AI-Powered Network Incident Resolution

North American Telecom Giant Speeds Incident Resolution Time with AI

AI-driven fault detection and faster root cause analysis at carrier scale.

Higher

Detection accuracy

Faster

Incident resolution

Scalable

AI foundation

About

A leading North American telecom operator was losing ground to manual error detection and slow root cause analysis. Fragmented monitoring data, labor-intensive triage, and inaccurate fault identification were eroding service reliability at carrier scale. Veltris built an AI-driven platform combining machine learning anomaly detection and LLM-powered explanations to transform how the operator detected, understood, and resolved network incidents.

AI turns fragmented network signals into actionable intelligence.

Challenge

Manual incident analysis couldn’t keep pace with carrier-scale operations.

Manual error detection and slow, often inaccurate root cause analysis across a high-volume telecom network created compounding reliability risks. Fragmented data across multiple monitoring sources limited visibility into overall network health and slowed engineering teams during incident triage.

PAIN POINTS

Labor-intensive manual error detection
Slow and inconsistent root cause analysis
Fragmented monitoring data across multiple sources
Limited visibility into network health and fault conditions

Solution

AI-powered intelligence built for faster network incident resolution.

Veltris combined supervised and unsupervised machine learning models to assign failure probabilities to network nodes and surface protocol-level anomalies. An automated telemetry pipeline processed Sigos KPI and Netscout PCAP data to calculate failure intervals and node probabilities.

LLM-driven explanations translated detected errors into human-readable insights, accelerating triage and root cause analysis. The solution was deployed as a production-grade AI platform using Python, Scikit-learn, Flask, Docker, and ReactJS with real-time monitoring.

STEP FLOW

Monitor → Detect → Explain → Resolve → Improve

AI finds the signal, explains the issue, and accelerates the response.

WHAT CHANGED IN THE WORK

Manual detection → AI-driven anomaly detection
Fragmented signals → Correlated network intelligence
Manual triage → Model-driven fault analysis
Complex errors → Human-readable LLM explanations

WHAT WE DELIVERED

ML-Based Anomaly Detection
Automated Network Telemetry Pipeline
LLM-Driven Error Explanations
Production-Grade AI Deployment

Ready to accelerate network incident resolution with AI?

Results

Faster detection. Smarter triage. Stronger network reliability.

KEY RESULTS DELIVERED

Higher

detection accuracy. Less manual review across high-volume data

Faster

incident resolution. LLM-driven explanations and root cause analysis

Scalable

AI foundation. Unified monitoring into actionable network intelligence

Why Veltris

Veltris combined AI/ML engineering, network telemetry, anomaly detection, and LLM capabilities to transform incident management at carrier scale. By connecting fragmented monitoring signals with model-driven fault detection and human-readable explanations, Veltris helped engineering teams identify issues faster, accelerate root cause analysis, and strengthen network reliability.

CAPABILITIES APPLIED

AI/ML Engineering
Supervised & Unsupervised ML
Network Telemetry Engineering
Anomaly Detection & Fault Probability Scoring
LLM-Driven Error Explanation
Real-Time Network Monitoring

Modernize network operations with
AI-powered incident resolution.