TLDR: NETSCOUT Systems, Inc. recently demonstrated its advanced AI-driven network operations as part of TM Forum’s NeuroNOC Catalyst project at DTW Ignite 2025. The initiative focused on developing self-healing network capabilities, showcasing how high-quality data and AI can significantly reduce manual troubleshooting and operational costs for telecom service providers.
WESTFORD, Mass. – NETSCOUT Systems, Inc. (NASDAQ: NTCT), a leading provider of observability, AIOps, cybersecurity, and DDoS attack protection solutions, recently announced its significant participation in TM Forum’s NeuroNOC Catalyst project. This innovation initiative, showcased at DTW Ignite 2025, set new records for the number of Communication Service Providers (CSPs) and countries represented, highlighting the industry’s keen interest in advanced network automation.
The NeuroNOC Catalyst project brought together a global alliance of telco and technology companies to explore how AI agents, closed-loop automation, and high-quality network data can enable self-healing operations across complex telecom environments. The primary objective was to dramatically accelerate fault detection and resolution in live network scenarios, moving towards a future of autonomous network management.
NETSCOUT played a pivotal role by deploying its Omnis AI Insights Solution, which includes the Omnis AI Sensor and Omnis AI Streamer tools. This solution was instrumental in delivering high-fidelity telemetry from 5G Standalone Radio Access Network (SA RAN) and Packet Core, which is essential for effective AI-driven operations. The Omnis AI Sensor leverages NETSCOUT’s signature deep packet inspection (DPI)-based, end-to-end network visibility, while the Omnis AI Streamer provides powerful analytics and filtering at the source via an open API-driven dataset.
During simulated service-impact scenarios, NETSCOUT’s solution enabled network operations center (NOC) engineers to swiftly identify subscriber registration issues, pinpoint the root cause using a curated large language model (LLM), and execute remediation steps with minimal human intervention. This demonstration underscored the practical application of AI in enhancing network reliability and efficiency.
A key takeaway from the project was the strong empirical validation of the critical need for high-quality, curated data to drive effective AI solutions. Participants emphasized that without accurate and reliable data, AI models are merely ‘hollow shells’ incapable of delivering valuable results. Data curation, therefore, is paramount to unleashing the full power of AI in network operations.
The project yielded promising results, demonstrating substantial benefits for communications service providers. These include an estimated 80% reduction in manual troubleshooting efforts and up to 50% lower operational costs. Furthermore, the initiative showed that streamlining data tokenization through AI models like AWS Bedrock could reduce data usage by as much as 80%, pointing to additional potential savings and efficiencies.
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Richard Fulwiler, Senior Director of Product Management at NETSCOUT, underscored the importance of data quality, stating, ‘Curated real-time data is essential for intelligent network operations.’ This sentiment reinforces NETSCOUT’s commitment to providing the foundational data visibility necessary for advanced AI and automation initiatives within the telecom industry.
