Doctor Strikes it Rich with AI Tool, $10m ARR in 6 Months | TJMLabs CEO Jonathan Adly

Jonathan Adly spent 15 years as a licensed pharmacist before writing the first line of code for TJM Labs in November 2024 — and 18 months later he's at $25M ARR, 40% EBITDA, 450 pharmacies, and $100M raised without ever making a pitch deck. His pharmacy AI agents cost $45,000 a year, replace 2–3 human technicians each, and now fill 500,000 prescriptions every day. In this episode he breaks down the $1M → $15M → $25M ARR run, why he took a $75M Series B from Elephant while already profitable, how he values an AI company at 15X+ off public comps, and the napkin math from 450 pharmacies to a $1.8B opportunity.

• 0:00Speaker 00

  What is TJM Labs selling to these pharmacies? What do they get when they pay you?

• 0:04Speaker 01

  Yeah, so they get an AI agent. And the AI agent does what a pharmacy technician or a pharmacy assistant does. Pharmacy is incredibly difficult space. An average retail mom and pop, their margin is 2%. Let's say a grocery chain, their pharmacies lose money. It's a loss leader. You come in and implement, it's no longer a loss leader.

• 0:26Speaker 00

  Can you give us the revenue growth from December 24 to December 25 to today?

• 0:30Speaker 01

  Yeah, so 1 million to about 15 to 25, 25 million. We had 40% EBITDA.

• 0:39Speaker 00

  What?

• 0:40Speaker 01

  Yes.

• 0:40Speaker 00

  That's insane. Hey folks, today we have a triple threat. Jonathan Adly spent 15 years working inside pharmacies before founding TJM Labs and combines three disciplines. He's a licensed pharmacist, an AI software engineer, and an operator. He previously worked on AI infrastructure at Kari RX and founded Gallon AI. He holds a PharmD, a postdoctoral residency, and an MBA. TJM Labs is a pharmacy AI platform that automates operational work, prescription intake, data entry, refills, and more. Jonathan, you ready to take us to the top?

• 1:15Speaker 01

  Yeah, I'm ready.

• 1:16Speaker 00

  All right. So take us into the story here first. What came first? Were you running, you know, did you get your doctorate first? Were you a pharmacist first? Were you a coder first? What started this?

• 1:27Speaker 01

  You know, I don't know how far back you want to go, but I remember I'm being in high school and we're talking about colleges was my parent and what you want to do. And classic immigrant story, they always want you to be a doctor. And I did not want to be a doctor. Actually, 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 the idea is go to pharmacy school, get your license, right? That was a really hard floor in your career because if you have a pharmacy license, you always have a job. Typically in pharmacy school, when you do well, they push you into academia. So I ended up publishing a paper, doing the residency, did some post doctor residency work.

• 2:11Speaker 01

  And I ended up, I would say, in a great job, very comfortable in a health system. Eventually promoted to do some admin work, understand where the money is as far as the pharmacy supply chain, all the government interventions that could be well meaning, but ends up having a secondary side effect. Seen, I would say, how the sausage gets made. And that made me want to be an entrepreneur. There's a great opportunity there. And that's when I started pursuing my MBA. So tried a couple of things, didn't really work out. I was a consultant, ended up working, I would say, first as a product person because

• 2:55Speaker 01

  I had the domain expertise, but eventually took on more and more actual engineering tasks. And self-taught myself. And at some point, you know, I was consulting, the same, the same problem showed up again and again and again. And it screamed, this is a product, not a consulting engagement. And that's how Gallon AI was born. Gallon AI was, did super well. And about a year and a half, it got acquired.

• 3:26Speaker 00

  Jonathan, what does super well mean? Did you raise capital for that company?

• 3:29Speaker 01

  I did not, I did not raise capital. It was bootstrapped, right? And we, when we got acquired, we had our first enterprise, like true enterprise customer. And if you have one enterprise customer, usually that means you can get the second and the third and the fourth.

• 3:44Speaker 00

  And was that a meaningful financial exit for you or no?

• 3:46Speaker 01

  It was 100% a meaningful financial exit. I would say, so Gallon AI ended up, it was a very similar software stack, like a company like Open Evidence. And Open Evidence, I think it's a unicorn now, like a multi-billion dollar valuation. The difference is Gallon AI was very BTB focused versus Open Evidence when they're just physicians can sign up and work out of it. Gallon AI exit was too early. Everybody would have said you sold way too early. You should have stuck around, right?

• 4:17Speaker 00

  What was revenue when you sold?

• 4:20Speaker 01

  I actually can't say because this is like our, under the NDA when we got acquired.

• 4:25Speaker 01

  But I can tell you that we first, when you sign an enterprise customer, usually there's like an implementation, it takes some time to ramp up their revenue. It would have been enough, again, as a bootstrap company, where I can just have this one enterprise customer and not do anything else.

• 4:40Speaker 00

  Are you talking like one customer paying seven figures above a million a year? Or was it more like, you know, 100,000 or 50,000?

• 4:47Speaker 01

  No, in healthcare, typically enterprise is more than 250K a year. Half a million is reasonable, but there is million, two million contracts.

• 4:58Speaker 00

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

• 5:04Speaker 01

  Kari RX was the acquirer. We shared, I would say, an enterprise customer. And it was an easy conversation. They were a great software company. AI was just starting to flume, so to speak. And Gallon AI had, I would say, the AI stack very early, ahead of the curve. So it was a great acquisition for both parties. And Kari exited six months ago. So they also got, they, they did well and they, they sold to someone else. And yeah, it was successful for me as a founder to have that first exit. Because that tells you, you can build at least the zero to one stage. And people don't realize this, zero to one is really hard.

• 5:45Speaker 01

  You know, one to 100, there's playbooks and I think can be executed on somewhat programmatically. But zero to one is, that's the part when there's no playbook for, you have to go and figure it out.

• 5:56Speaker 00

  Why did you feel like you had to leave Kari to build this business versus building it inside of Kari?

• 6:03Speaker 01

  I think I just miss being a founder, right? I had the vision, especially early in AI, that it's going to evolve extremely fast, that we should not, to treat AI, not as software, not like software plus plus, but as true labor replacement. And that was way too early to convince the whole mature and successful organization that that's the way to do it, right? Everybody in that period was thinking like, I want to be the system of record for this specific slice of industry. And that would be the way. Versus I was like, I don't care about being a system of record. I want to be the system of action.

• 6:44Speaker 00

  But if we fast forward to today here in middle of 2026, what is TJM Labs selling to these pharmacies? What do they get when they pay you?

• 6:53Speaker 01

  Yeah, so they get an AI agent. And the AI agent does what a pharmacy technician or a pharmacy assistant does. So if you imagine, if you walk into a pharmacy, typically the doctor have sent an electronic prescription, which is basically, you know, a regulated version of an email or a fax, or they gave you a hard copy of a prescription that you walk into the pharmacy. Typically, you know, pharmacy has staff that spending the whole day typing these prescriptions into their system of record. And then the pharmacist double checks it and then dispenses it to you as a patient. What these bots do is they replace the data entry.

• 7:34Speaker 01

  And typically, one of these agents, bots, is two to three pharmacy technicians.

• 7:40Speaker 00

  How do you structure your price? And what's the average pharmacy paying you today per year to use your technology?

• 7:47Speaker 01

  Yeah, so our average ACV is about 45K a year, right? And if you notice, that is slightly less than the salary of a pharmacy technician. And that's how we price it. You can hire a digital worker for 45K a year, or you can hire three human worker and that will cost you 150K a year, right? Or more if you count benefits and payroll and all of that stuff. An idea is these agents work 24/7. You train them once and they work with you forever. You can always retrain them and make them do additional things.

• 8:22Speaker 00

  And did you always price this way, even going back to 2020, last year when you launched?

• 8:26Speaker 01

  Yeah, that was the vision. The vision was always digital workers. We didn't think perceived pricing makes any sense in the AI world, right? We don't think task also makes sense. This is very close to tokens.

• 8:41Speaker 01

  Tasks could be very ambiguous. Imagine you're hiring someone and you tell them you're going to only work on this one task and that's it. Typically, that's not how people work.

• 8:49Speaker 00

  According to your blog, you officially launched in 2025. But when did you write the first line of code for the platform?

• 8:55Speaker 01

  Yeah, we wrote the first code actually November 2024. I remember it was a holiday break. We had our first customer essentially win a really rush contract. They had to hire about 200 technicians for, and they had four weeks to do that. That is a monumental, difficult task to hire this amount of people and train them. So it was pretty disparate and you know, disparate times called for disparate solutions. So that's how our company was formed. We said, let me try it. Let me see how can, how many bots agents we can put in. So instead of hiring 200, maybe have to hire only 100, right?

• 9:36Speaker 01

  But it ended up being extremely successful and over time, as the AI capability get better and, and, and higher, the solutions just kept getting better and the tasks that it can do kept expanding as well.

• 9:51Speaker 00

  So that was your first customer. I assume your ability to get that deal was just from your network being at Kari and your experience in the pharmaceutical space, correct?

• 9:58Speaker 01

  Hundred percent. And this is, if you think about it, it was like an immaculate inception because pharmacy is a very regulated flow. Was there's no test sandboxes. You actually have to work on real prescriptions, real medication that can cause serious harm. So you cannot just have a landing page. I'm going to do this and people will just say, here's the keys to the kingdom. Cause you might actually kill somebody. You have to have a ton of trust built into the system already over many, many, many years, right? In order for people to say, great, go ahead. Here's access to our production system. Let's go live.

• 10:37Speaker 00

  So how did you convert your years of experience in the space, your network into hard paying customers at TGM Labs? How did you get the first 10 customers?

• 10:48Speaker 01

  Yeah, it's all trust. You know, I think the first customer was obviously a relationship that was built over many years. The second customer was literally a friend of the first customer. And he's like, I see what you have done for them. Please do it for us too. The third customer, this is when I got convinced I need a sales team, was one of my customers in an airplane sitting next to a guy and they were chatting. And he showed him like what these agents are doing. And the guy came down from the airplane and called my cell phone and said, I want that too. And we stayed this way until about 30 pharmacies or so.

• 11:28Speaker 00

  Fast forward to today, how many, how many customers do you work with today?

• 11:32Speaker 01

  We have about 450 pharmacies.

• 11:34Speaker 00

  The average you gave me earlier, is that per location or per pharmacy?

• 11:39Speaker 01

  That's per bot, per digital worker, right? And a pharmacy can get 100 digital workers if they're big enough.

• 11:45Speaker 00

  But if you look at your pharmacies today, I imagine some of them have 100 locations. Some of them are mom and pops with one or two locations. What's the average number of bots per your 450 pharmacies, would you guess?

• 11:56Speaker 01

  I would say probably 2.5, almost three. So.

• 12:01Speaker 00

  Okay, okay. Interesting. Can I, can I take that math? I mean, 2.5 bots at 45 grand per year per bot means each of the 450 pharmacies would be somewhere around $100,000, 112,000 paying you per year. Is that accurate?

• 12:16Speaker 01

  Yeah. And then obviously there's segments.

• 12:18Speaker 00

  So we can obviously back into your revenue that way, right? I mean.

• 12:21Speaker 01

  I mean, I can tell my revenue, my revenue was about, it was close to 25 million ARR, right? That's not, that's not a secret.

• 12:28Speaker 00

  And what growth rate does that represent? If you go back to December of 2025, where did you finish there?

• 12:33Speaker 01

  The first customer I just told you about, we stayed nine months for them. And that's obviously a massive customer, a million plus a year. We only started selling to other pharmacies February 2025. So it's been a year and like five months now.

• 12:50Speaker 00

  Can you give us the revenue growth from December 24 to December 25 to today?

• 12:54Speaker 01

  Yeah. So 1 million to about 15 to, to 25, 25 million.

• 13:01Speaker 00

  That's incredible. So the last six months, you've added about 10 million bucks of ARR. How the heck did you do that? Expansion or new customers?

• 13:08Speaker 01

  Yeah. I mean, look, it's a good product. We know what we're doing. It's a, what we call it is a 15 year overnight success. Pharmacy is incredibly difficult space. An average retail mom and pop, their margin is 2%. When you come in and implement solution for this, they go from a 2% margin to an 8% margin. Who would not want this, right? As a small and Bob business in America. These enterprise customers, like they say a grocery chain, their pharmacies lose money. It's a last leader. You come in and implement is no longer a last leader. We had an incredibly strong team. We made two acquisitions during that time. And yeah, there's a reason we raised the 100 million in like six months. So.

• 13:50Speaker 00

  How much of the revenue growth was inorganic? It came from the acquired companies?

• 13:55Speaker 01

  Less than 5%. We, we essentially had the acquisition more for the team than anything.

• 14:02Speaker 00

  I see. Okay. Let's jump into that story. So you've been pretty aggressive on the capital front. This press release you put out said a 10 million Series A, a 15 million A1 and 75 million from Elephant. What was the timing on those?

• 14:16Speaker 01

  Yeah. So Series A, December 2025, right? Series A1, March. Series B, June.

• 14:28Speaker 00

  Take me back to the 10 million Series A in December because you were already doing significant revenues at this point. You said 15 million of ARR. Why raise it? I mean, I guess why raise the money at all? And if not, why not go all in? Why not raise as much as you possibly could?

• 14:43Speaker 01

  That's a great question. I didn't want to raise actually. I didn't, to this day, we still don't have an investment deck. The only reason I raised that Series A, because there's exceptional, there was two exceptional teams out there, right? They were doing, I would say adjacent things that very easily can overlap what we're doing. Their both were pharmacies and I was like, if we can combine, it will be almost like the Manhattan Project. We actually internally called it the Manhattan Project. You combine all of these great minds in one room. There's no one else. Maybe like five guys in the whole world that can do that. We have all five.

• 15:22Speaker 00

  So just to be clear, you bought, you used some of that 10 million to buy Encore RX and Pharmasol? Or was the original 10 million just for Encore RX?

• 15:30Speaker 01

  10 million was Encore and then 50 million was for Pharmasol. Obviously the acquisition numbers are not exactly the same and we did some other stuff with the money, not exclusively acquisitions.

• 15:40Speaker 00

  And the combined revenue of Encore and Pharmasol, you said was under like two or three million bucks when you bought them. Quote 5% of your total ARR.

• 15:48Speaker 01

  Yeah, yeah. So yeah.

• 15:49Speaker 00

  Okay, okay. Really team led. Okay. Take me into the Elephant round, Series B, which just closed a couple of days ago. Why do this deal and was this all primary or secondary as well?

• 15:58Speaker 01

  We decided in the beginning, we decided not to announce. Again, we had this mindset of like, we are, there's a company called the Viva, a public company, they're S and B 500. No one ever heard of them. But if you are a pharma company, they are your software provider, right? We were like, our role model is Viva. How we raise money, how we work is, is Viva. So it's the same mentality. We don't want to announce. However, we have decided that announcing is a good idea because as we're hiring and we're ramping up, it helps a lot with talent. People don't want to jump in our company and then essentially the company goes away. When you say we raised 75 million, it just makes conversations a lot easier. So.

• 16:40Speaker 00

  Okay. That didn't answer my question though. You also have to manage dilution as a founder, right? So does Elephant come in and put all this on the balance, just operating capital? Or was there some liquidity in secondary for early employees? How did you view the money?

• 16:52Speaker 01

  Yeah, there was some liquidity. It was all secondary. I also believe if you raise, there has to be a secondary. I don't believe in raising without secondaries. A lot or a little depends on the dynamic and what do you feel the market is, is, looks like. We want to have a force balance sheet where we never have to raise again if we don't want to. And we're still profitable and continue to be profitable and M and A. We really believe M and A is, is important. Right now, it's the same as a transition between cloud and on premise. And you have to run. You cannot walk because that transition is going to finish in like a year from now. And whoever is the AI agent category winner is the category winner.

• 17:35Speaker 00

  Jonathan, you said you are profitable. So you had a lot of leverage going into the Elephant round. You probably didn't have to do the deal. Can you share? Are we talking like 10% profit margin or more?

• 17:44Speaker 01

  No, we had 40% EBITDA.

• 17:47Speaker 00

  What?

• 17:48Speaker 01

  Yes.

• 17:49Speaker 00

  That's insane. Wow. So what were you doing? Now I'm even more curious, why take external money and take all the dilution? It's just really because you want to single to the market. Hey, listen, come work for us. We've got a lot of money. We want the best talent.

• 18:02Speaker 01

  Yeah. Look, at the end of the day, I had, I came from an acquisition already. I sold a company, right? If I'm strictly thinking about this like money, like I don't have to work. I can just sit at home and do nothing. The idea is.

• 18:16Speaker 00

  You're a builder. You would never do that.

• 18:18Speaker 01

  Yes, exactly. And I tried doing that and it didn't work out. The idea is you want to build a company of a significant proportion. You want, you play the game for the love of the game. At the end of the day, right? And if you want to win the category, right? If you want to be a classic vertical software company, if you want to be the Viva, right? If Viva's for pharma, TGM Labs is for pharmacy, you have to raise money and you have to be aggressive, right? And you have to basically one, run, run as fast as you can. And raising, people don't realize this. Every raise is a miracle because someone actually puts their hand in their pocket and gives you lots of money to go build your company. You don't want to raise when you need to. That's actually the opposite.

• 19:03Speaker 01

  You want to raise when you don't need to, because that's when the easiest way to do it.

• 19:08Speaker 00

  The biggest question I get is Nathan, I'm considering raising, what valuations are the markets giving AI companies today? I'm going to assume you're not able to share your exact valuation, but can you maybe give us a range? You know, was it 20 to 30x or more or less? Coach us up there.

• 19:22Speaker 01

  People always sometimes raise on vibes. I have, I always like to look at the public companies and see, right? What is their multiple? And if someone has a 10x multiple, like Figma, when they went public, they had like a 15x multiple or something like that, or maybe 10x multiple. And they were growing 40% a year. If I'm growing 10x.

• 19:44Speaker 01

  Right, then it's not going to be less than 15x a year. That doesn't just make any sense. So even in these conversations, when you want a valuation, you always want to based on on on real numbers and not just vibes. And again, it would be good to know what's your total market. Right? What's your forecast? Because if you raise at a high valuation, it actually comes back and bites you.

• 20:04Speaker 00

  So are you able to confirm you guys were greater than a 15x multiple?

• 20:08Speaker 01

  Yeah, yeah, easily, yeah.

• 20:10Speaker 00

  And how, to your point, it's a good one. There is such thing as too high evaluation. So what multiple is too much? Like, how do you figure out the cap is?

• 20:18Speaker 01

  When I talked to the elephant folks, we sat down and we said, okay, let's make a vision.

• 20:25Speaker 01

  How could we hit 100 million ARR? And we have a good relationship. Sometimes people don't have good relationships with their investors. I do. And we sat down and did the napkin mass and it'll be like, okay, so here it is. Does that valuation make sense? If we execute on this forecast? And is the plan to execute on it realistic? And if it is, then great, the valuation makes sense. But if you're sitting there saying, you know, I think when SpaceX went public, they said like the total addressable market is like all the money in the world. Okay, you know, that's so that's what I'm saying is like, but I can, I can say I have 450 pharmacies today. I know there are 16,000 pharmacies out there. Right?

• 21:06Speaker 01

  I can go from 450 to 1,000 to 3,000. Great. So easily I can backtrack into the valuation from that.

• 21:14Speaker 00

  Yeah, 16,000 pharmacies with 2.5 bots each at 45,000 per bot per year is 1.8 billion of ARR. That would be a wonderful place for you to hit.

• 21:28Speaker 00

  Okay, I imagine one of the usage metrics you track is how many millions of prescriptions are your bots filling every month or year? What's that number?

• 21:35Speaker 01

  We are doing, last we checked about three months ago, about half a million scripts per day.

• 21:39Speaker 00

  Your bots are processing half a million of prescriptions per day?

• 21:42Speaker 01

  Yeah.

• 21:44Speaker 00

  Wow.

• 21:44Speaker 01

  And that's just bots are doing other stuff, but prescriptions are easy to kind of wrap your head around.

• 21:49Speaker 00

  That's wild. Well, hey, look, I think a lot of people are going to want to follow your story. If they want to follow you online, where can they, where can they find you?

• 21:55Speaker 01

  The best way is LinkedIn, you know, Jonathan Hadley. We are very, this is LinkedIn is our outbound channel. The team does great work there.

• 22:04Speaker 00

  Guys, there you have it. He comes from years in the pharmaceutical space as a coder, as a, as a PhD, as a doctor, you know, got all the good stuff. Launched TJM Labs back in 2024. Revenue as of recording is about 25 million bucks of ARR. Again, that's up 10 million just in six months, an incredible growth rate. Ultimately, the goal is to scale to additional pharmacies, help them get more efficient in this age of AI. Jonathan, thank you for taking us to the top.

• 22:30Speaker 01

  Thanks, Nathan.

• 22:32Speaker 00

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