E-E-A-T

Experience: the author has provably worked with the topic themselves — real projects, case studies rather than secondhand theory. Expertise: subject matter depth and accuracy. Authoritativeness: third parties reference the source — mentions, links, speaking appearances, credentials. Trustworthiness: transparent details (imprint, author, contact information), correct facts, no overstated promises.

E-E-A-T isn't a single ranking factor but a quality principle that shows up across many signals — from author attribution through consistent business data to external mentions.

Concrete steps: articles with real, named authors including their qualifications (structured with Person schema); case studies from genuine projects; verifiable credentials (speaking engagements, memberships, awards) made visible; consistent business information across your website, profiles and directories; and no invented figures — a single disprovable fact damages the credibility of your entire source.

E-E-A-T isn't a switch you flip but the sum of many signals — most of them within your control. On your website: author boxes with name, role and credentials on every expert article, an About page featuring real people instead of stock photos, complete imprint, source citations for any figures or legal claims. Experience becomes visible through concrete project examples, firsthand observations and details only someone who actually does the work would know — that's what separates a genuine article from generic commodity content.

Off-site, consistent business data across directories and industry portals matters, as do genuine reviews, mentions in trade media, speaking engagements and association memberships. These external signals are especially relevant to AI systems because they're hard to fake: a company consistently described as a specialist in a subject across multiple independent sources gets cited as a trustworthy source far more often than one making the same claim only on its own website. A practical starting point: clean up your own website first, then maintain consistent information on your five most important external profiles.

E-E-A-T was developed for human Google quality raters, but the principle applies to AI systems too: whether a source gets cited depends on similar credibility signals — visible authorship, external confirmation, consistent facts. For GEO that means: author markup, consistent business data and external mentions — the things that already build E-E-A-T — are simultaneous GEO levers. Build E-E-A-T rigorously, and you automatically improve your AI visibility.

One important boundary: E-E-A-T isn't built quickly — it develops over time through real projects, real credentials and external mentions. Tactical inflation — bought links, fake reviews, padded author profiles — does more long-term harm than short-term good. That's the difference from technical GEO measures like schema markup or llms.txt, which are far faster to implement and show results.