Abstract: A system can include one or more memory devices that can store instructions thereon that, when executed by one or more processors, cause the one or more processors to receive a natural language query for information associated with a first entity, generate a vector embedding based on the natural language query, search a vector database having a plurality of vector embeddings to identify a set of vector embeddings of the plurality of vector embeddings that are associated with the vector embedding, identify metadata that corresponds to respective vector embeddings of the set of vector embeddings, select a first vector embedding that corresponds to the first entity, generate a prompt based on the natural language query, generate a response to the natural language query using a machine learning model, and provide an electronic message that includes the response to the natural language query.