AI Enhanced IOT Monitoring Dashboard

AI Enhanced IOT Monitoring Dashboard DashAI, an integrated platform that allows manufacturing companies to collect, analyse and visualise data collected in their plants in real time using generative AI. It stems from the need to efficiently and intuitively analyse the large volume of data generated by highly sensorised plants, typical of the Industrial Internet of Things (IIoT) world. The application was conceived with two main objectives from both a functional and technological point of view. Functionally, the platform aims to improve and expand access to data analysis, enabling data-driven decision-making at all levels, using an interface that allows information to be obtained through natural language. From a technological point of view, the challenge is to integrate the flow of real time data from plants with the analysis potential offered by generative AI models. DashAI's software architecture is based on a modular multi-agent system that exploits state-of-the-art artificial intelligence models, such as GPT-4o. This approach allows users, who interact with the system via natural language, to customise and improve the insights generated over time; moreover it gives the possibility of effortlessly expand the platform with new functionalities based on client needs. The core functionalities of the platform include data analysis and data source integration capabilities. From the point of view of analysis, there is a page for generating insights via natural language and one for real time visualisation of collected data. On the integration side, there is a functionality to input data from new sensors via MQTT connection or uploading files containing historical data. A market analysis was conducted to place DashAI's value proposition within the IIoT market, including a business plan that follows the SaaS model. The results of the project demonstrate significant technical achievements, such as the integration of natural language processing, real-time data monitoring, and custom report generation. The platform has received positive feedback from industry leaders like Ferrari, Iveco, and Stellantis, who have recognized its potential to improve operational efficiency and enable data-driven decision-making. Future development plans for DashAI include the integration of advanced alerting systems, enhanced multi-sensor data analysis and continuous optimization of the user experience. In conclusion, this thesis presents a comprehensive overview of the DashAI project, highlighting its contribution to the field of Industrial IoT by developing a scalable, user-centric platform that supports complex industrial data insights. The project lays the foundation for continuous innovation in smart industrial solutions, addressing the evolving demands of modern industrial organizations.