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Covariate Selection Using Machine Learning for Clinical Models
HMS developed an interactive application for a global pharmaceutical company to support ML-based covariate selection. It provides a structured approach to selecting covariates and helps assess their relevance for pharmacometric models.
Cloud-Native Statistical Computing Environment for Clinical Trials
HMS replaced a 25-year-old SAS system at Boehringer Ingelheim with a cloud-native Statistical Computing Environment on AWS. The platform integrates SAS Viya and supports automated validation in a GxP-regulated environment.
AI in Clinical Trials: Machine Learning for the Analysis of Clinical Data
Machine learning can complement traditional statistical analysis of clinical data. HMS supported Novartis in exploring its potential to predict tumor control and safety outcomes and to identify relevant baseline factors.
Automated Content Tagging for CRM Free-Text Data
HMS developed an automated content-tagging solution for unstructured CRM free-text data. Using large language models, the solution assigns content to predefined categories and structures it for search, grouping, and downstream analysis.
Text-to-SQL: Retrieve Company Data via Chat
HMS developed a Text-to-SQL solution for an international financial institution that translates quantitative business questions into database queries and presents the results in an interactive format.
Sales Analytics Platform for Bayer Vital
Bayer Vital combines customer management with modern technology to respond quickly to changing market conditions. Analytics provide insights that help improve sales and marketing activities across distribution channels.
A Lab in Your Pocket: Data-Driven Decisions
HMS developed a modular software solution for trinamiX consisting of a mobile app, a cloud backend, and a web-based customer portal. The solution enables real-time transfer and analysis of sensor data from an infrared detector.
Planning and Analysis of Clinical Trials in a Validated R Environment at medac
To analyze clinical trial data, HMS designed and implemented a validated R environment within a GxP framework. Controlled package versions and versioned code support traceable analyses on Microsoft Windows.
Global BI at BioHorizons Camlog
BioHorizons Camlog worked with HMS to develop a robust data warehouse and Power BI model, providing a reliable foundation for efficient reporting.
Role-Based Document Chatbot with Agentic RAG
HMS developed a chatbot for a global pharmaceutical company that searches internal documents based on user roles and makes relevant information accessible through an AI-powered interface.
Maintaining and Modernizing the SAS Environment for Regulatory Reporting
HMS supports a leading IT service provider in the financial sector in modernising and optimising its SAS environment for regulatory reporting.
Customer Journey Analytics with an Automated CRM Data Pipeline
HMS developed an automated CRM data pipeline for Customer Journey Analytics. The solution integrates data from multiple sources and provides quality-assured data on a daily basis for CRM analytics and customer journey insights.
SAS Viya on Azure: Migration and Long-Term Platform Operations
HMS migrated the SAS 9.4 environment of a major European energy and utilities company to SAS Viya on Microsoft Azure. Since the migration, HMS has been responsible for the operation, maintenance, and further development of the platform.
Identify Pharmaceutical Trends Earlier with AI-Powered News Analysis
HMS developed a platform for an international pharmaceutical company that analyses news, clusters topics, and highlights emerging developments. This gives business teams a stronger basis for market monitoring and regular updates.
Automated Market and Competitive Analysis with Agentic AI
HMS developed an Agentic AI solution for a global pharmaceutical company that automatically collects and reviews market and competitive intelligence and makes it available for reports and ad hoc queries.
Application Modernization with GenAI: From SAS to Python on AWS
A leading insurance group modernised a core SAS application on AWS. HMS designed the target architecture and migration approach, implemented the modernisation, and evaluated the use of GenAI to support the migration from SAS to Python.
Enterprise Search with AI for Complex Research Queries
HMS developed a RAG-based chatbot for a global chemical company. The system handles complex research queries in natural language and delivers traceable answers with source citations.
SAS Viya Migration at an Automotive Bank
HMS migrated an existing on-premises SAS platform to SAS Viya on Azure and implemented a scalable cloud infrastructure based on Kubernetes.