Executive Summary

  • Hybrid NOC deployment designed for low latency and a complete audit trail.
  • Ticket summarization + classification + next-best-action suggestions, grounded on internal runbooks.
  • 41% deflection on repetitive tickets and MTTR reduced by 32% with confidence gates + auto-escalation.
  • p95 latency 420ms → 180ms via vLLM tuning, prompt caching, and KV cache optimization.
  • Production observability: deflection, rollback rates, citation coverage, and cost per ticket.

Before / After

Metric
Before
After
Improvement
p95 latency
420ms
180ms
-57%
Deflection rate
0%
41%
+41pt
MTTR (index)
100%
68%
-32%
Cost per ticket (index)
100%
76%
-24%

Timeline

W1-2

Discovery + evaluation set

Ticket taxonomy, runbooks inventory, and a measurable benchmark (routing accuracy, citations coverage, safe escalation rate).

W3-5

MVP in staging

RAG ingestion pipeline, triage workflow, and guardrails (confidence gates, forced citations, rollback paths).

W6-8

Production rollout

Integration with ITSM tooling, observability dashboards, and on-call-safe escalation policies.

Decisions & Trade-offs

Grounding

Choice: Runbook-grounded RAG with strict citations
Alternatives: fine-tuning only
Why: Fast iteration, reduced hallucinations, and explainability for operators.
Risks: Stale runbooks → wrong suggestions.

Safety

Choice: Confidence thresholds + auto-escalation
Alternatives: always-answer assistant
Why: Protect on-call operations: assist when confident, escalate when uncertain.
Risks: Over-escalation if thresholds are too strict.

Serving

Choice: vLLM with KV cache tuning and prompt caching
Alternatives: TensorRT-LLM
Why: Balanced throughput and low p95 latency for interactive triage.
Risks: Batching too aggressively can harm p95.

Vector layer

Choice: Hybrid FAISS + Milvus
Why: Fast local retrieval for hot runbooks + scalable collections by domain.
Risks: Two retrieval paths need consistent observability.

Stack & Architecture

Models

  • 8B quant INT4
  • Embedding model (in-house)

Serving

  • vLLM
  • KV cache + prompt caching

Vector

  • FAISS + Milvus

Security

  • Guardrails + safe escalation
  • Role-based access
  • Audit logs

→ View Full Reference Architecture

SLO & KPI

NOC triage p95 < 200ms

✓ Achieved 180ms

Deflection ≥ 35% with safe escalation

✓ Achieved 41%

ROI & Unit Economics

Formula: ROI = (ΔProd + ΔQuality + Risk avoided) − (Capex/amm + Opex)
  • ΔTCO ↓ 24% (indexed baseline)
  • MTTR ↓ 32% via routing + suggested resolutions
  • 41% deflection on repetitive tickets

Risks & Mitigations

Risk: Runbooks drift → wrong suggestions → Mitigation: automated sync + freshness alerts + canary eval.
Risk: Over/under-escalation due to thresholds → Mitigation: staged rollout with shadow mode and per-queue tuning.

Lessons learned

  • Grounding + citations beat “smarter prompts” for operator trust.
  • Latency work is mostly caching and batching discipline, not bigger GPUs.
  • Deflection is only good if rollback and escalation are first-class.

Testimonials

"We cut noise and sped up incident handling without compromising safety."

— NOC Operations Lead

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