How Our AI Visibility Analysis Works

We perform a comprehensive 6-step technical analysis to determine if your website is properly configured for AI discovery and citation by ChatGPT, Claude, Perplexity, and other AI systems.

💡 The Challenge: Many websites inadvertently block AI crawlers or lack the structured data AI systems need to understand their content. Our tool identifies these issues and provides specific solutions.

Our 6-Step Analysis Process

Each step checks a critical component of AI visibility. We provide specific, actionable recommendations based on current AI system requirements and web standards.

Step 1: AI Crawler Access Check

We scan your robots.txt to see if AI bots can access your site

Common Issue

Many websites inadvertently block AI crawlers through restrictive robots.txt configurations

Our Analysis

We check if your robots.txt allows these documented AI crawlers:

What We Check:

GPTBot (ChatGPT) - OpenAI's official web crawler for training data

ClaudeBot (Anthropic) - Anthropic's crawler for constitutional AI training

PerplexityBot - Real-time search and citation crawler

Google-Extended (Bard) - Google's AI training data crawler

CCBot (Common Crawl) - Open dataset used by multiple AI systems

Technical Example:

# ✅ AI-friendly robots.txt configuration
User-agent: GPTBot
Allow: /

User-agent: ClaudeBot  
Allow: /

User-agent: PerplexityBot
Allow: /

User-agent: Google-Extended
Allow: /

Analysis Impact:Proper AI crawler access is fundamental to AI visibility

Source:Based on official OpenAI, Anthropic, and Perplexity documentation

Step 2: Technical Accessibility Audit

We verify your site meets the technical requirements AI crawlers need

Common Issue

Technical barriers can prevent AI systems from accessing and indexing content

Our Analysis

We test essential technical requirements for AI crawler compatibility:

What We Check:

HTTPS/SSL certificate validation (industry standard for secure crawling)

Response time optimization (faster sites get crawled more frequently)

HTTP status code validation (200 OK responses ensure successful crawling)

Redirect chain analysis (excessive redirects can break crawler paths)

Content accessibility without JavaScript (most AI crawlers don't execute JS)

Technical Example:

# Technical requirements we verify:
✅ HTTPS enabled (SSL certificate valid)
✅ Response time optimized
✅ HTTP 200 status codes
✅ Clean redirect chains
✅ Content accessible without JavaScript

Analysis Impact:Technical issues can significantly impact AI crawler success rates

Source:Based on web crawling best practices and AI company guidelines

Step 3: Structured Data Analysis

We analyze your schema markup to see how well AI can understand your content

Common Issue

Unstructured content is harder for AI systems to parse and cite accurately

Our Analysis

We scan for schema.org structured data that enhances AI comprehension:

What We Check:

JSON-LD format detection (W3C recommended structured data format)

FAQ schema for Q&A content (helps AI systems answer user questions)

Article schema for blog posts (enables proper content attribution)

Product schema for e-commerce (powers AI shopping recommendations)

Organization/LocalBusiness schema (establishes entity recognition)

Technical Example:

{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "mainEntity": [{
    "@type": "Question", 
    "name": "How do I check AI visibility?",
    "acceptedAnswer": {
      "@type": "Answer",
      "text": "Use an AI visibility checker to analyze your robots.txt, structured data, and technical configuration for AI crawler compatibility."
    }
  }]
}

Analysis Impact:Rich structured data significantly improves AI content understanding

Source:Schema.org standards and Google's structured data guidelines

Step 4: Content Structure Evaluation

We evaluate how AI-friendly your content organization is

Common Issue

Poorly structured content is difficult for AI systems to extract and cite

Our Analysis

We analyze your content structure for optimal AI readability:

What We Check:

Semantic HTML usage (proper H1-H6 hierarchy, lists, sections)

Clear content hierarchy and logical information flow

Meta descriptions and title tag optimization for context

Alt text for images (provides context for visual content)

Internal linking structure (helps AI understand content relationships)

Technical Example:

<!-- ✅ AI-optimized content structure -->
<article>
  <h1>Main Topic Title</h1>
  <section>
    <h2>Specific Question or Subtopic</h2>
    <p>Clear, direct answer with supporting details...</p>
    <ul>
      <li>Structured list of key points</li>
      <li>Factual information with context</li>
    </ul>
  </section>
</article>

Analysis Impact:Well-structured content improves AI citation accuracy

Source:Web accessibility standards and AI content processing research

Step 5: Discoverability Assessment

We check how easily AI systems can find and index your content

Common Issue

Content that's hard to discover won't be included in AI knowledge bases

Our Analysis

We verify these discoverability factors:

What We Check:

XML sitemap presence and validity (provides content roadmap for crawlers)

URL structure and canonicalization (prevents duplicate content issues)

Alternative content formats like RSS/JSON feeds (multiple access points)

LLMs.txt file detection (emerging standard for AI crawler permissions)

Content freshness indicators (helps AI identify up-to-date information)

Technical Example:

<?xml version="1.0" encoding="UTF-8"?>
<urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">
  <url>
    <loc>https://yoursite.com/important-page</loc>
    <lastmod>2025-01-15</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.8</priority>
  </url>
</urlset>

Analysis Impact:Strong discoverability increases AI indexing likelihood

Source:XML sitemap protocol and search engine optimization standards

Step 6: AI Visibility Score Calculation

We combine all factors into a single, actionable score with prioritized recommendations

Common Issue

Without clear priorities, it's difficult to know which optimizations will have the most impact

Our Analysis

Our scoring system weighs factors based on their impact on AI visibility:

What We Check:

AI Access Control (40% weight) - Foundation for any AI visibility

Structured Data Quality (30% weight) - Critical for AI content understanding

Content Structure (20% weight) - Important for accurate citations

Technical Performance (10% weight) - Supporting infrastructure

Bonus points for advanced optimizations (up to +10 points)

Technical Example:

# Example Score Calculation
AI Access: 85/100 (× 40%) = 34.0 points
Structured Data: 70/100 (× 30%) = 21.0 points  
Content Structure: 90/100 (× 20%) = 18.0 points
Technical: 95/100 (× 10%) = 9.5 points
Bonus Features: +5.0 points
Final Score: 87.5/100

Analysis Impact:Comprehensive scoring helps prioritize optimization efforts

Source:Weighted scoring methodology based on AI system requirements

Why AI Visibility Matters

Growing AI Usage

AI assistants are increasingly used for research and recommendations
Businesses report increased referral traffic from AI citations
Structured content receives more accurate AI citations

Technical Requirements

AI crawlers require explicit access permissions
Structured data improves content comprehension
Technical optimization affects crawler success rates

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Last updated: July 1, 2025
Author: Alex Kim, AI Visibility Specialist