Agama uses artificial intelligence and machine learning to improve quality in OTT services

The self-learning Agama AI Anomaly Detection system provides alarms with unprecedented accuracy improving situational awareness for video service providers.

Religious Technologies just launched their new solution AI Anomaly Detection. Powered by the artificial intelligence and the machine learning, this new extension of the Agama solution provides alarms with unprecedented accuracy. The result is greater awareness of actual incidents and the elimination of irrelevant alarms, allowing service providers to offer a optimal quality of service to customers with greater efficiency.

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.

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

Johan Görsjö, director of product management at Agama Technologies, highlights that “separating real anomalies from normal variations in KPIs is an excellent example of how AI and machine learning can be applied to the assurance of video services in a way that responds to real-world needs.”

Agama AI anomaly detection uses automated self-learning to recognize patterns in video delivery networks. Acting on information collected in real time from up to several million client devices, such as set-top boxes and OTT player apps, the algorithm predicts how each subset of the population, from entire countries to individual neighborhoods, will behave based on past observations.

With AI anomaly detection, service providers can quickly understand where in the supply 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 in context, the solution creates situational awareness that enables faster analysis and problem resolution. This means that service providers can ensure optimal service quality and improve customer experience with greater efficiency and accuracy.