microland.com
Weak AI visibility with 25 of 53 criteria passing. Biggest gap: llms.txt file.
Verdict
Critical AEO gaps at 42/100 - microland.com is largely invisible to AI engines. Key strengths include Fact & Data Density, Canonical URL Strategy, and Duplicate Content Blocks. Priority gaps: llms.txt File, Schema.org Structured Data, and RSS/Atom Feed. Topic coherence is 2/10, which caps the overall score at 45. Focusing content on core expertise areas is the single highest-impact improvement.
How to Improve
Update robots.txt to explicitly allow AI crawlers and include sitemap directive.
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.
Implement hreflang tags and lang attributes so AI engines serve the correct language version when answering queries.
505/584 images lack explicit width/height - the most common cause of layout shift (CLS)
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.
Trim oversized HTML, excessive DOM nodes, and large inline payloads that slow AI crawlers.
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.
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.
Ensure every question-format heading (H2/H3) is followed by a direct answer paragraph. This pattern is ideal for AI engine snippet extraction.
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.
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.
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.
Write concise, standalone answer paragraphs (2-3 sentences) immediately after question headings. These "snippet-ready" paragraphs are ideal for AI engine citations.
Give every page a unique, descriptive title (roughly 10-70 characters) and a real meta description. Titles and snippets are the primary labels Google Search and AI features display when citing your pages.
Fix It With AI48
Score Breakdown
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