Dr. Qichao Hu

And then second, the filtering stage, that in the traditional process, you have senior scientists, principal scientists coming out with this idea.

And then those candidates are sent to the junior scientists basically in the lab to make these things.

And then a junior scientist, for example, can try different, maybe 10 to 20 different formulations a day.

by hand.

So now as part of AI, you have this dry lab, white lab.

So the idea of creation, think of that as dry lab, basically computer ideas.

And then this idea of filtering, this is a white lab.

So you go to the lab and then instead of human scientists, you have what's called a lab, autonomous lab.

It's basically a high throughput robot that will do 5,000 formulations in one morning.

Yeah.

Yeah.

Yeah.

And then it's like perfect accuracy, no error.

So that can reduce the filtering from, again, several weeks, another several weeks, even months to just days.

And then the last one, probably the biggest bang for the buck is the validation.

Validation takes a long time.

It takes several years traditionally.

So once you have enough data and you can train these machine learning models, you only need to capture just the first probably two weeks of testing.

And then you will know, you will know it's end of life.

So, so each basically take each phase and then you can shrink what was originally years now to weeks, weeks, if not days.