E-E-A-T for AI: Trust Signals Answer Engines Look For
E-E-A-T stands for Experience, Expertise, Authoritativeness and Trustworthiness — Google's framework for content quality, now equally relevant to AI answer engines deciding which page to cite. The difference is that AI engines read trust signals mechanically, from markup.
The signals machines can actually read
A human reviewer senses credibility from design and prose. An engine reads specific artifacts.
- Authorship: a visible byline plus an author declared in Article schema, ideally linking to an author page.
- Freshness: datePublished and dateModified in schema, mirrored by visible <time> elements.
- Provenance: outbound links to authoritative sources — standards bodies, government data, peer-reviewed work.
- Identity: an Organization schema whose sameAs array connects the site to Wikipedia, Wikidata, LinkedIn and Crunchbase.
Why anonymous content loses
When two pages contain the same fact, engines prefer the one with provenance: a named author, a date and citations. Anonymous, undated content is a dead end for a system that must justify its answers — some engines surface the author and date directly in their citations.
The fix costs minutes per page: name the author, show both dates, and cite two credible sources. Maveriko's E-E-A-T checks verify each of these on every scan.
Entity-level trust
Above page level, engines ask whether the publisher itself is a known entity. A Wikidata entry, a consistent name across profiles and a complete Organization schema make the brand resolvable. That entity graph is 40% of Maveriko's GEO score, because for recommendation-style questions the engine recommends brands, not URLs.