Our client is a leading Telecommunication Services Company with operations in 26 countries and 142,000 employees worldwide. The Group has a global customer base of 259 million people, including 214 million mobile customers and 22 million fixed broadband subscribers.
Our client is a leading Telecom Player with various servers at multiple locations who did not have an automated solution to detect and fix anomalies, monitor the server health parameters. They were engaged in manual processes to monitor the systems, which was time consuming, prone to human error and resulted in huge monetary losses during downtime. The customer wanted to automate the anomaly detection process to minimize down time and increase monitoring effectiveness.
PureSoftware, with its expertise in this area, saw an opportunity to automate the anomaly detection process through a built-in Machine Learning (ML) ability that learns about anomalies, understands their patterns, analyses them, and predicts them before they occur. We also developed neural network-based models on the historical time series data, which helps the system to predict anomalies well in advance and perform self-healing. The application was able to perform the following functions:
Automatic ticket management
Detection of corrective threshold.
As a result of the new process, our client saw reduction in monetary loses significantly because of automatic anomaly prediction and remediation.
The company now continues to experience improved anomaly prediction accuracy and significant time savings.
Our client experienced 100% detection in anomalies and a close to 100% system uptime directly attributed to the Elastic search AI enablement assistive learning Process and error correction mapping.