---
title: "Case Study: Hardware-Depth Infrastructure Observability | x101"
url: https://www.x101.ai/case-studies/infrastructure-observability
description: "How a Tier-1 telecom operator in North America gained its first unified view of a large server estate with x101, down to hardware sensors, catching around 70% of issues before service impact."
---

# A server estate seen down to the sensor, and acted on before it breaks

Telecom · North America

**A Tier-1 telecom operator in North America gained its first unified view of a very large server estate with x101, surfacing around 70% of hardware issues before service impact and resolving infrastructure incidents around 40% faster.** There had been no central view of the fleet, and no view at all of the hardware beneath it. x101 unified server telemetry with hardware sensor data from every management controller, then moved the operation from reacting to alerts to acting ahead of them.

- **Industry**: Tier-1 telecom operator
- **Scope**: Infrastructure and hardware observability
- **Estate**: Thousands of servers, heavy data workloads
- **Depth**: Server telemetry plus hardware sensors

Last updated: 23 September 2026

- **First** One central view of the server estate, where none existed
- **100%** Of hardware management endpoints connected centrally
- **~70%** Of hardware issues surfaced before service impact
- **~40%** Faster mean time to resolve on infrastructure incidents
- **~50%** Reduction in manual health-checking effort
- **~60%** Of routine remediations executed under governance

## The challenge

The operator runs a very large fleet of physical and virtual servers carrying heavy, continuous data workloads. Nothing monitored that infrastructure centrally, and the hardware layer was effectively invisible: **every management controller was an island**, reachable one server at a time.

- **No central observability**: Server health lived in scattered tools and manual checks, with no single picture of the estate.
- **Hardware data locked per server**: Temperature, power and component health sat in thousands of separate management interfaces.
- **Failures announced themselves**: Degrading components were discovered when something broke, not before.
- **No headroom for surprises**: The servers ran heavy data workloads around the clock, so a small thermal or capacity drift turned into a service-affecting incident quickly.
- **Manual, repetitive checking**: Engineers walked server lists by hand to verify health, which was slow, partial and unsustainable at estate scale.
- **An alert-then-react posture**: Everything happened downstream of a failure, nothing ahead of it: there was no forecasting and no preemption.

## What x101 does

Lightweight agents were deployed across the estate and the hardware layer was connected directly through its native protocol and APIs. Every signal, server telemetry, workload metrics, temperature, power and component health, is correlated into one governed layer, and **x101 turns it from a dashboard into decisions**: insights, recommendations and governed actions taken before alerts become outages.

1. **Telemetry from every node**. Agents across thousands of servers stream server, workload and resource telemetry continuously into one place.
2. **The hardware layer, centralised**. Management controller integration pulls temperature, power, fan and component health from every server, centrally, for the first time.
3. **One correlated picture**. Hardware sensors, server metrics and workload behaviour are correlated across the whole estate rather than read tool by tool.
4. **Insight, not just charts**. x101 analyses the correlated stream and surfaces what matters: emerging thermal drift, failing components, capacity risk, each with a recommendation attached.
5. **Action before breakage**. Where a pattern predicts failure, action is taken ahead of it, workloads moved, throttling applied, components flagged, through approved and reversible records.
6. **The baseline keeps improving**. Every outcome feeds back into detection thresholds and procedures, so the baseline keeps improving and the estate gets better at recognising its own failure signatures.

> **Governance across every stage.** Role-based access, approval gates on production-changing actions, a full audit trail, and rollback on every automated remediation. Autonomy is graduated: each class of preemptive action earned its place against live results before running unattended.

## The impact

The estate went from invisible to instrumented, and from reactive to preemptive. Hardware stopped failing by surprise, engineers stopped walking server lists, and the heaviest workloads gained thermal and capacity visibility they had never had.

- **~70%** Caught before impact: Hardware issues surfaced and handled before service was affected.
- **~40%** Faster resolution: Shorter mean time to resolve on infrastructure incidents.
- **~50%** Less manual checking: Health verification stopped being a walk through server lists.
- **100%** Hardware centralised: Every management endpoint reporting into one governed layer.

Infrastructure observability before and after x101

| Dimension | Before | After · with x101 |
| --- | --- | --- |
| Estate visibility | Scattered tools, no central view at this level | One governed layer across the estate, server telemetry through to hardware sensors |
| Hardware layer | Management interfaces disconnected, checked one server at a time | All endpoints centrally connected. Temperature, power and component health in one place |
| Failure posture | React after breakage or a threshold breach | Around 70% of hardware issues surfaced and handled before service impact |
| Resolution speed | Slow, manual cross-checking across silos | Around 40% faster mean time to resolve on infrastructure incidents |
| Operations effort | Manual health walks across thousands of servers | Around 50% less manual checking, with around 60% of routine remediations automated. Under governance, with rollback available |
| Output | Raw dashboards, where they existed at all | Insights and recommendations with governed, reversible actions attached |

> Connecting the hardware layer changed the physics of the operation. When temperature, power and component health are visible across every server centrally, and something is watching the trend lines, failures stop being surprises and start being work orders.
>
> Solution summary · x101 infrastructure 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**: Server, workload and hardware sensor data read as one picture instead of thousands of islands. [Explore](https://www.x101.ai/#unify)
- **From Answer to Action**: A predicted failure becomes a governed, reversible action rather than a notification. [Explore](https://www.x101.ai/#act)
- **Instant Analytics**: Thermal, power and capacity trends answered from live data, charted and explained. [Explore](https://www.x101.ai/#analytics)
- **AI Explainability**: Every recommendation carries the correlated evidence that produced it. [Explore](https://www.x101.ai/#explainability)
- **Governed by Design**: Approval gates on production-changing actions, with rollback and a full audit trail. [Explore](https://www.x101.ai/#governance)
- **No Vendor Lock-in**: Deployed inside the operator's own environment, alongside the estate it watches. [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 North America. Improvement figures reflect the target and expected outcomes of the deployment and are indicative; actual results vary with estate scope, workload profile and rollout phase.

## Keep reading

- [27,000 devices, one governed NOC](https://www.x101.ai/case-studies/carrier-network-observability): Three ingestion paths unified at carrier scale, with a measured 43% MTTR reduction.
- [Day-2 operations at node scale](https://www.x101.ai/case-studies/day2-operations): Health checks, log analysis and mass patching, governed across thousands of nodes.
- [All case studies](https://www.x101.ai/case-studies): Eleven deployments across telecom networks, enterprise IT and workforce functions.

## See your estate down to the sensor.

Agent-based telemetry, hardware-layer integration and governed action in one platform, deployed inside your environment.

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