About

Laboratory results inform models; models design the next experiments. While running ML and Comp Bio teams, we learned that the biggest obstacle to better AI in R&D was closing the feedback loop with the wetlab. We needed a way to query, trace, and govern heterogeneous data at scale.

We founded Lamin in 2022 to build an open-source, zero-lock-in solution to this problem. We started with a "git for R&D data" that enabled traceability and versioning alongside a biological data catalog. Today, LaminDB is a programmable memory layer for R&D that scales from personal projects to pharma-scale enterprise deployments.

We are grateful to work with thousands of scientists and engineers across academia, biotech, and global pharma. Together, we build a better foundation for data-driven R&D.