How a Tier-1 telecom operator in India replaced a reactive, alarm-heavy NOC model with Ops Singularity's agentic AI fault management, running 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 ~12.5 million 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, driving 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; 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 or commercial LLM endpoints entirely.
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.
Consolidates alarms, KPIs, tickets, inventory & topology into one normalised source of truth, with topology stitching.
LLM-based primary-vs-symptom reasoning, 3GPP-grounded and topology-validated, emits a clean AI incident with confidence & citations.
Statistical & time-series ML surfaces degradation signatures ahead of service-affecting alarms. ML-only, no LLM.
Auto-generates operator-grade RFO, RCA and Plan of Action on approved templates, with ticket enrichment & closure.
Right message to the right stakeholder across email & SMS: SLA timers, escalation and delivery tracking.
Time-series ML forecasts capacity exhaustion, congestion and service degradation, driving preventive maintenance.
The defining constraint was data sovereignty: operational, customer-impact and topology data could not leave the operator's approved environment. Ops Singularity was deployed entirely on-premises, both the platform and the AI models, so intelligence runs where the data lives.
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 incidents; symptomatic noise & flapping suppressed automatically |
| Root cause analysis | Manual, multi-SME, hours per complex incident | Automated, explainable RCA with confidence & citations; 3GPP-grounded, topology-validated |
| Mean time to resolve | Long detection-to-resolution lifecycle | ~50% faster across defined incident families |
| First-time-right | Frequent repeat & bouncing tickets | ~60% permanent resolution on defined types |
| Failure posture | Reactive; action only after breach | Proactive; anomalies & capacity risk forecast ahead of impact |
| Operations effort | Touch-heavy L1/L2 triage & 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 |
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.
Ops Singularity 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.
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 quality and rollout phase.