AI-driven anomaly detection for telecom QoE and QoS
MedUX helps operators monitor QoE data in real time, apply flexible rules and use AI-driven workflows to identify anomalies, prioritize impact and accelerate action.
Traditional methods of monitoring network performance often rely on manual intervention: time-consuming and error-prone. Yet networks become more complex and the volume of data increases. Rapid identification of impairments is a must. Transform real time data into value added insights leveraging Machine Learning techniques.
With MedUX, operators can deal with analysis of network reliability, availability and quality of service (QoS) parameters leveraging machine learning, artificial intelligence techniques and using a real-time automation driven approach.
Degradation spikes, service interruptions, location-specific impairment, OTT issues, before/after deviations.
NOC, service assurance, CX teams, network quality, analytics teams.
Dashboards show what changed; anomaly detection helps identify where action is needed first.