Our needs are to improve the current TRL level and build a more feature-rich platform to suit the needs of the 5G/6G/Cloud/Edge ecosystem.

Detailed market analysis, a technical roadmap of new features, an effective marketing campaign, proof of concept demonstrations to potential clients.

The NANCY AI-based Energy-Efficient RAN Orchestration solution is highly replicable thanks to its modular, standards-compliant design, containerized microservices, and integration through open, interoperable interfaces. Powered by UBITECH’s Maestro orchestrator, it can be deployed across heterogeneous RAN environments—legacy, virtualized, or cloud-native—and easily adapted to public, private, or hybrid network setups. Its AI models are retrainable with operator-specific datasets, enabling rapid tuning to different traffic patterns and energy-usage profiles, while the embedded security and privacy mechanisms can be tailored to sectoral or regulatory needs. Supported by clear documentation and strong technology-transfer processes, the solution can be efficiently reproduced, customized, and scaled by operators, vendors, and industrial partners.

The NANCY AI-based Energy-Efficient RAN Orchestration solution significantly contributes to sustainability by reducing the energy footprint of radio access networks through intelligent, real-time optimization powered by UBITECH’s Maestro orchestrator. By dynamically adjusting network parameters based on traffic demand, environmental conditions, and operational targets, it minimizes unnecessary power usage, lowering both OPEX and CO₂ emissions. Its modular, cloud-ready architecture promotes resource-efficient deployment, while enhanced security and privacy safeguards support long-term, trustworthy operation. The solution’s adaptability across diverse RAN environments further enables widespread adoption, amplifying its positive environmental impact and supporting greener, more sustainable telecommunications infrastructures.