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Entity SEO

Entity SEO and Knowledge Graph Optimization

AI systems reason about entities, not pages. Your brand either exists as a recognized entity in the Knowledge Graph, with verified attributes and co-citation signals, or it is an anonymous web page that AI models cannot confidently attribute. Entity SEO fixes the latter.

30%
Higher AI citation rate for entities with Wikidata presence
4x
More Knowledge Panel appearances after entity build
0.71
Branded mentions to AI visibility correlation (Ahrefs)
12wk
Typical timeline for Knowledge Graph recognition

What Entity SEO Covers

Five pillars of Knowledge Graph presence.

A recognized entity has verified attributes, authoritative references, and co-citation signals that AI systems use to confirm identity and trustworthiness. We build all five pillars.

Wikidata and Wikipedia Presence

Wikidata is the machine-readable backbone of the web's entity graph. Google, ChatGPT, and other AI systems pull entity attributes from it. We build and maintain accurate Wikidata entries for your organization, key products, and leadership, and where applicable, pursue Wikipedia notability.

  • Wikidata entity creation and attribute population
  • sameAs cross-linking to authoritative databases
  • Wikipedia article assessment and editing support
  • Annual entity attribute maintenance

Structured Data Density

Schema.org markup is how you communicate entity attributes to AI systems in a machine-readable format. We implement full structured data coverage across your site: Organization, Product, Person, Service, and Event schemas with the attribute depth that feeds AI knowledge bases.

  • Organization schema with full attribute coverage
  • Product and Service schema implementation
  • Person schema for key team members
  • sameAs attribute linking to verified sources

Co-Citation Strategy

AI systems derive entity trustworthiness partly from co-citation signals: being mentioned alongside recognized authorities in relevant contexts. We build your co-citation presence through strategic content placement, roundup inclusions, and industry publication mentions.

  • Co-citation target research and mapping
  • Industry publication mention campaigns
  • Roundup and "best of" content placement
  • Brand mention monitoring and attribution

Knowledge Panel Optimization

Google Knowledge Panels are a direct signal that your entity is recognized in the Knowledge Graph. We improve the attributes Google surfaces in your panel: description, social profiles, founding information, key products, and associated entities.

  • Knowledge Panel claim and verification
  • Panel attribute accuracy audit
  • Social profile cross-linking
  • Entity attribute correction campaigns

Entity Recognition Monitoring

We track Knowledge Graph presence through direct monitoring of how AI systems describe your entity. Monthly prompt tests ask major LLMs to describe your organization, identify your key products, and name your leadership. Accuracy and completeness improve as entity signals strengthen.

  • Monthly entity recognition prompt testing
  • Attribute accuracy tracking
  • Knowledge Graph connection monitoring
  • Competitive entity gap analysis

Our Entity SEO Process

Audit, architecture, build, track.

Entity recognition does not happen by accident. It requires deliberate infrastructure: structured data, verified external references, and co-citation signals built over time.

Phase 01

Entity Audit

We test how AI systems currently describe your organization: ask ChatGPT, Perplexity, and Gemini about your company, products, and leadership. The accuracy and confidence of those responses tells us the current state of your entity recognition. We also audit your existing structured data, Wikidata presence, and sameAs connections.

Phase 02

Entity Architecture Design

We map the full entity graph you need: organization, products, services, leadership, and their interconnections. Then we identify the authoritative external sources (Wikidata, Crunchbase, LinkedIn, industry databases) that should hold sameAs references to each entity, and the co-citation contexts where your brand should be present.

Phase 03

Implementation and Outreach

Structured data implementation happens in one sprint. External entity work, Wikidata editing, sameAs link building, and co-citation campaigns require ongoing outreach. We run both in parallel, with technical implementation first to give Google and LLMs something accurate to parse.

Phase 04

Entity Recognition Tracking

Monthly prompt testing tracks whether AI systems describe your entity more accurately over time. We measure attribute completeness (does the AI know your founding year, primary service, key clients?), co-citation frequency, and Knowledge Panel completeness. These metrics compound: stronger entity signals create more accurate AI descriptions, which reinforce the entity further.

Entity SEO in Practice

How a fintech company became a recognized entity in 4 months.

The Challenge

A fintech company with strong product-market fit and 15,000 monthly organic visitors had zero Knowledge Graph presence. When asked about payment processing companies, every major LLM named their competitors. Our client was either ignored or described inaccurately. The problem was not brand awareness. It was entity recognition: no Wikidata entry, no sameAs markup, no structured data, no co-citation signals that would let AI systems confidently attribute information to them.

Our Solution

We built their entity architecture from the ground up: Wikidata entry with full attribute population, Organization schema with sameAs links to Crunchbase, LinkedIn, and their industry association profiles, Person schemas for the leadership team, Product schemas for their three core offerings, and a co-citation campaign placing them in fintech comparison articles alongside Stripe, Adyen, and Braintree.

Results Achieved

None to active
Knowledge Panel Appearance
Full attribute panel at 10 weeks
0% to 78%
AI Entity Accuracy
Attribute accuracy in LLM responses
+340%
LLM Co-citations with Competitors
Named alongside known entities
+65%
Branded Organic Traffic
Knowledge Panel driving direct navigational search

FAQ

Entity SEO frequently asked questions

Entity SEO is the practice of establishing your brand, products, and key individuals as recognized entities in AI and machine-learning knowledge bases, principally the Knowledge Graph. Traditional SEO treats your site as a collection of pages. AI systems treat the web as a collection of entities. A brand that exists as a recognized entity with verified attributes gets cited more consistently and accurately than a brand that AI systems can only infer from page content.
The Knowledge Graph is Google's structured database of entities and their relationships. It powers Knowledge Panels, Featured Snippets, and the entity understanding that feeds into Google Gemini and AI Overviews. It also connects to Wikidata, which is a primary source for many other LLMs. If your organization is not in the Knowledge Graph as a verified entity, AI systems that rely on it will either ignore you or describe you inaccurately when asked directly.
sameAs is a Schema.org property that links your entity on one platform to its representation on authoritative external databases: Wikidata, Wikipedia, Crunchbase, government company registries. When AI systems encounter your brand, they use sameAs links to pull verified attributes from those external sources. A brand with rich sameAs connections gets described with more accuracy and confidence than one without them.
Traditional link building chases PageRank. Co-citation chases entity association. When authoritative sources mention your brand alongside recognized entities in your space, AI systems learn that you belong in the same category. A piece in TechCrunch that mentions your payment processing company alongside Stripe and Adyen tells the AI "this is a credible player in the payment processing space." That association signal feeds entity trust independently of whether the link passes PageRank.
No. Wikipedia helps, but Wikidata is more important and more achievable. Wikidata has no notability requirements and accepts any organization. A complete, accurate Wikidata entry with sameAs connections to your other profiles is the foundation. Wikipedia adds a credibility signal and additional attribute data, but it requires passing notability guidelines. We start with Wikidata, then assess Wikipedia eligibility.
Technical structured data implementation shows in Google's understanding within 4-6 weeks. Wikidata entries typically get picked up within 6-8 weeks. Knowledge Panel changes from entity attribute improvements take 8-12 weeks. The full effect on LLM entity recognition compounds over 3-6 months as new sameAs connections, co-citations, and entity signals accumulate and get incorporated into LLM knowledge bases.

Start with a Free Entity Audit

Find out how AI systems currently describe your brand.

We run your brand, products, and leadership through structured prompts across major LLMs and test attribute accuracy. Then we show you what it would take to become a confidently recognized entity.

  • Free entity recognition audit across 4 LLMs
  • Knowledge Graph gap assessment
  • sameAs connection inventory and recommendations