Key Takeaways
- Syngenta has selected TetraScience’s Tetra OS platform to automate data transformation across its Crop Protection R&D organization, replacing manual data exchange and transcription workflows that have historically slowed scientific decision-making.
- The Tetra Scientific Data Foundry will centralize analytical data from chromatography and mass spectrometry instruments, converting raw outputs into a standardized, AI-ready format and linking siloed laboratory data into a single searchable repository.
- TetraScience will forward-deploy its Sciborgs team — scientist-engineers specializing in the intersection of science, data, and AI — to guide Syngenta through implementation, adoption, and ongoing improvement across multiple sites.
- The rollout includes platform hosting, maintenance support, and TetraU training for Syngenta scientists and IT staff, with a goal of building a reusable data and AI foundation for future R&D and quality use cases.
- Syngenta’s Digital Automation Lead, Claudio Battilocchio, cited standardizing and harmonizing data at scale as fundamental to accelerating the speed and quality of scientific discovery within the company’s R&D operations.
Syngenta Selects Tetra OS for R&D Data Automation
TetraScience, the Scientific Data and AI Company, announced on April 22, 2026, that Syngenta has selected its Tetra OS platform to drive digital automation and data transformation within the company’s Crop Protection R&D organization. The deployment is aimed at eliminating manual, ad hoc data exchange and transcription processes that have historically delayed scientific decision-making.
How the Tetra Scientific Data Foundry Will Be Deployed
At the center of the implementation is the Tetra Scientific Data Foundry, which will centralize and harmonize analytical data from a range of instruments, including chromatography and mass spectrometry systems. The Foundry converts raw instrument data into a standardized, AI-ready format and links previously siloed data sources into a single, searchable repository described by TetraScience as a “scientific memory.”
This unified data layer is designed to enable consistent data sharing with downstream tools and applications across Syngenta’s R&D sites.
“Delivering end-to-end data automation across our R&D organization requires a unified foundation — one that eliminates data silos, connects laboratory assets and systems, and transforms raw scientific data into accessible, actionable insight to drive the future of our science,” said Claudio Battilocchio, Digital Automation Lead R&D at Syngenta. “The capabilities provided by TetraScience offer that foundation, enabling us to standardize and harmonize data at scale across our R&D landscape. Such capabilities are fundamental to how we are transforming R&D — accelerating the speed and quality of scientific discovery, addressing productivity for data management, and ultimately strengthening our ability to develop the innovations that help farmers feed a growing world.”
Sciborgs and On-Site Support at Syngenta
TetraScience will deploy its Sciborgs — a team of scientist-engineers operating at the nexus of science, data, and AI — directly to Syngenta sites to oversee implementation, adoption, and continuous improvement. TetraScience describes the Sciborg role as translating technical design into daily practice and embedding best practices across laboratory locations.
“Science has been trapped in an artisanal past — fragmented data, bespoke integrations, and manual workflows that don’t scale,” said Patrick Grady, CEO of TetraScience. “Syngenta understands that the future belongs to organizations willing to industrialize their scientific data infrastructure. By deploying our Data Foundry, Syngenta is improving efficiency and building the foundation for a new era of compounding scientific intelligence.”
Training and Future Use Cases
The implementation package includes platform hosting, maintenance support, and TetraU training for Syngenta scientists and IT teams. TetraScience and Syngenta intend the deployment to create a reusable data and AI foundation capable of supporting additional R&D and quality use cases as the program expands across the organization.
