VIVO is a data-driven Digital Twin platform that integrates heterogeneous IoT data, Artificial Intelligence models, and predictive analytics, enabling continuous monitoring and decision support for the management of complex environments and infrastructures.

Through data-driven processes, the platform promotes and accelerates the energy, environmental, and digital transition, transforming complex information into sustainable and measurable decisions.

Through its technological ecosystem, VIVO:

• collects and integrates energy and environmental data from multiple sources

• monitors consumption, emissions, and renewable energy performance

• generates real-time indicators and insights

• develops predictive models to anticipate future scenarios

Thanks to interactive dashboards, automated alert systems, and decision-support tools, VIVO enables stakeholders to:

• promptly identify critical issues

• assess environmental and operational impacts

• define effective and measurable mitigation strategies

VIVO transforms data into operational knowledge and concrete decision support, enabling a more conscious and sustainable management of complex systems.

VIVO KEY STRENGTHS

VIVO was designed as an integrated ecosystem where IoT, GIS, 3D technologies, and Artificial Intelligence operate in a coordinated way, through a single user interface and a continuous data flow from acquisition to decision-making.

• Cross-domain adaptability: VIVO’s functional logic is independent from the application domain: platform behavior is driven by the configuration of data, drivers, and models. The same technological core can adapt to radically different contexts — such as energy networks, marine ecosystems, port infrastructures, and buildings — simply by reconfiguring data sources, models, and visualization layers.
• Scalability: Its microservices architecture allows independent scaling of each component, supporting both small-scale installations and enterprise multi-organization deployments with complete tenant data segregation. User, group, and permission management is granular and fully configurable through the interface.
• IoT platform and edge computing: VIVO acquires field data through intelligent and configurable drivers compatible with heterogeneous sources, including physical sensors, online services, data lakes, and traditional databases. The acquisition layer normalizes different data streams into a unified data model, making information comparable and analyzable regardless of its origin.
• Openness and integration: Configurable drivers allow seamless integration with physical sensors, cloud services, and heterogeneous data lakes without technological constraints, protecting existing investments and ensuring continuity with current IT infrastructures.
• AI/ML, IFC, and GIS technologies: VIVO leverages Artificial Intelligence and Machine Learning algorithms to generate contextualized forecasts tailored to the specific deployment domain. The platform integrates BIM/IFC models for 3D infrastructure representation and combines them within a georeferenced GIS environment, providing a spatial, temporal, and predictive interpretation of monitored phenomena.
• Native analytics and decision-support tools: VIVO does not simply collect and visualize data: it interprets real-world phenomena through predictive algorithms calibrated to the specific operational context. AI/ML, GIS, and IFC models work together to provide spatial, temporal, and forecasting insights, transforming raw data into operational knowledge that supports strategic decision-making and closes the loop between measurement and action.