The first computational
debugging layer for
biological workflows.

Loci turns failed biological experiments into actionable intelligence — starting with CRISPR, where failures are frequent and costly.

fig. 01 experimental failure as signal, not noise
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§ 01

Scientists run experiments that take months and cost thousands. When they fail, no one knows why. We change that.

With dozens of interacting variables shaping biological outcomes, no system or person can reliably pinpoint the causes of failure.

Loci 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.

§ 02

Three reasons we win where others can't.

01

A new category of data.

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.

02

A new mode of collaboration.

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. Loci is the channel that connects them.

03

We model how, not just what.

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.

§ 03

In just over a month since inception.

1st
Place — Wharton Undergraduate Healthcare Pitch Competition
50+
Postdocs, PIs & graduate students surveyed across academia and biotech
7,000
Papers powering our MVP via a custom annotation algorithm
25,000
In funding from winning America's Startup Pitch Competition
§ 04

See it work.

Our MVP is built on a custom annotation algorithm trained over 7,000 papers. Try it in your browser.

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