Abstract: Vector-based hybrid search for chatbots is provided. A system receives, via a chatbot, a user query. The system generates, using an artificial intelligence model, a vector representation based on a combination of the user query, historical queries, and corresponding responses and identify a cached response corresponding to the user query in a semantic cache using the vector representation. The system executes, based on the cached response, a hybrid search operation including retrieval of first and second results having a first and second accuracy value by execution of a first and second search process on a first and second data source. The system selects one of the first or the second results based on modeling the first and the second accuracy value and displays, responsive to the user query, an output corresponding to the selected results.

Abstract: The present disclosure relates generally to the digitization of documents and more particularly, to a system, method and computer program which integrates multiple trained machine learning ensembles to identify, extract, and map a data set. The method, for example, includes receiving a data set from sources; identifying ensembles, each ensemble comprising machine learning models and each ensemble to determine an outcome; identifying a type for the data set based on a vendor type and the data set; executing a section detection module to identify sections of the data set and classify the sections; executing a page classification module; generating associations between the sections and the classifications; transforming, based on the association, the sections, the classifications, and the type, the data set into a second file type; and presenting the transformed data set for integration into a capital management system.