One of the region’s largest Tier-1 mobile network operators runs a vast estate of cell sites and towers across a demanding coverage area. As the network grew, keeping every site healthy became harder, and faults were surfacing too late to prevent disruption. To bring down repair times, cut unplanned downtime, and control field engineering costs, the operator partnered with AIQU to build an autonomous predictive maintenance platform that could see problems coming and act before subscribers felt them.
One of the region’s largest Tier-1 mobile network operators had no single view of its distributed tower telemetry or radio access network performance. Faults surfaced late, which pushed up repair times, caused unplanned cellular downtime, and drove field engineering costs higher.
Key challenges included:
AIQU designed and deployed an end-to-end autonomous predictive maintenance model. It brought real-time sensor data, environmental conditions, and historical tower degradation patterns into one machine learning system. The platform worked across the network to:
The model delivered measurable improvements within six months of deployment.
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