Abstract: Systems and methods for context-aware query interpretation using Generative Artificial Intelligence (GenAI) models is disclosed. The system receives a request for generating a response corresponding to a domain from user. The system identifies entities and an intent of the request received. Also, the system determines candidate tables, fields, metrics, and/or dimensions. The system generates candidate executable database query followed by validating the candidate executable database query. The system generates output data by executing the validated executable database query. Moreover, the system computes a confidence score for the validated executable database query. The system generates a response based on the computed confidence score and the generated output data. Consequently, the system outputs the response on a user interface of a user device.

Abstract: Systems and methods for detecting, classifying, and managing impediments are disclosed. For example, embodiments may be related to impediments in project management. The proposed systems and methods are configured to evaluate data harvested from multiple different sources (in different formats), identify potential impediments that may be described or present in the data, and classify said impediments based on whether the impediment is non-technical or technical. In addition, the proposed systems implement a technical solution of active learning combined with reinforcement learning to produce a feedback loop that, over each iteration, improves the accuracy of the impediment classification. The impediment management assistant is configured to identify impediments from various inputs sources across industries with an AI-based self-learning capability, providing a robust and accurate model even with only a limited training dataset.

Abstract: Systems and methods for detecting, classifying, and managing impediments are disclosed. For example, embodiments may be related to impediments in project management. The proposed systems and methods are configured to evaluate data harvested from multiple different sources (in different formats), identify potential impediments that may be described or present in the data, and classify said impediments based on whether the impediment is non-technical or technical. In addition, the proposed systems implement a technical solution of active learning combined with reinforcement learning to produce a feedback loop that, over each iteration, improves the accuracy of the impediment classification. The impediment management assistant is configured to identify impediments from various inputs sources across industries with an AI-based self-learning capability, providing a robust and accurate model even with only a limited training dataset.