Entity SEO for AI: Ensuring LLMs Recognize Your Brand

Updated December 17, 2025

Google rankings no longer guarantee AI visibility. A B2B SaaS company can rank #2 for its core category keyword and remain completely invisible when prospects ask ChatGPT for recommendations. Competitors appear with detailed explanations while the higher-ranked company never enters the conversation. This is the entity gap, and it explains why strong SERP positions no longer capture buyer attention when 89% of B2B buyers have adopted generative AI for vendor research.

Traditional SEO relies on matching keywords to pages. AI search relies on connecting concepts to entities. If LLMs cannot verify your brand as a distinct entity with specific attributes, they will not cite you. This guide outlines how to force that recognition using our CITABLE framework and the technical infrastructure that makes AI citation possible.

Entity SEO vs. traditional SEO: The strategic shift

Traditional SEO optimizes for strings of text. Entity SEO optimizes for things and their relationships. This distinction matters because LLMs organize knowledge differently than search engines organize information.

Natural language processing and machine learning have shifted how search engines work. Algorithms now prioritize entities and their relationships over keyword frequency. This shift means you need to focus on content relevance and topical depth rather than keyword density. Traditional SEO strategies rooted in keyword research focused primarily on difficulty and search volume. Entity SEO focuses on whether AI systems can confidently identify what your brand is, what you do, and how you relate to other concepts.

At Discovered Labs, we built our CITABLE framework specifically to address the entity recognition challenge that traditional SEO ignores

Key terminology

Before diving deeper, here are the core concepts you need to understand:

• Entity SEO: Optimizing your brand's presence in knowledge graphs so AI systems recognize and cite you as a distinct, verified thing rather than just a collection of keywords
• Knowledge Graph: A database of entities (people, places, companies, concepts) and their relationships that AI systems use to understand the world
• Organization Schema: Structured data markup that explicitly tells AI systems who your company is, what you do, and how to verify that information
• AEO (Answer Engine Optimization): The practice of optimizing content to be cited by AI-powered answer engines like ChatGPT, Claude, and Perplexity
• Verifiable Data: Facts about your company that multiple trusted sources confirm

Comparison: Traditional SEO vs. entity SEO

The difference is fundamental. Entities represent concepts and have relationships with other entities, while keywords are specific words searchers type into queries. You can rank #1 on Google and still be invisible to ChatGPT. LLMs pull answers from sources they have seen consistently across their training data and that meet high confidence thresholds. Traditional SEO signals like backlinks and technical optimization do not directly determine which brands LLMs cite.

How LLMs understand brands: The role of knowledge graphs

Google's Knowledge Graph launched in 2012 with the mantra "things, not strings." Within seven months, it cataloged 570 million subjects and 18 billion related facts. By 2020, Google claimed over 500 billion facts about five billion entities. Modern LLMs adopt and expand this entity-based approach when they retrieve information.

When ChatGPT generates an answer, it draws on a probabilistic map of entities and their relationships. The model has learned connections like: "Discovered Labs" -> is an -> "Agency" -> specializes in -> "AEO" -> serves -> "B2B SaaS." If those connections exist with high confidence in the training data, the LLM will cite your brand. If they do not exist or conflict with other sources, your brand stays invisible.

The confidence problem

LLMs avoid hallucination by only citing information they have high confidence about. High uncertainty scores signal the model is less confident, which triggers the system to either abstain from responding or warn users of potential hallucinations. When LLMs see conflicting information or lack supporting sources, their confidence scores drop and they skip citing your brand entirely.

For your team, this means lost pipeline. When your entity signals conflict, prospects researching with AI never see your brand. The deal goes to a competitor before your sales team hears about the opportunity.

The business impact is significant. According to Forrester, 89% of B2B buyers have adopted generative AI, naming it one of the top sources of self-guided information in every phase of their buying process. Research from Magenta Associates shows that 66% of UK senior decision-makers use AI tools like ChatGPT, Microsoft Copilot, and Perplexity to evaluate potential suppliers. If you are not an entity in the knowledge graph, you are not in the consideration set.

How to establish your brand as an entity in AI knowledge graphs

Establishing entity status requires three coordinated efforts: structured data implementation, third-party validation, and content clarity. At Discovered Labs, we address these through specific components of our CITABLE framework. Your team can implement these three priorities in parallel over 90 days.

Step 1: Implement organization schema (the "E" in CITABLE)

The "E" in CITABLE stands for Entity graph and schema. This means explicitly defining relationships in your copy and using structured data to communicate them to AI systems. Think of Organization Schema as a passport that explicitly identifies your company to AI systems. It states who you are, what you do, and how to verify your information.

Google recommends using JSON-LD for structured data because it is the easiest solution for website owners to implement and maintain at scale. You can place JSON-LD in the head or body section of your HTML.

Here is an example of Organization Schema for a B2B SaaS company:

{
  "@context": "https://schema.org",
  "@type": "Organization",
  "name": "Your Company Name",
  "url": "https://www.yourcompany.com",
  "logo": "https://www.yourcompany.com/images/logo.png",
  "description": "Your Company provides project management software for distributed enterprise teams",
  "email": "contact@yourcompany.com",
  "telephone": "+1-555-555-5555",
  "address": {
    "@type": "PostalAddress",
    "streetAddress": "123 Main Street",
    "addressLocality": "San Francisco",
    "addressRegion": "CA",
    "addressCountry": "US",
    "postalCode": "94102"
  },
  "sameAs": [
    "https://www.linkedin.com/company/yourcompany",
    "https://www.crunchbase.com/organization/yourcompany",
    "https://www.wikidata.org/wiki/Q123456789",
    "https://twitter.com/yourcompany"
  ]
}

The sameAs property matters most. Use it to unambiguously link your organization to authoritative external sources like Wikipedia, Wikidata, Crunchbase, LinkedIn, and your official website. This property establishes a relationship that search engines use for entity reconciliation, helping AI systems connect your schema to established entities across the web.

For B2B SaaS products specifically, use WebApplication schema to describe your product. WebApplication is a subtype of SoftwareApplication and distinguishes cloud-based software accessed through browsers from installed software.

We see these errors cost clients 4-6 weeks of progress because they trigger low confidence scores:

• Mismatched information where schema data does not match visible page content
• Using generic SoftwareApplication when WebApplication better describes your product
• Missing required properties like name, description, and url
• Fake or inflated ratings that violate Google guidelines

Validate your implementation using the Rich Results Test for Google-specific validation and the Schema Markup Validator for general schema.org syntax checking.

Step 2: Build third-party validation (the "T" in CITABLE)

The "T" in CITABLE stands for Third-party validation through reviews, user-generated content, community discussions, and news citations. External validation from Wikipedia, Crunchbase, and G2 carries more weight with AI models than claims on your own website. If multiple authoritative sources confirm who you are and what you do, the LLM's confidence increases.

Priority platforms for B2B validation:

1. Wikidata: Create a Wikidata entry even if you lack a Wikipedia article. Google, ChatGPT, and Bing pull from Wikidata to enrich their results. Create a free account, verify no existing record exists, then add structured declarations including date of creation, founder, website, workforce, and social profiles.
2. Crunchbase: Any registered and socially authenticated user can add a profile page. Required information includes logo, contact email, phone number, and a description between 2 and 10,000 characters. Crunchbase focuses on technology companies, so profiles outside this scope may be removed.
3. LinkedIn Company Page: Your personal profile must be older than seven days and cannot be newly created. You need an email address with a domain unique to your company (Gmail and Hotmail cannot be used). Your LinkedIn profile strength must be rated Intermediate or All Star to create a company page.
4. G2 and Capterra: G2 allows only one profile per vendor for services. Products must be B2B software or services associated with a specific vendor. After submitting your claim request, expect approval within 1-3 business days.
5. Industry directories: For SaaS companies, explore inclusion on directory sites like BuiltWith, ProductHunt, and industry-specific listings.

NAP consistency matters: NAP stands for Name, Address, and Phone Number. Inconsistent NAP data across your listings can drop your performance by 16%. When all your listings match, AI systems confidently cite your information. When discrepancies exist, confidence drops and competitors get cited instead.

Step 3: Optimize content clarity (the "C" in CITABLE)

The "C" in CITABLE stands for Clear entity and structure with a 2-3 sentence BLUF (Bottom Line Up Front) opening. Write your About page and homepage to explicitly state who you are and what you do in the first three sentences. This is not marketing copy optimization. It is entity definition that AI systems parse for confidence.

Use this checklist when you audit your homepage and About page:

• ✓ First sentence names your company and category with specific nouns (e.g., "Discovered Labs is the AEO agency for B2B SaaS companies")
• ✓ Second sentence states your core value proposition in concrete terms
• ✓ Third sentence establishes credibility through quantified proof points
• ✓ All entity attributes use explicit language, not implied positioning

Avoid vague positioning like "We help businesses grow." Instead, be specific: "Discovered Labs helps B2B SaaS companies get cited by ChatGPT, Claude, and Perplexity when prospects research solutions."

For deeper guidance on implementing these principles, our 28-point AEO implementation checklist provides step-by-step instructions for technical and content optimization.

Measuring AI visibility: Metrics that matter for revenue leaders

Traditional SEO metrics like rankings and organic traffic do not capture AI visibility. You cannot report citation rates to your board using Google Analytics. You need different measurements that connect to pipeline.

Citation rate tracks how often your brand appears in AI-generated answers for relevant queries. A baseline audit might reveal you appear in 5% of buyer-intent queries while competitors appear in 35%. The goal is to increase this systematically. Our median client sees citation rates improve from 5% baseline to 40-50% within 6 months.

Share of voice measures your presence relative to competitors. Track your mention rate across ChatGPT, Claude, Perplexity, and Google AI Overviews. Focus on which content changes and channels drive the biggest citation increases. Our article on AI citation patterns breaks down how each platform selects sources differently.

Conversion quality differs significantly for AI-sourced traffic. AI-sourced traffic converts at higher rates than traditional search traffic because prospects arrive with research complete. The AI system has already filtered options and recommended your brand as a fit. According to Semrush research, the average AI search visitor is 4.4 times as valuable as the average visit from traditional organic search based on conversion rate. This is why tracking AI-referred leads separately in your CRM matters.

Our article on measuring AI citation ROI breaks down exactly how to attribute pipeline to AI visibility improvements and report these metrics to your board.

How Discovered Labs engineers entity recognition

At Discovered Labs, we built our entire methodology around the entity recognition problem. Our CITABLE framework addresses all seven components required for AI citation:

• C: Clear entity and structure (2-3 sentence BLUF opening)
• I: Intent architecture (answer main + adjacent questions)
• T: Third-party validation (reviews, UGC, community, news citations)
• A: Answer grounding (verifiable facts with sources)
• B: Block-structured for RAG (organized sections, tables, FAQs, ordered lists)
• L: Latest and consistent (timestamps + unified facts everywhere)
• E: Entity graph and schema (explicit relationships in copy)

We start with an AI Visibility Audit that maps exactly where you appear (and do not appear) across ChatGPT, Claude, Perplexity, and Google AI Overviews for your buyer-intent queries. This baseline shows your "entity gaps" and the specific places where competitors dominate and your brand does not register as a recognized entity.

The audit reveals whether your brand exists as a recognized entity or if you are just a collection of keywords that AI cannot confidently cite. From there, we engineer the structured data, third-party validation, and content clarity required to close those gaps.

For companies evaluating whether to build these capabilities in-house or work with a specialized partner, our 90-day ROI comparison breaks down the timeline, resource requirements, and expected outcomes of each approach. You can also review our framework for making the build vs. buy decision.

Book an AI Visibility Audit to see if your brand exists as an entity in the knowledge graph. We will show you exactly where you stand, where your competitors appear, and the specific entity signals you need to build. You will leave with a prioritized roadmap you can present to your CEO and board.

Frequently asked questions

What is the difference between entity SEO and traditional SEO?

Traditional SEO optimizes pages for keyword rankings on Google. Entity SEO optimizes your brand's presence in knowledge graphs so AI systems recognize you as a distinct, verifiable entity and cite you in their answers. The technical requirements differ: backlinks help traditional SEO but do not directly determine which brands LLMs cite.

How long does it take for an LLM to recognize a new brand entity?

Establishing entity recognition typically takes 3-4 months of coordinated effort across structured data implementation, third-party validation building, and content optimization. According to IndexLab research, most businesses start seeing early citation improvements within 4-6 weeks, with full optimization occurring by month 4-6. The timeline depends on your starting point and competitive landscape.

Do I need a Wikipedia page to be an entity?

No. Wikipedia helps but is not required. Wikidata entries are more accessible and often under-exploited by companies. Crunchbase, LinkedIn Company Pages, and G2 profiles all contribute to entity validation. Start with Wikidata if you lack the notability for a full Wikipedia article, then build Crunchbase and LinkedIn company profiles. The key is consistent, verified information across multiple authoritative sources.

What signals do LLMs look for to cite content?

LLMs evaluate entity clarity (can they identify who you are and what you do?), third-party validation (do trusted sources confirm your claims?), information consistency (does your data match across platforms?), and content structure (can they easily extract facts from your pages?). High confidence across these signals increases citation likelihood.

Why does my Google #1 ranking not translate to AI visibility?

Google ranks pages based on backlinks, keywords, and technical SEO signals. ChatGPT, Claude, and Perplexity pull answers based on entity recognition and validation across trusted sources. These are fundamentally different systems. A Google #1 ranking means your page matched a keyword query. An AI citation means your brand registered as a verified entity in the knowledge graph with clear attributes and third-party confirmation. Traditional SEO does not build the entity signals AI systems require.

Key terminology glossary

Knowledge Graph: A database structure that represents entities (things like companies, people, concepts) and the relationships between them. Google's Knowledge Graph contains over 500 billion facts about 5 billion entities. LLMs use similar structures to understand and retrieve information about brands.

Named Entity Recognition (NER): A sub-task of information extraction that locates and classifies named entities in text into categories like organization names, locations, and products. NER helps LLMs understand context and generate relevant responses when users ask about specific companies.

Structured Data: Code added to your website in formats like JSON-LD that explicitly tells search engines and AI systems what your content represents. Organization Schema is the structured data type for company entities, including properties like name, description, address, and links to third-party profiles.