E-E-A-T Analysis
Experience, Expertise, Authoritativeness, Trustworthiness — the trust signals AI systems and search engines use when deciding whether you're reliable enough to mention, cite, or recommend.
About E-E-A-T
E-E-A-T stands for Experience, Expertise, Authoritativeness, and Trustworthiness — a framework used to evaluate whether a business appears credible, knowledgeable, established, and safe for customers. This report looks at the trust signals AI systems and search engines may use when deciding whether your business is reliable enough to mention, cite, or recommend.
Not every signal applies to every business. A local restaurant, resort, private investigator, contractor, or professional service company may not need a Wikipedia page, national media coverage, or formal licenses to be trustworthy. Some industries require credentials; others rely on reviews, testimonials, local citations, case studies, and visible proof of real customer experience.
That's why this report separates traditional E-E-A-T signals from practical trust signals — and why every gap it finds becomes a task in your Playbook, prioritized by what will actually move trust for your business.
Two companies offer the same service. One shows named experts, real credentials, case studies, and press. The other has a generic team page. AI recommends the first — not because it's better, but because it can be trusted on the evidence it found.
The four dimensions we score
A benchmark — and a better question
The goal isn't just to look credible — it's to find the trust gaps that matter most, and make it easy for customers, search engines, and AI systems to understand, trust, and recommend your business.
What your gaps mean
Each thin pillar points to a specific kind of proof.
The goal is simple: give AI so much proof that recommending you is the safe, obvious answer — not a risk it has to hedge.