All Foundries

Commodity Management & Trading

Transforming trading ecosystems through data-driven insights and secure, agile platforms that empower global commerce.

Customer & Marketing Strategy

Reimagining customer engagement with personalized, connected experiences that build trust and long-term value.

Digital Engineering

Turning ideas into scalable digital solutions—accelerating transformation through design, technology, and engineering excellence.

Station Studio

Redefining mobility hubs with digital innovation to create smarter, sustainable experiences for people and businesses on the move.

Supply Chain & Industry 5.0

Building resilient, intelligent supply networks that anticipate change and deliver efficiency with purpose.

Sustainability

Driving responsible innovation to help organizations achieve net-zero goals and create a positive impact for future generations.

Physical AI

Physical AI gives machines, robots and physical operations the ability to perceive their environment, decide what matters and act in real time. Factories, plants, stores and infrastructure become systems able to sense conditions, learn from them and improve on their own, closing the gap between the physical and the digital world.

Next-Gen Infrastructure

From networks as infrastructure to connected platforms that enable critical, autonomous and intelligent operations.

Retail

From satisfaction surveys to EBITDA: closing the loop on customer pain points

A department store retailer in Mexico turned a biannual NPS programme into a closed-loop customer listening mechanism, connecting feedback to pain-point initiatives and incremental EBITDA.

From satisfaction surveys to EBITDA: closing the loop on customer pain points

Retail

Unified returns management

A retailer processing more than two million returns a year moved from channel-specific workflows toward one digital returns system. The initiative connects request creation, status tracking and routing across sales and return channels.

Unified returns management

Contact

Robot for Autonomous Inspection in Data Centers

In a data center, a small anomaly can become an operational issue before the next manual round reaches it. This initiative equips a multinational media company with an autonomous inspection approach designed for continuous, repeatable observation across dense server environments. A mobile robot combines visual, thermal and environmental sensing with autonomous patrols, real-time analysis, remote teleoperation and API-based integration, creating a more scalable way to monitor critical assets without removing human oversight.

Autonomous quadruped inspection robot patrolling between server racks in a high-density data center.

Autonomous patrols

Predefined inspection routes

Multimodal sensing

Visual, thermal and environmental signals

Real-time detection

Anomalies analysed during inspection

Remote intervention

Teleoperation available when required

Data center technician inspecting a server rack beside an autonomous robot during a coordinated operational check.

Robot for autonomous inspection in Data Centers

A robot-enabled inspection model that brings consistent sensing, earlier anomaly visibility and remote operational control into the physical heart of the data center.

Data center reliability cannot depend on what a manual round happens to catch

Objectives

The initiative aimed to establish a more efficient, precise and scalable inspection model for a complex data center environment. The robot needed to navigate narrow aisles, uneven flooring and potential obstacles while collecting the signals required to identify overheating, unusual vibration, damaged equipment, abnormal server LEDs, unusual noise and hotspots. The operating model also needed to keep specialists in control, enabling remote intervention for detailed inspection when an autonomous patrol identified a condition that required closer attention.

Opportunity

Beyond automating a patrol route, the opportunity is to create a reusable inspection layer that can connect physical assets, operational teams and existing platforms. API integration can place robot-generated observations into established workflows, while a digital twin can provide a training environment for navigation and inspection scenarios. The source also indicates that the architecture is intended to accommodate new functionality over time. The deployment status and approved roadmap for these capabilities should be confirmed before publication.

Making continuous inspection work in a dense, critical environment

The challenge was not simply to put a robot in a server room. The initiative needed to combine safe physical movement, multiple inspection signals, timely operational response and integration with existing ways of working.

Key Challenges

Autonomous inspection robot navigating a narrow data center aisle with dense server racks and a raised floor threshold.

1: Navigate constrained infrastructure
The solution needed to move autonomously through narrow aisles, high-density rack layouts, uneven flooring and potential obstacles without disrupting the operating environment.

Robot-mounted camera and sensor array examining server equipment for visual, thermal and environmental anomalies.

2: Interpret diverse anomaly signals
A single visual check was not enough. The inspection model needed to recognise asset categories and combine RGB, LiDAR, thermal, acoustic and environmental observations to identify different types of abnormal condition.

Technician responding to an amber equipment alert at a server rack while an inspection robot remains nearby.

3: Turn detection into timely action
The initiative required inspection data to be collected and analysed in real time, with alerts reaching the control room quickly enough to support an appropriate response.

Technicians connecting an autonomous inspection robot to a service and data docking station inside a data center.

4: Fit existing operations and evolve
Autonomy could not become an isolated workflow. The solution needed human override, connection to existing platforms and an architecture able to support additional functionality over time.

Solution

An autonomous inspection layer with human control built in

NTT DATA developed a robot-enabled approach that links autonomous movement with multimodal sensing, real-time anomaly detection and operational integration. Each capability addresses a distinct requirement of the inspection process while preserving a route for specialist intervention.

Autonomous route execution

The robot patrols predefined inspection routes and is designed to navigate the data center environment, including tight aisles, uneven surfaces and other potential obstacles.

Multimodal asset inspection

RGB cameras, LiDAR, thermal imaging and environmental sensors support asset recognition and the detection of conditions such as overheating, unusual vibration, equipment damage, abnormal LEDs, unusual noise and hotspots.

Real-time analysis and alerting

Inspection data is collected and analysed as the robot moves. When the system identifies an anomaly, it can send an alert to the control room to support a faster operational assessment.

Teleoperation and platform connectivity

Operators can take direct control when a closer inspection is needed. APIs support integration with existing platforms, while the digital twin provides a training environment and the architecture allows for future functional extensions.

Impact

A stronger foundation for continuous data center operations

The autonomous inspection approach supports less dependence on manual rounds and more consistent observation of critical assets. By combining repeatable patrols, multiple sensor types, real-time anomaly detection, alerts and remote intervention, it can help teams standardise inspection, strengthen operational visibility and respond earlier to emerging conditions. These are supported value signals from the source, not measured outcomes. Any claim relating to labour reduction, downtime, efficiency, safety, reliability or continuity requires quantified client evidence before publication.

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