Friday, August 22, 2025

Accelerating AI in Life Sciences: TetraScience & Snowflake

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TetraScience, the Scientific Data and AI Cloud, announced it is teaming up with Snowflake to enable joint customers to unlock the value of their scientific data and reap the benefits of the Scientific AI revolution. The collaboration will allow the seamless sharing of scientific data between the Tetra Scientific Data and AI Cloud and Snowflake’s AI Data Cloud so that life sciences organizations can generate previously unattainable insights with diverse and multimodal datasets, dramatically improving their pace and quality of decision-making across the biopharma value chain.

The Tetra Scientific Data and AI Cloud is purpose-built to transform raw, siloed scientific data into analytics-ready and AI-native datasets. By combining TetraScience’s expertise in scientific data and workflows with the Snowflake AI Data Cloud, organizations gain seamless access to scientific data and the agility to develop advanced analytics and AI-driven use cases. Scientific teams can collaborate on data effortlessly across groups and geographical boundaries, leveraging the security and privacy safeguards of Snowflake’s AI Data Cloud.

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“Our collaboration with Snowflake puts unprecedented operational insights and AI-ready scientific data directly in the hands of laboratory and data scientists,” says Patrick Grady, CEO of TetraScience. “Whether developing custom dashboards or running advanced analytics or AI models, researchers can now focus on science and innovation rather than data preparation.”

“The combination of TetraScience’s deep scientific expertise with Snowflake’s AI Data Cloud creates a new opportunity for customers to gain efficiencies and scalability,” says Lisa Arbogast, Industry Principal, Life Sciences at Snowflake.

Several pharmaceutical organizations leveraging Snowflake already realize the benefits of seamlessly accessible, analytics-ready, and AI-native scientific data from TetraScience. This enables diverse use cases and digital initiatives across the biopharma value chain, such as monitoring the health and utilization of scientific instruments to enhance lab efficiencies and automating the ingestion and labeling of assay reports for secure, streamlined collaboration.

Source: Businesswire

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