number of employees, and state filing requirements.”<\/p>\n\n\n\n }<\/p>\n\n\n\n },<\/p>\n\n\n\n {<\/p>\n\n\n\n “@type”: “Question”,<\/p>\n\n\n\n “name”: “What documents do I need to bring to my first meeting with an accountant?”,<\/p>\n\n\n\n “acceptedAnswer”: {<\/p>\n\n\n\n “@type”: “Answer”,<\/p>\n\n\n\n “text”: “Bring your prior-year tax returns, profit and loss statements, bank statements, receipts for deductible expenses, and any correspondence from the IRS or state tax authority.”<\/p>\n\n\n\n }<\/p>\n\n\n\n }<\/p>\n\n\n\n ]<\/p>\n\n\n\n }<\/p>\n\n\n\n Write answers in 40 to 60 words. Start each answer with the core fact. Avoid promotional language. AI engines skip self-promotional claims and prefer factual, verifiable statements.<\/p>\n\n\n\n Add FAQ schema to every service page, blog post, and resource page that contains question-and-answer content. The more FAQ-structured data your site provides, the more answer-ready content AI engines have to work with.<\/p>\n\n\n\n Review schema and AggregateRating schema let you display star ratings, review counts, and individual testimonials in a format that search engines and AI platforms can read. For local businesses, these schema types build the trust signals that AI engines weigh when deciding which source to cite.<\/p>\n\n\n\n Trust is a core ranking factor in AI search. When an AI engine must choose between two plumbing companies to cite, the one with verified reviews embedded in structured data has a measurable advantage. AggregateRating schema summarizes your overall rating, while individual Review schema highlights specific customer experiences.<\/p>\n\n\n\n Here is an AggregateRating example:<\/p>\n\n\n\n {<\/p>\n\n\n\n “@context”: “https:\/\/schema.org”,<\/p>\n\n\n\n “@type”: “Plumber”,<\/p>\n\n\n\n “name”: “Coral Gables Plumbing Pros”,<\/p>\n\n\n\n “aggregateRating”: {<\/p>\n\n\n\n “@type”: “AggregateRating”,<\/p>\n\n\n\n “ratingValue”: “4.8”,<\/p>\n\n\n\n “reviewCount”: “147”,<\/p>\n\n\n\n “bestRating”: “5”<\/p>\n\n\n\n }<\/p>\n\n\n\n }<\/p>\n\n\n\n This tells machines that the business has a 4.8 out of 5 rating based on 147 reviews. AI engines use this data to assess credibility when generating answers about local service providers.<\/p>\n\n\n\n Google has strict guidelines for review schema. Only use reviews that are genuinely collected by your business. Do not mark up reviews from third-party platforms like Yelp or Google itself, as this violates Google’s structured data policies. Use first-party reviews collected through your own website or review system.<\/p>\n\n\n\n AI search engines use structured data for AI search as a shortcut to build accurate entity profiles. Instead of parsing unstructured HTML to guess your business name, location, and services, they read your schema and treat it as a verified data source for generated answers.<\/p>\n\n\n\n Google AI Overviews, ChatGPT, and Perplexity each handle structured data differently, but the principle is the same. Machines prefer structured facts over unstructured text because structured data is less ambiguous and more reliable.<\/p>\n\n\n\n When ChatGPT search encounters a page with complete LocalBusiness schema, it can confirm the business name, location, and hours without reading the entire page. This saves processing time and increases the likelihood that the page is cited in a generated answer.<\/p>\n\n\n\n A local restaurant in Boulder, Colorado, added Restaurant schema with AggregateRating markup, completed its Google Business Profile, and unified its NAP (name, address, phone) data across all platforms. Within 90 days, the restaurant began appearing in AI-generated answers to local dining queries across multiple platforms. The key was consistency: schema, GBP, and on-page content all told the same story.<\/p>\n\n\n\n Validate your schema markup using Google’s Rich Results Test for rich-result eligibility and Schema.org’s Markup Validator for full compliance. Testing ensures your code is syntactically correct, your data is accurate, and search engines can read it without errors.<\/p>\n\n\n\n Testing is not optional. A single missing bracket, an incorrect @type, or a mismatched business name can render your entire schema unreadable. AI engines that encounter broken schema will skip it and move to the next result.<\/p>\n\n\n\n Google Rich Results Test<\/strong> (search.google.com\/test\/rich-results): Paste a URL or a code snippet to check whether your schema qualifies for Google’s rich results. Note that this tool only validates schema types that power rich results, not all schema types.<\/p>\n\n\n\n Schema.org Markup Validator<\/strong> (validator.schema.org): Tests your full schema against the Schema.org specification. Use this for a comprehensive check that covers types Google may not surface as rich results, but AI engines still read.<\/p>\n\n\n\n Run validation after every change to your schema. Set a quarterly reminder to recheck your schema, because business hours, review counts, and service offerings drift over time. Outdated schema is worse than no schema because it sends incorrect signals to AI engines.<\/p>\n\n\n\n The most common schema markup for seo mistakes include using the wrong @type, marking up content that does not exist on the page, inconsistent NAP data across schema and Google Business Profile, and adding review markup for third-party reviews that violate Google’s guidelines.<\/p>\n\n\n\n Wrong @type<\/strong>: Using “LocalBusiness” when a more specific type like “Dentist” or “AutoRepair” is available. Specificity helps AI engines categorize your business accurately.<\/p>\n\n\n\n Invisible data<\/strong>: Adding schema for content that does not appear on the visible page. Google requires that structured data reflect the content users can see. Marking up information that only exists in schema is considered spam.<\/p>\n\n\n\n NAP inconsistency<\/strong>: Your business name, address, and phone number must match exactly across your schema, your Google Business Profile, your website footer, and every directory listing. Even minor differences like “LLC” vs. “L.L.C.” can create confusion.<\/p>\n\n\n\n Third-party review markup<\/strong>: Marking up reviews that come from Google, Yelp, or TripAdvisor on your own website. Google explicitly prohibits this. Only mark up reviews collected directly through your own site.<\/p>\n\n\n\n Schema markup is a technical requirement for any local business that wants to be visible in both traditional search and AI-generated answers. The schema markup examples in this guide cover the three types that deliver the most impact: LocalBusiness, FAQ, and AggregateRating. Each one gives AI engines a machine-readable shortcut to your business data.<\/p>\n\n\n\n Implementation does not require a developer for most businesses. JSON-LD code can be generated using free tools like Google’s Structured Data Markup Helper or Merkle’s Schema Markup Generator. The harder part is maintaining accuracy over time, which requires quarterly audits and consistent updates.<\/p>\n\n\n\n Online Ranking AI<\/a>AI SEO program<\/a>free AI audit<\/a>If your business needs help implementing schema markup alongside a full AI search optimization<\/strong> strategy, deploys and validates schema as part of every . Start with a to see what structured data your site is missing.<\/p>\n\n\n\n JSON-LD is the recommended format by Google and the most widely supported by AI engines. It sits in a script tag and does not alter your visible page content. Most CMS platforms and schema generator tools output JSON-LD by default.<\/p>\n\n\n\n Schema markup does not function as a direct ranking factor. However, it enables rich results, improves local map pack eligibility, and helps AI engines cite your business accurately. These indirect benefits can lead to increased visibility and click-through rates.<\/p>\n\n\n\n Start with LocalBusiness schema on your homepage and contact page, FAQ schema on service and blog pages, and AggregateRating schema if you collect first-party reviews. Add more types like Service or Product schema as your content grows.<\/p>\n\n\n\n Yes. AI search engines still parse FAQ-structured content when generating answers. FAQ schema helps ChatGPT, Gemini, and Perplexity identify question-answer pairs on your page. The content itself also improves user experience and topical depth.<\/p>\n\n\n\n Audit your schema quarterly. Update it whenever your business hours change, your review count grows significantly, your services expand, or you add new locations. Outdated schema sends incorrect signals to both search engines and AI platforms.<\/p>\n","protected":false},"excerpt":{"rendered":" Search engines and AI platforms process billions of web pages every day. Most of those pages are formatted for human readers, with menus, images, and styling that machines must interpret. Schema markup eliminates that guesswork by providing structured, machine-readable data directly in your page’s HTML. For local businesses, the stakes are high. When someone asks […]<\/p>\n","protected":false},"author":2,"featured_media":3028,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[13],"tags":[40,46],"class_list":["post-3027","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-seo","tag-ai-seo","tag-schema"],"yoast_head":"\nFAQ Schema: Feeding Answers to AI Engines<\/h2>\n\n\n\n
Review and AggregateRating Schema: Building Trust Signals<\/h2>\n\n\n\n
Structured Data for AI Search: How AI Engines Use Your Schema<\/h2>\n\n\n\n
How to Test and Validate Your Schema Markup<\/h2>\n\n\n\n
Common Schema Markup Mistakes to Avoid<\/h2>\n\n\n\n
Conclusion<\/h2>\n\n\n\n
Frequently Asked Questions<\/h2>\n\n\n\n
What is the best schema markup format for local businesses?<\/h3>\n\n\n\n
Does schema markup directly improve my Google ranking?<\/h3>\n\n\n\n
How many schema types should I add to my website?<\/h3>\n\n\n\n
Is FAQ schema still worth implementing after Google retired FAQ rich results?<\/h3>\n\n\n\n
How often should I update my schema markup?<\/h3>\n\n\n\n