How to Add Schema Markup for AI Search: A Step-by-Step Guide for Australian Businesses

Adding schema markup for AI search means embedding structured data code — specifically JSON-LD blocks — into your website’s HTML so that AI engines like Google AI Overviews, ChatGPT, Perplexity, and Bing Copilot can identify exactly who you are, what you offer, and which answers on your pages are worth citing. Schema markup turns your content from text that AI has to interpret into labelled data that AI can extract with confidence. Google confirmed in April 2025 that structured data gives websites an advantage in search results, and pages with properly implemented schema show 73% higher selection rates in AI Overviews compared to unmarked content. This guide walks you through every schema type that matters for AI visibility, with practical JSON-LD examples you can copy and adapt for your own site.

What Schema Markup Actually Does for AI Search

Schema markup is a small block of code, written in JSON-LD format, that sits inside a <script> tag on your page. It tells search engines and AI platforms what each piece of content is — not what it says, but what it represents. The difference matters enormously for AI citation.

Think of it this way: when ChatGPT or Google AI Overviews scans your page, it needs to answer two questions before it can cite you. First, does it recognise your brand or author as a trustworthy entity? Second, can it extract a clean, structured answer from your content without guessing where that answer starts and ends?

Schema markup helps with both. It sharpens entity recognition so the AI is confident about who you are, and it creates extraction-friendly blocks so the AI can pull a clean answer without ambiguity. As The HOTH’s 2026 analysis puts it: the whole game is confidence plus extractability.

Two major platforms have now confirmed that schema markup helps their AI systems understand content:

• Google AI Overviews: In April 2025, the Google Search team confirmed that structured data gives websites an advantage in search results — making schema verified infrastructure, not speculation.
• Microsoft Bing Copilot: Fabrice Canel, Principal Product Manager at Microsoft Bing, confirmed in March 2025 that schema markup helps Microsoft’s LLMs understand content for Copilot responses.

An Otterly.ai experiment running from December 2025 to March 2026 found that while most AI platforms cannot directly fetch or interpret schema code when asked, pages with schema saw a 377% increase in SERP features and a 1,500% increase in AI Overview appearances within Google’s ecosystem. The evidence is clear: schema markup is essential infrastructure for AI visibility.

The 7 Schema Types That Matter Most for AI Visibility

Schema.org contains over 800 types, but a small handful does the heavy lifting for AI search. Here are the seven you need, roughly in order of priority.

1. Organization Schema (Foundation of Entity Recognition)

This is where AI entity recognition begins. Organization schema tells an AI system that your brand name represents a real, verifiable company rather than a random string of text. Without it, an AI has to infer what you are from context — and inference creates ambiguity.

The single highest-leverage field inside Organization schema is sameAs. This field links your brand to other trusted profiles that AI models already know — your LinkedIn company page, your Facebook page, your Google Business Profile. When you include sameAs links, you are telling the AI: “The company you’ve seen referenced on LinkedIn and Google Maps is the same entity as this website.”

Here is a practical example for an Australian business:

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Organization",
  "@id": "https://example.com.au/#organization",
  "name": "Your Business Name",
  "url": "https://example.com.au",
  "logo": "https://example.com.au/logo.png",
  "description": "Brief description of your business and services",
  "telephone": "+61-7-XXXX-XXXX",
  "email": "info@example.com.au",
  "address": {
    "@type": "PostalAddress",
    "streetAddress": "123 Main Street",
    "addressLocality": "Gold Coast",
    "addressRegion": "QLD",
    "postalCode": "4217",
    "addressCountry": "AU"
  },
  "sameAs": [
    "https://www.linkedin.com/company/your-business",
    "https://www.facebook.com/yourbusiness",
    "https://www.instagram.com/yourbusiness",
    "https://maps.google.com/?cid=YOUR_GOOGLE_CID"
  ]
}
</script>

Pro tip: Use the @id field (like "https://example.com.au/#organization") as a unique identifier. This lets you reference the same organisation entity from other schema blocks across your site without duplicating the full object every time.

2. LocalBusiness Schema (Essential for Local Businesses)

If you serve customers in a specific geographic area — a restaurant, dental practice, law firm, trades business, or agency on the Gold Coast — LocalBusiness schema is non-negotiable. It tells AI systems your service area, opening hours, and the specific category of business you operate.

Use the most specific subtype available: Dentist, Restaurant, LegalService, RealEstateAgent, Plumber, or ProfessionalService. The more precise the type, the stronger the entity signal.

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "ProfessionalService",
  "@id": "https://example.com.au/#localbusiness",
  "name": "Your Business Name",
  "url": "https://example.com.au",
  "telephone": "+61-7-XXXX-XXXX",
  "priceRange": "$$",
  "address": {
    "@type": "PostalAddress",
    "streetAddress": "123 Main Street",
    "addressLocality": "Gold Coast",
    "addressRegion": "QLD",
    "postalCode": "4217",
    "addressCountry": "AU"
  },
  "geo": {
    "@type": "GeoCoordinates",
    "latitude": "-28.0167",
    "longitude": "153.4000"
  },
  "openingHoursSpecification": [
    {
      "@type": "OpeningHoursSpecification",
      "dayOfWeek": ["Monday","Tuesday","Wednesday","Thursday","Friday"],
      "opens": "08:30",
      "closes": "17:00"
    }
  ],
  "areaServed": {
    "@type": "City",
    "name": "Gold Coast"
  }
}
</script>

3. Article and BlogPosting Schema (Authorship and Freshness)

Every blog post and article on your site should carry Article or BlogPosting schema. This tells AI engines who wrote the content, when it was published and last updated, and which organisation published it. These are critical freshness and authority signals.

AI engines prioritise recent, attributed content. An article with clear authorship and a recent dateModified timestamp is far more likely to be cited than an anonymous, undated page.

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Article",
  "@id": "https://example.com.au/blog/your-post/#article",
  "headline": "Your Article Title",
  "datePublished": "2026-06-15",
  "dateModified": "2026-06-15",
  "author": {
    "@type": "Person",
    "name": "Author Name",
    "url": "https://example.com.au/about/author-name",
    "sameAs": [
      "https://www.linkedin.com/in/author-name"
    ]
  },
  "publisher": {
    "@id": "https://example.com.au/#organization"
  },
  "image": "https://example.com.au/images/featured-image.jpg",
  "description": "A brief description of the article content"
}
</script>

Notice how the publisher field references the Organization’s @id instead of repeating the full organisation details. This is the @graph approach in action — connecting entities cleanly.

4. FAQPage Schema (AI’s Favourite Answer Format)

FAQPage schema is one of the most powerful schema types for AI citation because it pre-packages your content in the exact question-and-answer format that AI engines love to extract. When an AI encounters FAQPage markup, it does not have to figure out which sentence answers which question — the relationship is explicit.

Critical rules for FAQPage schema in 2026:

• Questions must match the visible page exactly. Not approximately, not in spirit — word for word. Google will penalise mismatches.
• Answers must match the visible page in substance. Schema answers can be slightly shorter but cannot contain claims the visible answer does not contain.
• Use one FAQPage block per page. Do not split your FAQs across multiple schema blocks.

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "mainEntity": [
    {
      "@type": "Question",
      "name": "How much does schema markup cost to implement?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "For most small business websites, schema markup implementation costs between $500 and $2,000 AUD as a one-off project. WordPress plugins like RankMath or Yoast can automate basic Article and Organization schema for free."
      }
    },
    {
      "@type": "Question",
      "name": "Does schema markup directly improve Google rankings?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Schema markup is not a direct ranking factor for traditional organic results. However, Google confirmed in April 2025 that structured data gives an advantage in AI-powered search features like AI Overviews, where pages with schema show 73% higher selection rates."
      }
    }
  ]
}
</script>

5. HowTo Schema (Step-by-Step Instructions AI Can Extract)

If your content includes step-by-step instructions — how to lodge a BAS, how to prepare a surface for painting, how to set up Google Analytics — HowTo schema tells AI engines exactly where each step begins and ends. This creates clean, numbered answer blocks that AI systems love to cite.

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "HowTo",
  "name": "How to Add Organization Schema to Your Website",
  "step": [
    {
      "@type": "HowToStep",
      "position": 1,
      "name": "Create the JSON-LD block",
      "text": "Open a text editor and create a new JSON-LD script block with your organisation details, including name, URL, logo, address, and sameAs links."
    },
    {
      "@type": "HowToStep",
      "position": 2,
      "name": "Add the code to your website header",
      "text": "Paste the JSON-LD block inside a script tag in your website's head section. In WordPress, use a plugin like RankMath or insert it via your theme's header.php file."
    },
    {
      "@type": "HowToStep",
      "position": 3,
      "name": "Validate with Google's testing tools",
      "text": "Run your page URL through Google's Rich Results Test at search.google.com/test/rich-results to confirm the schema is valid and error-free."
    }
  ]
}
</script>

6. Service Schema (Tell AI What You Actually Sell)

Each service page on your website should carry Service schema describing what is offered on that specific page. This is particularly important for professional services, agencies, and trades businesses where the services might otherwise be ambiguous to an AI.

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Service",
  "@id": "https://example.com.au/services/seo/#service",
  "name": "Search Engine Optimisation",
  "description": "Monthly SEO management for Australian businesses including keyword research, on-page optimisation, and link building.",
  "provider": {
    "@id": "https://example.com.au/#organization"
  },
  "areaServed": {
    "@type": "Country",
    "name": "Australia"
  },
  "serviceType": "Search Engine Optimisation"
}
</script>

7. BreadcrumbList Schema (Site Structure for AI Navigation)

BreadcrumbList schema tells AI engines how your pages connect to each other in a hierarchy. When combined with WebPage schema, it helps AI systems understand the topical structure of your site — which pages are parents, which are children, and how content clusters together.

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "BreadcrumbList",
  "itemListElement": [
    {
      "@type": "ListItem",
      "position": 1,
      "name": "Home",
      "item": "https://example.com.au/"
    },
    {
      "@type": "ListItem",
      "position": 2,
      "name": "Services",
      "item": "https://example.com.au/services/"
    },
    {
      "@type": "ListItem",
      "position": 3,
      "name": "SEO",
      "item": "https://example.com.au/services/seo/"
    }
  ]
}
</script>

How to Connect Schema Types Using @graph (Your Site’s Knowledge Graph)

The real power of schema markup for AI search is not individual blocks — it is how those blocks connect to form a coherent knowledge graph about your business. The @graph array lets you wrap multiple related entities into a single JSON-LD block where they cross-reference each other.

When schema is implemented with stable @id values and a @graph structure, it behaves like a small internal knowledge graph. AI systems do not have to guess who you are and how your content fits together — they can follow explicit connections between your brand, your authors, and your topics.

Here is what a connected @graph block looks like on a blog post page:

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@graph": [
    {
      "@type": "Organization",
      "@id": "https://example.com.au/#organization",
      "name": "Your Business Name",
      "url": "https://example.com.au",
      "logo": "https://example.com.au/logo.png",
      "sameAs": [
        "https://www.linkedin.com/company/your-business",
        "https://www.facebook.com/yourbusiness"
      ]
    },
    {
      "@type": "WebPage",
      "@id": "https://example.com.au/blog/your-post/#webpage",
      "url": "https://example.com.au/blog/your-post/",
      "name": "Your Article Title",
      "isPartOf": {
        "@id": "https://example.com.au/#website"
      }
    },
    {
      "@type": "Article",
      "@id": "https://example.com.au/blog/your-post/#article",
      "headline": "Your Article Title",
      "datePublished": "2026-06-15",
      "dateModified": "2026-06-15",
      "author": {
        "@type": "Person",
        "@id": "https://example.com.au/#author",
        "name": "Author Name"
      },
      "publisher": {
        "@id": "https://example.com.au/#organization"
      },
      "mainEntityOfPage": {
        "@id": "https://example.com.au/blog/your-post/#webpage"
      }
    },
    {
      "@type": "BreadcrumbList",
      "itemListElement": [
        {
          "@type": "ListItem",
          "position": 1,
          "name": "Home",
          "item": "https://example.com.au/"
        },
        {
          "@type": "ListItem",
          "position": 2,
          "name": "Blog",
          "item": "https://example.com.au/blog/"
        }
      ]
    }
  ]
}
</script>

This single block tells any AI engine: this is an article published by this organisation, written by this author, living at this URL, and sitting in this position within the site hierarchy. Every entity references the others cleanly through @id pointers.

Step-by-Step: How to Implement Schema Markup on Your Website

Knowing which schema types matter is only half the job. Here is how to actually implement them on your site, whether you use WordPress, Shopify, or a custom-built website.

Step 1: Audit Your Current Schema

Before adding anything, check what you already have. Run your homepage, a service page, and a blog post through Google’s Rich Results Test. Also use the Schema Markup Validator for a broader check. Note what schema types are already present and which are missing or broken.

Step 2: Start With Organisation Schema on Every Page

Your Organisation schema should appear on every page of your site, not just the homepage. In WordPress, the simplest approach is to add it via your theme’s header.php file or use a plugin like RankMath Pro, which generates Organisation schema automatically. For Shopify, add the JSON-LD block to your theme.liquid file.

Step 3: Add Article Schema to Every Blog Post

Most modern SEO plugins (RankMath, Yoast, SEOPress) automatically generate Article or BlogPosting schema for your posts. Check that the output includes author, datePublished, dateModified, and publisher fields. If your plugin does not include sameAs links for the author, consider adding those manually or via a custom code snippet.

Step 4: Add FAQPage Schema to Pages With FAQ Sections

If you have an FAQ section on a service page or blog post, add FAQPage schema for those questions and answers. Remember: the questions and answers in the schema must match the visible page content exactly. RankMath Pro includes an FAQ block for WordPress that generates the schema automatically.

Step 5: Add Service Schema to Each Service Page

Create a separate Service schema block for each individual service page. Link each Service back to your Organisation using the @id reference. Include a clear description, serviceType, and areaServed.

Step 6: Add BreadcrumbList Schema

Most SEO plugins generate BreadcrumbList schema automatically if breadcrumbs are enabled. Verify that the output matches your visible breadcrumb navigation. Mismatches between visible and schema breadcrumbs can confuse AI systems.

Step 7: Connect Everything With @graph

If your SEO plugin does not automatically use @graph arrays (most modern ones like RankMath do), consider consolidating your separate JSON-LD blocks into a single @graph block that cross-references entities via @id. This creates the cleanest signal for AI systems.

Step 8: Validate and Monitor

After implementation, run every page type through the Rich Results Test. Fix any errors or warnings. Then set up ongoing monitoring — check your schema quarterly, especially after theme updates or plugin changes that might break the markup.

Schema Markup for WordPress: The Practical Toolkit

WordPress powers over 40% of the web and most Australian small business websites. Here is how to implement schema effectively on WordPress.

Option 1: RankMath Pro (Recommended)

RankMath Pro is the most comprehensive schema solution for WordPress. It automatically generates Organisation, Article, BreadcrumbList, and WebPage schema. It includes a dedicated Schema Generator that lets you add FAQPage, HowTo, Service, LocalBusiness, Product, and 20+ other schema types to any page without writing code.

Key settings to configure in RankMath:

• Go to RankMath > Titles & Meta > Local SEO and fill in every field — business name, address, phone, logo, opening hours, and social profiles (these become your sameAs links)
• Under RankMath > Schema Templates, create reusable templates for your service pages and FAQ sections
• Enable the FAQ Block in the WordPress editor to generate FAQPage schema automatically when you add FAQ sections to posts

Option 2: Manual JSON-LD via Header Code

If you prefer full control, add JSON-LD blocks directly to your site. In WordPress, you can add global schema (Organisation, LocalBusiness) via Appearance > Theme File Editor > header.php or through a plugin like Insert Headers and Footers. For page-specific schema, use the RankMath custom code field or add JSON-LD blocks within your content using a Custom HTML block.

Option 3: Yoast SEO

Yoast SEO also generates automatic schema using its @graph approach. Configure your Organisation details under Yoast > Settings > Site Representation. Yoast’s schema is solid for basic types but has fewer options for custom schema types like Service or HowTo compared to RankMath.

Common Schema Mistakes That Hurt AI Visibility

Getting schema right is important, but getting it wrong can actively harm your AI visibility. Here are the most common mistakes Australian businesses make.

1. Schema-Content Mismatch

The biggest mistake is having schema that says one thing while the visible page says another. If your FAQPage schema contains an answer that does not appear on the visible page, Google will ignore the markup and may penalise your site’s structured data trust score. Every schema claim must be verifiable in the visible content.

2. Missing sameAs Links

Organisation and Person schema without sameAs links leaves your entity ambiguous. AI systems use sameAs to cross-reference your brand against profiles they already know. Without those links, the AI has to guess whether “Blue Coast Plumbing” on your website is the same entity it has seen on Google Maps, LinkedIn, and Facebook. Include every verified profile URL.

3. Stale dateModified Values

If your Article schema shows a dateModified from 2023 but the content has been updated since then, you are sending a negative freshness signal. Configure your CMS to update dateModified automatically whenever a post is edited, or update it manually when you refresh content.

4. Duplicate or Conflicting Schema

Multiple plugins generating schema simultaneously is a common problem on WordPress. If RankMath and another plugin both generate Organisation schema, the conflicting signals can confuse AI systems. Audit your pages using the Rich Results Test and disable duplicate schema sources.

5. Using Microdata Instead of JSON-LD

While Google technically supports Microdata and RDFa, JSON-LD is the recommended format. It is easier to maintain, easier to validate, and less prone to breaking when your page layout changes. If your site still uses inline Microdata, migrate to JSON-LD.

6. Ignoring Product and Review Schema for Ecommerce

Australian ecommerce sites often neglect Product and AggregateRating schema. For AI-powered product recommendations — which are growing rapidly through ChatGPT’s Merchant Program and Perplexity’s Buy with Pro — these schema types help AI engines understand pricing, availability, and customer sentiment.

What the Research Says: Does Schema Actually Help AI Citations?

The honest answer is nuanced. Schema markup is confirmed infrastructure for Google’s AI ecosystem, but its direct impact on other AI platforms is more limited. Here is what we know from the data.

The Google Ecosystem: Strong Evidence

Within Google’s ecosystem, the evidence is compelling. Pages with properly implemented structured data show 73% higher selection rates in AI Overviews compared to unmarked content. The Otterly.ai experiment documented a 1,500% increase in AI Overview appearances after implementing FAQ, Organisation, and Article schema. Google has explicitly confirmed that structured data provides an advantage.

ChatGPT, Perplexity, and Claude: Indirect Benefits

The Otterly.ai experiment found that six out of seven AI platforms could not directly fetch or correctly interpret schema markup when asked. ChatGPT, Perplexity, Claude, and others largely process page content through their own natural language understanding rather than parsing JSON-LD blocks directly.

However, schema still benefits these platforms indirectly. Clear entity markup improves your presence across the web — better Google results, better knowledge panel data, better profile connections — which feeds into the training data and retrieval sources these AI platforms use. Schema is not a shortcut, but it strengthens the overall entity signals that all AI systems rely on.

The Bottom Line

Schema markup does not guarantee AI citations. What it does is increase the probability that AI systems can confidently identify your brand and extract clean answers from your content. For Australian businesses competing for visibility in AI search, that probability increase is the difference between being cited and being invisible.

Schema Markup Priority Checklist by Business Type

Not every business needs every schema type. Here is a priority list based on your business model.

Local Service Businesses (Tradies, Restaurants, Clinics)

1. LocalBusiness (use most specific subtype) — non-negotiable
2. Organization with complete sameAs links
3. Service for each service page
4. FAQPage for common customer questions
5. BreadcrumbList for site structure
6. Article for blog content

Professional Services (Lawyers, Accountants, Consultants)

1. ProfessionalService (LocalBusiness subtype)
2. Person for each practitioner with sameAs to LinkedIn profiles
3. Organization with sameAs links
4. Service for each practice area
5. FAQPage for legal/financial questions
6. Article with clear author attribution

Ecommerce Businesses

1. Organization with sameAs links
2. Product with price, availability, and AggregateRating
3. BreadcrumbList for category hierarchy
4. FAQPage for product and policy questions
5. Article for buying guides and blog content

Agencies and B2B Services

1. Organization with comprehensive sameAs links
2. Service for each service offering
3. Person for team members and authors
4. Article with author and publisher connections
5. FAQPage for service-related questions
6. BreadcrumbList for site navigation

Testing and Validating Your Schema Markup

Implementation is only as good as your validation process. Use these tools to test your schema before and after deployment.

Google Rich Results Test

Available at search.google.com/test/rich-results, this tool validates your structured data specifically against Google’s requirements. It shows which rich results your page is eligible for and flags any errors or warnings. Test every page template on your site — homepage, service page, blog post, and contact page.

Schema Markup Validator

Available at validator.schema.org, this tool validates all Schema.org markup without Google-specific restrictions. It is useful for catching structural errors that the Rich Results Test might miss.

Google Search Console

After implementation, monitor the Enhancements section in Google Search Console. It reports schema errors and valid items across your entire site over time, catching issues that single-page tests miss — like template errors affecting hundreds of pages.

Manual Spot Checks

Every quarter, manually check three to five pages across different templates. Schema can break silently during theme updates, plugin updates, or CMS migrations. A quarterly audit catches these issues before they compound.

Frequently Asked Questions

What is schema markup and why does it matter for AI search?

Schema markup is structured data code (JSON-LD format) that you add to your website’s HTML to tell AI engines exactly what your content represents — your business identity, your services, your articles, and your FAQs. It matters for AI search because it helps engines like Google AI Overviews, ChatGPT, and Perplexity identify your brand as a trustworthy entity and extract clean answers from your content with confidence.

Does schema markup directly improve Google rankings?

Schema markup is not a direct ranking factor for traditional organic search results. However, Google confirmed in April 2025 that structured data gives an advantage in AI-powered search features. Research shows pages with schema have 73% higher selection rates in AI Overviews compared to pages without it.

Which schema types are most important for AI visibility?

The seven most important schema types for AI visibility are Organization (with sameAs links), LocalBusiness (for local businesses), Article/BlogPosting, FAQPage, HowTo, Service, and BreadcrumbList. Organization and FAQPage schema tend to deliver the strongest entity recognition and citation benefits.

Can I add schema markup without coding knowledge?

Yes. WordPress plugins like RankMath Pro and Yoast SEO generate most schema types automatically. RankMath’s Schema Generator lets you add FAQPage, HowTo, Service, and other types without writing code. Shopify and Squarespace also have built-in schema for basic types like Organization and Product.

How do I test if my schema markup is working correctly?

Use Google’s Rich Results Test (search.google.com/test/rich-results) to validate against Google’s requirements, and the Schema Markup Validator (validator.schema.org) for broader validation. After deployment, monitor the Enhancements section in Google Search Console for ongoing error tracking.

Does schema help with ChatGPT and Perplexity, or just Google?

Schema has the strongest direct impact within Google’s ecosystem (AI Overviews and AI Mode). A 2026 experiment found that ChatGPT, Perplexity, and Claude cannot directly fetch or interpret schema markup. However, schema indirectly benefits these platforms by strengthening your overall entity signals and web presence, which feeds into their retrieval and training data.

How often should I update my schema markup?

Audit your schema quarterly, and always check it after theme updates, plugin changes, or site redesigns. Article schema should update dateModified automatically whenever content is edited. Organisation and LocalBusiness schema should be updated whenever your business details change — new address, phone number, or social profiles.

What is the sameAs field and why is it so important?

The sameAs field in Organization or Person schema links your entity to other trusted profiles — LinkedIn, Facebook, Google Business Profile, Wikipedia — that AI models already know. It disambiguates your brand by telling AI systems that these different profiles all represent the same entity. This is the single highest-leverage field for AI entity recognition.

Next Steps: Get Your Schema AI-Ready

Schema markup is not a one-time project — it is ongoing infrastructure that needs to evolve as AI search matures. Start with Organisation schema and sameAs links, then work through the priority list for your business type. Validate everything with Google’s testing tools, and audit quarterly.

If the technical side feels overwhelming, or if you want expert implementation that covers all seven schema types with a proper @graph structure, Titan Blue’s AEO team can handle the full implementation. We also offer AI readiness audits that assess your current schema alongside 30+ other factors that determine whether AI engines can find, understand, and cite your business.

The businesses that get schema right now are building the entity foundation that AI engines will rely on for years to come. The ones that wait will find it increasingly difficult to catch up as AI search becomes the default way Australians find businesses, products, and services.

Talk to our team about getting your structured data AI-ready.