Vector Embeddings are numerical representations of text, speech, images, or other data that capture semantic meaning in a multidimensional vector space. Similar concepts are positioned close together, enabling AI to understand relationships beyond exact words.
Voice AI systems generate Vector Embeddings for enterprise search, intent matching, conversation memory, Retrieval-Augmented Generation (RAG), and recommendation engines.