68% Faster Incident Resolution
The average root cause analysis time was reduced from over two hours to less than 30 minutes.
An AI-first implementation that accelerates root cause analysis and enables proactive network operations.
Industry Telecommunications
Company Internet Service Provider
Location South Africa
The average root cause analysis time was reduced from over two hours to less than 30 minutes.
The system automatically correlated duplicate and related alerts into consolidated incidents, allowing engineers to focus on real issues.
Customer support teams were empowered with AI-assisted diagnostics, which reduced unnecessary escalations to senior network engineers.
The system detected patterns of network degradation before customers experienced outages, laying the foundation for predictive operations.
The client is a leading South African Internet Service Provider (ISP) serving both enterprise and consumer customers across the country. As network coverage and customer demand expanded, the Network Operations Center (NOC) struggled to keep pace with the increasing alert volumes. The client sought a smart solution to address the growing complexity of incidents and their reliance on experienced engineers.
Engineers manually investigated hundreds of daily alerts across multiple monitoring, telemetry, ticketing, and device management systems before identifying the actual root cause.
Support teams lacked the technical visibility needed to resolve network issues independently, causing frequent escalations to Level 2 support and senior NOC engineers.
Operations remained largely reactive, with incidents investigated only after customers began experiencing outages, latency, or packet loss.
We built an autonomous AI-powered Network Operations Agent using an agentic architecture. Our team used technologies like LLMs, Retrieval-Augmented Generation (RAG), multi-agent orchestration, and knowledge graph integration.
They connected these AI modules directly to the client's monitoring platforms, OSS/BSS systems, and ticketing environment. Four AI agents continuously monitor network activity, correlate incidents, identify probable root causes, and recommend the most effective resolution steps.
Continuously ingests telemetry, health metrics, and alerts from multiple monitoring platforms to maintain real-time visibility across the network.
Correlates related alerts into a single actionable incident, eliminating duplicate notifications and dramatically reducing alert fatigue.
Combines live telemetry, historical incidents, knowledge repositories, and operational documentation to determine the most probable root cause within minutes.
Generates recommended remediation steps, prepares incident summaries for engineers, assists customer support teams with diagnostics, and escalates only when human intervention is required.
When network degradation begins, the Monitoring Agent immediately detects abnormal telemetry
The Incident Detection Agent groups multiple related alerts into a single incident instead of flooding engineers with duplicate notifications
Then, the Root Cause Analysis Agent compares current network behavior with historical incidents, baseline performance, and internal documentation to identify the likely cause
Finally, the Troubleshooting Agent prepares recommended remediation steps and a complete incident summary for engineers, enabling faster decision-making and reducing manual investigation
The ISP transformed its Network Operations Center from a reactive support function into an AI-assisted operational intelligence platform. Engineers now spend less time investigating incidents, support teams resolve more customer issues without escalation, and the organization can identify potential network problems before they impact customers.
The multi-agent architecture also establishes the foundation for predictive maintenance and future self-healing network capabilities.
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