A leading mobile network operator serving more than 10 million subscribers in Sub-Saharan Africa was struggling to manage over 1.2 million alarms generated each week across power, transmission, and RAN domains. Fragmented monitoring tools and incompatible data models made it difficult to correlate events, identify root causes, and restore network performance quickly. The result was delayed fault resolution, increased operational pressure, and degraded customer experience.

Using the Totogi Ontology, the operator unified alarm data into a shared semantic model aligned with TM Forum standards. AI automatically mapped operational data structures and applied advanced correlation techniques to detect cascading failures and identify true root causes in real time, without replacing existing systems or requiring complex integrations.

Within one week, more than 1.2 million alarms were compressed into just three actionable root causes, reducing alarm noise by 97% and improving mean time to resolution by more than 75%. The operator gained clear, real-time visibility across domains and a reusable AI foundation for future automation and service assurance use cases.