Jared Tangney

And so that was the first question.

And that's the first thing we had to show is that this works conceptually.

So the first years were really around building prototypes to...

be able to prove that it does correlate.

And even Rich was very skeptical at first.

He's the first to admit that at first he's like, hey, if you can't show me this works in people, this is not worth my time.

But we were able to show that and our first in human studies were very successful.

And then from there, really the next leap goes to, okay, well, we've shown that we can track glucose in the skin.

Now, how do we get to something that is factory calibrated, that doesn't need to be calibrated by the user?

And that is really the biggest leap, and that's probably been the biggest leap for people with diabetes, is no longer needing to calibrate these systems.

But in order to go from something that can be calibrated, when you calibrate a sensor, you can remove that user-to-user difference, you can remove that sensor-to-sensor difference,

Without that calibration, you're relying only on information you have during manufacturing.

So that was really the next big step is how do we create something that is predictable, right?

That I know how it's going to perform in you and I know how it's going to perform in me without any additional context or information.

So that was really the next big focus was getting to that factory calibration.

Yeah, we came at it from both angles.

We knew that sensing in that space would be or should be a viable compartment for sensing.

From a chemistry perspective, we didn't want to totally reinvent the wheel there.

We have this enzyme called glucose oxidase that really all the CGM systems use, very robust, manufactured and high volume now because of all the systems that are on the market.

So we said, well, can we take that same method of chemistry?