Protecting Crops Today to Feed Billions Tomorrow
SyngentaDatabricks · Delta Lake · Unity Catalog +3
Syngenta built Gaia, the first crop protection R&D data platform for analytics and innovation, on Databricks to unify siloed data across 70-plus countries. The platform cut data access times from as long as nine months to minutes, reduced time to value by 70%, decreased engineering costs by 50%, and now publishes over 200 data products handling more than 2 million queries per month.
Overview
Syngenta built Gaia, the first crop protection R&D data platform for analytics and innovation, on Databricks to unify siloed data across 70-plus countries. The platform cut data access times from as long as nine months to minutes, reduced time to value by 70%, decreased engineering costs by 50%, and now publishes over 200 data products handling more than 2 million queries per month.
This entry has 14 published fields tied to exact passages in an immutable source capture.
Inspect the highlighted sourceThe challenge
Syngenta's research and development (R&D) team struggled with siloed data across 70-plus countries, legacy systems and months-long delays to access critical insights. Even experienced analysts struggled to figure out what data existed and where it was stored, and scientists could wait as long as nine months before they had the information needed to start a project.
The solution
Syngenta turned to Databricks as the foundation for Gaia, the first crop protection R&D data platform for analytics and innovation, built on Databricks and AWS with a federated Data Mesh approach. Delta Lake brought Syngenta's diverse R&D data into one trusted environment, Unity Catalog acted as Gaia's central governance solution, and Databricks SQL became the core engine driving day-to-day work with pipelines, queries and BI dashboards. Syngenta also began testing AI/BI Genie, Databricks' conversational interface for natural language data questions, and Databricks MLflow and MLOps to unify machine learning efforts.
Reported business value
The time to data access dropped from as long as nine months to just a few minutes, while overall time to value was reduced by 70%. Data engineering costs fell by half. Gaia now handles more than 2 million queries per month, supporting about 320 direct users across R&D, while powering more than 60 downstream systems and applications that reach over 1,200 data citizens across the business.
Sources
Open any source and check the claim yourself — that is the point of the register.
This record was researched and written with AI assistance, and its claims were checked against the sources above. (EU AI Act art. 50 transparency notice.)
Other life sciences entries in the register.
Novo Nordisk accelerates clinical documentation and drug development with Claude
Novo Nordisk, a global pharmaceutical company headquartered in Europe, built NovoScribe, an AI-powered documentation platform using Claude Code, Amazon Bedrock and MongoDB Atlas, to automate clinical study report (CSR) generation. Claude helped cut writing times on CSRs by 90%, reducing time spent producing clinical study documentation from 10+ weeks to 10 minutes, and delivered a 95% reduction in resources needed to create device verification protocols. The platform expanded from clinical trial reports to device protocol documentation and patient materials, and an 11-person development team, including non-technical staff, now prototypes features using Claude Code.
Novo NordiskClaude Code · Amazon Bedrock · MongoDB AtlasNovartis: Accelerating Drug Development with AI-Powered Clinical Trial Transformation
Novartis partnered with AWS Professional Services and Accenture to modernize their drug development infrastructure and integrate AI across clinical trials, with the goal of reducing trial development cycles by at least six months. The initiative built a GXP-compliant data mesh platform on AWS with Databricks for processing, enabling AI use cases including protocol generation and an intelligent decision system (digital twin). Early results from the patient safety domain showed 72% query speed improvements, 60% storage cost reduction, and 160+ hours of manual work eliminated. The protocol generation use case achieved 83-87% acceleration in producing compliant protocols.
NovartisAmazon Web Services (AWS) · Databricks · Amazon S3 +8
Phagos uses generative AI on AWS to match bacteriophages to bacterial infections
Phagos, a Paris-based biotech startup, uses generative AI models built with Amazon SageMaker AI to match bacteriophages to target bacteria for phage therapy, replacing a manual trial-and-error process. The AI models cut wet-lab testing needs by 50% and reduced phage-candidate screening time by 99.5%, from 29 hours to 10 minutes per bacteria. Phagos has treated more than half a million animals in France and can now develop a new treatment in two months versus 10+ years for traditional antibiotic development.
Phagos· FranceAmazon SageMaker AI · Amazon EC2 · Amazon S3 +1Was this helpful?
Your feedback helps us improve our use case database