---
title: "Case Study: 27,000+ Devices in One Governed NOC | x101"
url: https://www.x101.ai/case-studies/carrier-network-observability
description: "How a carrier-scale Tier-1 operator in India unified fault and performance data from 27,000+ network devices with x101, cutting MTTR by a measured 43% and automating 85% of routine operations."
---

# 27,000 network devices, one governed operations centre

Telecom · India

**A carrier-scale Tier-1 operator in India brought more than 27,000 routers, switches and devices under one governed operations centre with x101, cutting mean time to resolve by a measured 43% and automating 85% of routine manual operations.** Fault and performance data that had arrived over three separate collection paths now lands in a single near-real-time layer, where engineers see investigated insight, ask for live values in plain language, and act through governed automation.

- **Industry**: Tier-1 telecom operator, carrier scale
- **Scope**: Network fault and performance, infrastructure and services
- **Estate**: 27,000+ routers, switches and devices
- **Ingestion**: Traps and metrics, event streams, polled collection

Last updated: 23 September 2026

- **27,000+** Devices under one pane, where no central real-time view existed
- **3** Ingestion paths unified into one correlated pipeline
- **43%** Reduction in mean time to resolve, measured in production
- **85%** Of routine manual operations automated under governance
- **~60%** Of incidents auto-created with probable cause attached
- **Seconds** From device event to investigated insight

## The challenge

The operator's estate, more than twenty-seven thousand devices, generated a continuous flood of fault and performance data over three different collection mechanisms. Nothing monitored those devices centrally in real time, so **what was happening inside the network was fragmented across collection silos** and the operations centre saw it late, partially, or not at all.

- **No central real-time view**: Twenty-seven thousand devices, and no single place to see their fault and performance state as it happened.
- **Three ingestion worlds**: Traps and metrics, event streams and polled collection each lived in its own pipeline, with no correlation across them.
- **Volume beyond human triage**: Carrier-scale telemetry arrived faster than any team could read it, and meaningful signals drowned in the stream.
- **Investigation as archaeology**: Root-causing a fault meant hopping across element managers and counters, device by device.
- **Dashboards without answers**: Views, where they existed, showed symptoms; a live value still meant logging into the device.
- **Network, infrastructure and services apart**: Device faults, server health and service state were monitored in separate tools with separate truths.

## What x101 does

x101 was deployed as the central intelligence layer for the operations centre: ingesting all three collection paths at carrier scale, processing fault and performance streams in near real time, and **putting investigation on the platform rather than the engineer**. Probable causes are suggested with evidence, approved procedures run automatically, incidents are raised with context, and engineers ask for live values in plain language instead of hunting through consoles.

1. **Three paths, one pipeline**. Traps and metrics, event streams and polled collection from 27,000+ devices land in one pipeline, normalised and correlated on arrival.
2. **Processed as it arrives**. Events and performance indicators are handled as they land, so degradation surfaces within moments rather than at the next reporting cycle.
3. **Investigated, not just displayed**. Each fault is investigated against topology, history and correlated signals, and the probable cause is presented with its evidence. The engineer decides.
4. **Live values, asked for in words**. Engineers ask for current values and states in plain language and get answers drawn from the live estate, alongside the central dashboards.
5. **Known patterns, handled**. Approved procedures run automatically on recognised patterns, incidents carry their context, and every action is reversible and audited.
6. **Network, infrastructure, services as one**. The same layer watches the infrastructure and the services riding on the network, so cause and impact are visible in one place.

> **Governed autonomy, deliberately bounded.** x101 suggests root causes rather than acting on its own judgment, automation is limited to predefined and approved procedures, and every incident, ticket and action carries a full audit trail and a rollback path. Autonomy is earned scope by scope, never assumed.

## The impact

The operations centre moved from fragmented, delayed device monitoring to one near-real-time picture of twenty-seven thousand devices, with investigation, live values and action available in the same place the fault appears.

- **43%** Lower MTTR: Measured in production within six months of go-live.
- **85%** Routine ops automated: Running as governed procedures rather than manual work.
- **27,000+** Devices in one view: Three ingestion paths correlated into a single pane.
- **~60%** Incidents auto-created: Raised automatically with probable cause and context attached.

Carrier-scale network operations before and after x101

| Dimension | Before | After · with x101 |
| --- | --- | --- |
| Device visibility | No central real-time view across the device estate | 27,000+ devices in one near-real-time fault and performance view |
| Data ingestion | Three collection paths in separate, uncorrelated silos | One pipeline, normalised and correlated on arrival |
| Investigation | Manual, device-by-device root-causing across consoles | Probable cause suggested with evidence attached, engineer in command |
| Access to truth | Log into the device to read an actual value | Live values and states answered in plain language |
| Response | Ad-hoc manual procedures, untracked | 85% of routine operations automated as governed procedures, 43% MTTR reduction. Both measured in production within six months of go-live |
| Coverage | Network, infrastructure and services in separate tools | One governed layer across devices, infrastructure and services |

> At twenty-seven thousand devices the question is never whether the data exists, it always does. The question is whether anyone can see it in time, trust what it means, and act on it safely.
>
> Solution summary · x101 carrier-scale network observability deployment

## The parts of x101 this uses

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

- **Unify**: Three collection paths, plus infrastructure and service state, read as one correlated picture. [Explore](https://www.x101.ai/#unify)
- **Agentic Orchestration**: Topology, history and signals investigated in parallel, converging on one probable cause. [Explore](https://www.x101.ai/#orchestrate)
- **Instant Analytics**: Live values and performance questions answered from the estate as it is right now. [Explore](https://www.x101.ai/#analytics)
- **From Answer to Action**: Approved procedures execute and incidents are raised with context, inside the same flow. [Explore](https://www.x101.ai/#act)
- **AI Explainability**: Every suggested root cause arrives with the evidence behind it. [Explore](https://www.x101.ai/#explainability)
- **Governed by Design**: Bounded autonomy, audited actions and a rollback path on everything. [Explore](https://www.x101.ai/#governance)

**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. The MTTR reduction and routine-operations automation figures are measured production results from this deployment; the remaining figures are indicative, and actual results vary with estate scope, telemetry profile and rollout phase.

## Keep reading

- [The loop that never skips a step](https://www.x101.ai/case-studies/self-healing-network): Discovery to quantitative closure, with full provenance on every automated fix.
- [Fault management at national scale](https://www.x101.ai/case-studies/ran-fault-management): Alarm floods compressed into incidents with evidenced root cause, fully on-premises.
- [All case studies](https://www.x101.ai/case-studies): Eleven deployments across telecom networks, enterprise IT and workforce functions.

## Bring your device estate into one governed operations centre.

Carrier-scale ingestion, near-real-time processing, investigation with evidence, and governed automation, deployed inside your environment.

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