novatools.org
Weak AI visibility with 27 of 48 criteria passing. Biggest gap: llms.txt file.
Verdict
Below-average AEO readiness at 48/100 - multiple areas need attention. Key strengths include Schema.org Structured Data, Q&A Content Format, and Clean, Crawlable HTML. Priority gaps: llms.txt File, Content Licensing & AI Permissions, and Speakable Schema. Topic coherence is 3/10, which caps the overall score at 50. Focusing content on core expertise areas is the single highest-impact improvement.
How to Improve
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.
Minimize blocking scripts and stylesheets in <head> to improve content availability for AI crawlers.
Trim oversized HTML, excessive DOM nodes, and large inline payloads that slow AI crawlers.
Generate a comprehensive sitemap with lastmod dates for all important pages.
Implement hreflang tags and lang attributes so AI engines serve the correct language version when answering queries.
Optimize compression, cache headers, redirect chains, and HTML payload size for faster AI crawler access.
Add rel="canonical" tags to all pages to prevent duplicate content confusion.
Top Opportunities10
Ensure blog content consistently covers your core expertise areas rather than scattering across unrelated topics. AI engines build authority models - a site about "Medicare coverage" that also publishes about humidifiers and groceries dilutes its topical authority.
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.
The same paragraphs appear on multiple pages. AI engines may only index one version and ignore the rest. Rewrite shared content so each page offers a unique perspective.
Ensure every question-format heading (H2/H3) is followed by a direct answer paragraph. This pattern is ideal for AI engine snippet extraction.
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.
Define the primary entity in the first 500 characters, use consistent terminology (same term 70%+), and add "unlike X" signals to help AI engines distinguish your topics.
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.
Sections within pages contain identical or near-identical text. LLMs may flag this as low-quality or thin content, reducing citation authority. Rewrite duplicate blocks with unique angles.
Place a concise 40-80 word answer block in the first 300 words of each page. Avoid throat-clearing openers like "In this article..." and lead with the answer.
Show direct use, testing, implementation, or lived experience with concrete observations, examples, screenshots, and lessons learned.
Fix It With AI38
Score Breakdown
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