Glossary
Semantic Alignment
Multi-modal embedding similarity between content themes and product categories. Deep matching beyond keywords.
Definition
Semantic Alignment is the measurement of conceptual fit between content, products, and audiences using multi-modal embedding similarity. Rather than matching on keywords alone, semantic alignment captures meaning: a creator discussing 'sustainability' aligns with eco-friendly products even if the word 'eco' never appears. Sentient OS uses embedding models to project text, images, and metadata into vector space where similarity indicates semantic fit. The Psychographic Layer computes semantic alignment for creator-brand matching. Content Intelligence uses it to assess content-product resonance. Keyword overlap matrices complement embeddings for interpretability. Semantic alignment enables matching that survives vocabulary variation, nuance, and implicit meaning-the kind of fit that keyword-based systems miss.
Why It Matters
Semantic alignment powers creator matching and content intelligence. Sentient finds fit by meaning, not just words-enabling matches that demographics and keywords cannot.
Related Pages
Related Terms
Persona Vectors
Mathematical representations of customers as points in complex space, enabling computable similarity and distance.
Content Intelligence
Understanding content resonance, format effectiveness, and semantic alignment with audiences.
Intent Recognition
Classifying strategic intent behind interactions (informative, transactional, social). A click is not just a click.
Vector Spaces
High-dimensional mathematical spaces where actors, products, campaigns become 'Persona Vectors.' Mathematics instead of databases.
Natural Language Processing (NLP)
AI technology for understanding human language in context. Used in Layer 2 for intent and tonality analysis.
Explore the Full Platform
See how these concepts come to life inside Sentient OS.