Why “Entity Authority” is the New Domain Authority

Why “Entity Authority” is the New Domain Authority

For 20 years, SEOs have worshipped at the altar of Domain Authority (DA). We chased backlinks from high-DA sites, assuming that “link juice” was the ultimate ranking signal.

In the age of LLMs (Large Language Models), DA is becoming a legacy metric. The new king is Entity Authority.

If you don’t understand the difference, you are optimizing for a search engine that existed in 2015, not the AI engines of 2025-2026.

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What Is an Entity? (And Why AI Cares)

To a search engine like Google or an LLM like GPT-4, an “Entity” is a thing—a person, place, organization, or concept that is distinct and identifiable in the real world.

Examples of Entities

• “Apple” (The Fruit) = Entity A
• “Apple Inc” (The Technology Company) = Entity B
• “Steve Jobs” = Entity C
• “Cupertino, CA” = Entity D
• “iPhone” = Entity E

Each is a discrete entity with its own properties, relationships, and context.

How LLMs See Entities

LLMs don’t think in “websites” or “domains.” They think in vector embeddings—mathematical representations of concepts in multi-dimensional space.

In the LLM’s “mind,” your brand (Entity) is positioned in semantic space relative to other entities. Example:

Entity: "HubSpot"
- Associated with: CRM, marketing automation, sales tools, Salesforce-competitor
- Sentiment: Positive (strong reviews), Authoritative (industry leader)
- Relationships: Used by, compared to Salesforce, alternative to Pipedrive
- Trustworthiness: High (featured on Forbes, TechCrunch)

The AI learns this “understanding” from how your brand appears across the web—consistency, mentions, sentiment, association.

Entity Authority = How well the AI understands and trusts your brand within a specific topic layer.

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The Shift: From “Links” to “Knowledge”

Domain Authority (DA): The Old Model (2005-2023)

What it measures: The quantity and quality of links pointing to a domain.

Formula (simplified):

Domain Authority = (Backlinks from high-authority sites) + 
                   (Anchor text relevance) + 
                   (Link velocity)

How Google used it: As a ranking signal. More links = higher ranking.

Limitation: Doesn’t account for how trustworthy or consistent your brand identity is.

Entity Authority: The New Model (2024+)

What it measures: The consistency and interconnectedness of facts about a brand in the Knowledge Graph and LLM training data.

Formula (conceptual):

Entity Authority = (NAP consistency across web) + 
                   (Knowledge Graph data accuracy) +
                   (Wikipedia/Wikidata presence) +
                   (Citation consistency) +
                   (Sentiment in reviews/mentions) +
                   (Co-mention with other trusted entities)

How LLMs use it: To decide whom to trust when synthesizing answers.

Advantage: Captures brand trustworthiness holistically.

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Real-World Case Study: Brand A (Low DA, High Entity) vs. Brand B (High DA, Low Entity)

Brand A: Emerging SaaS Tool

Metric	Value
Domain Authority (Moz)	28
Total Backlinks	312
Brand Mentions (monthly)	450
Sentiment	89% positive
Wikipedia Entry	Yes (up-to-date)
Wikidata Entry	Yes (complete)
G2 Rating	4.8/5 stars, 320 reviews
Founder LinkedIn Followers	45K
Recent Media Coverage	Forbes, TechCrunch, VentureBeat

Entity Authority Assessment: HIGH (Brand is consistent, well-understood, trusted)

Brand B: Legacy B2B Software

Metric	Value
Domain Authority (Moz)	54
Total Backlinks	2,847
Brand Mentions (monthly)	180
Sentiment	62% positive (older reviews), declining
Wikipedia Entry	Yes (outdated, hasn’t been updated in 3 years)
Wikidata Entry	Incomplete (missing founder, description outdated)
G2 Rating	3.2/5 stars, 50 reviews
Founder LinkedIn Followers	8K
Recent Media Coverage	None in last 12 months

Entity Authority Assessment: MEDIUM-LOW (Brand data is scattered, outdated, sentiment declining)

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The Question: Which Does ChatGPT Recommend?

User asks ChatGPT: “What’s the best project management tool for remote teams in 2025?”

Traditional SEO prediction (based on DA): Brand B wins (DA 54 > DA 28). More backlinks, higher authority.

Actual LLM prediction (based on Entity Authority): Brand A wins. Here’s why:

1. Consistency: Brand A’s description, positioning, and team info are identical across website, LinkedIn, Wikipedia, G2
2. Recency: Brand A has recent media coverage (Forbes, TechCrunch), signaling ongoing relevance
3. Sentiment: 89% positive mentions vs. 62%, tells LLM “customers trust this”
4. Knowledge Graph completeness: Wikipedia + Wikidata up-to-date, tells AI “this entity is well-documented”
5. Authority signals: Founder has 45K followers, recent funding news, active thought leadership

Result: ChatGPT cites Brand A first, even though Brand B has 10x more backlinks.

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How to Build Entity Authority (4 Core Strategies)

Strategy 1: Establish “SameAs” Relationships (Semantic Linking)

Tell the AI: “These are all the same entity.”

Implementation using Schema Markup:

{
  "@context": "schema.org",
  "@type": "Organization",
  "@id": "https://yourbrand.com",
  "name": "Your Brand Name",
  "url": "https://yourbrand.com",
  "sameAs": [
    "https://www.linkedin.com/company/your-brand",
    "https://www.crunchbase.com/organization/your-brand",
    "https://www.g2.com/products/your-brand",
    "https://twitter.com/yourbrand",
    "https://www.wikipedia.org/wiki/Your_Brand"
  ],
  "founder": {
    "@type": "Person",
    "name": "Founder Name",
    "sameAs": "https://www.linkedin.com/in/founder-name"
  }
}

What this does: Explicitly tells search engines and LLMs: “We’re the same entity across all these platforms.”

ROI: High (5 minutes to implement, high impact).

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Strategy 2: Niche Down (Be the Big Fish in a Specific Pond)

It’s easier to build Entity Authority for a specific category than a broad one.

Example:

• ❌ “Software” (too broad, you’ll never dominate)
• ✅ “Healthcare CRM” (more specific, easier to own)
• ✅ “CRM for distributed healthcare teams” (even more specific, highest authority probability)

Why niche matters: When an LLM evaluates “top tools for distributed healthcare teams,” it’s easier for it to identify you as THE expert if you’ve explicitly positioned as such across Wikipedia, G2, content, founder messaging.

Action items:

• Define your niche (specific industry + use case)
• Update LinkedIn company description to match
• Update Wikipedia (if you have entry) to include niche explicitly
• Write content specifically for that niche (helps AI understand your category)
• Get reviewed on niche-specific platforms (Healthcare IT News, industry forums)

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Strategy 3: Co-Occurrence (Get Mentioned Alongside Other Authorities)

LLMs learn associations. Get mentioned in the same breath as trusted competitors.

Goal: Train the AI: “When thinking about [category], include this brand.”

Tactics:

1. Appear in “Comparison” articles
   • “HubSpot vs. Salesforce vs. [Your Brand]”
   • Contact publication: pitch comparison article
   • ✅ Offer to be interviewed for the story
2. Publish co-citation content
   • Article: “Why [Your Brand] is better than Salesforce for [use case]”
   • Article: “Top 5 Salesforce alternatives (and when to use each)”
   • Goal: Natural mention of you + competitors in same context
3. Get mentioned on authority platforms
   • G2, Capterra “Best of” lists (which include your brand + competitors)
   • Industry analyst reports (Gartner, Forrester)
   • When report mentions “Leading CRM tools,” you’re listed alongside Salesforce

Why it works: When LLM training data shows “Brand A, Brand B, [Your Brand]” mentioned together 1,000 times, the AI learns they’re in the same category.

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Strategy 4: Wikipedia & Wikidata (The Entity Authority Shortcut)

Wikipedia and Wikidata are the AI’s source of truth.

If your brand exists on Wikipedia (and is well-maintained), LLMs treat that as canonical information about you.

Implementation:

Step 1: Check if you qualify for Wikipedia

• Wikipedia requires “notability” (significant coverage in reliable sources)
• Most B2B SaaS isn’t notable enough yet
• Check: Does your company have Wikipedia entry? If yes, go to Step 2. If no, skip to Wikidata.

Step 2: Optimize your Wikipedia entry (if it exists)

• Update: Founding date, headquarters, key executives, notable product launches
• Ensure: All facts are cited (citations = sources Wikipedia trusts)
• Add: Recent awards, funding, acquisition news
• DO NOT edit yourself (Wikipedia has conflict-of-interest rules). Hire a Wikipedia editor if needed.

Step 3: Create/Optimize your Wikidata entry (easier than Wikipedia)

• Visit: wikidata.org
• Search for your company
• If not found: Create new entity (10 minutes)
• If found: Update fields:
  • Instance of: Organization
  • Headquarters location: [City, Country]
  • Founded: [Date]
  • Official website: [URL]
  • CEO/Founder: [Link to their Wikidata entry]
  • Description: [One sentence value prop]
  • Logo: [Upload image]
  • Industry: [Category]

ROI: Medium effort (2-4 hours), high impact (directly influences Knowledge Graph).

Example Wikidata entry for Geolify:

Instance of: Software company

Founded: 2023

Headquarters: San Francisco, California

Official website: geolify.ai

CEO: [Person name]
Description: AI visibility tracking platform for brands

Industry: MarTech, SaaS

Funded by: [Investors]

Once you have a Wikidata entry, Google’s Knowledge Graph picks it up automatically.

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The Technical Shift Explained (Why LLMs Care More About Entity Authority)

Old Model (Google Ranking Algorithm)

Query: "best CRM"
→ Crawl website 
→ Count backlinks
→ Analyze keyword density
→ Rank #1 if authority high + keyword match

Measurement: Domain-level (your whole site gets a DA score)

New Model (LLM Decision-Making)

Query: "best CRM for distributed remote teams"
→ Retrieve text from top 100 sources on the topic
→ Parse entity mentions (Salesforce, HubSpot, Pipedrive, [Your Brand])
→ Evaluate entity consistency (are facts about [Your Brand] consistent across sources?)
→ Evaluate sentiment (are mentions positive or negative?)
→ Decide: Rank by entity authority + sentiment score
→ Synthesize answer citing top 3 entities

Measurement: Entity-level (your brand’s reputation/consistency)

Key difference: The AI doesn’t care about your domain’s DA. It cares about whether “Your Brand Entity” is well-understood and trusted.

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Takeaway: The New Authority Playbook

Stop obsessing over Moz DA or Ahrefs DR. A DA-20 site can beat a DA-80 site in AI search if the DA-20 site has higher Entity Salience for that specific topic.

Your new playbook:

1. Consistency: Ensure your brand data is identical across web (NAP, description, founder info)
2. Documentation: Create/update Wikipedia and Wikidata entries
3. Semantics: Use Schema Markup to link all your entities together
4. Co-citation: Get mentioned alongside industry leaders
5. Sentiment: Build positive brand sentiment (reviews, media coverage)

The result: An AI engine that confidently says, “This brand is an authority in [niche]. I’m recommending them.”

That’s Entity Authority. And it’s worth infinitely more than a high Domain Authority score.