AI for Telecommunications Operations
Are you striving for network operations and customer care excellence?
AI Care Suite
95% ticket avoidance and a 98% reduction in resolution time for network and service-related issues
Power Saving Advisor
Up to 25% energy savings and 90% reduction in manual operations with zero impact on customer experience.
Network Advisor
Up to 90% reduction in manual troubleshooting and 100% consistency in network diagnostics.
NOC Automation
Complete triage analysis,
90% classification accuracy,
100% consistency.
Unlock the Full Potential of Your Telecommunications Operations
Efficient Network Operations
Automate detection, diagnosis,
and resolution of network issues.
Customer satisfaction
Automatically handle customer complaints – before they happen.
Manage Data Overload
Integrate data from multiple sources
to generate advantanced cross-correlated insights.
Reduce Energy Cost
Dinamically maximize power savings while guaranteeing Quality of Service.
Whitepapers
RAN Power Savings
Practical Consideration for Leveraging AI to Automate Energy-Saving Features in Telecom RAN Networks
Energy Savings: a key challenge and opportunity in Mobile Networks
Paper about practical recommendations and real-life examples of achieving the mentioned power savings.
The Age of Telecom Network Automation
We are in the dawn of a new era in what regards telecommunications networks’ reality, not another incremental step or evolution.
Implementing Operational AI in Telecom Environments
The last decade has witnessed a tremendous transformation of the telecom market, placing operators into a complex situation.
In the spotlight
The (Rocky) Path Towards Zero-Touch Network Automation
Mobile networks are growing exponentially in complexity. As operators deploy and maintain multi-vendor 2G, 4G, and 5G environments—often coexisting within the same infrastructure—network engineers face an overwhelming volume of alarms, performance counters, and KPIs to interpret. Manual provisioning, troubleshooting, and intervention are no longer scalable or sustainable in such dynamic ecosystems. Traditional network assurance has relied heavily on manual investigation, where engineers spend hours correlating metrics, logs, and configuration data to identify the root causes of anomalies.
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