Ai visibility checker
Free ai visibility checker: enter your domain to get an indicative AI-citeability score — checks llms.txt, JSON-LD, FAQ structure, title, and headings.
What this grader checks:
Enter your domain above and click Grade my site to get an indicative AI-visibility score. These are indicative checks, not a guarantee of AI citation — AI models use hundreds of signals. This tool surfaces the most actionable structural improvements for CRE practitioners.
llms.txt file present
The primary signal LLMs use to discover and understand your site structure.
Structured data (JSON-LD)
Schema.org types (FAQPage, Organization, SoftwareApplication) that AI models consume to build entity graphs.
FAQ / Q&A structure
Question headings or FAQPage schema — AI models preferentially cite pages that directly answer questions.
Title tag and meta description
Basic entity signals that tell AI models what your page is about.
Heading coverage (H1 + ≥3 headings)
Clear topic hierarchy that gives AI models section-level context for your content.
Score: 0–100 across 5 weighted checks (llms.txt 25 pts, JSON-LD 25 pts, FAQ 20 pts, title+meta 15 pts, headings 15 pts). These are indicative checks, not a guarantee of AI citation.
Where AI changes the answer
AI citation is not random. LLMs preferentially surface content that matches a set of detectable structural signals — the same signals this grader checks. But the score this tool returns is an indicative assessment, not a guarantee of AI citation. No tool can promise a specific LLM will cite your site; AI models use hundreds of signals at inference time, many of which are not publicly documented. What this tool CAN do is identify the most actionable structural gaps in the signals that are known to matter. **The five checks that consistently correlate with AI citability for CRE firms:** **1. llms.txt (25 points).** The llms.txt protocol (analogous to robots.txt for AI crawlers) is the primary signal LLMs use to understand your site structure. A well-structured llms.txt lists your key pages, structured data types, and AI-relevant content explicitly. Most CRE websites don't have one — which means AI crawlers are guessing at your content structure instead of reading a declaration. **2. JSON-LD structured data (25 points).** Schema.org types — FAQPage, Organization, SoftwareApplication, Article — tell AI models not just what your page contains, but what category it belongs to and how the entities on the page relate to each other. A CRE firm with JSON-LD for its services, FAQs, and founding dates is a dramatically more confident citation target than one with no structured data. **3. FAQ and Q&A structure (20 points).** AI models are trained on the web and they preferentially cite content that directly answers questions — because users ask questions. Pages with explicit question headings (H2/H3 ending in "?") or FAQPage schema score significantly better on citability because their content structure matches the query pattern AI uses to locate answers. **4. Title and meta description (15 points).** These are not just for traditional SEO. They are the primary entity-identification signals in any crawled page. A title that includes your entity name + primary topic and a meta description that summarizes your value proposition give AI models a reliable, short-form representation of what you do — which directly influences how confidently they cite you. **5. Heading coverage (15 points).** AI models parse heading hierarchies to build a topic outline of your page. A page with a single H1 and a wall of body text gives AI models no topic segmentation to work with. A page with H1 + several descriptive H2s and H3s is far more parseable — and therefore more citable for specific sub-topics. **What this grader does not check.** This tool makes a single unauthenticated request to your domain's root URL and llms.txt. It does not crawl sub-pages, analyze content quality, evaluate backlink authority, assess topical authority depth, or measure any LLM's actual citation behavior. Improving these five structural signals is the first step — not the last.
Questions real estate teams ask
What is AI visibility and why does it matter for CRE firms?
AI visibility refers to how likely an AI assistant (ChatGPT, Claude, Perplexity, Gemini) is to surface and cite your firm's content when a user asks a relevant question. For CRE firms, this matters because AI-assisted research is increasingly common among investors, operators, and brokers — and firms that appear in AI-generated responses get top-of-funnel exposure that bypasses traditional search. Structural signals like llms.txt, JSON-LD schema, and FAQ structure are the most actionable levers for improving AI citability.
What exactly do the five checks in this grader measure?
The grader checks five structural signals that are known to correlate with AI citability: (1) llms.txt presence — the AI-crawler declaration file at your domain root; (2) JSON-LD structured data — schema.org types in a <script type='application/ld+json'> tag; (3) FAQ/Q&A structure — FAQPage schema or question headings; (4) title tag and meta description — basic entity identification signals; (5) heading coverage — H1 plus at least 3 total headings for topic hierarchy. Each check is weighted (25/25/20/15/15) and deterministic — the same page always produces the same score.
Is this score a guarantee that AI models will cite my site?
No — and this is intentional. The score is an indicative assessment of detectable structural signals, not a guarantee of AI citation. AI models use hundreds of signals at inference time, including content quality, topical authority, recency, and factors that are not publicly documented. A score of 100 on this grader means your site has strong structural signals — it does not mean any specific AI model will cite your content for any specific query. Improving your structural signals is the first step toward better AI citability, not a guarantee of it.
How do I improve my AI visibility score?
Address the failed checks first: (1) Add a /llms.txt file at your domain root listing your key pages and content types. (2) Add JSON-LD structured data for your organization, services, and FAQ content. (3) Structure your key pages with explicit question headings (H2/H3 ending in '?') or FAQPage schema. (4) Ensure every page has a descriptive title tag and meta description. (5) Use a clear heading hierarchy (H1 + descriptive H2/H3 sections). Beyond structural signals, publishing high-quality, specific, question-answering content in your area of expertise — CRE investing, underwriting, market analysis — is the most durable path to AI citability.
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