How to Check AI Bot Access: A Technical Audit

A page opening in your browser does not show that automated systems can reach the same content. A session, firewall, redirect, or the way content loads can produce different results. An AI bot access audit is done to surface these differences on a per-URL basis.

In the audit, look for answers to three separate questions: Is the request accepted, can the required content be read, and is the page in a suitable state for the relevant search system? Resolving an access problem is valuable technical progress; however, on its own it does not guarantee being cited or having your brand recommended. A healthy report does not mix these stages together.

Don't limit the audit to a single homepage

Choose sample URLs from different templates such as homepage, product, price, category, and guide. The problem may appear only in a specific page type. For example, a product description may be public while price information does not load without a session.

Write the expected content for each URL in advance. A product page should show the product name, a core feature, and variant information; a guide should show the title and the main description. If the audit consists only of the "a 200 status code came back" result, empty or missing content can slip through.

Give priority to pages that are commercially important and that you expect to be a source in AI answers. Before reviewing hundreds of unused old URLs, check access to the information needed at the moment of decision.

robots.txt, indexing, and access are different

robots.txt manages which URLs crawlers can access. Google states clearly that this file is not a method for hiding a page from search results. Blocking a page from being crawled and controlling its indexing are different operations. Google robots.txt guide.

For this reason, the robots rule, the HTTP result, and any indexing directives should appear on separate lines in your checklist. Do not rely on the robots file to protect private content; access control is a separate mechanism.

Do not evaluate bot names within a single "AI bots" group either. Their purposes for training, search, or fetching a page on behalf of a user may not be the same. Make the permission decision based on the role in the relevant provider's current documentation. Do not make a blanket permission change on the assumption that opening one bot will make you visible across all products.

Examine the security layer and page content together

You can use the representative control table below in the audit. A finding indicates the next review step, not the definite cause of the problem.

Observation	Area to review	Completion criterion
403 or a verification screen	Firewall and bot rules	The targeted legitimate request reaches the required content
Repeated redirects	URL and language redirects	Consistent access to the correct final page
200 but content missing	Delivered HTML and load flow	The main information is present within the response
Page in the wrong language	Language selection and location behavior	The requested language version is accessible
Unexpected canonical	Page matching settings	The preferred URL is specified correctly

Changing only the bot name in the request header does not conclusively prove the real provider's access. To support the result, use server logs and, if available, the verification tools the provider offers. The test request and a real bot visit should be shown separately in the report.

Read the requirement for Google AI features correctly

Google states that, to be a supporting link in AI Overviews and AI Mode, a page must be indexed and eligible to show a snippet in search. These features do not additionally require special AI files or special schema markup. Google AI features documentation.

This information shows that you should not treat adding llms.txt as a replacement for all the technical work. An inaccessible product page or a misdirected guide does not fix itself when a separate file is created. Keep structured data consistent with visible content too; do not add a feature the product does not have to the markup.

When you consider Maya's llms.txt guide, separate the file's purpose from the platforms' verified requirements. Tie technical choices to a measurable problem.

Close each finding by retesting it

A technical task's card should include the URL, the finding, the evidence, the responsible person, the change, and the retest result. "The firewall was updated" shows that the work was done; it does not on its own show that the page is now accessible.

After the fix, use the same URL and the same test conditions. Check that the required text arrives, and date the result. Then run visibility tracking separately: did technical access recover, and did source selection change in new AI answers?

For an initial check, you can review the scope of the Maya Site Auditor. At the end of the audit, aim for a short, verifiable task list that shows which problem was solved on which page, rather than a single score.