Ontada leveraged Microsoft’s Azure OpenAI Service Batch API and Azure AI Foundry to build its ON.Genuity platform, which processes 150 million unstructured oncology documents to automatically extract nearly 100 critical data elements across 39 cancer types. They integrated the new platform with their structured iKnowMed system using Azure Databricks for data ingestion and Azure Document Intelligence for text extraction, transforming manual chart review processes into an automated workflow that supports clinical decision-making and life science product development.

Implemented a hybrid cloud architecture by moving one-third of its servers to Azure public cloud and another third to Azure Local for edge capabilities, managed via Azure Arc to seamlessly integrate on-premises and cloud environments for advanced industrial AI use cases.

Bradesco integrated Microsoft's Azure generative AI into their virtual assistant BIA using services like Azure OpenAI and Data Lake, enabling BIA to quickly generate answers and documents, improving efficiency and response capacity for managers and clients.

Molslinjen built an AI-powered dynamic pricing engine and demand forecasting system using Azure Databricks, integrating a custom capacity management module for near real-time data processing. They implemented the solution by combining Azure Service Fabric for centralized data storage, Power BI for real-time analytics accessible to operational staff, and Azure Static Web Apps for a web interface, thereby streamlining pricing, loading, and marketing workflows.

Syndigo, a content management and syndication company, uses Azure AI Services and Azure OpenAI Service to enhance its data insights and optimize customer experiences. By leveraging best-of-breed language models, they build and deploy large-scale data analytics projects, providing actionable insights to clients.

Parexel implemented Microsoft Azure solutions by leveraging AI-powered capabilities within Azure Databricks and Power BI to consolidate and analyze data from over 100 disparate sources into a centralized data lake, eliminating labor-intensive manual reporting. The solution automated data aggregation and standardized data governance, optimizing clinical trial management and accelerating the delivery of actionable insights.

Network Rail implemented an AI-driven analytics platform called "insight" leveraging Azure Databricks and Azure Machine Learning to process vast amounts of data from its extensive railway infrastructure. The solution integrated diverse data streams—including track geometry, ultrasonic testing, and weather data—using Azure Data Factory and delivered visual insights through Power BI to enable predictive maintenance and efficient asset management. Agile collaboration with Microsoft architects streamlined maintenance scheduling and transformed labor-intensive processes into near real-time, cost-effective decision-making workflows.