Glossary
Vector Spaces
High-dimensional mathematical spaces where actors, products, campaigns become 'Persona Vectors.' Mathematics instead of databases.
Definition
Vector Spaces are high-dimensional mathematical spaces where entities-customers, products, campaigns, content-are represented as points (vectors). In Sentient OS, actors become 'Persona Vectors' that enable computable similarity and distance calculations. Two customers with similar vectors are behaviorally similar; a product vector close to an audience vector indicates strong fit. This approach replaces database lookups with mathematical operations: similarity is computed, not queried. Vector spaces support archetypal clustering (discovering behavioral segments), semantic alignment (matching content to audiences), and causal analysis (understanding influence and control). The architecture operates in hundreds of dimensions, capturing nuance that demographic or keyword-based systems miss. Vector spaces are the mathematical foundation of Sentient's intelligence.
Why It Matters
Vector spaces are Sentient OS's core mathematics. Every capability-persona modeling, creator matching, anomaly detection-depends on high-dimensional vector computation.
Related Pages
Related Terms
Persona Vectors
Mathematical representations of customers as points in complex space, enabling computable similarity and distance.
Archetypal Clustering
Unsupervised learning to identify behavioral clusters like 'The Skeptical Innovators' rather than demographic groups.
Semantic Alignment
Multi-modal embedding similarity between content themes and product categories. Deep matching beyond keywords.
Causal Analysis
Going beyond correlation to understand causality. Not 'Who knows whom?' but 'Who controls whom?'
Anomaly Detection
Identifying artificial patterns in traffic (bots, fake engagement) through vector space anomalies, not keywords.
Explore the Full Platform
See how these concepts come to life inside Sentient OS.