NetAI GraphIQ Demo with Irfan Lateef

Why It Matters

By automating root‑cause analysis and delivering near‑real‑time fault correlation, GraphIQ dramatically cuts outage resolution time, boosting network reliability and operational cost efficiency for tier‑one operators.

Key Takeaways

• NetAI GraphIQ ingests CLI, SNMP, gNMI, and cloud telemetry.
• Graph Neural Network runs on Nvidia H100 GPU for root‑cause analysis.
• Platform supports on‑prem, air‑gapped, and cloud deployment models.
• Demonstrated 5‑minute alarm correlation and 10‑minute mean‑time‑to‑repair improvement.
• Multi‑layer topology visualization enables real‑time anomaly detection and performance optimization.

Summary

The video showcases NetAI’s GraphIQ platform, a next‑generation AI‑ops solution that stitches together device‑level data—CLI configs, SNMP traps, gNMI streams, and even cloud telemetry—into a unified graph model. Van Latif walks through the functional architecture, highlighting how the platform builds multi‑layer topologies, feeds them to a Graph Neural Network (GNN) engine, and delivers fault correlation, anomaly detection, and performance insights. Key technical points include an injection layer that pulls configuration via SSH, real‑time SNMP performance data, and layer‑2/3 discovery using CDP, LLDP, and OSPF. The GNN runs on an Nvidia H100 GPU, delivering deterministic root‑cause analysis within seconds. Deployment is flexible: fully air‑gapped on‑prem servers, hybrid cloud‑GPU offload, or end‑to‑end in Google Cloud, allowing tier‑one operators to scale without owning specialized hardware. During the live demo, the platform processed 12,000 alarms, identified 2,500 root causes, and reduced mean‑time‑to‑repair to ten minutes, with alarm correlation completed in five minutes. A simulated link‑down on a LAX‑NYC circuit triggered real‑time alarm ingestion, GNN reasoning, and a causal timeline that pinpointed the configuration change as the primary fault. The implications are clear: operators can achieve ten‑fold efficiency gains, proactively detect anomalies before congestion escalates, and leverage cloud scalability while keeping sensitive network data on‑prem when required. GraphIQ positions itself as a competitive AI‑ops offering for large‑scale service providers seeking faster, more accurate network assurance.

Original Description

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.

Presented by Irfan Lateef, VP System Engineering. Recorded live at Networking Field Day 40 in San Jose on April 10, 2026. Watch the entire presentation at https://techfieldday.com/appearance/netai-presents-at-networking-field-day-40/ or visit https://TechFieldDay.com/event/nfd40 or https://NetAI.ai for more information.