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In this functional architecture deep dive, Irfan Lateef, Sales Engineering and Business Development lead, demonstrates the practical application of NetAI’s graph neural network (GNN) for large-scale networking. Lateef details the platform’s multi-layered ingestion process, which pulls configuration data via SSH CLI to build a comprehensive graph of the network, alongside real-time telemetry from SNMP, Syslogs, and GNMI. This data is processed on high-performance NVIDIA H100 GPUs to perform fault management, correlation, and anomaly detection. The system provides a multi-layer topology visualization that spans from physical links and Layer 3 routing to complex overlays like MPLS, VXLAN, and GRE tunnels, allowing operators to see exactly how issues propagate across the network fabric.
The presentation features a live demonstration where a simulated link failure between Los Angeles and New York triggers a cascade of OSPF and interface alarms. Unlike traditional tools that would flood an operator with thousands of separate tickets, the GNN engine distills these into a single deterministic root cause. Lateef showcases the “Evidence Timeline” and “Causal Chain,” which provide a human-readable explanation of how the AI arrived at its conclusion, tracing the blast radius from the initial configuration change through downstream symptoms. This transparency is designed to build the operator trust necessary for auto-remediation, where the system can automatically execute scripts, such as a no shutdown command, to resolve the issue in seconds, effectively achieving Level 4 or 5 autonomous operations.
Addressing the practicalities of deployment, Lateef explains that NetAI offers flexible models including air-gapped on-premise installations for security-conscious tier-one operators and cloud-based deployments for rapid scalability. The platform is designed to replace AIOps fatigue”with a tool that delivers immediate ROI by focusing on materially significant anomalies rather than subjective noise. By integrating with existing ITSM tools like Jira and ServiceNow, NetAI aims to be the single pane of glass that bridges the gap between different technical silos. The session concludes by emphasizing that while LLMs are limited to what they have been trained on, the GNN’s structural understanding of network protocols allows it to solve novel problems deterministically, reducing Mean Time to Repair (MTTR) by a factor of ten.
Personnel: Irfan Lateef
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