Beyond Network Monitoring: Leveraging Network LLMs for Root Cause Analysis

Telecom networks generate more data than their operations teams can monitor, analyze, or keep up with. As a result, network operators face constant pressure from increased device volumes, a growing user base, and ever-changing customer expectations. Most operators are still managing their network with a static toolset, reactive workflows, and manual decisions that fail to scale. Faults take longer to find and fix, and customers feel the impact.

Tech Mahindra is changing that with AI-native network operations. The solution combines large language models (LLMs), agent-based AI workflows, and digital twin simulation. The LLM reads network data, AI agents diagnose faults and recommend fixes, and the digital twin tests each fix before it reaches the live network. Operators move from responding to issues to resolving them in real time. This operational solution gives operators a blueprint for the future of intelligent network operations.

Why Network Operations Centers Fail to Turn Data into Action

Network operations centers (NOCs) generate vast amounts of data but fail to interpret, correlate, and act on it in real time, resulting in high mean time to repair (MTTR). Static dashboards provide inconsistent results and create more issues.

Analyzing context across data sources is difficult, misrouted and repeated truck rolls increase operational costs, and service delays result in a suboptimal customer experience and impact the net promoter score (NPS).

How an AI-native Architecture Built on Three Pillars Helps the Telecom Industry

The LLM can understand the networks. AI agents can diagnose and recommend. The digital twin can test and validate. Together, they create Tech Mahindra’s unified AI-native architecture, where network data can be transformed into real-time actions.

Pillar 1: Network LLM

Tech Mahindra developed a telecom LLM. Using parameter-efficient fine-tuning (PEFT), it reads and reasons over different types of telecom data, including logs, alarms, and structured performance metrics. The LLM powers intelligent assistants that can interpret multiple telecom signals and anomalies, determine the root cause, and propose multiple solutions for the field engineer.

The network LLM enables the network to triage itself. It’s the first-response unit on call for every network problem.

Pillar 2: Multi-Agent AI Workflow

The multi-agent system covers the entire network diagnostic use case (from detection to decision), with a different agent handling each stage of the diagnostic process. Such systems follow a modular design to facilitate integration with process automation and tools and allow agents to make independent decisions. Such a system can perform collaborative root cause analysis (RCA) while reducing human-in-the-loop intervention, as these agents reason like human experts and come up with optimal solutions.

Pillar 3: Digital Twin

A digital twin is like a virtual runtime environment for the network in which behavior is simulated. Operators can use these environments to plan network layout, troubleshoot coverage and performance issues, and visualize coverage. When integrated with other supporting tools and workflows at this layer, the digital twin can eliminate site visits while optimizing antenna placement and signal strength. With inputs from real-time sources (Internet of Things [IoT] data) and use of virtual reality (VR) technology, network planning and troubleshooting can become more insightful, experiential, data-driven, and testable.

Orchestration That Scales Securely Across Telecom Ecosystems

What really sets this framework apart is orchestration. Built on the TechM Orion framework and powered by NVIDIA AI Enterprise, it enables the development of GenAI offerings for clients. It supports scalable deployments across the telecom ecosystem. It includes integrated guardrails for secure, auditable operations. Its modular design allows rapid adaptation to client environments.

Measurable Results for Telecom Operators

The framework is more than automation. It also delivers resilient, intelligent, customer-centric operations. The framework creates tangible benefits:

• 70% quicker network design iterations
• Up to 80% time saved in tower inventory and site capture
• Significant reduction in false escalations and repeat field interventions
• Improved NPS due to faster, more accurate service resolutions

The Road Ahead

As AI moves from experimentation to enterprise integration, telecom presents one of the most compelling use cases and one of the biggest opportunities. With this LLM-led, agent-driven network framework, we are not just solving today’s operational bottlenecks but also laying the foundation for zero-touch networks of the future.

This initiative reflects who we are at our best: Innovators, engineers, and transformation partners. Whether you are building AI-native workflows, driving automation for clients, or scaling telecom platforms, this is the kind of work that positions TechM at the forefront of GenAI adoption.

These figures speak volumes about the reach and feasibility of adoption that mobile technology has achieved, making mass adoption of technologies like GenAI and enterprise digital transformations a reality.