What came first? Were you running-- Were-- You know, did you get your doctorate first? Were you a pharmacist first? Were you a coder first? What, what started this?

You know, I don't know how far back you, you wanna go, but, um, I remember being in high school and we're talking about college.

This was my parents and what you wanna do.

And, uh, classic immigrant story.

They always want you to be a doctor, and I did not wanna be a doctor.

Actually, um, healthcare in general was not something I'm interested about.

So we ended up basically fighting for six months, and pharmacy was the compromise.

And then how did you go from Gallon AI to CareRx? Was CareRx the acquirer of Gallon?

Jonathan, why is that? When you think of this as a scientist, why is it that implementing AI in a specific sector of the business, how is it making that big of a difference? A million dollar savings for CarePoint, that's amazing.

Yeah.

I mean, look, so the LLM layer, that's where we do the AI layer.

It's really, the best way to think of it, it's a surgical procedure where the variance between things that are happening is too high for rules, right? But stable enough where you don't have like a a million things that you have to address.

So for example, the most common example that we see is when you calculate the day supply.

There's rules how you calculate the day surprise, but if you look at all the different scenarios, the cream, the eye drops, the insulins, all of the stuff, the rules are not enough.

And this is where kind of LLM come in and this is where it makes things so much easier.

It's not the order entry or it's not what TGM Labs is is implementing, what examples are we now seeing our techs and even maybe some of our pharmacists leaning in towards that is helping us generate more revenue?

Yeah.

Yeah.

Uh, I-I mean, look, so the LLM layer, that's where we, we do the AI layer.

It's really-- The best way to think of it, it's a, it's, it's a surgical procedure where the variance between things that are happening is too high for rules, right? Like, but stable enough where you don't have like a, a, a million things that you have to address, right? So, for example, the most common example that we, we, we see is like when you calculate the day supply.

There's rules how you calculate the day supplies, but if you look at all the different scenarios, the cream, the, the eye drops, the insulins, all of the stuff, the rules are not enough.

And this is where kinda LLM come in, and this is where it makes things so much easier.

Uh, when you try to match a patient profile, when there's like duplicates, right? Again, you need some reasoning, but not a lot of reasoning.

What examples are we now seeing our techs and even maybe some of our pharmacists leaning in towards that is helping us generate more revenue?

Jonathan, why is that? When you think of this as a scientist, why is it that implementing AI in a specific sector of the business, how is it making that big of a difference? A million-dollar savings for CarePoint, that's amazing.

Yeah, I mean, look, so the LLM layer, that's where we do the AI layer.

It's really, the best way to think of it, it's a surgical procedure where the variance between things that are happening is too high for rules, right? But stable enough where you don't have like a...

a million things that you have to address.

So for example, the most common example that we see is when you calculate the day supply.

there's rules how you calculate the day surprise.

But if you look at all the different scenarios, the cream, the eye drops, the insulins, all of the stuff, the rules are not enough.

What examples can either of you give to our listeners, many of them pharmacy owners themselves, where we know that if we take up processes, if we take up order entry, aspects of repeating tasks tedious things that these humans are doing now you're moving them to other ways of generating revenue for the organization that isn't tedious it's not the order entry it's not what what tgm labs is is implementing what examples are we now seeing our techs and even maybe some of our pharmacists leaning in towards that is helping us generate more revenue