TetraScience + Databricks: Joining forces to unlock scientific data and accelerate the Scientific AI revolution
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We are catalyzing the Scientific AI revolution by designing and industrializing AI-native scientific data across the entire scientific value chain, and across scientific verticals, beginning with life sciences. We bring this AI-native data to life in a rapidly growing suite of next-generation lab data management solutions, scientific use cases, and AI-based scientific outcomes.
Scientific data, found in such vital industries as life sciences, chemicals, agriculture, materials, and energy, is among the world’s largest and fastest growing datasets and is upstream to the transformational breakthroughs required to solve humanity’s grand challenges.
The inexorable increase in computational power, democratization of cloud computing, and emergence of more powerful neural networks has led to much hope and hype in the realm of scientific discovery.
That said, in order for Scientific AI to become a commercial reality, large-scale, liquid, well-engineered, and compliant scientific datasets will need to be designed, assembled, and managed.
Today, the world’s scientific data is trapped in >10M silos, yielding subscale, proprietary, and/or unstructured data with no practical utility for predictive analytics, let alone AI. Four structural obstacles conspire to propagate these silos and inhibit data liquidity, data engineering, and AI utility.
Our founders recognized that a sui generis approach — both technical and commercial — was required to enable the design and industrialization of scientific AI-native data.
To enable the assembly of large-scale and liquid scientific data sets, and to engineer sophisticated scientific data models comprising taxonomies and ontologies that are actionable by AI in furtherance of real-world scientific use cases, TetraScience has taken one-of-a-kind, full-stack approach.
