Entity Signals

Entity Signals are the structured data and digital presence markers that help AI engines understand who you are, what you do, and whether to trust you. They're the foundation layer of Machine Relations.

Why Entity Signals Matter

Before an AI engine can recommend your brand, it needs to understand your brand as a distinct entity — not just a keyword. Entity signals tell AI engines: "This is a real organization, with these capabilities, in this category, trusted by these sources."

Pages with well-implemented schema markup are 2-4x more likely to appear in Google AI Overviews. In head-to-head studies, when two competing pages target the same query, only the page with clean schema implementation appears in the AI Overview result. Same query, same content quality, dramatically different AI visibility.

Key Entity Signals

• Schema.org markup — Organization, Product, Service, Person structured data in JSON-LD format on your site
• Google Knowledge Graph — Your brand's Knowledge Panel and entity associations
• Wikipedia / Wikidata — Entries that establish notability and enable entity resolution
• Consistent NAP — Name, address, phone consistent across platforms
• sameAs links — Connecting your website to LinkedIn, Crunchbase, GitHub, and other canonical profiles
• Authoritative mentions — Tier-1 publications referencing your brand by name, which AI engines weight heavily during retrieval

Entity Signals in Practice

Strong entity signals mean AI engines can confidently identify your brand and associate it with the right category. Weak entity signals mean the AI may confuse you with competitors, misattribute your capabilities, or omit you entirely. This is the entity resolution problem — and signals are how you solve it.

The technical layer is inseparable from earned media. Schema markup tells AI what your content means. Earned authority tells AI whether to trust it. Entity optimization is the discipline of aligning both layers so your brand becomes unambiguous to machines.