---
title: "Case Study: Sovereign AI Fault Management for a Tier-1 Network | x101"
url: https://www.x101.ai/case-studies/ran-fault-management
description: "How a Tier-1 mobile operator in India replaced reactive, alarm-heavy fault management with x101: around 60% less alarm noise, 50% faster resolution, running fully on-premises."
---

# A national mobile network's fault management, rebuilt on x101

Telecom · India

**A Tier-1 mobile operator in India removed around 60% of its alarm noise and resolved incidents around 50% faster after rebuilding fault management on x101, running 100% on-premises with no public AI service involved.** The reactive, alarm-heavy operations model was replaced end to end, deployed entirely inside the operator's own data centre, with every AI model running on the operator's own infrastructure.

- **Industry**: Tier-1 mobile operator
- **Scope**: Network operations centre · fault management
- **Deployment**: 100% on-premises
- **Data**: Fully sovereign
- **Duration**: 1 to 1.5 years

Last updated: 23 September 2026

- **~60%** Alarm noise removed through correlation and deduplication
- **~50%** Faster mean time to resolve across incident families
- **~60%** First-time-right resolution on defined incident types
- **~40%** Improvement in front-line operations efficiency
- **20–40%** Fewer incidents through early warning, phased
- **100%** On-premises AI, no public AI service involved

## The challenge

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.

- **Alarm floods and fatigue**: Static, hardcoded correlation rules missed complex multi-domain patterns, and real incidents were buried under symptomatic noise.
- **Slow, expert-dependent root cause work**: Finding the true cause meant several specialists manually interpreting logs, performance data, topology and history, stretching resolution times.
- **Resolutions that did not hold**: Manually matching an incident to a playbook led to mismatched steps, repeat tickets and incidents that bounced between teams.
- **Reactive by design**: Work started only after a hard failure or threshold breach. There was no forecasting of degradation and no way to act before customers felt it.
- **Every step needed a person**: Triage, diagnostic validation and recovery were all manual, which set the size and cost of the operations floor.
- **Data that cannot leave the building**: Network, topology and customer-impact data had to stay inside approved environments, which ruled out every public AI service.

## What x101 does

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.

1. **One source of truth**. Alarms, performance data, tickets, inventory and topology are consolidated and normalised into a single operational picture, with topology stitched together across domains.
2. **Cause separated from symptom**. x101 tells the primary fault from its symptoms, grounded in the operator's standards and validated against live topology. The result is one clean incident with its confidence score and citations.
3. **Early warning**. 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.
4. **The write-up, drafted for you**. 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.
5. **The right people told**. The right message reaches the right stakeholder over email and SMS, with response timers, escalation paths and delivery tracking all handled by the platform.
6. **Looking ahead**. Forecasting models flag capacity exhaustion, congestion and service degradation before they arrive, so maintenance is scheduled instead of scrambled.

> **Governance runs across every stage.** Each request carries the permissions of the person asking, confidence thresholds decide what x101 may do on its own, high-impact actions wait for human approval, and every decision is written to an immutable, replayable record.

## Deployed where the data lives

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.

### The platform

Inside the operator's data centre

- **Private deployment.** x101 runs inside the operator's own data centre, with no dependency on an external cloud provider.
- **Carrier-grade resilience.** Redundant, multi-instance services with automatic scaling and disaster recovery, built for round-the-clock operation.
- **Data stays inside the estate.** x101 reads the existing inventory, fault and performance systems in place, and nothing crosses an approved boundary.
- **Enterprise security throughout.** Single sign-on with multi-factor authentication, role-based access at every level, and encryption in transit and at rest.
- **Complete audit record.** Every question, retrieval, action, approval and result is logged with its own transaction identifier.

### The AI models

The operator's own, on its own hardware

- **In-house inference.** Language models are served on the operator's dedicated hardware, and model selection is restricted to those on-premises services.
- **No public AI service involved.** No prompt, alarm, ticket or network detail is ever sent to a commercial AI endpoint.
- **Tuned to this network.** On-premises tuning and retrieval over the operator's standards, equipment documentation and resolution history produce answers specific to this estate.
- **Models trained on operator data.** Detection and forecasting models are trained, versioned and retrained in-house, with drift monitoring, and are never externalised.
- **Guardrails at every interaction.** Input and output checks stop prompt manipulation, sensitive-data leakage, unsupported conclusions and unapproved actions before they reach a person or a device.

## The impact

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.

- **~60%** Less noise: Alarms compressed into actionable incidents, primary cause named.
- **~50%** Faster resolution: A shorter lifecycle from first detection through to closure.
- **~40%** Efficiency gain: Manual triage and ticket handling avoided across the front line.
- **20–40%** Fewer incidents: Prevention and faster isolation, phased in as forecasting matured.

Fault management before and after x101, across seven dimensions

| 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 incidents. Symptomatic noise and flapping suppressed automatically |
| Root cause | Manual, several specialists, hours per complex incident | Automated and explainable, with confidence and citations. Grounded 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

## The parts of x101 this uses

Nothing here was built for one customer. Each capability below is standard platform behaviour, applied to a network operations problem.

- **Unify**: Alarms, performance data, tickets, inventory and topology read as one picture instead of six separate systems. [Explore](https://www.x101.ai/#unify)
- **Agentic Orchestration**: Specialist agents work correlation, detection, drafting and communication in parallel, and their findings converge into one incident. [Explore](https://www.x101.ai/#orchestrate)
- **Company Knowledge**: Standards, equipment documentation and years of resolved faults become the grounding for every answer. [Explore](https://www.x101.ai/#knowledge)
- **AI Explainability**: Citations, reason codes and confidence scores on every incident, with the full trail preserved. [Explore](https://www.x101.ai/#explainability)
- **Governed by Design**: Permissions per user, approval gates on high-impact actions, and a signed record of every step. [Explore](https://www.x101.ai/#governance)
- **No Vendor Lock-in**: The whole platform and its models running on the operator's own premises, entirely self-contained. [Explore](https://www.x101.ai/#no-lock-in)

**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.

## Keep reading

- [Network change, simulation-led](https://www.x101.ai/case-studies/network-change-management): The same operator: risk scored and impact simulated before every change is approved.
- [All case studies](https://www.x101.ai/case-studies): Eleven deployments across telecom networks, enterprise IT and workforce functions.
- [How the platform works](https://www.x101.ai/platform): How a question becomes an evidenced answer and a completed action.

## Bring sovereign AI to your operations.

Correlation, evidenced root cause, early warning and forecasting as one governed platform, deployable entirely on your premises, with your models and your data staying inside your network.

- [Book a demo](https://www.x101.ai/contact)
- [See how x101 compares](https://www.x101.ai/compare)