AI Operational Ticketing & Resolution Engine
Architecting a production Retrieval-Augmented Generation (RAG) + LLM Classifier pipeline to triage, diagnose, and route thousands of incoming network outage tickets in real time.
Complexity & Initial Bottlenecks
An enterprise telecom infrastructure provider received over 12,000 network outage tickets monthly. Manual triage averaged 4.2 hours per ticket, delaying incident response for SLA-bound tier-1 enterprise clients.
Architecture & Technical Implementation
VEINTECH engineered an event-driven architecture where incoming tickets are converted into semantic embeddings, matched against existing network topology knowledge bases (PostgreSQL pgvector), and processed by an LLM Classifier to pinpoint severity and root cause instantly.
Business Impact & Performance ROI
Triage latency dropped 88% from 4.2 hours to under 3 minutes. Automated incident routing accuracy reached 94.8%, saving Network Operations Center (NOC) engineers over 1,800 manual hours monthly.
System Execution & Processing Pipeline
Ingestion & Webhook
Incoming incident tickets stream via encrypted webhooks into fault-tolerant Apache Kafka queues.
Vector Search (RAG)
Semantic retrieval queries pgvector for historical resolution logs and relevant network topology diagrams.
LLM Diagnostic Engine
Fine-tuned LLM synthesizes root cause analysis and drafts initial remediation action plans.
Automated Routing
Critical incidents are escalated directly to specialist engineering tiers with comprehensive summaries.
Why Manual Incident Triage Failed at Scale
In large-scale telecom networks, a single fiber cut can trigger hundreds of downstream router alerts. Manual triage overwhelmed NOC operators with duplicate noise. VEINTECH’s AI Classifier intelligently clusters correlated alerts into a single root-cause incident and surfaces verified remediation steps.
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