Get Your Content Indexed and Understood by AI Models

Understanding how LLMs access your content

AI models work through a specific pipeline. Understanding each stage tells you where your visibility is actually breaking — and which fix will move it.

User-agent: GPTBot
Allow: /

User-agent: ChatGPT-User
Allow: /

User-agent: Bingbot
Allow: /

Schema.org Article
  ├─ author (Person + credentials)
  ├─ datePublished
  ├─ dateModified
  └─ publisher (Organization)

<h1>Primary topic</h1>
  <h2>Major subtopic</h2>
    <h3>Supporting detail</h3>
  <h2>Related subtopic</h2>

All four stages have to work. We audit every one of them, then hand the findings to your team or implement them ourselves alongside technical AI SEO and schema and structured data.

Technical implementation for LLM visibility

Here is what actually changes on your site.

Robots.txt configuration

AI model crawlers check robots.txt to decide whether they are allowed to fetch your pages. Directives are per user agent, so a permissive wildcard rule does not guarantee AI access, and a restrictive one can silently switch it off.

# Allow AI crawlers
User-agent: GPTBot
Allow: /

User-agent: ClaudeBot
Allow: /

User-agent: Google-Extended
Allow: /

User-agent: PerplexityBot
Allow: /

User-agent: Bingbot
Allow: /

# Keep private paths out
Disallow: /admin/
Disallow: /private/

Sitemap: https://visibility.partners/sitemap.xml

• Serve it from the root: /robots.txt
• Declare each AI user agent explicitly rather than relying on wildcards
• Keep admin, staging and sensitive paths disallowed
• Reference your sitemap from robots.txt
• Verify real crawler hits in server logs after deploying

Essential schema markup for LLM visibility

Structured data labels your content type, author, publication date and publisher. It is the cheapest way to remove ambiguity during the understanding stage.

Article schema

{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "Article title",
  "description": "Brief description",
  "author": {
    "@type": "Person",
    "name": "Author Name",
    "jobTitle": "Author Title",
    "url": "https://visibility.partners/author/jeremy-osborn"
  },
  "publisher": { "@type": "Organization", "name": "Visibility Partners" },
  "datePublished": "2026-01-15",
  "dateModified": "2026-08-19"
}

Author credentials and affiliation

{
  "@context": "https://schema.org",
  "@type": "Person",
  "name": "Expert Name",
  "jobTitle": "Head of AI Search",
  "affiliation": { "@type": "Organization", "name": "Your Company" },
  "knowsAbout": ["Generative engine optimization", "Technical SEO"],
  "sameAs": ["https://www.linkedin.com/in/…"]
}

FAQPage schema

{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "mainEntity": [
    {
      "@type": "Question",
      "name": "Question text",
      "acceptedAnswer": { "@type": "Answer", "text": "Answer text" }
    }
  ]
}

• Use JSON-LD in the document head rather than inline microdata
• Validate against the schema.org validator and Rich Results Test
• Implement at template level so new pages inherit it
• Start with your highest-value pages, then roll out

HTML structure for LLM understanding

Models parse HTML to infer hierarchy, topics and relationships. Clean semantic structure is what makes a passage extractable in isolation.

<article>
  <h1>Main topic</h1>
  <p>Direct answer in the first paragraph.</p>

  <h2>Major subtopic</h2>
  <p>Context and explanation…</p>

    <h3>Supporting detail</h3>
    <p>Specifics, data, examples…</p>

  <h2>Related subtopic</h2>
  <table><caption>What the data shows</caption>…</table>

  <p>According to <cite>Source Name</cite>, …</p>
</article>

• One H1 per page stating the main topic plainly
• Nested H2/H3 hierarchy with no skipped levels
• Related sections grouped into coherent topical clusters
• Descriptive headings written for humans, not keyword slots
• Original data, examples and clear source attribution

Expertise, authority and trust signals

Models weigh who is speaking as heavily as what is said. Anonymous content is retrievable but rarely citable when a credentialed alternative exists.

<section itemscope itemtype="https://schema.org/Person">
  <h3>About <span itemprop="name">Author Name</span></h3>
  <p itemprop="description">
    <span itemprop="jobTitle">Title</span> with X years in Field.
  </p>
  <ul>
    <li>Certification: …</li>
    <li>Education: …</li>
    <li>Publications: …</li>
  </ul>
</section>

• Named author bios with verifiable credentials on every substantive page
• Organisation-level proof: track record, coverage, recognitions
• Citations and outbound links to credible primary sources
• Original research and data that only you can publish
• Consistent entity identity across your site and third-party profiles

Entity work sits alongside this — see entity optimization and digital PR for AI citations.

Freshness signals

Retrieval systems favour current sources for most commercial questions. Freshness is a maintenance discipline, not a one-off deploy.

<meta property="article:published_time" content="2026-01-15T09:00:00Z" />
<meta property="article:modified_time" content="2026-08-19T11:30:00Z" />

<!-- Mirror the same values in your Article JSON-LD -->
"datePublished": "2026-01-15",
"dateModified": "2026-08-19"

• Update priority content on a scheduled cadence
• Change modification dates only when substance actually changed
• Show a visible 'last updated' line for human readers
• Refresh outdated statistics and examples
• Retire or consolidate pages you will not maintain

Our LLM visibility process

LLM visibility: the foundation for GEO and AEO

LLM visibility is the prerequisite. If models cannot find or interpret your content, neither generative engine optimization nor answer engine optimization will produce citations. Think of it as technical SEO for AI. Once the foundation is solid, GEO and AEO layer cleanly on top — and our methodology sequences them in that order for exactly that reason.

Related services and reading

• Technical SEO agency
• AI visibility audit
• AI visibility monitoring