Glossary
Embedding Similarity
Measuring how well a piece of content or creator aligns with a product - based on actual behavioral fit, not just follower count.
Definition
Embedding Similarity is the mechanism by which the Psychographic Layer measures audience-product fit. Content and audience vectors are aligned in high-dimensional space (in the DNA layer). Fit is not a category label - it is a computable distance and direction in vector space. Multi-modal embeddings fuse language, visual, and behavioral signals into a unified representation. The result is a precise alignment score that goes beyond keyword matching to capture deep semantic resonance between what a creator's audience cares about and what your product offers.
Why It Matters
Embedding similarity replaces guesswork with geometry. Fit is computable, explainable, and deterministic.
Related Pages
Related Terms
Vector Spaces
A mathematical space where people, products, and content are represented so that 'closeness' means compatibility. The foundation for precise matching. Mathematics instead of databases.
Semantic Alignment
Multi-modal embedding similarity between content themes and product categories. Deep matching beyond keywords.
Psychographic Layer
Command Center Module IV - deep psychological fit through semantic alignment, keyword overlap, and demographic mapping.
DNA Layer
Layer 4 of the five-layer architecture - builds a living behavioral profile for every person, product, and brand to enable precise matching. Mathematics instead of databases.
Persona Vectors
Mathematical representations of customers as points in complex space, enabling computable similarity and distance.
Explore the Full Platform
See how these concepts come to life inside Sentient OS.