Can AI find, understand and cite your website?

Owners often ask one question—“Can AI find my website?”—that covers several different things. A crawler reaching a page, an agent understanding its purpose, and an assistant citing it in an answer are separate observations.

Audit your website’s public signals

Start with what you can check

1. Access: does the intended public page return useful content? Do its robots rules and indexing controls match your intentions?
2. Orientation: do clear titles, readable pages and a concise llms.txt explain what the site offers?
3. Discovery: can a relevant query find the right publisher and page?
4. Citation: does an actual answer cite the site for a recorded prompt, engine and time?
5. Action: can an authorized agent complete the intended operation?

Our website audit reads a bounded public snapshot of the first two. Our directory helps with discovery. The audit does not query AI answers or confirm search indexing.

How do I get cited by ChatGPT?

Make the site accessible to the relevant search systems and publish useful, specific information that can support an answer. Explain products, documentation, policies and original evidence clearly. Keep dates and claims accurate, link to primary material and avoid hiding the useful content behind an unnecessary interaction.

Crawler access is not a citation guarantee. Neither publishing llms.txt nor verifying a directory listing establishes that an assistant will mention a business. Search, training and user-triggered fetchers can have different access rules; review them separately using the publisher’s crawler documentation.

Measure citations with evidence

Keep the exact prompt, engine, date, cited URL and answer context. Repeat a small, relevant prompt set over time and record changes to the site. A single answer is a sample, not a stable visibility score. Absence from one provider’s stored dataset does not establish absence from all AI answers.

A useful citation record separates a direct link to your domain, a mention without a link, and an assistant recommending your product. Each answers a different question. Private owner observations should remain separate from public directory ranking.

What llms.txt helps with

A well-written file gives an agent a deliberate starting point. It can reduce time spent guessing site structure and highlight the source of truth. Check an existing file or generate an editable draft, then review it before publishing.

Avoid collecting scores without useful changes

Use diagnostics to fix a specific problem: an HTML page served as a text file, a blocked useful path, missing documentation or a stale link. Google’s guidance for AI search experiences emphasizes original, useful content, accessible pages and an effective page experience.

Own a listed site? Connect its verified listing to manage its public information and review measured activity within this directory. That activity is not a count of every agent visiting your website.

More: WebMCP: give agents a clearer way in. · Check what an MCP endpoint actually returns. · Agentic commerce needs evidence behind the interface. · Find the right documentation before using it. · The llms.txt project · What is llms.txt? · llms.txt examples · llms.txt best practices · checker · generator