A comprehensive solution for improving a legacy search system over multi-modal data using Anyscale and MongoDB was presented. The solution consists of a scalable, multi-modal data indexing pipeline that performs complex tasks like batch inference, vector embedding generation, and inserting data into a search index. A performant hybrid search backend is also implemented that combines lexical text matching with semantic search capabilities. Additionally, a simple user interface for interacting with the search backend was created. The solution utilizes Anyscale platform as the AI compute platform and MongoDB cloud as the central data repository. Enterprises dealing with large volumes of multi-modal data often require robust search systems to address limitations such as inadequate support for unstructured data and dependence on data quality and relevance.