Moderate AI visibility with 32 of 53 criteria passing. Biggest gap: llms.txt file.
Verdict
Below-average AEO readiness at 54/100 - multiple areas need attention. Key strengths include Internal Linking Structure, Direct Answer Paragraphs, and Canonical URL Strategy. Priority gaps: llms.txt File, Q&A Content Format, and RSS/Atom Feed.
How to Improve
robots.txt blocks Google-Extended - content is excluded from Google AI training and grounding
Add a machine-readable llms.txt file at your domain root that describes your site, services, and key pages for AI engines.
Create a comprehensive llms-full.txt with detailed page descriptions, content summaries, and topic taxonomy.
Update robots.txt to explicitly allow AI crawlers and include sitemap directive.
Trim oversized HTML, excessive DOM nodes, and large inline payloads that slow AI crawlers.
Ensure clean, well-structured HTML with proper meta tags, HTTPS, and parseable content for AI crawlers.
Generate a comprehensive sitemap with lastmod dates for all important pages.
Optimize compression, cache headers, redirect chains, and HTML payload size for faster AI crawler access.
Minimize blocking scripts and stylesheets in <head> to improve content availability for AI crawlers.
722/741 images lack explicit width/height - the most common cause of layout shift (CLS)
Implement hreflang tags and lang attributes so AI engines serve the correct language version when answering queries.
Top Opportunities10
View allPublish original research, statistics, case studies, or proprietary data that AI engines can cite. Unique data points make your content a primary source rather than a derivative one.
Add question-based headings (H2/H3) throughout your content. Use "What is...", "How does...", "Why should..." patterns that match how users query AI assistants.
Use HTML tables for comparison data and ordered/unordered lists for features, steps, and specifications. Structured data formats are directly extractable by AI engines for answers.
Expand articles to 1000+ words with structured H2/H3 sections, comparison tables, and expert analysis. Thin content (under 300 words) is rarely cited by AI engines. Deep, well-structured articles demonstrate expertise.
Include "our analysis", "our data", "our testing" phrases backed by original research or proprietary data. 52.2% of AI-cited posts contain owned data signals.
Add inline citations to external sources, "According to [Source]..." attribution phrases, and a Sources section at the end of key articles.
Include dateModified schema, visible last-updated dates, and time elements on content pages. Fresh content signals help AI engines prioritize your pages over stale alternatives.
Include clear definition patterns ("X refers to...", "X is defined as...") for key terms and concepts. Definition-style content is highly citable by AI engines answering "what is" queries.
Write 20-25 word self-contained answer sentences immediately after each H2 heading. 72.4% of AI-cited posts use this pattern - it gives engines a ready-made snippet to quote.
Add specific numbers, percentages, statistics, and data points throughout your content. Fact-dense content gives AI engines concrete data to cite rather than vague claims.