E-E-A-T Signals for AI: How Agents Evaluate Trust

From Google’s E-E-A-T to AI trust evaluation

Google formalized E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) to evaluate website quality. Since 2024, AI agents like ChatGPT Shopping, Perplexity Shopping, and Google AI Mode use these same criteria to decide which stores to recommend to buyers.

The fundamental difference: Google evaluates E-E-A-T to rank pages in search results. AI agents evaluate E-E-A-T to recommend one store over another in a conversation. The consequence is direct: a store that doesn’t project E-E-A-T signals readable by AI crawlers never gets recommended.

AI agents don’t browse your site like a human. They read HTML, JSON-LD, policy pages, and structured data. Every E-E-A-T signal must be readable in plain text to exist in the AI world.

The 4 E-E-A-T pillars applied to agentic commerce

Experience: proof of real activity

Experience measures whether your store has a real track record of commercial activity. AI agents look for signals of proven history:

• Customer review volume: an AggregateRating with reviewCount > 50 signals real activity. A store with no reviews or just 3 reviews is treated as unproven.
• Review freshness: recent reviews (< 90 days old) prove ongoing activity. Reviews from 2022 on a site in 2026 signal a potentially abandoned store.
• Response time: FAQ pages, responses to reviews, and mentions of active customer service reinforce the experience signal.

To learn how to implement AggregateRating, see our guide on AggregateRating for Shopify.

Expertise: demonstrable product knowledge

Expertise is measured by the depth and precision of your product information. AI agents favor stores that demonstrate mastery of their domain:

• Expert content: detailed product descriptions with precise technical terms (INCI composition in cosmetics, nutritional values in food, technical specs in electronics).
• Industry certifications: certifications (organic, vegan, cruelty-free, OEKO-TEX, ISO) in schema.org prove expertise validated by third parties.
• Expert endorsements: endorsements by professionals (dermatologists, nutritionists, veterinarians) with verifiable credentials strengthen the expertise signal.

Expertise without proof is an unverifiable claim. See our guide on Claims & Proof to structure your evidence.

Authoritativeness: reputation and external recognition

Authoritativeness measures your store’s recognition by external sources. AI agents evaluate:

• Domain age: a domain registered for 5 years carries more authority than a 3-month-old domain. AI agents cross-reference this data with WHOIS registries.
• Brand mentions: references to your brand on third-party sites, press articles, or comparison sites reinforce authority.
• Backlink profile: the quality and diversity of sites linking to yours is an authority signal that AI agents inherit from search engines.

Trustworthiness: coherence and verification

Trustworthiness is the central pillar. A store can have experience, expertise, and authoritativeness, but if it displays inconsistent information, AI agents penalize it:

• Verified claims: every marketing promise (free shipping, 30-day returns, satisfaction guarantee) must be backed by an accessible policy page and corresponding structured data.
• Accessible policies: /policies/shipping-policy and /policies/refund-policy pages must exist in HTML, not only in JavaScript.
• Data coherence: the displayed price must match the price in the Offer schema. The displayed rating must match the AggregateRating. Any divergence is a distrust signal.

To structure your schema.org data, see our guide on Schema.org for Shopify.

Concrete signals AI agents read

AggregateRating in JSON-LD

The most impactful E-E-A-T signal for AI commerce. Agents read JSON-LD directly:

{
  "@type": "AggregateRating",
  "ratingValue": "4.7",
  "reviewCount": "324",
  "bestRating": "5"
}

A store with 324 reviews and a 4.7/5 rating in JSON-LD gets recommended before a store without AggregateRating, even if the latter has better products.

Certifications in schema.org

Certifications (organic, vegan, cruelty-free) must be in structured data, not just displayed as images:

{
  "@type": "Product",
  "hasEnergyConsumptionDetails": {
    "@type": "EnergyConsumptionDetails",
    "hasEnergyEfficiencyCategory": "https://schema.org/EUEnergyEfficiencyCategoryA"
  },
  "additionalProperty": [
    {
      "@type": "PropertyValue",
      "name": "Certification",
      "value": "COSMOS Organic"
    }
  ]
}

About page with verifiable facts

An “About Us” page that says “We are passionate about quality” provides zero E-E-A-T signal. A page that says “Founded in 2018, 15,000 customers, workshop in Lyon, ISO 9001 certified” provides verifiable facts that AI can cross-reference with other sources.

Accessible and complete policy pages

Shipping and return pages must be in native HTML and contain precise information: delivery times in days, return conditions, costs. An empty page or a JavaScript-only page invisible to crawlers is a negative E-E-A-T signal.

Common mistakes that destroy your E-E-A-T signals

1. Badges as images without alt text: a Trustpilot badge displayed as <img src="trustpilot-badge.png"> without alt text is invisible to AI crawlers. The fix: expose the data as JSON-LD AggregateRating.

2. Reviews loaded only via JavaScript: review widgets that load reviews via JavaScript after initial render are invisible to AI crawlers that read raw HTML. The fix: inject reviews via SSR (server-side rendering) or JSON-LD.

3. Unverifiable claims: “Best value for money” or “Category leader” without a source, study, or certification is an empty claim. AI agents treat it as marketing noise and penalize it.

4. Missing or broken return policy: if /policies/refund-policy returns a 404 error, the AI agent considers the return policy nonexistent, even if it’s mentioned in the footer.

5. Price mismatch between schema and display: a price of 29.90 EUR displayed on the page but 39.90 EUR in the Offer schema creates a maximum distrust signal. AI may exclude the product from its recommendations entirely.

Priority E-E-A-T checklist for AI

1. Implement AggregateRating in JSON-LD with reviewCount and bestRating
2. Expose certifications as additionalProperty in the Product schema
3. Write an “About Us” page with verifiable facts (founding date, customer count, location, certifications)
4. Verify that /policies/shipping-policy and /policies/refund-policy are accessible and complete
5. Remove marketing claims not backed by structured proof
6. Convert image-only badges to JSON-LD data
7. Verify coherence between displayed values and schema.org values
8. Publish expert content with verifiable sources

To assess your international readiness, also see our guide on GEO Readiness.

Related articles

• Claims & Proof: Credibility in the Eyes of AI
• AggregateRating: Making Your Reviews AI-Readable
• Conversational Content: Writing for Humans AND AI
• Understanding Your GEO Score: 9 Factors Explained
• Schema.org Product: Why and How on Shopify

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