Neural Embeddings are numerical vector representations generated by deep learning models that capture the semantic meaning of words, sentences, audio, or documents. Similar concepts are positioned close together within the embedding space.
Voice AI platforms use Neural Embeddings for semantic search, Retrieval-Augmented Generation (RAG), intent matching, document retrieval, and conversation understanding. They improve search accuracy beyond traditional keyword matching.