Scientific Data Infrastructure for Biologists

A suite of devtools built specifically for computational biology work—including experiment tracking, data provenance, visualization, and data sharing. Researchers doing computational work lack good infrastructure and spend time on tooling instead of science.

The convergence of cheap sequencing, explosion in multiomics datasets, and AI-driven drug discovery has pushed computational biology into every serious research lab, yet the tooling layer remains embarrassingly fragmented—researchers still cobble together shell scripts, Dropbox folders, and ad-hoc Jupyter notebooks to manage work that costs millions of dollars to produce. Benchling owns the wet-lab notebook space but has little meaningful presence in the computational/data infrastructure layer, leaving a real gap. The $5k–20k/mo revenue band is plausible for a small number of paying institutional or biotech customers, but it requires landing accounts that go through procurement, legal, and IT security review, which compresses early-stage velocity badly. The single most likely failure mode is the classic academic-to-commercial mismatch: researchers want the tool, but they don't hold budget, and the people who do hold budget aren't feeling the pain directly—making the sales cycle long and conversion rates low enough to kill the business before it scales.