Telecom / IT Ops

Network Operations: Predictive Ticket Suppression & Automated FLT

Eliminate false-positive alert storms and auto resolve Level 1 tickets with ML-powered pattern recognition and agentic triage.

75%

Fewer Tickets

8-min

MTTR

65%

Auto-Resolved

$420K

Saved

Challenge & Solution

Why single method approaches fall short, and what replaces them.

The Challenge

12,000+ false-positive tickets generated per month

NOC engineers spend 60% of time on non-issues

Alert fatigue leads to missed incidents

Manual triage averages 45 minutes per ticket

The Solution

ML models learn normal patterns and suppress false positives while agentic auto-triage diagnoses, correlates, and resolves real issues autonomously predictive and agentic working in cohesion.

Workflow Components

8 coordinated components across agents, rules, and models.

Pattern Learning ML

Anomaly Detection

Intelligent Ticket Mgmt

Context Analysis

Diagnostic Agent

Correlation Agent

Triage Decision

Documentation Agent

Workflow

End to end execution path, including branch points and human gates.

Device Event

→

Pattern Recognition

→

Suppress?

→

Yes

Auto-Close

Context Analysis

→

Diagnostics Agent

→

Correlation Agent

→

Triage

→

Auto-Resolve

Escalate

→

Enrich & Route

Results

Measured in production.

75%

Ticket reduction

90%

False-positive elimination

65%

L1 auto-resolution

8-min

Mean time to resolve

$420K

Annual savings

Why Multi-Method Matters Here

No single technique covers the whole problem.

ML Learns Patterns

Models distinguish normal fluctuations from real anomalies.

Rules Enforce Policy

Deterministic logic governs escalation thresholds and SLAs.

Workflows Orchestrate

Multi-step diagnostic sequences run in coordinated order.

LLMs Understand Context

Natural language analysis of logs, alerts, and documentation.