This is like giving the mobile network its own team of smart digital engineers who constantly watch how it’s performing, spot problems early, and automatically fix or optimize things before customers notice.
Manual, reactive network operations: human engineers spending huge time monitoring KPIs, troubleshooting faults, tuning parameters and planning capacity upgrades in complex 4G/5G networks, leading to higher costs, slower incident response, and inconsistent quality of service.
Deep, proprietary network performance and topology data; integration into existing OSS/BSS and radio/network management workflows; telco-grade reliability and compliance requirements that make the solution sticky once embedded.
Hybrid
Time-Series DB
High (Custom Models/Infra)
Real-time inference and decision-making at telco scale (massive volumes of time-series metrics and alarms) while ensuring safety, determinism, and regulatory compliance.
Early Adopters
Focus on AI agents specifically tuned for end-to-end mobile network operations (from RAN to core), embedded in Deutsche Telekom’s own network and OSS/NMS stack, rather than generic cloud AI or standalone analytics—positioning this as an operator-grade, operations-native AI automation layer.