The operator runs one of India's largest radio access networks: hundreds of thousands of sites and millions of cells, generating around 12.5 million alarms every day. Fault management was still anchored to a reactive, rule-based, alarm-centric model that had stopped scaling with the network.
Static, hardcoded correlation rules missed complex multi-domain patterns, and real incidents were buried under symptomatic noise.
Finding the true cause meant several specialists manually interpreting logs, performance data, topology and history, which stretched resolution times.
Manually matching an incident to a playbook led to mismatched steps, repeat tickets and incidents that bounced between teams.
Work started only after a hard failure or a threshold breach. There was no forecasting of degradation and no way to act before customers felt it.
Triage, diagnostic validation and recovery were all manual, which set the size and cost of the operations floor.
Network, topology and customer-impact data had to stay inside approved environments, which ruled out every public AI service.
One platform took fault management from reactive and manual to proactive and closed-loop. Six coordinated capabilities turn raw operational signals into a single actionable incident, an evidenced root cause and an approved action, with human approval and governance applied at every stage.
Alarms, performance data, tickets, inventory and topology are consolidated and normalised into a single operational picture, with topology stitched together across domains.
x101 reasons across the whole picture to tell the primary fault from its symptoms, grounded in the operator's standards and validated against live topology. The result is one clean incident carrying its confidence score and its citations.
Statistical and time-series models surface degradation signatures before they become service-affecting alarms. This path is pure machine learning, with no language model in the prediction.
x101 produces the operator-grade reason for outage, the root cause analysis and the plan of action on approved templates, then enriches and closes the ticket behind it.
The right message reaches the right stakeholder over email and SMS, with response timers, escalation paths and delivery tracking handled by the platform.
Forecasting models flag capacity exhaustion, congestion and service degradation before they arrive, so maintenance is scheduled instead of scrambled.
The defining constraint was sovereignty: operational, topology and customer-impact data could not leave the operator's approved environment. x101 was deployed entirely on the operator's own premises, platform and AI models together, so the intelligence runs where the data already sits.
Moving from a reactive, manual model to a closed-loop one changed the economics of the operations floor: less noise, faster resolution, fewer repeat incidents and a smaller, higher-value team footprint.
Alarms compressed into actionable incidents with the primary cause named.
A shorter lifecycle from first detection through to closure.
Manual triage and ticket handling avoided across the front line.
Prevention and faster isolation, phased in as forecasting matured.
| Dimension | Before · reactive and manual | After · x101, on-premises |
|---|---|---|
| Alarm handling | Millions of raw alarms, static rules, constant fatigue | Around 60% compressed into unique actionable incidentsSymptomatic noise and flapping suppressed automatically |
| Root cause | Manual, several specialists, hours per complex incident | Automated and explainable, with confidence and citationsGrounded in operator standards, validated against topology |
| Time to resolve | Long detection-to-resolution lifecycle | Around 50% faster across defined incident families |
| Resolution quality | Frequent repeat and bouncing tickets | Around 60% resolved permanently on defined types |
| Failure posture | Reactive, action only after a breach | Proactive, with degradation and capacity risk forecast ahead of impact |
| Operations effort | Every triage and recovery step needed a person | Around 40% efficiency gain, low-touch under governance |
| Data and AI posture | Public AI services off-limits, no safe path to adopt | Fully on-premises, sovereign and auditable end to end |
By running the platform and its AI models entirely on its own premises, the operator gained modern AI in the network operations centre without ever compromising data sovereignty, turning a compliance constraint into an advantage.
Solution summary · x101 network fault management deployment
Nothing here was built for one customer. Each capability below is standard platform behaviour, applied to a network operations problem.
About this case study. The customer's identity is withheld at their request and is referred to throughout as a Tier-1 telecom operator in India. Improvement figures reflect the target and expected outcomes of the deployment and are indicative; actual results vary with network scope, data availability and deployment phase. Technical, model and infrastructure specifics are intentionally generalised.