Multi-vector search improves information retrieval by using multiple semantic representations to capture different aspects of meaning in a query. Instead of relying on a single embedding vector, the system generates several, each representing a distinct interpretation of the request. This increases recall and accuracy, especially when queries are complex or ambiguous. For enterprises, multi-vector search ensures that AI-powered search engines and knowledge retrieval systems surface the most relevant and contextually appropriate results.