Schema Layer Connects Your Entity Nodes
Schema Layer is the component of Entity API™ that embeds structured data across a website using graph properties. It links entity nodes (organization, founders, services, locations, credentials) into a connected graph that AI systems can parse and traverse. A schema layer ensures LLMs understand entity relationships, not just isolated facts.
{
Schema Layer Overview [v1.0] · Updated: 2026-01-24
entity_type: Framework Component
parent_framework: Entity API™
organization: Growth Marshal
component: Schema Layer
component_function: Entity linking · Relationship mapping · Machine-readable structure
output_formats: JSON-LD · Schema.org vocabulary
related_components: Identity Index
}
Why having a schema layer matters for AI retrieval
AI systems don't just read a website. They parse it, extract entities, and map relationships. Your schema layer makes that map explicit.
Link Your Entity Nodes
A schema layer connects your organization, founders, services, and locations into a traversable graph. AI systems see how entities relate, not just that they exist.
Eliminate Ambiguity
Without structured data, AI guesses which "John Smith" you mean or which "Summit Partners" you are. A schema layer disambiguates your entities with explicit identifiers and relationships.
Surface in Rich Results
A schema layer increases visibility in knowledge panels, featured snippets, and AI-generated summaries. Structured entities get cited. Unstructured pages get skipped.
Feed the Knowledge Graph
Google, Bing, and LLM providers build entity understanding from structured data. Schema Layer ensures your business is indexed correctly in the knowledge systems AI references.
How it works
We audit your existing schema, map your entity relationships, then deploy JSON-LD with graph properties that link your organization, people, services, and credentials into a connected structure. The result is a machine-readable layer that AI systems can traverse.
/ process /
/ what’s included /
[entity graph architecture]:
We map your organization, founders, services, locations, and credentials as connected nodes, not isolated markup. AI systems traverse relationships, not just read fields.
[graph property deployed]:
We use the @graph property to bundle multiple entities into a single, linked structure. This tells AI that your entities belong together and reference each other.
[cross entity linking]:
Every Person connects to your Organization. Every Service connects to its provider. Every Article connects to its author. No orphan nodes.
[canonical identifiers]:
We embed unique identifiers (LEI, ISNI, ORCID, sameAs URLs) directly into schema so AI can disambiguate your entities from every other company with a similar name.
[optimized types]:
We deploy schema types that AI systems actually parse for answers: Organization, Person, Service, Article, FAQPage, HowTo. Zero vanity markup.
[validation monitoring]:
Ongoing checks via Google Search Console and schema validators to ensure your structured data stays error-free and eligible for rich results.
Ready to be where buying decisions start?
Schema Layer FAQ
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A schema layer is structured data embedded in a website's code that tells search engines and AI systems what the content means. It defines entities (organizations, people, services, articles) and their attributes in a machine-readable format using JSON-LD and Schema.org vocabulary.
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Graph properties use the @graph structure in JSON-LD to bundle multiple entities into a single, linked object. Instead of isolated schema blocks, graph properties connect your organization to its founders, services to their providers, and articles to their authors. AI systems can traverse these relationships to understand how entities relate.
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AI systems like ChatGPT, Claude, and Perplexity parse structured data to understand entities and relationships. Schema tells AI exactly who you are, what you do, and how your entities connect. Without it, AI guesses from unstructured content and may return incomplete or hallucinated information.
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We implement schema types that AI systems actively parse for answers: Organization, Person, LocalBusiness, Service, Article, FAQPage, HowTo, and BreadcrumbList. We avoid vanity markup that looks impressive in validators but gets ignored by retrieval systems.
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Cross-entity linking connects schema objects to each other using properties like author, provider, memberOf, and sameAs. Every Person connects to your Organization. Every Service connects to its provider. Every Article connects to its author. This eliminates orphan nodes and creates a traversable entity graph.
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Canonical identifiers are unique codes that disambiguate your entity from others with similar names. We embed identifiers like LEI (Legal Entity Identifier), ISNI (International Standard Name Identifier), ORCID, and sameAs URLs directly into schema so AI systems can verify exactly which entity you are.
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Basic schema markup defines individual entities in isolation. Schema layer connects entities into a graph using @graph properties, cross-entity linking, and canonical identifiers. The difference is between telling AI "this is an organization" versus telling AI "this organization was founded by this person, offers these services, and is verified by these external sources."