Products and Services
AIOps in the mobile core network utilizes artificial intelligence and machine learning to automate, monitor, and optimize operations. AIOps plays a vital role in the core, transforming vast and complex data flows into intelligent automation. It facilitates proactive management of the core infrastructure, helping to ensure reliable services through more efficient monitoring and incident response.
RADCOM Mobile Core AIOps combines deep telecom domain expertise with intelligent assurance and integrated AI-driven operations to automate and proactively ensure mobile core network performance. Covering a wide range of customizable use cases, the solution enables intelligent, real-time incident detection, analysis, remediation, and resolution, to ensure quality customer experiences.
See how RADCOM Mobile Core AIops solution
works
RADCOM Mobile Core AIOps offers AI Alerts and virtual network operations center (vNOC) that enables cost-effective and automated anomaly detection and workload management. Using AI and ML, the solution provides data visualizations that assist in root cause analysis and resolve faults quickly and efficiently. A machine learning baseline is generated and analyzed based on historical data. New incoming information data points are then constantly compared, triggering an alert when an anomaly is detected. Alerts are sent to users via various delivery and real-time streaming channels such as Slack and other BSS/OSS solutions. This helps ease network engineers’ workloads, reducing time to detect and resolve faults.
RADCOM Mobile Core AIOps provides operators with control over the amount of AI-related data delivered, preventing information overload. Operators use filters or rules that can be adjusted to focus on top-priority tasks, or break down according to the type of tasks and levels of severity. This significantly reduces mean-time-to resolution, freeing network teams to spend more time optimizing services. The solution also supports bringing your own analytics container (BYOAC), which can be adapted and tested for telecom-specific use cases.