Embeddings

Embeddings are dense numeric representations of text that capture semantic relationships between words, phrases, or documents in a vector space. Similar meanings map to nearby vectors. For SEO, recommendation, and AI messaging workflows, embeddings enable search, clustering, and personalization: systems can match user queries to relevant prompts, messages, or content, improving relevance and discoverability without relying solely on exact keyword overlap. Embeddings are dense numerical vectors that represent text in a way that captures semantic similarity, so related words or documents map to nearby points in vector space. They are produced by specialized models and used for search, clustering, and recommendation. For SEO and AI messaging use cases, embeddings allow teams to match user intent to relevant prompts, content, or responses without relying solely on exact keyword matches.