Agama introduces self-learning AI Anomaly Detection – enhancing situational awareness for video service providers

Agama Technologies, the specialist in video service quality and customer experience, has introduced AI Anomaly Detection. Powered by artificial intelligence and machine learning, this new extension to the Agama solution provides alarms with unprecedented precision. The result is enhanced awareness of real incidents and elimination of irrelevant alarms, which enables service providers to deliver optimal service quality to customers with greater efficiency.

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To deliver video services that meet or exceed customer expectations, service providers must act quickly when quality and usage Key Performance Indicators (KPIs) deviate from their normal range. What is normal, however, can change over time and vary greatly depending on the time of day or day of the week. These fluctuations limit the usefulness of alarms based on fixed thresholds. A more intelligent approach is the way forward.

Agama’s new feature AI Anomaly Detection automatically identifies anomalies based on information from every subscriber and provides actionable alerts, clear visualization of detected anomalies and powerful interactive analytics.

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“We are excited to introduce the new AI Anomaly Detection feature,” says Johan Görsjö, Director of Product Management at Agama Technologies. “Separating actual anomalies from normal variations in KPIs is an excellent example of how AI and machine learning can be applied to video service assurance in a way that addresses real-world needs.”

Agama’s AI Anomaly Detection employs automated self-learning to recognize patterns in video delivery networks. Acting on information collected in real-time from as many as several million client devices, such as set-top boxes and OTT player applications, the algorithm predicts how each subset of the population, from whole countries down to individual neighborhoods, will behave based on past observations.

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With AI Anomaly detection, service providers can quickly understand where in the delivery chain anomalies occur, what the current situation is, and what has happened before and after the detected anomalies. By detecting real anomalies and putting them into context, the solution creates situational awareness that enables faster analysis and problem resolution. This means that service providers can assure optimal service quality and improve customer experience with greater efficiency and accuracy.

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