A telecom carrier handles an enormous volume of customer interactions — tens of thousands of support calls and messages a day — keeping the call center perpetually overloaded. At the same time, subscriber data, billing information and network traffic are among the most sensitive data categories, legally required to be stored in-country and unable to be processed through cloud platforms hosted abroad.

Beyond the customer-support pressure, analysing network data to detect anomalies and optimise operations is also difficult given a data volume too large to handle manually.

An AI assistant combined with an automated call center can automate 50–60% of calls and messages, cutting customer wait time by 60–70% and reducing call-center operating costs by 30–40%. On the technical side, a network-analytics and billing-fraud-detection system can move anomaly detection from a manual process taking hours to days down to near real time, while cutting revenue loss from fraud or billing leakage by 40–60%.

Because the entire system runs on infrastructure located in-country, the carrier both meets the subscriber-data localization requirement and harnesses the power of AI to improve customer experience and network operating efficiency.