Platforms for sustainable growth

Artificial Intelligence, Extended Reality and the Internet of Things are key enabling technologies for building competitiveness in companies of any size. 

Innovation is a continuous process that optimises business processes and raises the quality of work and products, reducing errors and improving the ESG rating.

Training
Remote Support
Competitive Analysis
Decision-Support
Business Intelligence
3D Virtualisation
Computer Vision
Anomaly Control
Optimisation
Predictive Maintenance

Artificial Intelligence

AI systems for data analysis, quality control and process optimisation

Mission-Critical AI

Mare Group’s Artificial Intelligence platform brings together Machine Learning, Computer Vision and predictive analytics 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 the group: Mare Group is present with shareholdings in autonomous guidance systems for drones and with AI-based counter-drone defence projects. The uses developed in the following areas build on this foundation.

Decision-Support

Mare Group’s AI systems process data from a variety of sources: management systems, sensors, production systems, corporate databases, reports and external sources, to provide a clearer reading of processes and performance. Data analysis identifies recurring patterns, correlations, critical issues and areas for improvement, giving technical teams, function managers and company management more effective tools to interpret scenarios and make decisions.

QA e Computer Vision

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 processes. Integrated with sensors and IoT systems, it turns visual information into alerts and indicators, to act during the process and not only downstream.

Business intelligence

The models’ results flow into dashboards, reporting and business intelligence tools. Numerical, textual and visual data become updatable views, distributed to the various roles in the organisation according to role and responsibility. This is the level where analysis, indicators and alerts reach those who operate, in a readable form. 

Virtual and immersive reality

Digital environments, simulation and immersive interaction

Training

Virtualisation and Digital Twins can simulate machinery, assembly lines, entire plants or even large areas of territory. In these environments, maintenance activities, safety procedures and operational simulations take place, through which personnel train faster and verify the skills acquired objectively, without risk and at very low cost.

Remote Support

A ticketing system integrates 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 management of interventions and a statistical dashboard for monitoring.

Cultural Heritage

XR technologies, gamification and multimodal immersive environments to enhance and make accessible artistic, cultural and environmental heritage, with an approach based on emotional engagement and interaction. The tools range from 3D scans and virtualisations to interactive 3D models, through to multi-projections, 3D mapping and holograms.

Predictive maintenance

Monitoring, diagnostics and predictive asset management

Predictive maintenance

It integrates IoT sensors, embedded hardware, data-acquisition systems, Machine Learning algorithms, Artificial Intelligence and Big Data Analytics to detect anomalies, degradation trends and potential failures. The system plans maintenance work, reduces the risk of downtime and improves the management of infrastructure, machinery and critical components.

Data collection

Asset operating data is collected through sensors, IoT devices and distributed hardware components. The information acquired covers vibration, temperature, consumption, stresses, structural parameters, environmental conditions and other indicators useful for monitoring.

Anomaly monitoring and analysis

The data collected is compared with 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.

Failure prediction

Trend analysis estimates how the asset’s condition will evolve over time. Maintenance is not managed only after a failure, but planned on the basis of measurable signals, risk thresholds and operating conditions.

Intervention management

The system defines maintenance procedures, generates alerts and manages intervention priorities. The information collected helps technical teams, plant managers and operational staff to organise activities based on the actual condition of the monitored assets.

Application sectors

Predictive maintenance is applied in 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 infrastructure settings.