What holds biomanufacturing back is finding the right conditions to maximize yield, and the capacity to execute the experiments that find them.
A realistic growth medium has around twenty components; testing them exhaustively would take more than two million experiments. The standard answer, Design of Experiments, assumes simple response models and fixed designs, so it can discard components in screening that later turn out to limit growth, while execution stays manual and analysis takes weeks to complete.
We at LABMaiTE with Smart Experiment close that loop. AI proposes which experiment to run next, robotics executes it around the clock, and computer vision reads every plate. Each pillar also stands alone if only one of the pillars is your biggest bottleneck now.
Our approach surpasses DoE in reaching campaign targets, in peer-reviewed wet-lab experiments, and in different benchmarks, internal or executed by customers.
We show how self-driving labs move bioprocess development from trial and error to targeted search, what the hardware has to do, what the algorithm decides, and where the remaining bottleneck sits in analysis.
If your bottleneck is only one of the above or all, we have a solution for you.
