Entity Recognition in AI Search: How AI Systems Learn About Your Brand
AI systems build knowledge graphs of entities — brands, people, products, places. Understanding how your brand is recognized (or misrecognized) as an entity is fundamental to AEO. Here's how it works and how to improve your entity recognition.
What is an entity in AI search?
In AI search terminology, an "entity" is a distinct, uniquely identifiable thing — a person, brand, place, product, or concept. Entities are the nodes in a knowledge graph, and the relationships between them form the graph's edges.
When an AI system knows about your brand as an entity — not just as a string of text appearing on web pages, but as a distinct node with properties (category, founding date, products, people associated with it) — it can answer questions about you more accurately and is more likely to cite your content in response to related queries.
Entity vs. keyword: the practical difference
Keyword-based recognition
AI knows "RankAsAnswer" is a string that appears on pages about AEO. It might mention the name but doesn't have confident knowledge about what it is.
Result: hallucinations, vague mentions, no citations
Entity-based recognition
AI knows RankAsAnswer is a SaaS company in the AEO category, with specific features, a known founding context, and connections to the AEO topic cluster.
Result: accurate citations, feature mentions, category authority
How AI systems build entity knowledge
AI language models build entity knowledge through two phases: training (learning from the corpus of text the model was trained on) and retrieval (real-time web search in models like ChatGPT Browse and Perplexity).
Training data phase
The model reads billions of web pages and builds entity representations from patterns in the text. Your brand's entity strength in a model's training data is determined by how often and how clearly it's mentioned across the web — Wikipedia, news articles, blog posts, directories, and your own site.
Control level: Indirect — improve web presence, get press mentions, maintain Wikipedia page if eligible
Retrieval phase (real-time)
Models like Perplexity and ChatGPT Browse retrieve current web content when answering queries. Here, Schema markup and structured entity data directly influence how the model understands your entity in real-time.
Control level: Direct — Schema markup, structured entity pages, llms.txt
Why entity recognition matters for AEO
Entity recognition affects AEO in two specific ways:
- ▸Citation probability: AI systems are more likely to cite content from entities they recognize as authoritative in a category. A recognized entity's content gets a trust multiplier in retrieval scoring.
- ▸Hallucination prevention: Strongly recognized entities generate fewer hallucinations because the AI has sufficient confident knowledge to answer accurately without fabrication.
- ▸Category authority: Entities recognized as experts in a category (via topical content and knowsAbout Schema) get priority citation for queries in that category.
Entity strength factors
Schema markup for entity recognition
For retrieval-based AI systems, Schema markup is the most direct and controllable method of entity signal injection. Think of your Organization Schema as your entity declaration — a structured statement of exactly who and what your brand is.
Critical Organization Schema properties for entity recognition
@typeUse the most specific type (SoftwareApplication, Corporation, LocalBusiness, etc.)nameExact legal/brand name — consistent across all Schema instances on your domaindescriptionA precise, 100-200 word description that includes your category, primary value prop, and key differentiatorsurlYour canonical homepage URLsameAsArray of URLs for your entity on external platforms — the richer the betterknowsAboutArray of topics your organization has expertise in — anchors you to category queriesfoundingDateISO 8601 format year or full dateThe sameAs strategy: building cross-web entity coherence
The sameAs property is how you tell AI knowledge graphs that your brand entity at yourdomain.com is the same entity as your LinkedIn page, your Crunchbase profile, your G2 listing, and every other place your brand exists online.
This cross-platform entity coherence is critical for entity strength. The more authoritative platforms that confirm your entity's existence and properties, the stronger your entity recognition becomes.
Wikipedia
Highest
Wikidata
Highest
LinkedIn Company
Very high
Crunchbase
High
Google Business Profile
High
G2 / Capterra
High (SaaS)
GitHub
High (tech)
Twitter/X Official
Medium
Facebook Business
Medium
Build the profile first, then add sameAs
Content-based entity signals
Beyond Schema, your content architecture sends entity signals:
- ▸Consistently use your brand name in the same way across all pages — entity recognition depends on consistent name usage
- ▸Publish consistently in your topical category — the more content you produce on your core topic, the stronger your category entity association
- ▸Get cited by other entities — when authoritative sites mention your brand, they strengthen its entity recognition in AI systems
- ▸Keep your About page accurate and comprehensive — it's often one of the most frequently crawled pages by AI systems seeking entity facts
Diagnosing your entity strength
Test your entity strength by asking AI systems directly:
- ▸Ask "What is [your brand]?" — a confident, accurate answer indicates strong entity recognition
- ▸Ask "Who are the founders of [your brand]?" — errors here indicate weak entity knowledge about your team
- ▸Ask "What category does [your brand] compete in?" — miscategorization indicates entity confusion
- ▸Run RankAsAnswer's Entity Identity Card report — it evaluates your Schema-declared entity properties against citation-readiness benchmarks