AI Visibility Metrics
Indexably evaluates your content using 16 distinct metrics. Single-page scans analyze 10 metrics focused on individual page optimization. Full-site scans add 6 additional categories that evaluate your website as a whole. Understanding these metrics helps you prioritize improvements for maximum AI visibility.
Single-Page Metrics
These 10 metrics are evaluated for every page you scan. Each measures a specific aspect of how well your content can be discovered, understood, and cited by AI platforms like ChatGPT, Perplexity, and Gemini.
1. Content Relevance
Measures how directly your content answers potential user questions. High-scoring pages have clear topic focus, address common queries early, and provide complete information. This matters because AI systems prioritize content that directly satisfies user intent without requiring additional sources.
2. AI Readability
Evaluates how easily AI systems can parse your content structure. This includes proper heading hierarchy, short paragraphs, bullet points, and semantic HTML. AI platforms process content structurally, so clear organization significantly improves comprehension and citation likelihood.
3. Citation Suitability
Assesses whether your content contains quotable facts, statistics, and authoritative statements. High scores come from specific data points, clear definitions, and expert statements with attribution. AI systems prefer citing content with concrete, verifiable claims over vague assertions.
4. Structured Metadata
Analyzes schema.org markup, meta tags, and Open Graph data. Structured data helps AI systems understand content context, relationships, and authority. Pages with comprehensive metadata are more likely to be correctly categorized and cited.
5. Crawl & Index Signals
Reviews page-level indexation signals affecting AI discovery. This includes title tags, meta descriptions, canonical tags, robots directives, and heading structure. If these signals are missing or misconfigured, AI systems may not properly discover or cite your content.
6. Page Freshness
Examines signals indicating content currency. This includes visible dates, recent timestamps, and references to current information. AI systems prefer citing current content, and freshness signals help establish that your information is up-to-date and maintained.
7. Domain Expertise
Measures authority and E-E-A-T indicators. High scores come from author credentials, organizational expertise pages, and content depth. AI platforms assess source credibility when deciding what to cite, favoring demonstrated expertise over anonymous content.
8. Multimodal Readiness
Evaluates non-text content optimization. Key factors include descriptive image alt text, video transcripts, and accessible charts. As AI systems increasingly process multiple content types, properly labeled media improves overall page understanding.
9. RAG/AI Retrieval Suitability
Measures how well your structure supports AI retrieval systems. AI platforms chunk content for processing—clear section boundaries, self-contained paragraphs, and consistent formatting help your content be accurately retrieved and cited.
10. Engagement Cues
Assesses social proof and credibility signals. These include reviews, testimonials, comment sections, and trust badges. AI systems use engagement signals as secondary indicators of content authority and community validation.
Full-Site Categories
These 6 categories are evaluated during full-site scans. They measure how well your entire website is optimized for AI discovery, going beyond individual page analysis to assess site-wide patterns and structure.
1. Site Structure
Evaluates how well your site is organized for AI navigation and understanding. This includes URL hierarchy, navigation patterns, breadcrumb implementation, and logical content grouping. Clear site architecture helps AI systems understand content relationships and importance.
2. Topic Coverage
Measures the depth and breadth of your content across topics. Sites with comprehensive topic coverage demonstrate authority in their subject area. AI systems favor citing sources that thoroughly cover their domain rather than sites with shallow or scattered content.
3. Internal Linking
Analyzes how your pages connect and support each other. Strong internal linking distributes authority, helps crawlers discover content, and establishes topical relationships. Pages with robust internal links are more likely to be discovered and understood by AI systems.
4. AI Discoverability
Measures how easily AI systems can find and index your content. This includes sitemap completeness, crawl efficiency, and content accessibility. Sites optimized for discoverability ensure their content reaches AI training and retrieval systems.
5. Content Distribution
Evaluates the balance and focus of content across your site. This assesses whether content is appropriately distributed, avoiding thin pages while maintaining topical consistency. Balanced content distribution signals a well-maintained, authoritative resource.
6. Technical Readability
Assesses technical factors that affect AI accessibility. This includes page speed, mobile optimization, JavaScript rendering requirements, and server response times. Technical barriers can prevent AI systems from fully accessing and understanding your content.