How Do I Audit a Store's AI Citation Readiness in One Hour?

Short answer: yes, and the order matters more than the depth. Most teams auditing their store for AI citation readiness start by reading their page copy, which is the slowest possible place to begin. The reasoning follows from how assistants actually assemble an answer: they retrieve candidate passages, verify the structured claims on the page, and check that the claims are consistent with what other sources say about you. A one-hour audit should follow that same order in reverse, testing the cheapest and most fatal blockers first.

Fifteen minutes per pass. Stop early if a pass fails badly — a store that fails the machine-reading pass does not benefit from a keyword review.

## Pass 1: Can a crawler reach the page at all?

Before any content question, confirm the assistant can retrieve the page. This is a fifteen-minute check, not a project.

1. Confirm the main product and collection URLs return HTTP 200 without a JavaScript render step. Use a plain text fetch, not a browser. 2. Check robots.txt for rules blocking the assistant crawlers you care about. Absence of an explicit allow is not the same as a block, but an explicit disallow on a shop path is fatal. 3. Check for bot-challenge middleware that returns a challenge page to non-browser user agents. 4. Confirm the canonical URL points at the version you want cited, not a filtered variant.

If a fetch returns a challenge page or a stale cache, fix that before doing anything else. Content work on an unreachable page is wasted effort.

## Pass 2: Is the answer text actually present?

Assistants quote text. If your answer exists only inside an image, a video, or a widget that renders client-side, it is not quotable.

1. Fetch each headline product page as plain text and search for the specific claims you would want quoted: price, material, dimensions, warranty length, shipping window, return terms. 2. Note every claim that appears on screen but not in the fetched text. 3. Note every "contact us for details" placeholder. Assistants treat an unstated attribute as an unanswerable question and route to a competitor who stated it.

This pass usually finds two or three attributes published only as images. Those are the fastest wins available anywhere in the audit.

## Pass 3: Are the claims structured and consistent?

Now check whether the claims are machine-readable, and whether they agree with each other.

1. Confirm product-level structured data is present and valid — name, price, currency, availability, brand, and identifiers where you have them. 2. Confirm FAQ or Q&A content on the page is marked up so individual question-answer pairs can be extracted, rather than buried in prose. 3. Cross-check the same attribute across three surfaces: the product page, the collection page, and your feed or marketplace listing. Conflicting values on the same attribute are worse than a missing attribute, because the assistant has to choose.

A quick way to run this is the free scanner at /audit, which flags missing, malformed, and conflicting fields in one pass and ranks them by how often they block citation. Use it as a starting inventory rather than reading every template by hand.

## Pass 4: Does the site answer real questions?

The last fifteen minutes are about coverage, not correctness.

1. Write down ten questions a buyer would ask an assistant about your category — not keyword phrases, but full questions, including comparison and constraint questions. 2. For each, check whether your own site contains a direct, extractable answer. 3. Mark the questions you can answer and nobody else in your set can. Those are the passages most likely to be selected when an assistant needs a definitive source.

Record the results as a short list: attribute, where it lives, whether it is extractable, whether it is consistent. That list is your fix queue, and it is normally shorter than teams expect.

## Why one hour is the right budget

Auditing is diagnostic, not corrective. A one-hour pass produces a ranked list of blockers; the fixing work is separate and should be batched by defect class rather than done page by page. Teams that try to audit and fix simultaneously in the same session usually finish neither.

A useful rhythm is a one-hour audit every month, a fix batch after each audit, and a measurement review to see whether citation rates on your target questions moved. The explanation of how citation measurement works differently from rank tracking is in /faq.

## FAQ

Run the four passes in order, keep the output as a list, and fix in batches. Start with the scanner at /audit if you want the inventory generated for you.