Good afternoon, everybody. Hopefully, it looks like everybody's enjoying their lunch. I'm going to get that right after this talk. My name is Vin Singh. I am the Founder, Chairman, and CEO of BullFrog AI. We are an AI machine learning company for drug discovery and development.
Basically, our goal is to increase the odds of success and reduce the time and investment required to discover and develop drugs. I will give you a few seconds to read over our forward-looking statements. All right, let us just jump into it. The pharmaceutical industry has a very big problem or multiple big problems, but they spend a lot of money, and it takes a long time to develop drugs. It takes 10 - 15 years, $1 billion-$2 billion to develop a drug.
Shockingly, Big Pharma still fails 50% of the time in phase III, which is unbelievable, which is really the reason I started this company nine years ago, in the very early days of the modern AI era. There is really an urgent need to fix this formula of failure, and that is what we are doing at BullFrog AI. If you want to know why you should be investing in BullFrog AI, I think this pretty much sums it up.
We have a proprietary AI machine learning platform that is very different than pretty much any other company out there. Our technology comes from the Johns Hopkins University Applied Physics Lab, also called APL, which is a multi-billion dollar subsidiary of Johns Hopkins that works on top-secret defense work. They developed this platform.
It won Innovation of the Year years ago, and we have continued to innovate and develop it much further over the years. We have an amazing strategic partnership, with an institute that has a proprietary dataset, brain-related dataset. We have our first commercial deal with Big Pharma earlier this year, and I will talk a little bit more about that.
We are a very, I think, well-run company from a financial perspective. We have no debt. We have a very low burn for an AI company, but especially one in our sector. With our technology, we are able to basically decode the complexity of biology. Make no mistake, this is by far the most complex and challenging application of AI. That is really because there is still a lot we do not know about biology. It is analogous to exploring outer space, right?
There are things we know, but there is still a lot we do not know. All the interactions that occur in our biological systems, it is very complex. With our technology, we are able to make predictions and uncover insights, make discoveries that can help solve the problems I laid out in the beginning here. As with AI, the other part of the problem here or the challenge is data. The two go together. You cannot have one without the other.
When you think about healthcare and drug development, the real challenge with data has been the rich datasets that have the gold, the information that can really lead to the revolutionary discoveries and hopefully someday cures. That data historically has been incredibly expensive to generate. But it has come down by orders of magnitude over the past, I will say, 10 years or so.
Combining AI with these rich datasets is going to lead to some amazing breakthroughs in drug development. This is an exciting time to be in this industry. We have a tech stack. It's a proprietary tech stack. There are three components to it, and they all solve really big problems that exist in pharma. The first is bfPREP.
That's a data prep solution. Having data that's AI-ready is a big problem. When it's not ready, you don't get the answers that you should be getting, and your compute costs go through the roof. We have a tool that can take very large, complex biomedical datasets and sometimes in hours or days, clean, harmonize, and structure that data so it's AI-ready. You might think that Big Pharma has this figured out.
They have clean data because they have all the money and the experience and resources. Well, they don't. It's a huge problem. We met with one top five pharma at a conference recently, and they informed us that they have 100,000 datasets that need to be cleaned and prepared. Quite shocking. The second piece is what we call bfLEAP. That's our analytics engine.
That's where we make our discoveries. The core of that was what we licensed from Johns Hopkins. Then the final piece is bfARENAS. As Satya Nadella, CEO of Microsoft, pointed out recently, rapid decision-making is really a key area of focus for anybody working in AI. When an AI presents you with multiple options, how do you know which one to move forward with? For what we do, it might be multiple drug targets or multiple biomarkers.
For you, it might be something different, like which company's stock should I be buying? I recommend BullFrog, by the way. Really understanding this, understanding disease pathways, understanding what are the root causes of disease, and that's something that we do very well, and we're doing that with our top five pharma partner. When you analyze this data, you build networks. We're able to actually identify the genes that are drivers of disease.
The ones that we're currently focused on are major depressive disorder, bipolar, and schizophrenia. Our work with major depressive disorder led to this deal with a top five pharma, and here it is. This is a big deal. Most of you probably don't realize how difficult it is to get a deal with Big Pharma, especially when you're a tiny company.
At the time that we got this done, which was in March, we had nine employees. They don't do deals with nine employees companies. They're looking for much bigger, more established companies to work with. As we went through the diligence process with them and convinced their leadership, their R&D people, their AI teams, we convinced them that, "Look, we have something that you guys don't have, and you can't do what we can do," and they acknowledged that, and that's why they wanted to work with us.
This was a huge win for us, and it's going to lead to many more opportunities. We're performing incredibly well on this project. We're identifying targets for major depressive disorder. They will select one of those, bring it in-house, and develop a drug for that target and take it forward.
As we are performing on this project and delivering, there's real interest in expanding the relationship. The exciting thing is there's four, five, six different areas, low-hanging fruit, that we can easily expand into that would give them very valuable information for what they're trying to do. These opportunities are multimillion, if not more, tens of millions.
We're pretty optimistic about this, and we think great things are going to come. Our strategy all along was land and expand, so we knew getting in the door is hard, but if you perform, you will be rewarded. Even though we're focused on drug target discovery with that particular relationship, we've also demonstrated our capabilities in late-stage clinical trials. I talked earlier about how much time and money is spent developing drugs.
Well, we helped this little company called Eleison Pharmaceuticals that had a failed phase III in pancreatic cancer. We analyzed a large, very messy dataset, and we identified a patient subgroup and biomarkers that led to a 3X improvement in survival. Basically, we've provided them with a formula to be successful in a phase III. That's a big deal, and that's something we intend on doing with our Big Pharma partner and others.
Based on what I've just told you can see we operate at the two ends of the spectrum: target discovery, which is the first step, phase III, which is the last step. The work we've been doing with this major pharma comes from data from the Lieber Institute for Brain Development. We had an arrangement with them where we had exclusive access to all of their data.
They have the largest collection of brains in the world for neuropsychiatric patients. These are diseases that historically have been diagnosed and treated, drugs developed based on behavior. Well, now we're layering on some amazing biology, and we think that's going to lead to drugs that actually work. We're seeing things like different subtypes of depression, for example.
It's not a one-size-fits-all disease. It's a very complex disease, as all brain-related diseases are, often involving multiple regions of the brain. Oops. Oh, sorry. What is our business model? The common thread is our tech stack, which I told you about. We use it for ourselves, like with these partnerships, but we're also selling it into the marketplace, and we're building a very substantial pipeline. Recently hired a commercial team.
We have about three dozen opportunities now in our pipeline, and we're aggressively moving to start closing these deals, get the wheels turning. But this first deal was really the catalyst, okay? That's the door opener. We've continued to hit some important milestones, and really, we're not a biotech. Okay? We're not looking to develop drugs. We're all about revenue. Generate revenue. I think I'm optimistic about this fourth quarter.
I think we're going to have multiple opportunities, and we're going to continue growing from there. Really excited about the direction we're headed. The other thing is we want to take our tech stack and make it an enterprise solution because our customers would love to interact with our technology, and we're going to call this the BullFrog Operating System for drug discovery and development.
Our goal is to deploy this in every pharmaceutical and biotech company in the world. We are on our way to doing that now. We are still finishing up the development work. We have a very experienced leadership team. I think everybody on this list has at least 30 years of experience, very accomplished. What this gives me, and some of these guys are fairly new. When I got the deal with the Big Pharma, I decided I need to build a leadership team around me.
This is a deep toolbox that is going to help us navigate through the opportunities and challenges going forward. The relationship with AI and pharma has gone through multiple waves. We are seeing some big things happen with Big Tech and Big Pharma. We are starting to see some acquisitions happen, which we had not seen for many years.
Things are starting to change, and it really comes down to are you executing? Are you adding value? Are you under-promising and over-delivering? So far, we are doing all of those things. We will see what this next wave brings us, but we are excited about where we stand and what is in front of us. Just to review, look, we are a very undervalued stock. We are not on the radar, and that is going to change. When we announced this top five pharma, we were not allowed to disclose the name of the company.
If we get an expansion, or I should say when we get the expansion deal with them, we will be able to announce it, and then everything is going to change, and we will become a known entity, and we will not be trading at an incredible discount to our peers, which is what is happening today. I think that is it. Well, no, it is not. Let me see. Well, I think that is it. I will take some questions. Yeah.
This sounds really exciting. Can you give a little background on the selling cycle for this first pharma and how you would expect the other two to five to happen in the timeline and so on?
Yeah. The first one took one year from the first conversation to closing the deal. I think that's sort of the generally accepted sales cycle, 9-1 8 months for Big Pharma. We already have others in our pipeline. I think there's an opportunity to actually close one or two of those this quarter. The sales cycle appears to be shrinking pretty significantly.
Like I said earlier, it's a big deal to get something done with pharma like this. You're validated in the eyes of the market. You're not seen as this high-risk, unknown entity. Right? That's going to continue. I think you're going to see a shrinking. The other thing is, I expect us to move away from pilot deals and go straight into standard deals, which as you know are pretty significant.
The deals that we're really going after, we'll call them Biobucks deals, okay? If you ever read about the billion-dollar, multi-billion dollar deal with an AI and a Big Pharma, those are called Biobucks. That means maybe there's $50 million upfront, there's milestone payments, there's royalties, right? So you have cash flow stream going way out into the future.
If everything is met, all those milestones and so forth, that's where you get the billion dollars. For us, that's transformational because we only burn-- We've hired some people, so we'll probably be burning $8 million, $8.5 million a year. One of these deals can make us a cash flow positive business. Yeah.
Do you have-- Are you providing more enterprise services or are you trying to get in there where you're leveraging the intelligence where you're actually having a stake in the finished product?
Right now it's more a fee for service when it comes to our tech stack, but as we have proof of our ability to add value, we don't want to give away a biomarker discovery or a target discovery for half a million dollars. But in the beginning, you got to do that. Then we'll move to a back-end economics deals.
I know that you've been pumping the top five. You're in a research game, I mean, the top five don't do research anymore. Right? So you're getting down to those smaller guys that are actually researching the compounds, providing them with the services in that time you're dealing-
Well, no, Big Pharma does discovery work. They do.
The five.
Yeah.
I mean most of the five. I'm sorry.
Yeah. There's a lot of biotechs in our pipeline as well. It's a mixed bag. Anybody else? Oh, yeah.
Since you're modeling underlying biology, are you working in companion diagnostics or any of that area?
Not right now, but we've thought about it. Some of that has to do with our licensing restrictions. The code base that we license is really for drug development. But the reality is the discoveries we make are perfect for companion diagnostics. But it's something we're looking at sort of as a next step. Okay. All right. Well, thank you