DATA CENTERS & HIGH TECH

AI-Powered Asset Digitization for Data Center Operations

When this data center high tech customer commissions a new facility, its operations teams need a complete and structured inventory of every installed asset before maintenance and operations can begin. Building that inventory by hand has historically taken months to years, and still left a meaningful share of assets unrecorded. For one data center, Rive digitized the asset base directly from the engineering documentation in a matter of hours, identifying a broader asset base than the manual survey had captured.

INDUSTRY

Data Centers & High Tech

Operational Context

Asset & Infrastructure Operations

Data Source

Engineering & Facility Documentation

The Challenge

Traditionally, general contractors created the asset inventory by physically walking the data center, locating each asset, recording its details, and entering the information into the customer’s asset inventory and maintenance management systems. This approach created several recurring problems:

The physical survey and data-entry process typically took months to complete.

Manual surveys often missed installed assets.

Assets that were not captured were not included in preventive maintenance programs.

Maintenance teams lacked immediate access to asset specifications, maintenance instructions, spare-parts requirements, and technician qualifications.

When an unrecorded asset failed, troubleshooting and repair took significantly longer.

Missing asset and dependency information raised the risk of extended outages, emergency procurement, and higher repair costs.

As a result, operations teams lacked a complete and reliable view of the installed asset base during a critical stage of data center handover and commissioning.

The Approach

At the end of construction, engineering, procurement, and construction firms hand the customer the complete set of engineering documents and models for the data center. These cover more than ten disciplines - including mechanical, electrical, plumbing, fire protection and life safety, building management systems, and controls and instrumentation - and are delivered as PDFs and proprietary formats such as Autodesk Revit. Individual files can be very large, and a full package may contain thousands of pages, drawings, schedules, and model elements.

These files hold detailed information about every installed asset: equipment specifications, identifiers, locations, and the relationships between connected systems. But because that information is spread across many documents and formats, extracting it by hand is slow and error-prone. Rive built an AI-powered engineering data pipeline that:

Ingests large engineering documents and design models.

Uses specialized AI agents to identify and extract asset information.

Standardizes and validates the extracted asset records.

Maps the relationships between interconnected assets and systems

Produces structured data ready to load into asset inventory, maintenance management, and operational systems.

The output is not only a set of individual asset records but also a knowledge graph representing how equipment and systems connect. Using that graph, Rive added an impact-analysis capability: operations teams can assess the downstream effect of an asset failure and see which systems, equipment, or workloads may be affected. If a cooling asset fails, for example, the system helps identify which downstream cooling zones, electrical systems, or GPU clusters are likely to be impacted.

Results

For one data center, the traditional manual process required a long physical survey and manual data-entry workflow, while Rive processed the same facility directly from its engineering documentation in a matter of hours.

Measure

Manual process

Rive

Asset coverage

Assets can be missed during manual capture

Broader asset coverage from engineering documentation

Processing

Manual extraction and data entry across multiple sources

AI agent-driven extraction and processing

Time elapsed

A substantial amount of time, spanning months to years

Completed in hours

Output

Asset data requiring further consolidation

Structured asset records with mapped relationships

Result

Slow, manual, and difficult to scale

Fast, repeatable, and scalable

Beyond speed and coverage, the output included both structured asset records and the mapped relationships between interconnected systems. Operations teams get a more complete and connected view of the installed asset base at handover.