Multi-Cloud Networking
Enables seamless integration across multiple cloud platforms. Ensures consistent networking policies and performance across diverse cloud environments.
Autonomous deployment with user controls
Automates network deployment processes while allowing user-defined parameters. Balances efficiency of automation with flexibility of customization.
Public-private roaming
Facilitates smooth transitions between public and private networks. Enhances connectivity options for users moving between different network domains.
Elastic scaling for control and data plane
Dynamically adjusts network resources based on demand. Optimizes performance and cost-efficiency by scaling up or down as needed.
Reverse Network Slicing
Enable efficient use of operator’s spectrum and public-private roaming while keeping your data secure and providing data governance. A must have feature for GDPR compliance.
Support carrier grade redundancy and geographical redundancy
Ensures high availability through multiple layers of backup systems. Distributes network resources across different locations to mitigate regional outages.
In-service upgrade
Allows for system updates without interrupting active services. Minimizes downtime and maintains continuous operation during software or hardware upgrades.
3GPP R17 compliant 4GEPC, 5GC and Wi-Fi Core
Adheres to the latest 3GPP Release 17 standards for mobile network cores. Supports integration of 4G, 5G, and Wi-Fi technologies in a unified core network.
NetOPs agent
Automates network operations and management tasks. Streamlines network maintenance and troubleshooting processes.
ML based Dynamic Network Slicing
Uses machine learning to create and adjust network slices in real-time. Optimizes network resource allocation based on changing traffic patterns and requirements.
ML based intelligent traffic detection and steering
Employs AI to identify and classify different types of network traffic. Intelligently routes data flows for improved performance and efficient resource utilization.
ML based DPI
Utilizes machine learning for advanced Deep Packet Inspection. Enhances security and network optimization through intelligent packet analysis.
ML based handover optimization
Applies machine learning algorithms to improve mobile device handovers between cells. Reduces connection drops and enhances user experience during network transitions.
ML based paging optimization
Uses AI to refine paging processes in mobile networks. Improves battery life and network efficiency by optimizing how devices are located and contacted.
ML based resource optimization
Leverages machine learning to allocate network resources more efficiently. Dynamically adjusts resource distribution based on predicted usage patterns and demand.
ML based end to end observability and predictive analysis
Provides comprehensive visibility into network performance using AI-driven analytics. Predicts potential issues and optimizes network behavior based on historical and real-time data.