Eeat

eeat — Experience · Expertise · Authoritativeness · Trust

Google's quality framework (the extra "E", Experience, added Dec 2022), the lens that decides whether content deserves to rank and be cited — and the strongest signal for YMYL (your-money-your-life) topics. Until now E-E-A-T lived only implicitly in geo-optimization's Trust-Signals subsection and brand-guardian's accuracy gate. This makes it a first-class, upstream consult: it reviews a draft BEFORE it ships and returns structured fixes, rather than being a back-end afterthought.

Advise, don't gate. E-E-A-T returns must_fix + suggestions; the owner integrates. brand-guardian remains the terminal accuracy gate; this is the upstream quality lens that feeds it.

When to use

• Producing/auditing content that must rank, be cited by AI engines, or carry authority — blog posts, landing pages, service pages, especially YMYL (health, finance, legal, safety — RestoreAssist/DR/CARSI are YMYL-adjacent).
• As a specialist-council consult on any content artifact (routing table: content/campaign/FACT tasks).

When NOT to use

• Pure keyword/SERP data → seo. AI-engine visibility infra → geo-optimization.
• Fiction / internal notes / non-published copy.

The four lenses (score each, cite the gap)

1. Experience — is there genuine first-hand experience? Real case studies, original photos/data, "we did/tested/measured this," specifics only a practitioner would know. Generic AI-sounding overviews score low. (This is the signal most often missing in generated content.)
2. Expertise — a named, credentialed author/entity? Bio, qualifications, relevant track record shown on-page. Anonymous or unattributed = weak.
3. Authoritativeness — is the site/author cited by others; does the page cite authoritative Tier-1/2 sources (pull via source-ingest)? Awards, memberships, recognized standards bodies (IICRC, ISO) named.
4. Trust — the load-bearing one. Accuracy (every load-bearing claim sourced), transparency (contact/about, real business, ABN), honest framing (no overclaim — ties to the no-fake-as-real rule), reviews/testimonials, secure/updated site.

Consult-response contract (feeds specialist-council)

{
  "specialist": "eeat",
  "verdict": "pass | needs-work | fail",
  "scores": {"experience": 0, "expertise": 0, "authoritativeness": 0, "trust": 0},
  "must_fix": [{"lens": "experience", "issue": "no first-hand signal — reads generic", "fix": "add a real case study / original data"}],
  "suggestions": [{"lens": "trust", "change": "cite the IICRC standard for the drying claim", "impact": "high"}],
  "evidence": ["source-ingest Tier-1 for the load-bearing stat"]
}

How to use

1. Read the draft. Score each lens 0–100 with the concrete gap.
2. For YMYL, weight Trust + Expertise hardest; a fail there is a fail overall.
3. Return the structured response; the owner folds must_fix in before shipping.
4. Cross-consult: pull authoritative citations via source-ingest; hand the accuracy check to brand-guardian as the final gate.

Guardrails

• No fabricated credibility — never invent authors, awards, or citations to score higher; flag the gap instead (the fix is to EARN the signal).
• Sourced trust only — every "trust" fix that adds a claim must point to a real Tier-1/2 source via source-ingest.
• Advisory lens, not the gate — brand-guardian decides ship/no-ship on accuracy.

Anti-duplication

Owns the E-E-A-T rubric. Does NOT do SERP/keyword data (seo), AI-engine infra like llms.txt/JSON-LD (geo-optimization — which now defers its Trust-Signals depth here), source scraping (source-ingest), or final accuracy gating (brand-guardian). It is the upstream quality lens the others consult.