Project Name
Ksolves Builds an Agentic AI Copilot Cutting NOC Resolution Time by 60%
A global network management platform used by telecom operators and ISPs to monitor broadband access infrastructure at scale had NOC and field teams spending disproportionate time manually correlating data across dashboards that were never designed to talk about the same incident. Diagnosing a fault meant switching between five or more monitoring tools, and junior technicians escalated the majority of incidents simply because tacit senior-engineer knowledge wasn’t available to them in the moment.
Through AI ML consulting services, Ksolves built an AI-powered Smart Button that sits directly on any network management page, reads its context, detects anomalies, and delivers conversational root cause insights without requiring the user to navigate anywhere else. Mean time to resolve dropped by 60%, and junior-to-senior escalations fell by 40%.
- Dashboard Fragmentation: NOC analysts had to manually correlate data across five or more monitoring tools to identify root causes, spending 30-45 minutes per incident on data gathering before remediation could even begin.
- Context Loss on Page Navigation: Every time a technician moved between dashboard pages, operational context was lost, with no layer understanding what the user was currently looking at or able to surface relevant insight proactively.
- Anomaly Detection Without Labels: Network telemetry data was largely unlabelled, ruling out supervised anomaly detection and requiring an unsupervised approach capable of detecting deviations with no prior incident classification.
- Senior Engineer Knowledge Dependency: Effective fault diagnosis relied on tacit knowledge held by experienced engineers, and junior technicians consistently escalated incidents that AI-assisted guidance could have resolved independently.
- No Memory Across Troubleshooting Sessions: Every interaction with existing diagnostic tools started from scratch, with no way to recall previous troubleshooting steps or correlate recurring patterns across sessions.
- High Mean Time to Resolve: The combination of manual data gathering, context loss, and escalation dependency was driving MTTR figures that were getting harder and harder to defend against SLA commitments.
Through AI ML consulting services, Ksolves built the Smart Button as a context-first agentic AI layer that wraps around the existing network management platform rather than replacing it, governed by one principle: zero additional navigation.
- Page-Level Context Engine: A context extraction layer reads the current dashboard page, active device group, time window, metric selection, and visible anomalies, passing this structured context to the AI agent as the foundation for every interaction.
- Agentic AI Reasoning Layer: A multi-step reasoning agent built on LangChain decomposes complex network fault queries into sequential diagnostic steps, calls relevant data retrieval tools, and synthesizes findings into a concise root cause summary.
- Unsupervised Anomaly Detection: An anomaly detection module using Isolation Forest and statistical threshold models applies to live telemetry, letting the agent proactively flag deviations in signal quality, throughput, or error rates before the user even asks.
- Memory-Enabled Conversation: Session and cross-session memory lets the AI agent recall previous troubleshooting steps, reference earlier anomalies in the same session, and recognize recurring fault patterns across multiple incidents.
- Conversational Root Cause Interface: A natural language chat interface embedded directly in the platform lets technicians ask follow-up questions, request deeper analysis of specific metrics, and get step-by-step remediation guidance in plain language.
Technology Stack
| Category | Technology |
|---|---|
| AI/ML | Agentic AI (LangChain + LLM) |
| AI/ML | Anomaly Detection (Isolation Forest) |
| Architecture | Context-Aware Page Engine |
| Database | Vector Database (Embeddings Store) |
| Platform | Memory-Enabled Conversation Layer |
- 60% Reduction in Mean Time to Resolve: The context engine and conversational root cause interface now deliver actionable diagnosis in under 10 minutes for common fault patterns, down from 30-45 minutes of manual correlation across five or more tools.
- 40% Reduction in Junior-to-Senior Escalations: The memory-enabled agent with guided troubleshooting workflows cut junior-to-senior escalation rate by 40%, freeing senior engineers for genuinely complex incidents only.
- 70% of Detectable Fault Types Caught Pre-Customer-Impact: The unsupervised anomaly detection layer now flags deviations proactively in real time, catching roughly 70% of detectable fault types before customer-impacting degradation occurs.
- 80% Fewer Dashboard Context Switches Per Incident: The Smart Button's page-level context engine eliminated cross-tool navigation for common diagnostic queries, cutting context switches per incident by roughly 80%, down from an average of 8-12.
“The Smart Button has fundamentally changed how our NOC operates. Our junior technicians now resolve incidents that used to require senior escalation, and they resolve them faster.”
– NOC Director, TelecommunicationsTelecommunications
NOC analysts on this platform used to spend 30-45 minutes per incident navigating five or more tools, with junior technicians escalating over half of all faults simply because AI-guided diagnosis didn’t exist yet. Ksolves built an agentic AI Smart Button that delivers real-time, context-aware root cause insights from a single click, backed by unsupervised anomaly detection and memory-enabled conversational troubleshooting.
Mean time to resolve dropped by roughly 60%, escalation rate fell by around 40%, and the anomaly detection layer now catches roughly 70% of detectable fault types before they reach customers. Context switches per incident dropped by about 80%, turning what used to be a tool-hopping exercise into a single conversation on the page the technician was already looking at.
The same memory and reasoning infrastructure is positioned to extend into proactive maintenance scheduling, predictive fault routing, and autonomous remediation workflows as the next phase of this work.
Is Your NOC Still Relying on Manual Data Correlation and Senior Engineer Escalation for Every Fault Diagnosis?
