Dynamic data portals and anomaly detection with AI agents

á á á áModern business and defense operations generate massive datasets. Detecting anomalies within these vast data streams is critical for identifying errors, frauds, or threads. However, traditional dashboards are inflexible and costly, especially for large, complex data.áá á á áTo address these challenges, we propose to leverage generative AI to achieve two key objectives. The first is to enhance the scalability of the existing RTutor.ai prototype using retrieval-augmented generation, enabling more efficient and dynamic data access. The second is developing a prototype for a Multi-Agent Anomaly Detection System for businesses and defense applications. This system leverages a coordinated swarm of AI agents: data agents preprocess datasets while monitoring agents apply statistical, machine learning, and time-series techniques to identify anomalies. áá á á áBuilding on our expertise with AI platforms such as RTutor.ai and Chatlize.ai, we aim to prototype a scalable system that integrates data management, interactive visualization, and AI-powered analysis. áThis innovative solution will deliver real-time analytics and autonomous anomaly detection, addressing challenges in data monitoring and decision-making.