Artificial Intelligence, Extended Reality and Internet of Things are enabling technologies that build competitiveness in companies of any size.
Innovation is a continuous process that streamlines business processes and raises the quality of work and products, reducing errors and improving ESG ratings.
The Mare Group Artificial Intelligence platform brings Machine Learning, Computer Vision and predictive analysis together in a single environment, applied to industrial, production and organisational contexts. It works on heterogeneous data: operating parameters, images, video and signals from sensors and industrial systems. The same technologies extend to aerospace and defence, a growing area for Mare Group: Mare Group holds stakes in autonomous guidance systems for drones and runs AI-based counter-drone defence projects. On this base rest the uses developed in the following areas.
The Artificial Intelligence systems process data from different sources: management software, sensors, production systems, corporate databases, reports and external sources, to return a clearer reading of processes and performance. Data analysis identifies recurring patterns, correlations, critical points and areas for improvement, giving technical teams, function managers and company leadership more effective tools to interpret scenarios and make decisions.
Computer Vision analyses images and video streams for quality control. It recognises defects, non-conformities and deviations from standards on products, components, surfaces, assemblies and processing operations. Integrated with sensors and IoT systems, it turns visual information into alerts and indicators, so action can be taken during the process and not only at the end.
The model outputs flow into dashboards, reporting and business intelligence tools. Numerical, textual and visual data become updatable views, distributed to the different roles in the organisation according to role and responsibility. This is the level where analysis, indicators and alerts reach the people who operate, in a readable form.
Virtualisation and Digital Twin can simulate machinery, assembly lines, entire plants or even extended areas of territory. In these environments personnel carry out maintenance activities, safety procedures and operational simulations, training faster and verifying acquired skills objectively, without risk and at very low cost.
A ticketing system combines real-time questionnaires, augmented reality and Artificial Intelligence to make interventions faster and more effective even without highly specialised personnel. It includes an archive of manuals and documentation, real-time intervention management and a statistical dashboard for monitoring.
XR technologies, gamification and multimodal immersive environments enhance artistic, cultural and environmental heritage and make it accessible, with an approach based on emotional engagement and interaction. The tools range from 3D scans and virtualisations to interactive 3D models, and on to multi-projections, 3D mapping and holograms.
It combines IoT sensors, embedded hardware, data acquisition systems, Machine Learning algorithms, Artificial Intelligence and Big Data Analytics to identify anomalies, degradation trends and possible failures. The system plans maintenance interventions, reduces the risk of downtime and improves the management of infrastructure, machinery and critical components.
Asset operating data is collected through sensors, IoT devices and distributed hardware components. The information acquired covers vibrations, temperatures, consumption, stresses, structural parameters, environmental conditions and other indicators useful for monitoring.
The collected data is compared against expected behaviour models. Through Machine Learning algorithms and predictive analysis, the system identifies deviations, anomalies and conditions that indicate progressive degradation or a possible malfunction.
Trend analysis estimates how the asset condition evolves over time. Maintenance is not managed only after a failure occurs: it is planned on the basis of measurable signals, risk thresholds and operating conditions.
The system defines maintenance procedures, generates alerts and manages intervention priorities. The information collected helps technical teams, plant managers and operational staff organise activities according to the actual condition of the monitored assets.
Predictive maintenance applies across several contexts: railway infrastructure, rolling stock and railway components, industrial plants, energy and consumption, buildings and structures, interconnected production processes, healthcare logistics and critical assets in industrial and infrastructural settings.