Run a full E-E-A-T audit on any URL in under 30 seconds. Get a scored breakdown across all four pillars — Experience, Expertise, Authoritativeness, Trustworthiness — with prioritised fixes and their estimated impact on AI citation rates.
The tool fetches your live page, parses the HTML for technical E-E-A-T signals, then passes the page content to an AI model for a deeper content-level review. The output is a scored report you can act on today.
Google introduced E-E-A-T in the 2022 update to its Search Quality Evaluator Guidelines. Adding the first "E" for Experience was a direct response to AI-generated content flooding search results — Google needed a signal that separated people who had actually done the thing from people who had read about it. LLMs have since absorbed the same logic into their own source selection.
Google doesn\'t publish the exact formula. What it does publish is the 176-page Search Quality Evaluator Guidelines, which trains human raters to score pages against E-E-A-T criteria. Those scores feed back into algorithm development — not directly into rankings, but as training signal for the systems that do.
In practice, Google tracks E-E-A-T through a combination of on-page signals, off-page signals, and entity signals. On-page: structured data, author markup, content accuracy, citation of primary sources. Off-page: link graph quality, brand mentions in trusted publications, co-citation patterns. Entity: whether your brand, author, or organisation appears in the Knowledge Graph with consistent NAP data (Name, Address, Phone).
PageRank is still a component of authority measurement. But modern Google weaves in link quality (not just quantity), topical relevance of linking domains, and anchor text distribution. A single link from a respected industry publication typically outweighs fifty links from unrelated directories.
For AI engines, the mechanism is different but the outcome is the same. LLMs are trained on text corpora where credible sources are naturally more prevalent and more frequently cited by other credible sources. A page with strong E-E-A-T signals appears more often in that training data, gets cited more by other trusted pages in it, and therefore carries more weight when the model decides what to reference in an answer.
E-E-A-T matters most for sites operating in competitive or high-stakes verticals — but the fundamentals apply to any site that wants AI engines to treat it as a credible source. Here\'s who gets the most out of this audit.
Most E-E-A-T problems are technical, not editorial. You don\'t need to rewrite your content from scratch. You need to give crawlers the right structured data to read it correctly. Below are the highest-impact fixes for each pillar — ranked by how quickly they affect citation rates.