Text Embeddings are numerical vector representations of words, phrases, or documents that capture their semantic meaning. Similar text produces similar vectors, enabling AI systems to compare meaning rather than exact wording.
Voice AI platforms use Text Embeddings for semantic search, Retrieval-Augmented Generation (RAG), intent matching, recommendation systems, conversation memory, and enterprise knowledge retrieval.