Entity Saturation Score

Extended definition

Entity Saturation Score measures how thoroughly AI knowledge graphs and vector databases understand your brand across all entity dimensions: what you are (category), what you do (products/services), who you serve (customers/markets), how you're related to other entities (partners, competitors, technologies), and key attributes (founded, location, size, differentiators). High saturation means comprehensive entity representation; low saturation means sparse, incomplete understanding. Saturation affects retrieval (incomplete entities get overlooked), accuracy (missing attributes cause misrepresentation), and authority (rich entities signal importance).

Why this matters for AI search visibility

AI engines can't accurately represent what they don't understand. Low Entity Saturation causes persistent accuracy problems: your category gets misstated, your differentiators don't appear, your use cases are misrepresented, or you're confused with competitors. High saturation ensures AI has the information needed to correctly position you when generating answers. For new brands or products, saturation predicts how quickly AI visibility can ramp: high saturation enables immediate visibility, low saturation creates a knowledge gap that requires months to fill through content and structured data.

Practical examples

• Established brand has 94% entity saturation enabling accurate representation across all AI platforms
• Startup with 31% saturation sees consistent misrepresentation of product category and target market
• Saturating missing entity attributes (competitors, use cases, technologies) through schema markup increases mention accuracy from 54% to 91%