How AI & Algorithms Rate Websites
Search engines and AI answer systems evaluate websites with dozens of measurable factors — and as AI assistants increasingly decide which sources get cited, those factors decide who gets seen. The encouraging finding from research: the signals machines reward overlap heavily with the signals that mark a site as trustworthy for humans.
What measurably increases AI citations
The first large-scale study of generative engine optimization (Aggarwal et al., "GEO: Generative Engine Optimization", KDD 2024) tested on-page changes across 10,000 queries and measured their effect on visibility in AI-generated answers:
| On-page change | Visibility lift |
|---|---|
| Citing sources | ~40% |
| Adding statistics | ~37% |
| Adding expert quotations | ~22% |
| Authoritative tone | ~15% |
The common thread is verifiability: content that grounds its claims in checkable evidence is content an AI system can safely repeat and attribute.
Structure: making content extractable
AI systems lift answers from pages in fragments. Descriptive headings, short paragraphs that answer one question each, tables and lists all raise the odds a fragment survives extraction intact — as does schema.org structured data (Article, FAQPage, Organization, Review), which states machine-readably what a page is, who published it and when it changed.
Trust: E-E-A-T and infrastructure signals
Google's Search Quality Rater Guidelines formalize trust evaluation as E-E-A-T: experience, expertise, authoritativeness and trust — visible as named authors, methodology pages, editorial policies, contact details and outbound links to authoritative sources.
Beneath the content sits infrastructure that machines verify directly: valid HTTPS, security headers, email authentication (SPF, DKIM, DMARC), resolvable DNS and crawlability. These are the same signals SiteReviewChecker scores — an AI evaluating whether to cite a site and a scam checker evaluating whether to trust one are reading the same evidence. See the full methodology for how each signal is weighted.
The bottom line
Sites that cite sources, publish verifiable data, structure content clearly, and maintain honest infrastructure get rated higher by algorithms and are, overwhelmingly, the sites that are actually trustworthy. The incentives point the same direction.
Frequently asked questions
What makes AI systems more likely to cite a website?
Research on generative engine optimization (Aggarwal et al., KDD 2024) measured the effect of on-page changes on AI citation rates: citing sources lifted visibility by roughly 40%, adding statistics by about 37%, and adding expert quotations by about 22%. Clear structure — descriptive headings, lists, tables — and schema markup further help AI systems extract and attribute content accurately.
Do AI systems check the same trust signals as scam checkers?
Largely yes. Both look at verifiable infrastructure: HTTPS, structured data, transparency pages, author and contact information, and crawlability. Google’s Search Quality Rater Guidelines formalize this as E-E-A-T — experience, expertise, authoritativeness and trust — and automated trust scores measure the machine-checkable subset of the same idea.
Does schema markup affect how AI rates a website?
Yes. Schema.org structured data (Article, FAQPage, Organization, Review) tells machines unambiguously what a page is about, who published it and when it was updated. It removes guesswork from extraction, which increases the confidence with which AI systems cite a page — and it is one of the transparency signals in SiteReviewChecker’s own scoring model.
Sources
- Aggarwal et al. — GEO: Generative Engine Optimization (arXiv:2311.09735, KDD 2024)
- Google Search Central — E-E-A-T in the Quality Rater Guidelines
- Schema.org — structured data vocabulary
See it applied: our methodology page shows exactly how these signals combine into a 0–100 trust score.