E-E-A-T for AI: trust signals engines can read
Experience, Expertise, Authoritativeness and Trust aren't just for Google. Here are the E-E-A-T signals AI engines can actually verify.
By Faraaz Khan
E-E-A-T: Experience, Expertise, Authoritativeness and Trust, is the set of credibility signals that help an engine decide whether to rely on you. For AI specifically, what matters is the subset a machine can *verify*: a clear identity, named authors, dates, citations, and an Organization in your structured data.
Trust signals an engine can actually read
- A real identity: an About page, contact details, and an `Organization` schema with verified `sameAs` profiles.
- Named authors and dates: `Article`/`BlogPosting` markup with an author and published/modified dates.
- Citations: link to primary, authoritative sources; being well-sourced is itself a trust signal.
- Consistency: the same facts about your company everywhere, so models converge on the truth.
- A clear methodology: explain how you know what you claim. (Ours is public.)
Why it's decisive for AI
An answer engine is staking its own credibility on what it cites. Given two comparable sources, it favors the one it can verify and trust. Strong, machine-readable E-E-A-T is what tips that decision your way, and it's a prerequisite for being recommended at all.
Run the checker to see how your trust signals score today.
Frequently asked questions
What does E-E-A-T stand for?
- Experience, Expertise, Authoritativeness and Trust, the credibility framework popularized by Google's quality guidelines, now equally relevant to how AI engines decide which sources to rely on and cite.
How do I show E-E-A-T to an AI engine?
- Make the signals machine-readable: a clear identity and About page, Organization and Article structured data, named authors with dates, citations to authoritative sources, and consistent facts about your business across the web.