AI degradation analysis
Key features
Unlock the power of AI to detect and analyse network performance issues in near real-time. Our cutting-edge solution enables proactive maintenance and rapid issue resolution, preventing disruptions before they impact your customers and ensuring optimal network performance.
AI-driven degradation detection
Harness the power of AI to monitor and detect performance degradation at the cell level, in real-time. By analysing both live and historical data, the platform ensures early identification of issues, allowing operators to take swift action before problems affect network performance.
Seamless NMS and OSS correlation
Automatically link degradation signs to NMS hardware alarms and OSS parameter changes, pinpointing the exact root cause of performance drops. This precision speeds up resolution times by directly identifying hardware or configuration issues.
Smart clustering of degradation events
Maximise efficiency by grouping related degradation events into clusters based on shared patterns. This contextual approach enhances analysis, enabling operators to focus on solving clusters of issues for faster, more impactful decision-making.
Experience
Resolve network issues before they impact customers, ensuring smooth service and reducing complaints.
Access
Centralise all network data for quick visibility and faster issue resolution.
Speed
Automate issue detection and resolution, enabling faster, more efficient problem-solving.
Key performance indicators
Customer experience challenges
Siloed and outdated systems make it difficult to access and analyse network data, limiting visibility into performance. This lack of integration delays issue diagnosis, preventing operators from optimising network efficiency and performance.
Fragmented infrastructure data
Siloed and outdated systems make it difficult to access and analyse network data, limiting visibility into performance. This lack of integration delays issue diagnosis, preventing operators from optimising network efficiency and performance.
High manual effort and delayed response
Manually extracting insights from fragmented systems slows down issue identification and increases operational costs. This reactive approach hinders the ability to prevent disruptions, reducing operational efficiency and delaying network improvements.
35-45
reduction in service degradation
20-30
boost in network performance
25-35
increase in operational efficiency
Applications for mobile networks
Byanat's cutting-edge AI platform revolutionises network management by automating key processes and enhancing operational efficiency.
