DG Twin
DG Twin develops digital twin solutions that create virtual counterparts of engineering systems, enabling industrial companies in transportation and energy generation to simulate, analyze, and optimize performance in real time. The company's data-driven engineering approach uses AI, machine learning, cloud computing, and IoT to collect operational data and deliver self-adaptive improvements that reduce emissions and resource use. Its digital twin technology supports applications such as increasing electric vehicle battery autonomy and improving demand-supply matching for renewable energy sources.
- Clean Technology
- Energy
- Internet of Things
- Artificial Intelligence
- Mobility & Transportation
- Software Only
- Data & Analytics
- Industrial Automation
Funding
Founders
Founder details are not available yet.
Product
Problem
Industrial companies in the energy and transportation sectors face increasing pressure to reduce environmental impact while maintaining or improving system performance. Traditional design and operational approaches often rely on physical testing and reactive maintenance, which are costly, time-consuming, and limit the ability to optimize complex engineering systems for sustainability.
Solution
DG Twin provides digital twin solutions that create virtual counterparts of engineering systems, enabling companies to simulate, analyze, and optimize performance in real time. By leveraging highly specialized digital technologies including AI, machine learning, cloud computing, and IoT, the company helps industrial clients in transportation and energy generation achieve more rational resource use and lower emissions. DG Twin's approach is based on "data driven engineering," collecting real-world operational data to identify improvement solutions aligned with economic and environmental sustainability goals. The company's digital twin technology enables continuous learning through IoT connectivity, allowing virtual models to adapt progressively to specific usage needs, improving battery autonomy, driving safety, and demand-supply matching for renewable energy sources.
Target Audience
Primary customers are industrial companies in the transportation and energy generation sectors seeking to improve sustainability and operational efficiency through digital transformation.
Features
- Digital twin technology that creates virtual replicas of engineering systems for performance optimization and environmental impact reduction
- Data-driven engineering approach using cloud computing, machine learning, IoT, and AI for real-time data collection and performance improvements
- Self-adaptive solutions that automatically customize to specific user and operational contexts
- Specialized IoT connectivity enabling continuous dialogue between physical systems and their virtual counterparts
- Proprietary technologies developed through funded European research projects, including integration into advanced interoperability platforms for future energy systems
- Ongoing R&D projects including RAYS (Real-time AI Driven Yield Optimization in Agrivoltaic Systems) for agrivoltaic plant optimization