The TANGO project has reached a successful conclusion, delivering a platform where citizens, businesses, and public institutions can share and use data safely, while protecting privacy, governance, and the environment.
The consortium brought together 37 organizations across Europe, including research institutions, technology providers, and industry partners, all working toward a shared goal: realizing the value of data without giving up control or trust.
Turning privacy and trust into action
At the core of TANGO’s mission was the development of practical technologies and frameworks that put privacy, user control, and interoperability first. Rather than relying on old centralized systems or isolated data silos, the project built a distributed platform that enables secure data sharing across sectors. Key features of the platform include:
- Decentralized identity systems that let users control their data
- Privacy-focused cryptography to protect sensitive information
- Transparent governance tools for fair and accountable data use.
The platform was tested in real-world areas such as public administration, finance, transport, and tourism, showing that it can meet real needs while remaining user-friendly and secure.
Responsible and sustainable AI
One of TANGO’s most innovative achievements was the integration of AI-supported infrastructure management. This AI goes beyond automation – it takes environmental sustainability and ethical consideration into account when handling data and computing resources.
The project developed ways to run data operations more efficiently, explain AI decisions to build trust, and securely authenticate users and devices, all while respecting privacy and regulations. Together, these improvements ensure that intelligent systems act as trusted partners, rather than mysterious “black boxes” that make decisions without oversight.
XLAB’s role: Making AI-driven infrastructure real
XLAB played a vital role in developing and integrating AI-supported infrastructure management. Using its expertise in applied and complex systems, XLAB helped the platform adapt dynamically to changing demands while keeping data private, reducing energy use, and remaining easy to use.
One of the most visible outcomes of this work was RENOPS’s energy-aware scheduler. As Urban Kos, Developer, who worked on the project, explains:
“One of the core challenges in developing RENOPS’s scheduler was designing a system that could integrate forecasts of renewable energy availability, carbon emissions, and electricity prices into practical scheduling decisions. Unlike traditional schedulers that run tasks at fixed times, our tool shifts energy-intensive jobs - from AI model training to data analysis - to periods when green energy is most abundant or costs are lowest.”
This required careful orchestration of forecasting, optimization, and execution logic. Urban continues:
“I’m especially proud of this contribution because we’re not just improving performance — we’re enabling software to actively reduce its environmental impact while respecting operational constraints. Turning abstract sustainability goals into a working, open-source scheduler has been immensely rewarding.”
In addition to RENOPS, XLAB contributed to:
- Designing the platform’s high-level architecture for AI management.
- Co-authoring key project deliverables on AI frameworks.
- Embedding machine intelligence into distributed data systems in a transparent and responsible way.
These efforts were essential in turning theoretical concepts into real technologies ready for large-scale testing.
A foundation for the future
With TANGO complete, its results are already shaping the future of European digital innovation. The project demonstrates that secure, privacy-respecting, and energy-aware data systems are achievable at scale, enabling smarter services, empowered citizens, and stronger digital economies.
We continue to push the frontier of trustworthy AI and infrastructure intelligence, bringing the promise of a more transparent, sustainable, and human-centered digital society closer than ever.
