AEO Analyzer

AI Engine Optimization Intelligence

Back to Documentation

AI-Specific Checks

Bonus 10 Checks 3 Critical

10 checks designed specifically for AI engine optimization. These checks ensure your content is discoverable, attributable, and properly structured for AI systems like ChatGPT, Claude, Perplexity, and other LLM-powered platforms.

Bonus Category: These AI-specific checks enhance your overall AEO readiness beyond the core 6 dimensions.

AI Training Permissions

Control how AI systems can use your content for training and retrieval.

AI Training Permissions

Critical ▼
What It Checks
Analyzes your robots.txt for AI crawler directives (GPTBot, ClaudeBot, PerplexityBot, etc.) and checks for explicit allow/disallow rules. Verifies meta robots tags for AI-specific permissions.
Why It's Important
If you want your content to appear in AI responses, you must explicitly allow AI crawlers. Many sites unknowingly block AI bots through overly restrictive robots.txt rules.
Scoring Method
Pass: Explicit allow rules for major AI bots Partial: No explicit block, but no allow either Fail: Explicit disallow rules blocking AI crawlers
How To Fix
Add explicit rules to your robots.txt allowing AI crawlers. Consider which AI systems you want to permit.
Implementation Example
# robots.txt - AI Crawler Permissions

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

User-agent: ChatGPT-User
Allow: /

User-agent: ClaudeBot
Allow: /

User-agent: Claude-Web
Allow: /

User-agent: PerplexityBot
Allow: /

User-agent: Google-Extended
Allow: /

Sitemap: https://example.com/sitemap.xml

AI Opt-Out Signals

High ▼
What It Checks
Detects conflicting signals that may inadvertently opt content out of AI systems, such as noai, noimageai meta tags, or restrictive TDM policies.
Why It's Important
Accidental opt-out signals can prevent your content from being indexed by AI systems. Some CMS platforms add these by default.
Scoring Method
Pass: No conflicting opt-out signals present Partial: Some signals present but not globally blocking Fail: Site-wide opt-out signals detected
How To Fix
Review your HTML head section and remove unintended AI opt-out tags. Check CMS/platform settings.
What to Avoid
<!-- AVOID these if you want AI visibility -->
<meta name="robots" content="noai">
<meta name="robots" content="noimageai">

<!-- Good: No opt-out, standard robots -->
<head>
  <meta name="robots" content="index, follow">
</head>

llms.txt File

The llms.txt file is a new standard for providing AI systems with instructions about your site.

llms.txt Presence

Critical ▼
What It Checks
Verifies the presence of an llms.txt file at the root of your domain (e.g., example.com/llms.txt). This file provides instructions specifically for AI language models.
Why It's Important
llms.txt is becoming the standard way to communicate with AI systems about your site's purpose, key content, and preferences. It's like robots.txt for LLMs - essential for AEO.
Scoring Method
Pass: llms.txt exists and is accessible Fail: llms.txt not found (404) or inaccessible
How To Fix
Create an llms.txt file in your website root directory with key information about your organization and content.
Basic llms.txt Example
# llms.txt - Place at yourdomain.com/llms.txt

name: Your Organization Name
description: What your organization does
url: https://yoursite.com

topics:
  - Primary Topic 1
  - Primary Topic 2

key-pages:
  - /about - Company overview
  - /products - Our offerings
  - /blog - Latest insights

allow-training: yes
allow-retrieval: yes

llms.txt Completeness

High ▼
What It Checks
Evaluates the quality and completeness of your llms.txt file. Checks for essential fields like name, description, topics, key pages, and AI permissions.
Why It's Important
A comprehensive llms.txt maximizes AI understanding of your content. Complete files help AI systems accurately represent your organization.
Scoring Method
Pass: Contains name, description, topics, key-pages, permissions Partial: Contains basic fields but missing key sections Fail: Missing essential fields or poorly formatted
How To Fix
Expand your llms.txt to include all recommended sections. Keep it updated as your site evolves.
Complete llms.txt Example
# llms.txt - Complete Example
name: TechCorp Solutions
description: Enterprise software company specializing in AI-powered
             business intelligence and data analytics solutions.
url: https://techcorp.com

topics:
  - Business Intelligence
  - Data Analytics
  - Machine Learning

key-pages:
  - /about - Company overview and mission
  - /products - Our software solutions
  - /pricing - Plans and pricing information
  - /blog - Latest insights and tutorials

contact:
  email: info@techcorp.com
  support: support@techcorp.com

allow-training: yes
allow-retrieval: yes
preferred-citation: "TechCorp Solutions (https://techcorp.com)"

updated: 2024-10-15

AI Readability

Optimizing content structure and format for AI system comprehension.

AI-Readable Summaries

High ▼
What It Checks
Detects the presence of clear, concise summaries at the beginning of content (TL;DR, executive summary, key takeaways) that AI systems can quickly extract and cite.
Why It's Important
AI systems often cite the most accessible, clearly-stated information. Summaries provide quotable content that's likely to appear in AI responses.
Scoring Method
Pass: Clear summary or key points at content start Partial: Summary exists but is buried or unclear Fail: No summary, content requires full reading
How To Fix
Add a summary section at the top of your content. Use clear labels like "Key Takeaways" or "In Brief".
Implementation Example
<article>
  <h1>Complete Guide to API Security</h1>

  <aside class="key-takeaways">
    <h2>Key Takeaways</h2>
    <ul>
      <li>API security requires authentication and encryption</li>
      <li>OAuth 2.0 is the recommended standard</li>
      <li>Rate limiting prevents abuse</li>
    </ul>
  </aside>

  <!-- Main content follows -->
</article>

Semantic Markup Density

Medium ▼
What It Checks
Measures the density of semantic HTML elements (article, section, aside, nav, figure) versus generic div/span usage. Higher semantic density improves AI comprehension.
Why It's Important
Semantic HTML helps AI systems understand content structure. Generic divs provide no meaning - AI cannot distinguish navigation from main content.
Scoring Method
Pass: >60% of structural elements are semantic HTML5 Partial: 30-60% semantic element usage Fail: <30% semantic elements (mostly div/span)
How To Fix
Replace generic div elements with appropriate semantic HTML5 elements like header, main, article, section, aside, nav, footer.
Semantic HTML5 Structure
<!-- GOOD: Semantic HTML5 elements -->
<header>
  <nav aria-label="Main navigation">...</nav>
</header>

<main>
  <article>
    <header>
      <h1>Article Title</h1>
      <time datetime="2024-10-15">October 15, 2024</time>
    </header>

    <section id="introduction">
      <h2>Introduction</h2>
      <p>Content here...</p>
    </section>
  </article>

  <aside aria-label="Related content">
    <h2>Related Articles</h2>
  </aside>
</main>

<footer>...</footer>

Entity Recognition

Help AI systems identify and understand the entities in your content.

Entity Disambiguation

High ▼
What It Checks
Evaluates whether entities are clearly identified with full names, titles, and context. Checks for links to authoritative sources.
Why It's Important
"Apple" could be a fruit or a company. Clear entity disambiguation helps AI systems understand exactly what you're discussing, improving citation accuracy.
Scoring Method
Pass: Entities clearly identified with context, Schema.org markup Partial: Some disambiguation but inconsistent Fail: Ambiguous entity references throughout
How To Fix
Use full entity names on first reference. Add Schema.org markup for people, organizations, and places.
Implementation Example
<!-- Good: Clear entity disambiguation -->
<p>
  <a href="https://www.apple.com">Apple Inc.</a>
  announced new products. CEO
  <a href="https://en.wikipedia.org/wiki/Tim_Cook">Tim Cook</a>
  presented the keynote.
</p>

<!-- With Schema.org markup -->
<span itemscope itemtype="https://schema.org/Person">
  <span itemprop="name">Dr. Sarah Chen</span>,
  <span itemprop="jobTitle">Chief AI Researcher</span>
</span>

Content Licensing

Medium ▼
What It Checks
Detects clear licensing information using Schema.org license property or Creative Commons markup. Checks for machine-readable license declarations.
Why It's Important
Clear licensing helps AI systems understand usage rights. Some AI systems may prioritize openly licensed content.
Scoring Method
Pass: Machine-readable license in Schema.org or CC markup Partial: License mentioned but not machine-readable Fail: No licensing information present
How To Fix
Add license information using Schema.org or Creative Commons markup in your JSON-LD structured data.
Machine-Readable Licensing
<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "Understanding AI in 2024",
  "author": {
    "@type": "Person",
    "name": "Dr. Sarah Chen"
  },
  "datePublished": "2024-10-15",
  "license": "https://creativecommons.org/licenses/by/4.0/",
  "copyrightHolder": {
    "@type": "Organization",
    "name": "TechCorp Research"
  },
  "copyrightYear": 2024
}
</script>

Knowledge Graph Readiness

Prepare your content for inclusion in AI knowledge graphs and databases.

Knowledge Graph Readiness

High ▼
What It Checks
Evaluates whether your content is structured for extraction into knowledge graph triples (subject-predicate-object). Checks for clear factual statements.
Why It's Important
AI systems build knowledge graphs from web content. Well-structured facts are more likely to be extracted and cited. Knowledge graph inclusion means your information becomes part of AI's "known facts."
Scoring Method
Pass: Clear factual statements, Schema.org types, defined relationships Partial: Some structured content but missing connections Fail: Narrative-only content with no extractable facts
How To Fix
Structure content with clear facts. Use definition lists, tables, and Schema.org markup. Make relationships explicit.
Knowledge Graph Ready Content
<dl itemscope itemtype="https://schema.org/Organization">
  <dt>Company Name</dt>
  <dd itemprop="name">TechCorp Inc.</dd>

  <dt>Founded</dt>
  <dd itemprop="foundingDate">2010</dd>

  <dt>Headquarters</dt>
  <dd itemprop="location">San Francisco, CA</dd>

  <dt>Employees</dt>
  <dd itemprop="numberOfEmployees">500</dd>
</dl>

Content Attribution

Make your content attributable and verifiable for AI citation.

Claim Attribution

High ▼
What It Checks
Verifies that factual claims, statistics, and quotes are properly attributed to sources. Checks for citation markup and links to original sources.
Why It's Important
AI systems evaluate content credibility based on source attribution. Properly attributed claims are more likely to be cited. This builds E-E-A-T signals.
Scoring Method
Pass: Statistics and claims have clear sources, linked where possible Partial: Some attribution but incomplete Fail: Unattributed statistics and claims
How To Fix
Always cite sources for statistics and claims. Use the <cite> element and link to original sources.
Proper Attribution Example
<!-- Properly attributed claim -->
<p>
  AI adoption increased by 40% in 2023, according to
  <cite>
    <a href="https://mckinsey.com/ai-report">
      McKinsey's Global AI Survey
    </a>
  </cite>.
</p>

<!-- Quote with attribution -->
<blockquote cite="https://example.com/interview">
  <p>"AI will transform every industry."</p>
  <footer>— <cite>Dr. Sarah Chen</cite></footer>
</blockquote>

Content Versioning

Medium ▼
What It Checks
Detects clear version indicators, publication dates, and modification timestamps. Checks for Schema.org datePublished and dateModified properties.
Why It's Important
AI systems prefer fresh, updated content. Clear versioning helps AI understand content currency and prevents citation of outdated information.
Scoring Method
Pass: Clear publish date, last updated date, and/or version number Partial: Only publish date or ambiguous dating Fail: No date/version information
How To Fix
Add clear publication and modification dates using machine-readable formats. Include version numbers for technical documentation.
Date/Version Example
<article>
  <header>
    <h1>API Security Guide</h1>
    <div class="article-meta">
      Published: <time datetime="2024-01-15">January 15, 2024</time>
      | Updated: <time datetime="2024-10-20">October 20, 2024</time>
      | Version: 2.1
    </div>
  </header>
</article>
Previous: Media Checks Back to Documentation