Hexagon brings AI-powered speed and scale to industrial data workflows with Azure

Hexagon’s industrial manufacturing customers struggled to extract actionable insights from complex engineering data and images, hindering business decision-making and delaying project delivery.

To solve this industrial data issue, Hexagon rebuilt its SDx platform on Microsoft Azure, using Azure AI services, Azure Kubernetes Service (AKS), and Azure SQL Database Hyperscale to automate and scale data workflows.

With SDx2, Hexagon customers are driving faster outcomes at scale, realizing more than 90% reductions in facility onboarding, and saving millions in productivity improvements and data processing.

For industrial manufacturers, the inability to extract insights from increasingly complex engineering documents and disconnected systems can slow decisions and delay projects. Hexagon, a global technology company specializing in industrial data and automation, faced the same challenge. The company’s Asset Lifecycle Intelligence division recognized that its existing HxGN SDx solution needed modernizing beyond its on-premises architecture. Customers use the solution to organize engineering documents, drawings, and related metadata for asset lifecycle information management.

Hexagon set out to evolve SDx into SDx2 as a software as a service (SaaS) multitenant cloud solution capable of processing, analyzing, and visualizing industrial data with far greater speed, scalability, and precision. Hexagon had the vision—it just needed the right technology platform and architecture to deliver it.

Building SDx2 with Azure AI Foundry, AKS, and Azure SQL Database Hyperscale

“We chose to build the SDx2 platform with Azure AI Foundry for its maturity, comprehensive support for the entire AI development lifecycle, and seamless integration capabilities,” explains Martin Bergmann, Lead Strategist for AI and Enabling Technologies at Hexagon. “The ready-made services in Azure helped us reduce our development efforts and technical debt, allowing us to get to market faster.”    

To modernize SDx and meet rising customer expectations, Hexagon rebuilt its platform on Microsoft Azure, bringing together AI, microservices, and high-performance data infrastructure into one cloud-native solution. Complex engineering documents that once required hours of manual processing are now contextualized automatically. The process on Azure AI Foundry relies on a portfolio of proprietary AI algorithms, Azure OpenAI in Foundry Models, and Azure AI Document Intelligence to extract accurate, structured data. The deployed machine learning models can be continuously refined through Azure Machine Learning to deliver improved results over time. Behind the scenes, Azure Kubernetes Service (AKS) helps ensure microservices scale on demand, while Azure SQL Database Hyperscale handles growing volumes of engineering data without bottlenecks.

To support large-scale customers, Hexagon uses Azure SQL Database Hyperscale tier to store tenants’ engineering data in elastic pools. This enables automatic scaling without manual oversight. “Our customers’ datasets can reach tens of terabytes,” says Jenkins. “Elastic pools expand storage automatically, eliminating the need to constantly monitor database size.”

“The ready-made services in Azure helped us reduce our development efforts and technical debt, allowing us to get to market faster.”

Martin Bergmann, Lead Strategist for AI and Enabling Technologies, Hexagon

Hexagon is getting solutions to market faster, thanks to fully automated, zero-downtime deployments fueled by AKS. It’s also realizing cost efficiencies by using the elastic-by-design architecture in Azure for horizontal scalability, which ultimately benefits its customers. The SDx2 platform’s multitenant, cloud-native SaaS design delivers optimal performance while reducing infrastructure overhead. Consequently, Hexagon can streamline automation, real-time analytics, and intelligent data processing at scale. 

“Now we can implement microservices, ensuring zero-downtime deployment while we dynamically scale specific business applications based on demand. That’s a big advantage over earlier solutions,“ says Dmitry Borodin, Technical Manager of AI Solutions at Hexagon.

Simplifying data ingestion and interaction

For Hexagon’s development teams, the Azure AI ecosystem—services like Azure Machine Learning and Azure OpenAI—proved instrumental in building new solutions. These significantly reduced the time and effort for Hexagon customers to complete tasks such as tag extraction from piping and instrumentation diagrams. The enhancements have led to better data quality and easier creation and maintenance of digital twins. 

“With our new contextualization services built on Azure Machine Learning and decades of domain knowledge, what used to take users anywhere from two to three days—to configure and extract tags from documents—now takes under an hour with very little configuration,” says Borodin. "And, we’ve improved the data quality and completeness.”

“AKS is instrumental in scalability, deployments, and handling peak demands for AI-first services, such as automated contextualization and metadata extraction from complex engineering documents and drawings.”

Dmitry Borodin, Technical Manager of AI Solutions, Hexagon

Combined with Azure OpenAI, this streamlined data ingestion makes deeper contextualization possible, allowing customers to build a wide range of value-added AI solutions.

Enabling microservices, dynamic scaling, and faster deployment cycles with AKS

The containerized applications in AKS made it possible for Hexagon to achieve seamless scaling and automatic workload distribution in its SDx2 solution. Now, Hexagon developers can implement microservices architecture and roll out SDx2 updates without disrupting critical business operations. “AKS is instrumental in scalability, deployments, and handling peak demands for AI-first services, such as automated contextualization and metadata extraction from complex engineering documents and drawings,” says Borodin.   

“Dynamic, traffic-dependent scaling was impossible before. Now we achieve horizontal scaling exactly where the bottleneck happens, allowing us to design new business functionalities around that scaling model.”

Dmitry Borodin, Technical Manager of AI Solutions, Hexagon

“Previously, with our on-premises product, even deploying urgent fixes required creating a new build and making it available for download and distribution, which could take two to three days,” says Ross Jenkins, Technical Director for the platform at Hexagon. “Now that we have individual service build and release pipelines in AKS, we can deploy fixes very quickly—typically in under an hour.”

Borodin notes, “Dynamic, traffic-dependent scaling was impossible before. Now we achieve horizontal scaling exactly where the bottleneck happens, allowing us to design new business functionalities around that scaling model.”

Creating data security and elasticity through Azure AI Foundry synergy

Using Azure AI Foundry products, including Azure OpenAI, Azure AI Document Intelligence, and Azure Machine Learning, Hexagon embedded intelligence across SDx2’s workflows while maintaining a low-friction developer experience. Microsoft’s client libraries and built-in identity tools helped the team incorporate these services into a unified architecture without worrying about security.  

“Every connection is secured from the outside world through the use of private endpoints, helping ensure our customers’ datasets stay protected.”

Ross Jenkins, Technical Director, Hexagon

“We use managed identity to restrict database access, enforce transparent data encryption for data at rest, and apply role-based access control across our clusters,” explains Jenkins. “Every connection is secured from the outside world through the use of private endpoints, helping ensure our customers’ datasets stay protected.” 

Innovating the industry through AI enhancements that deliver business value  

Bringing SDx2 to life required more than a technical vision—it took hands-on collaboration across design, development and deployment. Microsoft worked closely with Hexagon during early feasibility testing, model experimentation, and architectural refinement to help ensure Azure services aligned tightly with the platform’s modular, AI-first design. That close engineering partnership helped accelerate delivery while keeping development scalable, highly secure, and performant.

One major Hexagon customer anticipates $1.6M in productivity improvements and up to $3.9 million savings in data acquisition, quality management, and contextualization. Another customer reduced facility onboarding time by more than 90% using SDx2’s AI-powered automation.

Within a legacy-heavy industry, Hexagon is helping redefine how modern industrial facilities are built and run. With Microsoft AI built into SDx2, Hexagon gives customers a more intuitive, automated, and adaptive way to manage industrial data, from natural language queries to intelligent document processing to continuous model refinement.  

“With Microsoft Azure as its foundation, SDx2 is helping our customers process, analyze, and visualize their industrial data with speed and completeness, driving real-time business intelligence, and that’s transformational,” concludes Bergmann.

Discover more about Hexagon on LinkedIn and YouTube. 