TetraScience

Biopharma R&D runs on data trapped in silos. Every instrument, ELN, and LIMS writes results in its own format, so most of what a scientist learns in one experiment never reaches the next. That's a big reason drug development costs keep climbing even as computing power gets cheaper. TetraScience builds Tetra OS, the operating system for scientific intelligence. It sits above and alongside a company's existing instruments, ELNs, and LIMS, works across any vendor, and turns raw scientific data into structured, AI-ready intelligence. Four components make it work. The Scientific Data Foundry ingests data from any source and converts it into governed, machine-readable data at scale. The Scientific Use Case Factory turns one-off workflows into reusable, validated ones deployed across sites. Tetra AI reasons and orchestrates on top of that foundation, grounded in real data instead of guessing. Sciborg teams, our embedded scientist-engineers, work inside customer organizations to get all of it adopted, not just installed. These parts compound. Better data makes workflows faster to build. Workflows generate better data. The result is intelligence that gets more valuable with use instead of resetting on every new project. Customers include Takeda, Bayer, Regeneron, Roche and and AstraZeneca, alongside partners like NVIDIA, Databricks, and Thermo Fisher Scientific. One top-20 pharma customer connected over 1,000 instruments, processed a petabyte of data in a year, and cut 20,000 hours of administrative work. Founded by Patrick Grady and Spin Wang, headquartered in Boston with a European base in Basel. Mission: radically improve and extend human life.