Global Energy Company
AssetOS: streamlining asset management with AI and a scalable architecture
Summary
A centralized asset management platform designed to organize, track, and optimize the use of critical assets across a global energy company, delivering significant annual workflow efficiency gains.
Leveraging advanced AI and cloud-based infrastructure, AssetOS simplifies workflows by providing teams with real-time insights, automation tools, and seamless integration capabilities. Built to handle large-scale environments, it ensures consistent asset quality and facilitates better decision-making by enabling teams to efficiently manage data, tools, and resources from a single interface.
Services
Industry
The Challenge
Maximizing the operational efficiency of assets across a global enterprise.
The organization faced fragmented asset data scattered across multiple systems, manual reporting processes consuming significant time, and a lack of real-time visibility into asset lifecycle performance. Teams were navigating between disparate tools, leading to inefficiencies in decision-making and operational workflows. They needed a unified platform that could consolidate asset information, integrate AI-driven intelligence, and scale across diverse regulatory, cultural, and technological requirements.
Problem 01
Fragmented systems and manual workflows
- ▪Asset data siloed across multiple tools and platforms
- ▪Manual reporting processes consuming significant time
- ▪No real-time visibility into asset lifecycle performance
- ▪Teams navigating between disparate systems for basic tasks
Problem 02
Scalability and integration demands
- ▪A solution needed to serve diverse global requirements
- ▪Regulatory, cultural, and technological complexity across regions
- ▪No unified platform to consolidate asset information
- ▪AI-driven intelligence required to accelerate decision-making
The Approach
We built AssetOS as a centralized digital hub, combining user-centered design with a robust, scalable architecture. The platform was structured around five core pillars to address every layer of the asset management value chain.
01
Data foundation and AI intelligence
A centralized Data Lake and robust datastores consolidate diverse data from all sources. AI copilots and intelligent search process vast datasets to uncover patterns and automate repetitive tasks.
02
Experience layer and integration
A user-centric frontend integrated with analytics and AI delivers seamless asset management experiences. A central API ensures secure, high-performance communication across all systems.
03
Infrastructure and scalability
Cloud-based infrastructure with clean architecture principles and 90% testing coverage. An API-first strategy enables flexibility, scalability, and seamless integration of advanced AI and ML capabilities.
Impact
A platform that transforms how a global organization manages its most critical assets, driving measurable efficiency and operational excellence.
Workflow Optimization
Significant workflow efficiency gains through centralized asset management, delivering measurable cost savings across the organization.
Lifecycle Management
Improvement in end-to-end asset lifecycle management and reporting, replacing fragmented manual processes with a single intelligent platform.
Accelerated Delivery
Accelerated software platform iteration cycles, with reduction in time spent on manual reports and faster deployment of new capabilities.
“AssetOS has transformed how our teams interact with critical data. What used to require navigating multiple systems and manual reports is now available through a single, intelligent interface. The AI-driven insights have accelerated our decision-making and given us the visibility we needed across our global operations.”
Global Energy Company
Methods
User-centered research paired with a modern, scalable technology stack to deliver an intelligent asset management platform.
User-centered design
Shadowing
Journey maps
Stakeholder interviews
C# / .NET
React
Python
Azure
Databricks
API-first architecture
Clean architecture
90% test coverage
AI copilots
AI search
Data Lake
Cloud infrastructure