Telecom

Telecom operators sit on some of the richest real-time data of any industry — network performance, usage patterns, device signals — and a lot of it never makes it past the OSS/BSS systems it was generated in. Meanwhile customers churn to whichever competitor's network didn't drop a call last week.

Key Challenges

Massive, Fast-Moving Network Data

5G and IoT have multiplied the volume and velocity of network data, and legacy OSS/BSS platforms weren't built to process it in anything close to real time.

Customer Churn in a Commoditized Market

With service largely interchangeable, retention increasingly depends on personalization and service quality signals most operators can't act on fast enough.

Unplanned Network Downtime

Outages carry direct SLA penalties and reputational cost, and reactive maintenance means problems get caught after customers already feel them.

Fraud and Revenue Leakage

SIM swap fraud, roaming fraud and subscription abuse are constantly evolving, and rules-based detection alone struggles to keep up.

AI Impact: Use Cases

Predictive Network Maintenance

Models trained on network telemetry flag equipment likely to fail before it does, shifting maintenance from reactive to planned.

Churn Prediction and Retention

Behavioral and usage models identify at-risk customers early enough for a retention offer to actually change the outcome.

Real-Time Fraud Detection

AI models score transactions and account activity in real time, catching fraud patterns that static rule sets miss entirely.

AI-Driven Customer Service

Agentic assistants resolve common billing and service requests instantly, reserving human agents for the cases that actually need judgment.

How We Can Help