AI-Powered Anomaly Detection

Automatically detect anomalies and accelerate Root Cause Analysis (RCA) and issue resolution.

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Extend Kalix Analytics with Automated Anomaly Detection

Filtering issues from the mass of data to which you are exposed is a growing challenge. With today’s network complexity manual investigations and legacy alerting mechanisms is not enough to give you the scale you need to identify and categorize anomalies.

We’ve introduced an automated approach that enables your team to operate more efficiently, exposing and prioritizing anomalies, both individually and in clusters. 

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AI-Powered Anomaly Detection Animated Illustration from Polystar

AI-Powered Anomaly Detection Explained

Anomalies represent deviations from expected performance in your network, indicating service impacting issues — now or in the future. AI-powered, anomaly detection means that you can automatically track KPIs, so you can focus on those that matter to your business.

Algorithms will automatically learn from the data and create the needed thresholds to automatically identify anomalies for different time series KPIs. Anomalies are grouped and tagged with priority indicators and show the number of customers impacted. 

 

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How Does Automated Anomaly Detection Boost Network Performance?

Self-Adapting

The AI powered anomaly detection function continuously learns from your unique input data to automatically adjust thresholds, so it’s optimized for your network – enhancing accuracy for your team and driving focused incident responses.

 

Cluster Anomalies

Group anomalies that relate to specific services – and view a timeline that highlights severity and frequency. You can view segments and dimensions associated with each deviation, enabling you to prioritize actions.

 

AI Summaries

Add your own troubleshooting steps and comments to detected anomalies through the integrated knowledge base management backend – so the solution learns from your inputs and generates tailored summaries and recommendations.

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More about Network Anomaly Detection in Telecom

Discover how emerging Artificial Intelligence (AI) and machine learning technologies can enhance operational efficiency in telecom operations. In this first article of a three-part series, Roman Šipula, Senior ML Engineer at Polystar, explores how AI-supported anomaly detection can help Network Operations Center (NOC) teams identify and address issues more effectively.

How AI-Powered Anomaly Detection Supports NOC Teams

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Discover how Polystar can elevate your incident isolation, tracking and performance delivered to your customers!

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FAQ - Anomaly Detection in Telecom Operations

  • AI-powered anomaly detection uses machine learning to continuously analyze network, service, and customer experience data to identify unusual patterns that may indicate faults, performance degradation, capacity issues, or emerging service risks.

    Unlike manual monitoring, AI can detect subtle deviations across large volumes of data in real time, helping telecom operators identify issues earlier and respond faster before customers are affected.

  • Anomaly detection helps reduce Mean Time to Repair (MTTR) by identifying issues as soon as they emerge and providing immediate visibility into abnormal network or service behavior.

    Advanced AI models can correlate related events, highlight likely root causes, and prioritize incidents based on impact. This enables operations teams to troubleshoot more efficiently, accelerate resolution times, and minimize service disruptions.

  • Traditional threshold monitoring relies on predefined limits and generates alerts only when a KPI exceeds a set value.

    AI anomaly detection learns normal behavior patterns from historical and real-time data and can identify unexpected changes even when thresholds are not breached. This enables earlier detection of emerging issues, adapts to changing network conditions, and reduces the need for constant manual threshold tuning.

  • False positives often occur when monitoring systems flag expected or harmless changes as faults. Common causes include static thresholds, seasonal traffic variations, planned maintenance activities, network upgrades, and temporary usage spikes.

    AI-driven assurance solutions reduce false positives by understanding normal operational patterns, incorporating context, and distinguishing genuine service risks from routine fluctuations, helping teams focus on the most important issues.

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