Turning behavior into vectors.
At its simplest, an embedding turns a piece of data (a user, a URL, a marketing "angle," or a business unit) into a list of numbers (a vector) that represents its meaning or intent in a multi-dimensional space.
Traditional Data: "User A visited URL X." (Rigid, exact match required).
Embedding Data: "User A's behavior vector is mathematically close to 'High-Intent Enterprise Buyer' and 'Cloud Security Interest'." (Flexible, predictive).
We use a sophisticated use case that moves beyond simple segmentation. Since we possess 3 billion behavioral data points, UPID, and B2B hierarchy, we construct a Semantic Identity Graph.
The system leverages state-of-the-art embedding models (like fine-tuned BERT or Transformer models) and Vector Databases (such as Pinecone, Milvus, or Weaviate) to index our vast libraries of behaviors and content.