Inturai offers a cutting-edge movement monitoring system that utilizes non-intrusive AI technology to deliver real-time, precise insights. By leveraging your existing infrastructure, Inturai reduces both costs and deployment time. This platform is designed to simplify the complex integration of diverse systems, making it an ideal solution for healthcare and IoT platforms. Key features include low-code integration, data control, and automated machine learning models, ensuring enhanced operational efficiency and privacy protection. Whether you need to monitor multiple events or streamline data across systems, Inturai delivers seamless monitoring and actionable insights through a single, easy-to-use interface.

This repository aggregates a comprehensive collection of multi-agent system components and distributed AI coordination techniques. It provides implementations of consensus algorithms, contract net negotiation protocols, auction-based task allocation, coalition formation strategies, and inter-agent communication frameworks. Users can leverage built-in simulation environments to model and test agent behaviors under varied network topologies, latency scenarios, and failure modes. The modular design allows developers and researchers to integrate, extend, or customize individual coordination modules for applications in robotics swarms, IoT device collaboration, smart grids, and distributed decision-making systems.

Industrial AI Agents is a Python-based toolkit designed to streamline industrial operations by deploying autonomous AI agents specialized in manufacturing tasks. It features a Data Ingest Agent for collecting sensor streams via MQTT and OPC-UA, a Diagnostics Agent for anomaly and fault detection, a Quality Agent for visual inspection using computer vision, and a Scheduling Agent for dynamic production planning. The framework supports containerized deployment with Docker and Kubernetes, enabling integration with existing IoT platforms and scalable real-time analytics.