AI SEO Audit

A traditional SEO audit is built around a ranked list of ten blue links. It weighs backlinks, keyword targeting, and the technical signals that move a page from position eight to position four. All of that still matters, and none of it describes how an AI assistant picks what to say.

An AI assistant does something different. It fetches a handful of pages, extracts what it can parse, and synthesises one answer naming two or three brands. There is no position four. You are in the answer or you are not, and what decides it is whether your pages can be read, understood, and trusted in a single pass.

This audit crawls up to 25 pages and scores six categories: AI accessibility, content structure, structured data, technical SEO, performance, and E-E-A-T signals. You get an overall score, a grade, and the specific issues behind each category.

AI accessibility is whether AI crawlers can reach your content at all, starting with robots.txt. It is first because everything else is irrelevant when it fails.

Content structure is whether a machine can tell what your page says. Clear headings, direct answers near the top, and self-contained sections extract cleanly. Pages that bury the answer in the eighth paragraph often do not get extracted at all.

Structured data is schema markup: Organization, Product, FAQ, Article. It is the difference between a model inferring what your page is about and being told.

Technical SEO covers the fundamentals that apply to any crawler: status codes, canonicals, titles, meta descriptions, internal links.

Performance matters because retrieval crawlers work under a timeout. A page slow enough to be abandoned is a page that was never read.

E-E-A-T signals are the markers of who stands behind the content: named authors, dates, citations, contact and about pages. Models lean on them when deciding what to trust.

If your site renders its content in the browser with JavaScript, most AI crawlers see an empty shell. They do not execute scripts the way Googlebot does, so a React or Vue app that has not been server-rendered or pre-rendered can be entirely invisible to them while looking perfect to you.

The audit flags this when it sees it, and it is worth acting on before anything else on the list. Adding schema markup to a page a crawler receives as an empty div changes nothing. Server-side rendering, static generation, or pre-rendering for bots all solve it; which one depends on your stack.

This is an audit of your site, not a measurement of your standing. A score of 95 means an AI system can read you properly. It does not mean any model recommends you, and treating the score as the goal is how teams end up with immaculate technical hygiene and no mentions.

Being recommended also depends on things no crawl can see: whether independent sources describe you, whether people discuss you where models look, whether your content answers the question someone actually asked. The audit clears the ground. What grows on it is a separate question, and the only way to answer it is to run the prompts your buyers use and record which brands come back.