Locus turns failed biological experiments into actionable intelligence — starting with CRISPR, where failures are frequent and costly.
With dozens of interacting variables shaping biological outcomes, no system or person can reliably pinpoint the causes of failure.
Locus is a biological debugging engine that transforms experimental results into actionable insight — compressing research timelines, reducing wasted effort, and accelerating biological innovation to improve human life.
Most AI companies compete on datasets that already exist. We compete on data that has been systematically destroyed. Using failure as training data has never been done — and unlocking it is a one-time opportunity.
Every failed experiment becomes a lesson the next lab inherits — better designs, fewer dead ends, real ROI. Researchers don't have to know each other to learn from each other. Locus is the channel that connects them.
Biotech AI optimizes for results — simulating biology for rapid discovery. But nobody models the experiment itself: the incubation, the cell prep, the hundred small human decisions that shape the outcome. That's where failure lives. That's where we look first.
Our MVP is built on a custom annotation algorithm trained over 7,000 papers. Try it in your browser.
Software & Data Infrastructure
UPenn '29 · BSE in Bioengineering
Product & Experimental Design
UPenn '29 · BA in Biology