Glossary
Embedding Similarity
Multi-modal vector alignment between content themes and product categories - computable fit in high-dimensional space.
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 (48 dimensions 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
High-dimensional mathematical spaces where actors, products, campaigns become 'Persona Vectors.' 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 5-Layer Architecture - every actor encoded as a point in 48-dimensional vector space. 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.