Customer Success Stories
Discover how customers across industries increase agility, optimize costs, and accelerate innovation using AWS
Creating lifesaving medicines faster
Genentech developed a generative AI system called gRED Research Agent, built using Amazon Bedrock Agents, to automate the time-consuming process of analyzing massive amounts of scientific data for drug discovery and biomarker validation. The solution is expected to save nearly 5 years of manual effort in biomarker validation across therapeutic areas, enabling scientists to focus on high-impact research and ultimately bring new medicines to patients faster.
Cloud for Social Impact
Scaling equitable maternal and newborn health using AWS
Access to timely, reliable, and culturally responsive health information is crucial for expectant mothers—but not always attainable. Jacaranda Health (Jacaranda) is working to expand access to critical health information to expectant parents in Africa who might not have access to the internet or medical facilities. The organization developed and implemented a digital health platform, PROMPTS, for mothers and babies using Amazon Web Services (AWS). PROMPTS uses two-way SMS exchange to empower mothers to seek and receive care at the right time and place throughout their pregnancy journey and for the first year after giving birth.
Improving Air Quality and Aiding Communities with ML
Across the Indo-Gangetic Plain (IGP), millions face shortened lifespans from breathing some of the world’s most polluted air. For decades, the lack of precise data on pollution sources has hindered efforts to address this crisis, and communities in the IGP have been fighting an invisible enemy.
Air Pollution Asset-Level Detection (APAD), a research project started with an innovation award from the Smith School of Enterprise and the Environment, University of Oxford, is changing this reality. APAD built custom machine learning (ML) models to analyze satellite imagery and identify pollution sources, using Amazon Web Services (AWS) infrastructure to store and process this massive dataset. By creating a comprehensive map of pollution sources, the organization is giving communities the evidence they need to make targeted interventions.
Toyota fuels innovation with data and AI
TMNA aims to transform its fragmented data landscape by creating unified, connected lakehouses across business units. The strategic vision focuses on breaking down data silos, enabling comprehensive data sharing, and implementing generative BI tooling to support self-service analytics and AI/ML use cases. By leveraging SageMaker Unified Studio, TMNA seeks to embed data-driven insights across manufacturing, sales, supply chain, and customer experience domains. The ultimate goal is to create a dynamic, governed data ecosystem that empowers decision-making and drives organizational innovation.
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