Closing the Diagnostics Gap in Production

NOC Root Cause Analysis Automation, powered by the AI-based AGILITY

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A Network That Outgrew Its Troubleshooting Model

Modern mobile networks are immensely complex ecosystems, multi-vendor, multi-layer, and constantly evolving. Yet for one leading US operator serving over 100 million subscribers, the approach to diagnosing packet-level issues had a clear opportunity for innovation and optimization.

Engineers spent hours sifting through PCAPs and network traces. Each incident triggered group triage sessions or “war rooms”, pulling senior engineers away from strategic projects to put out fires.

Over time, this pattern became more than an operational burden, it became a strategic risk.

As the VP of Network Operations described:

“I thought I had at least 100 engineers with the skills and domain knowledge to perform this triage. The reality is that I only have a handful of experts. These are the same people who are assigned to strategic automation and AI initiatives. Instead, we have to pull them into war rooms, working overtime and weekends to resolve the issues. The current manual process represents a significant risk to the business. It is slow, costly, reliant on in-demand resources and often customer-impacting.”

The cumulative effect was slower resolution, higher OPEX, and growing fatigue among expert engineers.

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Knowledge Bottlenecks and Tool Limitations

Even with advanced monitoring platforms and OSS systems in place, automated troubleshooting remained an unmet promise.

The core challenge wasn’t just resource scarcity; it was knowledge accessibility:

• Critical insights were buried across PCAPs, documents, and tribal knowledge.

• Tier-2 teams lacked the contextual intelligence to resolve issues independently.

• Escalations to Tier-3 became routine, stretching already limited expertise even thinner.

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Issue Identification Support with AI-Driven Diagnostics

To close this gap, Reailize deployed one of its NOC Automation Suite use cases, built on the AGILITY Platform from B-Yond, an AI-powered framework designed to automatically translate PCAP traces into actionable insights.

The solution reimagined how diagnostics are performed by introducing intelligence at every step of the process:

• Automated PCAP Diagnostics at Scale: ML-based models analyze packet data across the network to surface the most probable root causes referencing Industry documentation.

• Dynamic Topology Extraction: automatically reconstructs the network path between nodes to provide immediate visual and contextual understanding.

• Conformance Checks: identifies discrepancies in protocols or configurations between network elements, pinpointing where behavior deviates.

• Automated Escalation Diagnostics: Upskills Tier-2 engineers to perform expert-level triage with guided AI support, reducing dependency on senior experts.

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‍Measurable Efficiency

Within months of implementation, the results were measurable and transformative:

• Mean Time to Resolve (MTTR): Reduced by up to 90% for network issues

• Tier-2 / Tier-3 Escalations: Decreased by 47%, enabling faster incident closure

• Talent Retention & Satisfaction: Night and weekend “war rooms” reduced dramatically

• Strategic Capacity: Senior engineers freed to focus on automation, AI, and roadmap initiatives
• VIP Case Handling: Faster diagnostics enabled immediate issue resolution for high-value accounts  

Tier-2 engineers now diagnose issues in real time with AI-assisted insights, while Tier-3 teams are re-engaged in innovation rather than emergency support.

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From Firefighting to Future-Ready Operations

This transformation went far beyond technical automation. It reshaped the operating model of the NOC:

• Scalable knowledge distribution allowed every engineer to act with expert-level insight for faster resolution.

• AI-based triage automation minimized human dependency in critical troubleshooting steps.

• Data-driven learning loops continuously improved diagnostic accuracy.

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The Reailize Perspective

At Reailize, we help operators bridge the gap between data and action. Every day, we demonstrate how AI can make operational knowledge a shared and scalable asset rather than a limited resource.

Reailize makes it real.

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