Using machine learning on metrics, logs and counters to spot problems in charging, provisioning and billing early, and to find the cause faster.
BSS platforms produce huge amounts of operational data: charging traffic counters, provisioning success rates, API latencies, logs and infrastructure metrics. Static thresholds either miss problems or raise too many alarms, especially after configuration changes or seasonal peaks.
AIOps uses machine learning to learn normal behaviour, detect real anomalies and point to the likely cause, so operations teams act sooner and with less noise.
Five generic capabilities, each building on the last.
Collect and visualise key business and technical metrics in one place.
Show seasonality and trends: daily peaks, month-end bill runs, campaigns.
Learn normal patterns and flag deviations in real time.
Correlate anomalies across components, logs and recent changes to suggest the cause.
Predict problems before they affect customers and trigger runbooks.
Sudden drops or spikes in charging requests, failure codes or response times.
Rising activation failures after a configuration or network change.
Bill runs slower than usual, or jobs failing in a pattern.
CPU, memory, disk and database anomalies linked to business impact.
Built from common open-source and cloud components.
Agents and pipelines gather metrics, counters and logs, ideally using the OpenTelemetry standard.
A time-series and search store for metrics and logs.
Models for seasonality, anomaly scoring and correlation, with configuration per use case.
Dashboards, alerts and links to runbooks and ticketing.
This page describes generic industry practice and public standards. It is not based on, and does not describe, any particular vendor's product or operator's systems.