We build the Autopilot for self-optimizing robotic biolabs.
Deploying and optimizing protocols on laboratory robots still demands months of manual debugging.
To address this, we use active learning techniques to shorten optimization timelines from months to weeks.
Our platform connects to equipment, reads sensor data to auto-debug workflows, and runs continuous loops of:
design - experiment - analyze - adapt
Save time on getting new automation protocols to work. Optimize them from low to high performance in a fraction of time.
Access complex automation through natural language. Visualize and validate protocols through simulations before deploying on real hardware.
Automatically determine which experiment to run next for maximal knowledge gain. Anomaly detection automatically flags unusual outcomes so you don't have to sift through mountains of data.
