Abstract: Artificial intelligence (AI)-based systems and methods for AI application development using codeless creation of AI workflows is disclosed. The system receives request for creating an artificial intelligence (AI)-based workflow from the user device. Further, the system obtains input data from data sources and pre-process the obtained data using AI based pre-processing model. Further, the system identifies plurality of AI and Generative AI service nodes to be executed on the pre-processed data. The system further generates an AI-based workflow by connecting AI and Generative AI service nodes. Further, the system generates a metadata for AI and Generative AI service nodes by executing each of the identified plurality of AI and Generative AI service nodes. The system validates the metadata based on AI-based rules. Furthermore, the system determines actions to be performed on the metadata based on results of validation and performs the set of actions on the AI-based workflow.

Abstract: A system and method for automating and improving data extraction from a variety of document types, including both unstructured, structured, and nested content, is disclosed. The system and method incorporate an intelligent machine learning model that is designed to intelligently identify chunks of text, map the fields in the document, and extract multi-record values. The system is designed to operate with little to no human intervention, while offering significant gains in accuracy, data visualization, and efficiency. The architecture applies customized techniques including density-based adaptive text clustering, tabular data extraction based on hierarchical intelligent keyword searches, and natural language processing-based field value selection.

Abstract: A system and method for automating and improving data extraction from a variety of document types, including both unstructured, structured, and nested content, is disclosed. The system and method incorporate an intelligent machine learning model that is designed to intelligently identify chunks of text, map the fields in the document, and extract multi-record values. The system is designed to operate with little to no human intervention, while offering significant gains in accuracy, data visualization, and efficiency. The architecture applies customized techniques including density-based adaptive text clustering, tabular data extraction based on hierarchical intelligent keyword searches, and natural language processing-based field value selection.

Abstract: A device may provide, to user devices, a set of objects that are to be used by the user devices as part of a collaborative virtual environment. The collaborative virtual environment may support a virtual workshop. The set of objects may include objects capable of being used to solve a problem that is presented to users within the virtual workshop. The device may receive data describing a set of solutions. The device may curate the data describing the set of solutions using one or more natural language processing techniques. The device may rank the set of solutions that has been curated using a ranking technique or a machine learning technique. The device may provide data identifying a highest ranked solution or data identifying the set of solutions for display within the virtual workshop or to additional user devices that are accessible outside of the virtual workshop.