Al-Driven Networks
Making Networks Smarter
Evolve into an Intelligent Network
AI-driven networks represent the evolution of telecommunications infrastructure from manually operated and reactively managed systems to intelligent networks capable of continuously analyzing conditions, anticipating performance issues, and optimizing operations in real time.
As 5G deployments scale and traffic patterns become increasingly dynamic, traditional operational approaches are no longer sufficient to manage network complexity efficiently. Artificial intelligence, machine learning, analytics, and automation enable networks to make faster and more informed decisions across planning, operations, and optimization functions. These technologies support use cases ranging from traffic forecasting and fault prediction to root cause analysis, energy optimization, and autonomous network operations.
Tejas Networks is contributing to this transformation through active participation in industry standards and the development of AI-driven capabilities across both wireless and wireline domains, including forecasting systems, root cause analysis, AI-native RAN, and intelligent network automation.
As 5G deployments scale and traffic patterns become increasingly dynamic, traditional operational approaches are no longer sufficient to manage network complexity efficiently. Artificial intelligence, machine learning, analytics, and automation enable networks to make faster and more informed decisions across planning, operations, and optimization functions. These technologies support use cases ranging from traffic forecasting and fault prediction to root cause analysis, energy optimization, and autonomous network operations.
Tejas Networks is contributing to this transformation through active participation in industry standards and the development of AI-driven capabilities across both wireless and wireline domains, including forecasting systems, root cause analysis, AI-native RAN, and intelligent network automation.
Intelligent Spectrum Utilization
AI-based techniques applied to channel state information feedback and beam management in the radio access network, enabling more efficient use of available spectrum and improved user experience in dense and interference-prone environments.
Positioning Enhancement
Improve device location accuracy in environments where GPS signals are degraded or unavailable — including indoor spaces, tunnels, and dense urban areas — enabling location-dependent services and emergency response capabilities.
Energy Optimization
AI-driven management of network element power states, enabling intelligent sleep scheduling that reduces energy consumption during low-traffic periods while maintaining readiness for rapid traffic recovery.
Fault Prediction & Prevention
Continuous monitoring of network performance parameters to detect anomalies and identify emerging failure patterns before they affect service — shifting network maintenance from reactive to proactive.
Autonomous Network Operations
Agentic AI systems that not only detect and diagnose issues but take remediation actions autonomously, enabling closed-loop network management where human operators focus on exception handling rather than routine optimisation.
Tejas is contributing to global standards and creating Al-driven solutions across wireless and wireline scenarios.
Universal Forecasting System
A zero-shot forecasting capability for network key performance indicators, enabling prediction of traffic patterns, congestion events, and performance trends without requiring model retraining for each new network context.
RCA Agent
An AI root cause analysis agent correlating real-time alarms across network layers, identifying faults origins instead of symptoms, accelerating issues diagnosis and reducing resolution times for network incidents.
AI-RAN
AI-native architectures for next-generation wireless networks, integrating intelligence directly into the radio access network to enable dynamic optimisation of radio resources, interference management, and mobility handling.
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