SiftServe scoring methodology – CORE-EEAT Content Benchmarks
Key takeaways
This page covers: How to read the scores; Dimension and Total Scores; Contextual Clarity (Agent experience, GEO); Organization (Agent Experience); Referenceability (Agent Experience); Exclusivity (Agent Experience); Experience (Human Experience); Expertise (Human Experience).
- The CORE-EEAT standard is SiftServe's 80-item framework for measuring page quality for AI crawlers and human readers.
- The standard applies to any page SiftServe processes; agent-experience dimensions (GEO) are compared between the original and sifted versions, while human-experience dimensions (SEO) apply only to the original page.
- Total Score = (GEO Score + SEO Score) / 2, where GEO Score = (C + O + R + E) / 4 and SEO Score = (Exp + Ept + A + T) / 4.
- Dimension weights vary by content type across 9 page types: Product Review, How-to Guide, Comparison, Landing Page, Blog Post, FAQ Page, Alternative, Best-of, and Testimonial.
- Every page is checked twice — once for the original, once for the sifted copy — and human visitors continue to see the original while the sifted copy is only shown to AI crawlers.
How to read the scores
Every page is checked twice using the same 80-item CORE-EEAT standard. The first check is for the original page, and the second is for the sifted copy that AI can read. We compare the agent-experience aspects, which focus on what AI crawlers and assistants can find and understand, between the original and sifted versions. The human-experience aspects, which include credibility signals for readers and search ranking, apply only to the original page. Human visitors continue to see the original, while the sifted copy is only shown to AI crawlers.
Dimension and Total Scores
GEO Score = (C + O + R + E) / 4
SEO Score = (Exp + Ept + A + T) / 4
Total Score = (GEO Score + SEO Score) / 2Weighted Scoring by Content Type
Weighted Score = Σ (dimension_score × weight)
| Dim / Page Type | Product Review | How-to Guide | Comparison | Landing Page | Blog Post | FAQ Page | Alternative | Best-of | Testimonial |
|---|---|---|---|---|---|---|---|---|---|
| C | 10% | 20% | 10% | 20% | 25% | 25% | 10% | 10% | 10% |
| O | 10% | 20% | 20% | 10% | 10% | 25% | 15% | 25% | 5% |
| R | 15% | 10% | 25% | 5% | 10% | 15% | 25% | 20% | 15% |
| E | 20% | 5% | 10% | 5% | 20% | 5% | 5% | 15% | 10% |
| Exp | 20% | 5% | 5% | 5% | 10% | 5% | 15% | 5% | 30% |
| Ept | 5% | 20% | 15% | 5% | 10% | 10% | 5% | 10% | 5% |
| A | 5% | 5% | 5% | 25% | 5% | 5% | 5% | 5% | 5% |
| T | 15% | 15% | 10% | 25% | 10% | 10% | 20% | 10% | 20% |
Contextual Clarity (Agent experience, GEO)
| Check | Experience | What good looks like |
|---|---|---|
| Intent Alignment | Agent | Title promise = content delivery |
| Direct Answer | Agent | Core answer in first 150 words |
| Query Coverage | Agent | Covers ≥3 query variants (synonyms, long-tail) |
| Definition First | Agent | Key terms defined on first use |
| Topic Scope | Agent | Explicitly states what is and isn't covered |
| Audience Targeting | Agent | States "this article is for…" |
| Semantic Coherence | Agent | Logical flow between paragraphs, no jumps |
| Use Case Mapping | Agent | Decision framework: when to choose A vs B |
| FAQ Coverage | Agent | Structured FAQ covering long-tail follow-ups |
| Semantic Closure | Agent | Conclusion answers the opening question + next steps |
References: GEO: Generative Engine Optimization (Aggarwal et al., KDD 2024) · Google: creating helpful, reliable, people-first content
Organization (Agent Experience)
| Check | Experience | What good looks like |
|---|---|---|
| Heading Hierarchy | Agent | H1→H2→H3, no level skipping |
| Summary Box | Agent | Has TL;DR or Key Takeaways section |
| Data Tables | Agent | Comparisons and specs presented in tables |
| List Formatting | Agent | Parallel items use bullet or numbered lists |
| Schema Markup | Agent | Appropriate JSON-LD (Article/FAQ/HowTo/etc.) |
| Section Chunking | Agent | Each section has single topic; paragraphs 3–5 sentences |
| Visual Hierarchy | Human | Key concepts bolded or highlighted |
| Anchor Navigation | Agent | Table of contents with jump links |
| Information Density | Agent | No filler; consistent terminology throughout |
| Multimedia Structure | Human | Images/videos have captions and carry information |
References: The RefinedWeb dataset: filtering web data for LLM training (NeurIPS 2023) · GEO: Generative Engine Optimization (Aggarwal et al., KDD 2024)
Referenceability (Agent Experience)
| Check | Experience | What good looks like |
|---|---|---|
| Data Precision | Agent | ≥5 precise numbers with units (%, $, ms) |
| Citation Density | Agent | ≥1 external citation per 500 words |
| Source Hierarchy | Agent | Primary sources first; ≥3 Tier 1–2 sources |
| Evidence-Claim Mapping | Agent | Every claim backed by evidence immediately after |
| Methodology Transparency | Agent | Sample size, steps, and criteria documented |
| Timestamp & Versioning | Agent | Last updated <1 year; version changes noted |
| Entity Precision | Agent | Full names for people/orgs/products; no "a company" |
| Internal Link Graph | Human | Descriptive anchor texts forming topic clusters |
| HTML Semantics | Agent | Uses <article>, <figure>, <time>, <cite> |
| Content Consistency | Agent | Data self-consistent; no broken links (404) |
References: GEO: Generative Engine Optimization (Aggarwal et al., KDD 2024) · Google: how structured data works
Exclusivity (Agent Experience)
| Check | Experience | What good looks like |
|---|---|---|
| Original Data | Agent | First-party surveys, experiments, or statistics |
| Novel Framework | Agent | Named, citable original framework or model |
| Primary Research | Agent | Original experiments/surveys with documented process |
| Contrarian View | Agent | Challenges consensus with evidence |
| Proprietary Visuals | Human | ≥2 original infographics, charts, or diagrams |
| Gap Filling | Agent | Covers questions competitors don't |
| Practical Tools | Human | Downloadable templates, checklists, or calculators |
| Depth Advantage | Agent | Deeper than competing content on same topic |
| Synthesis Value | Agent | Cross-domain knowledge combination (A+B=C) |
| Forward Insights | Agent | Data-backed predictions and trend analysis |
References: Google: creating helpful, reliable, people-first content · GEO: Generative Engine Optimization (Aggarwal et al., KDD 2024)
Experience (Human Experience)
| Check | Experience | What good looks like |
|---|---|---|
| First-Person Narrative | Human | Contains "I tested" or "We found" + action verbs |
| Sensory Details | Human | ≥10 sensory words (smooth, heavy, bright) |
| Process Documentation | Agent | Step-by-step process with timeline |
| Tangible Proof | Human | ≥2 original photos/screenshots with timestamps |
| Usage Duration | Human | States "after X months of use…" |
| Problems Encountered | Agent | Shares ≥2 real problems + solutions |
| Before/After Comparison | Human | Shows change, improvement, or difference |
| Quantified Metrics | Agent | Measurable experience data (time, cost, success rate) |
| Repeated Testing | Human | Multiple tests or long-term tracking |
| Limitations Acknowledged | Agent | States "we only tested X scenario" |
References: Google: E-E-A-T in the Search Quality Rater Guidelines · Google Search Quality Rater Guidelines (PDF)
Expertise (Human Experience)
| Check | Experience | What good looks like |
|---|---|---|
| Author Identity | Human | Byline + avatar + bio (>30 words) |
| Credentials Display | Human | Relevant degrees, certs, years of experience |
| Professional Vocabulary | Agent | Accurate industry jargon, no misuse |
| Technical Depth | Agent | Parameters, thresholds, examples are actionable |
| Methodology Rigor | Agent | Analysis method is reproducible |
| Edge Case Awareness | Agent | Discusses ≥2 exceptions or "when this doesn't apply" |
| Historical Context | Human | Shows knowledge of the field's evolution |
| Reasoning Transparency | Agent | "We chose A over B because…" with tradeoffs |
| Cross-domain Integration | Agent | Connects knowledge across fields |
| Editorial Process | Human | "Reviewed by" or "Fact-checked by" labels |
References: Google Search Quality Rater Guidelines (PDF)
Authority (Human Experience)
| Check | Experience | What good looks like |
|---|---|---|
| Backlink Profile | Human | Cited by authoritative sites (.edu, .gov, leaders) |
| Media Mentions | Human | "Featured in" with media logos |
| Industry Awards | Human | Displays relevant industry awards or recognition |
| Publishing Record | Human | Conference talks, publications, patents |
| Brand Recognition | Agent | Brand has search volume |
| Social Proof | Human | Authentic user testimonials with real details |
| Knowledge Graph Presence | Agent | Has Wikipedia entry or Google Knowledge Panel |
| Entity Consistency | Agent | Brand/author info consistent across the web |
| Partnership Signals | Human | Shows partnerships with authoritative organizations |
| Community Standing | Human | Active and influential in professional communities |
References: Google: E-E-A-T in the Search Quality Rater Guidelines · Google Search Quality Rater Guidelines (PDF)
Trust (Human Experience)
| Check | Experience | What good looks like |
|---|---|---|
| Legal Compliance | Human | Privacy Policy + Terms of Service present |
| Contact Transparency | Human | Physical address or ≥2 contact methods |
| Security Standards | Human | Site-wide HTTPS, no security warnings |
| Disclosure Statements | Agent | Affiliate links disclosed (veto if missing) |
| Editorial Policy | Human | Content standards and review process published |
| Correction & Update Policy | Agent | Has corrections page or changelog |
| Ad Experience | Human | Ads <30% of page; no intrusive popups |
| Risk Disclaimers | Agent | YMYL topics have necessary disclaimers |
| Review Authenticity | Agent | Reviews show authenticity signals |
| Customer Support | Human | Clear return policy, complaint channels, response SLA |
References: Google Search Quality Rater Guidelines (PDF)
How AI systems read your pages
Primary sources on how AI crawlers fetch and select content, and how generative engines like OpenAI, Anthropic, Grok etc choose what to cite:
- OpenAI: overview of OpenAI crawlers (GPTBot, OAI-SearchBot, ChatGPT-User)
- Anthropic: how ClaudeBot crawls the web
- GEO: Generative Engine Optimization (Aggarwal et al., KDD 2024)
- Google Search Quality Rater Guidelines (PDF)
Human visitors continue to see the original, while the sifted copy is only shown to AI crawlers.