How a Tier-1 telecom operator in India replaced a reactive, alarm-heavy NOC model with an agentic AI fault-management platform that runs entirely inside its own data centre, with no dependency on public LLMs.
The operator runs one of India's largest radio access networks: hundreds of thousands of sites and millions of cells generating alarm volumes in the order of ~1.25 crore alarms per day. Fault management was still anchored to a reactive, rule-based, alarm-centric operating model that no longer scaled.
Static, hardcoded correlation rules missed complex multi-domain patterns and buried real incidents under symptomatic noise.
Root-cause analysis meant multiple experts manually interpreting logs, KPIs, topology and history, which drove high MTTR.
Manual playbook mapping led to mismatched steps, repeat tickets and bouncing incidents.
Incidents were handled only after a hard failure or threshold breach, with no early anomaly forecasting or prevention.
L1/L2 triage, diagnostic validation and recovery all required manual intervention, inflating headcount and cost.
Network, customer-impact and topology data could not leave approved environments, ruling out public and commercial LLM endpoints entirely.
The Solution
A single agentic AI platform modernised RAN fault management from reactive and manual to intelligent, proactive and closed-loop. Six coordinated capabilities turn raw operational signals into a unique actionable incident, an evidence-grounded root cause, and approved action, with a human-approval and governance band across every stage.
Governance
Supervisor and governance across every stage: RBAC, confidence-and-action policies, human approval gates, configurable guardrails, and an immutable, fully auditable reasoning chain for every decision.
Consolidates alarms, KPIs, tickets, inventory and topology into one normalised source of truth, with topology stitching.
LLM-based primary vs. symptom reasoning, 3GPP-grounded and topology-validated, emitting a clean AI incident with confidence and citations.
Statistical and time-series ML surfaces degradation signatures ahead of service-affecting alarms. ML-only, with no LLM in the path.
Auto-generates operator-grade RFO, RCA and Plan of Action on approved templates, with ticket enrichment and closure.
Right message to the right stakeholder across email and SMS, with SLA timers, escalation and delivery tracking.
Time-series ML forecasts capacity exhaustion, congestion and service degradation, driving preventive maintenance.
Every incident and RFO is grounded in approved knowledge (3GPP standards, OEM documentation and historical RCA/RFO) and carries source citations, reason codes and a confidence score. No hallucinated recommendations, no ungrounded actions.
High-impact actions route through human approval. Operator feedback, resolution outcomes and RFO edits feed back continuously to retrain detection models and tune templates, so the system gets sharper with every incident.
In Depth
The defining constraint was data sovereignty: operational, customer-impact and topology data could not leave the operator's approved environment. NetSingularity was deployed entirely on-premises, both the platform and the AI models, so intelligence runs where the data lives.
Platform · On-Prem
Cloud-native, private deployment. Kubernetes-native architecture deployed inside the operator's own data centre, with no reliance on external hyperscalers.
Carrier-grade resilience. N+1 high availability, multi-replica services, auto-scaling and DR-ready design for 24×7 SNOC operation.
Data never leaves the estate. Integrates with existing inventory, fault and performance systems in place, so data stays within approved boundaries end to end.
Enterprise-grade security. SSO/MFA, role-based access at platform and service level, service-to-service encryption, secrets management, and encryption in transit and at rest.
Immutable audit. Every prompt, retrieval, tool call, approval and action is logged with a unique transaction ID for complete traceability.
AI Model · On-Prem
On-prem LLM inferencing. Generative AI is served on dedicated in-house GPU infrastructure, with model and inference selection restricted exclusively to on-premises LLM services in the operator's workspace.
Zero public-LLM dependency. No prompt, alarm, ticket or network detail is ever sent to a commercial or public LLM endpoint.
Domain & OEM-aware. On-prem fine-tuning and retrieval-augmented generation over 3GPP, OEM and historical RCA knowledge produce telecom-grade, operator-specific answers.
ML models trained on operator data. Anomaly-detection and forecasting models are trained, versioned and retrained in-house with drift detection, and never externalised.
Guardrails at every interaction. Configurable input/output scanners block prompt injection, sensitive-data leakage, hallucinated RCA and unapproved actions before they reach a user or a device.
Moving from a reactive, manual model to an AI-driven, closed-loop one changed the economics of the NOC: less noise, faster resolution, fewer repeat incidents, and a smaller, higher-value operations footprint.
| Dimension | Before: reactive & manual | After: AI-driven & on-prem |
|---|---|---|
| Alarm handling | Millions of raw alarms, static rules, high fatigue | ~60% compressed into unique actionable incidentsSymptomatic noise and flapping suppressed automatically |
| Root cause analysis | Manual, multi-SME, hours per complex incident | Automated, explainable RCA with confidence and citations3GPP-grounded, topology-validated |
| Mean time to resolve | Long detection-to-resolution lifecycle | ~50% faster across defined incident families |
| First-time-right | Frequent repeat and bouncing tickets | ~60% permanent resolution on defined types |
| Failure posture | Reactive, with action only after breach | Proactive, with anomalies and capacity risk forecast ahead of impact |
| Operations effort | Touch-heavy L1/L2 triage and recovery | ~40% headcount efficiency; low-touch under governance |
| Data & AI posture | Public LLMs off-limits; no safe GenAI path | 100% on-prem, sovereign, fully auditable |
Noise reduction
Alarm-to-incident compression with primary-cause identification.
Lower MTTR
Shorter end-to-end detection-to-resolution lifecycle.
Headcount efficiency
Manual triage and ticket handling avoided across L1/L2.
Fewer incidents
Proactive prevention and faster isolation, phased in.
By running an agentic AI platform and its language models entirely on-premises, the operator unlocked GenAI-grade fault management without ever compromising data sovereignty, turning a compliance constraint into a competitive advantage.
NetSingularity delivers correlation, GenAI RFO, anomaly detection and prediction as one governed platform, deployable fully on-premises, with your models and your data staying inside your network.
Thousands of change requests a month across hundreds of change categories, assessed by checklist and delivered by hand. Seven coordinated flows turn each CR into a risk-scored, simulation-backed, auditable operational outcome.
O-RAN gives operators vendor choice, and with it more moving parts to coordinate on every site turn-up. One automation layer now carries planning data, field capture, provisioning, configuration and verification end to end.
A pan-India optical backbone built over years from more than a dozen equipment vendors, each with its own element manager on its own island. One cloud-native OSS now normalizes all three layers into a single operational picture.
Customer identity withheld by request and referred to throughout as a "Tier-1 telecom operator in India." Improvement figures reflect the target and expected outcomes of the AI-driven, on-premises fault-management deployment and are indicative; exact results vary by network scope, data availability and deployment phase. Internal technical, model and infrastructure specifics are intentionally generalised.
