Azure AI and Cloud Modernization Accelerates Industrial Data Workflows for Hexagon
Value Results Summary
Hexagon’s Asset Lifecycle Intelligence division serves industrial manufacturing customers who manage vast amounts of complex engineering data and technical drawings. Historically, extracting actionable insights from these disconnected systems was a manual, time-intensive process that delayed project delivery and hindered business decisions. To overcome the limitations of its legacy on-premises architecture, Hexagon collaborated with Microsoft Azure to develop SDx2, a cloud-native SaaS platform designed to process and visualize industrial data with high speed and precision.
The company rebuilt its platform using a suite of advanced services, including Azure Kubernetes Service (AKS) for dynamic scaling and Azure SQL Database Hyperscale to manage datasets reaching tens of terabytes. Hexagon integrated Azure AI Foundry, Azure AI Document Intelligence, and Azure OpenAI to automate tag extraction and document contextualization. This transition to a microservices architecture allowed for zero-downtime deployments, while Azure Machine Learning enabled the continuous refinement of proprietary AI models to improve data quality.
For sales and pre-sales teams, this implementation demonstrates how migrating to a managed AI ecosystem reduces technical debt and accelerates time-to-market for complex SaaS solutions. Post-sales and customer success teams can highlight the drastic improvements in data quality and the ease of maintaining digital twins through automated contextualization. Competitive analysts will find significant value in Hexagon's ability to achieve horizontal scaling at the microservice level, providing a blueprint for modernizing legacy industrial software into high-performance, secure cloud platforms.
