How Greg Geehan Creates Healthcare AI Startups Through Scale Up Labs Venture Studio

Anthony Codispoti : Welcome to another edition of the Inspired Stories podcast where leaders share their experiences so we can learn from their successes and be inspired by how they’ve overcome adversity. My name is Anthony Codispoti and today’s guest is Greg Geehan, co-founder and managing partner at ScaleUp Labs, a venture studio dedicated to creating high impact AI and smart systems ventures in the healthcare and advanced manufacturing space. Their mission is to use blueprint AI to spot real problems, build lean startups, and turn smart ideas into fast moving companies engineered for early wins and lasting impact. Now under Greg’s leadership, ScaleUp Labs has garnered attention for its unique approach to launching AI driven solutions guiding entrepreneurs to transform innovative concepts into flourishing enterprises. Greg also brings a wealth of experience from his roles at Excel Hub Venture Partners and Social Impact Partners, supporting disruptive technologies and global growth. He has successfully guided early stage ventures through funding rounds and international expansions, highlighting his knack for nurturing transformative projects. Among ScaleUp Labs milestones is forging strategic partnerships to accelerate AI advancements in local and global markets.

Greg’s dedication to innovation helps shape the future of advanced manufacturing and healthcare. And before we get into all that good stuff, today’s episode is brought to you by my company, AdBac Benefits Agency, where we offer very specific and unique employee benefits that are both great for your team and fiscally optimized for your bottom line. One recent client was able to add over $900 per employee per year in extra cash flow by implementing one of our innovative programs. Results vary for each company and some organizations may not be eligible.

To find out if your company qualifies, contact us today at adbackbenefits.com. All right, back to our guest today, co-founder, managing partner of ScaleUp Labs. Greg, I appreciate you making the time to share your story today. Yeah, thanks for having me, Anthony. And I want to make sure that I got the pronunciation of your last name correct. It’s Guillen.

Greg Geehan : Guillen. All right, you’re right. And everybody says it a different way, so it’s no problem.

Anthony Codispoti : I appreciate your flexibility on that. So, Greg, you co-founded ScaleUp Labs almost three years ago. Can you quickly take us through your career progression that kind of led you into this space?

Greg Geehan : Yeah, a lot of people ask me that because it’s kind of a winding, it could be a winding adventure. You know what I found when I got out of college was I didn’t know what I wanted to do. I got out in marketing information technologies and I’ve never done anything in that space beyond trying to help out my company’s growth over the years. But so I ended up, I got out in September 11, 2001 was kind of like as the world imploded around us, I was backpacking your up right after school and came back and had no job prospects.

You know, the entire economy shut down. So I did things like sales. I ended up teaching for a year trying to figure out what I wanted to do. And then I ended up at a nonprofit company where I ended up meeting my wife, who was originally from Poland, and kind of started to understand the business context at that point. It turned out that I wasn’t making the money I needed to make and very expensive, like Boston in that company. And so I decided to make a move into the private sector. And so I joined a startup. And the startup was in healthcare.

And I’d never done anything healthcare didn’t really know a lot about it. And during that process, they’d just been acquired by a bigger company. And then they were bought and sold multiple times, it went public, they changed their name three times, they went private again, private equity owned. And through this entire process, I kept taking on more and more of the company from the operational and relationship side. And I started to realize like I really like the energy of never having enough resources, which can be very stressful.

But, you know, understanding is like, how do you make your resources work to the point where you can hire the right people at the right time for really what it’s a transformative technology. Up to that point, I started a couple of companies on my own, you know, like early on, I was always doing things like buying and selling gum at school, like in sixth, seventh grade. And, you know, so I had this like this this idea of like how entrepreneurship should work, I didn’t really understand it.

And so, while I when I decided to move on from that company, and I started looking at opportunities, I said, I’m going to move into startups, I’m going to join one earlier stage, ended up not doing that at all going to a bigger company. That but what was really cool about it is that they were in healthcare, but they worked, it’s Athena health, they were working with startups in healthcare sector. So at the time that in incubator, they’re investing in startups, there was, you know, a marketplace where we’re like connecting them in, there was an accelerator. And so I joined this really, really dynamic, interesting space that was really helping startups to grow.

Anthony Codispoti : And that was the funding coming from Athena. So it was, yeah, you’ve got a pretty solid foundation under you then.

Greg Geehan : That’s right. That’s right. And I joined on the operational side. But I was working, you know, as a small team, and we were all working together. And from this, I, you know, Boston at the time was moving from, you know, being a little bit of a venture backwater, where there was a lot happening in the, in the like pharma tech, life sciences, things like that, where there was a lot of investment happening. But it really started to shift and concentrate investments into the Boston ecosystem into things like digital health. And that’s where I found my interest. And I ended up, because I was really connected to these startups, I started to go into the Boston ecosystem. And at the time, this is pre COVID, right? Right up until COVID, when this was happening, you could go into Kendall Square downtown Boston, and there be on a Thursday night, you go to venture cafe at the Cambridge Innovation Center, where it’s entrepreneurs, executives, industry leaders, educators coming together in one spot, and just talking about networking, or there’d be presentations like the country of what’s called South Korea, or Canada, or France, they would bring startups over into this one one location, you go there for a couple hours, have a couple drinks, and then you go to a law firm across the street, who would be hosting a pitch night for another another sector that they were supporting, and you can walk down the street to another event that was hosted, let’s say by a venture, a venture fund.

So there’s always things that were happening here. And so I was able to start bringing some really interesting technologies and companies to the table at Athena. And through that, I started to really get interested in investing. So I joined an investment group that at the time was looking for people to co-invest alongside the fund, actually, venture partners. And I really started the group in Boston that was focusing on technologies in the healthcare sector. And so this got me really interested into how do you start to build out networks within the startup ecosystem, but how do you start thinking about from an investment perspective? So that led me to Excel Hub venture partners. So we actually ended up like co-founder and I were looking first in Poland, which is where I have connections, my wife is from there, and there was an increasing ecosystem similar to what we saw here in Boston at the time.

And we’re saying, hey, if we invest in a company in Poland, bring them to the US, you’re going to see a 4x increase in the valuation of the company just by bringing them to the US and getting investment here.

Anthony Codispoti : And why is that? Just because sort of the cost of labor to sort of get it off the ground is lower there?

Greg Geehan : Yeah, it’s actually an ecosystem issue. And so it’s not just the capital, but companies that are outside of certain ecosystems, like let’s call it the West Coast, Silicon Valley, Boston, LA, New York, you have a couple major issues. One is that you can get a lot of early stage funding, usually government backed, but you’re not going to get the next funding round as easily. There’s just not as much money in it. And so a startup gets this early funding and they need to immediately get to revenue production. And so EBITDA becomes the driving factor in that company as opposed to growth. And so in a place like the US, what we’re saying is, I’m going to give you a bunch of money right now. And I want to see you grow 100x so I can see my return. Because I know the next round, there’s an investor that’s out there that will give that company a lot more money.

In a place like Europe or Latin America, or really most other places outside the US, and even places in the US, you don’t have that next round. Or if you do, it’s really small. So the only way you can grow is to do it through revenue production. And so when we think about it is like, if you’re using your revenue in order to grow your company, you have far less of it because of your margins, etc.

You’re still trying to grow in order to capture that next big growth cycle. So what happens is you bring a company that has a great technology, great founder, great opportunity, global position, like technology opportunity, and you bring them to the US, and you find an investor here, they’re just going to give them a bunch of money and say, grow. All of a sudden, you’re leveling that company up to what a US investment valuation would be. Because it’s no longer tied to EBITDA, it’s now tied to, hey, what’s the future and the future is big. We have a bigger market here. We’ve been doing this a lot longer than everywhere else. Yeah.

Okay. We found this in Latin America. We found this everywhere. So when we moved to Latin America as part of Excel Hub, because a lot closer time zones and things like that, but there’s great technologies everywhere, great founders everywhere.

Anthony Codispoti : And so how did this kind of eventually lead to the inspiration for starting Scala Labs?

Greg Geehan : Yeah. So when Excel Hub was looking at these companies come in and we ended up meeting our other co-founders, so there’s a couple of companies that came together. We are all thinking the same way, all small businesses here in Boston, all multiple startups under their belts, investments, things like that. And so we had started to find companies in Latin America and Europe to bring to the US through our accelerator programs. And we saw the same big forex valuation increase. We proved it out a number of times. It was really, we were getting a lot of traction, great companies.

But we kept going back to our roots of we’re creators at heart. And we’ve all started multiple companies. We like to take a company from like a negative one to zero and then zero to one. And so what we realized is that we could try to force ourselves into being traditional investors like venture capital, or we can go back to our roots and actually start the companies from scratch. And that’s the concept of a lab, or venture studios as you might call it. And so what we realized is that we also have a really, really interesting value proposition that not everybody has.

There’s actually two of them. One is that we have this international presence. So we know how to get devalued or undervalued assets, undervalued entrepreneurs that can make a real difference. And two is that we’re right at the forefront of major changes within the AI sector.

Right. So it’s not just AI is a tool. There’s a lot of development happening. But we actually sit kind of in the MIT ecosystem. So my co funders are helping to develop the next version of what you’d call identified AI, the protocols around how they’re all going to communicate small, you know, small language models to small language models. And because of that, because we have this ecosystem here, we can connect these great technologies to solve real business needs into the US and make them stronger by bringing these other tools in that we have access to. And this creates a really strong value proposition. And because of that, we’re now able to do rapid prototyping for new businesses. We have what we call blueprint AI.

It’s like what you call like an engine, right? We can take all these market signals. We talk to corporations. We talk to technology.

So like these are international spaces as well as, you know, what are we hearing here? And then we talk to investors and say, what is it that people are investing in? We’re able to take that information and start to put together a rapid prototyping process that does not require a lot of resources on our side because we’re using this IP that we’ve created internally to create rapid prototypes. We can get that into market in 30 days. We then can figure out how’s it responding. And we can then make iterations until we figure out if we have a real product. And then that product can become a company.

Anthony Codispoti : Let’s unpack a little bit. What you’re saying about AI here. I talked to a lot of my guests about AI, but most of us are using it in terms of like chat GPT or Claude or one of the other like similar large language models out there. And I don’t know, we feed in some context, some text, and it gives us some feedback. And it’s super helpful. I hear a lot of my guests tell me that it’s really improving their efficiencies in a great way.

But I feel like what you’re involved with and what you’re seeing in the way that you’re using it is on another level that a lot of people outside that space probably aren’t really tied into. So when you talk about AI becoming a gentified, I think what you’re saying is the AI agents that you can build to do particular tasks. Can you maybe give an example of that?

Greg Geehan : Yeah, no, absolutely. So AI is a tool. And if you think about things like a large language model, when you can sell it for $20, when you have a for free or the premium version for $20 a month per person, there’s not a lot of value being created there. There’s, you know, it’s something that has a that can create a lot of micro value to somebody if they know how to use it. So how do you talk to your data? Most people can use it to like create documents and things like that. But most people don’t realize you can actually use it to talk to your data by importing your data into what’s called chat sheet BT and then asking questions that can actually produce a lot of information.

And a lot of people aren’t familiar with that being a real value that you can create. In some ways, that model is going to continue, large language models are going to continue to add value, but it’s going to become more of the within the fabric of how businesses occur. It’s like the internet. It’s going to be the internet. The future of value is going to be created on different value propositions.

And that’s really tied to a few different things. One is agentic AI. So these are agents and how do you build out agents that work with other agents to create value on top of value?

So you have one agent doing one thing, you may have three agents that take what that agent has, create other things and you can create value to it. Second is small language models. So small language models is when you take these bigger, large language models and you put them out onto the edge. So this is, in our company, we have a lot of data. We can train models on our data that stays with our our walls and we can structure value creation based off of our IP. We can use that to create value as out to the outside world.

Products, consulting, other things that other companies can consider to be value. And then the third one is around decentralized AI. So decentralized AI is when you’re communicating across with other decentralized AI. So it’s taking out of the large language models and then figuring out how to communicate across your small language models and your agentic AI. So when we’re working within a space and we’re looking at building a company, we’re not just taking, can I build a wrapper on top of a chat GPT, we’re actually building these proprietary models that create value, that use agentic AI in order to build value on top of these small language models. And then we’re working with decentralized AI in order to communicate with other small language models. So that’s the future.

And if the companies are not actually putting that natively into their businesses or figuring out how to create value within their businesses, they’re going to be left behind in the longer term.

Anthony Codispoti : So the difference between a small language model and a large language model, in my understanding correctly, a small language model is it’s trained on your particular set of data. That’s right. What it is that you have that’s sort of proprietary to your company. Okay. And so it’s kind of walled off in a way so that, you know, chat GPT, you know, can learn from kind of what I’m inputting to it. And, you know, that can in some way shape or form, perhaps get disseminated to other folks, but on a small language model, that’s, it’s kind of a, there’s like a firewall around it. So that’s right.

Greg Geehan : From a value perspective, if you can enter it into chat GPT and get it and get a result, everybody can. There’s no differentiation. If you’re training on your own proprietary data, you can create something that has value for your business.

Anthony Codispoti : And so how does somebody create their own small language model?

Greg Geehan : Well, I, you know, that there’s, there’s technology experts who understand how all that work. You know, it’s not something you just go and do. You need to, you need to understand it.

And there’s going to be a lot of value that’s created for, you know, the, the people who really understand this in the future. I’m, I’m really, really fortunate. I’m not, I’m not on the technical side. I’m more on the business side, but I have three partners within our organization that are really, really in the depths of creating this next level of AI, which is called small language models. And they are building that out with us, with the input across the entire organization.

Anthony Codispoti : Can you give an example of multiple agents, AI agents kind of Yeah.

Greg Geehan : So, okay. So we have a, we have a company called Meshify. And Meshify, the way that, that it works is you have an agent. The whole premise of this is that we believe that your traditional structure around CRMs, let’s call it, is going to disappear as a, as AI becomes stronger.

Mostly it’s because data is dirty. And most of these in the, most of these systems, and the way you’re going to interact with it is going to be very different. It’s going to be much more intuitive. It’s not going to be like put it into a certain table and then we’ll access it in a certain way.

And somebody has to make a decision on if it brings value to the company or not. So Meshify, what it can do is it can actually interface with your Gmail. What’s called Gmail. If you use a business Gmail, it will actually look through all of your data.

It can, that’s your emails incoming. Start to figure out where there’s, where there’s opportunities. It can then go into your internal, let’s call it your small language model. So in a practical sense, let’s say that I have, you know, I’m trying to do an, I’m trying to do invoices.

I’m trying to create a quote or an invoice or something like that. Right. And I know there’s this, there’s all this information. I have 2,000 SKUs. I need to be able to figure out based on what they’re asking for, what the right quote or proposal could be, and then get it out to the right people at the right time. So inside, the agent looks through your emails, starts to look at where the value is. You have other agents who are going out to your data, to your internal data to figure out, here are the right, right potential opportunities that would solve their problem.

You have other ones that are figuring out the value, other ones that do research on the outside to figure out how you craft a message. And what it will do is overnight while you’re sleeping, it will start to craft out all of these emails and proposals. When you walk in in the morning, you can just look through it. You’re going to find stuff that you’ve forgotten about, you know, like an email that you missed, things like that. This is going to bring it to light and help you really very quickly get off these proposals to the right people asking the work for the right thing and putting the right thing in there. And then it learns over time.

How did we do so it gets stronger? The future of this is on the other side, if that other company that you’re trying to run a proposal with has a version to mesh by or it’s a similar one, the agents behind the scenes can actually negotiate with each other. Because one has the value, one knows the value on our side, you think it’s hard talking and start to say, hey, here’s a potential opportunity.

So when you walk in the morning, you kind of have it already figured out. Now you need to add your own value to it. Like the future value is not, A, I can’t do that, and it’s not going to be able to do that for a while. It can create its kind of its idea of it. Now you come in and you have basically everything kind of figured out and you can solve it. So that’s an example of how agents can work behind the scenes and circumnavigate a CRM or other processes, school, go drive, things like that.

Anthony Codispoti : That’s really interesting. Okay, so I’ve got a better understanding of how multiple agents can be working together sort of behind the scenes. I’ve got a better understanding of the small language models. Help me wrap my head a little bit more around decentralized AI.

Greg Geehan : So decentralized AI is when you have basically other small models that are working outside of an LLM. Consider an LLM, a centralized data source. And so a centralized data source has lots of data. It’s been trained on everything, right? So call it your chat GPT. Now you start taking it and you put it onto your computers and you’re doing training internally. So you’re creating your small language models. Eventually you have things like on your phone, your phone has more and more AI tied into it. You’re going to start being able to do processing and learning that’s hyper personalized.

Hyper personalized means that when I pick up my phone and do certain things with my apps and I have my day to day things that that it does, it’s going to hyper personalized information and the way that my phone works with me. Now to connect all of that, right now you go into one centralized location, which is the LLM and you say, hey chat GPT, here’s my prompt and it provides output. Decentralized means when you have like multiple small language models or like multiple edge devices that are connecting. How do you connect all of those to provide value?

So if I go into one place, how am I managing the data coming from here into my small language model and then how and what are the ways that they’re going to be able to connect and communicate?

Anthony Codispoti : Okay, so that’s super cool. Thank you for that explanation. Now I really want to hear about Blueprint AI and your rapid prototyping. Can you walk us through a specific example? Because when I think of rapid prototyping, I think of like 3D printers, we’re making you know a physical part, but you’re talking about it in a very different way, like your rapid prototyping like software and AI companies.

Greg Geehan : Right, yeah, it’s so if you think about in the life cycle of a startup, generally it’s somebody makes a decision that they want to start a company because they’ve seen some sort of problem or they think they have a problem and they spend a lot of time whiteboarding it, putting together pitch presentations, starting to think through how to code it, what is the problem, what’s the major problem, and what could be the solution that can solve that problem. And that’s a lot of manual work that goes into it.

It requires somebody who kind of knows the space. Then you have the problem of figuring out does the customer actually want it and you have things like jobs to be done, you have whole industries that all they do is interview people to find out, will they actually buy the solution that you put together. Then you code it and you iterate it and you eventually put something out into the market that you need to build. One of the major reasons that so many companies fail in the startup space, which is the majority of them, is because the ability to sustain that and create and understand the value is housed within a certain individual that is the entrepreneur. And so finding the right person, the right problem, the right solution, and the right ability to execute on it is really hard.

So now what we can do within Blueprint AI is actually to start to rapidly iterate on a lot of those processes using Agentec AI. So for example, jobs to be done, well-known practice costs a lot of money, it takes a lot of time. We have actually built out internally the ability for you to pose a problem or solution and we we can capture a lot of that from like our thesis on talking to corporates, etc. And then what that does is systematically use agents to start to figure out what is the mission and the vision, what’s the problem, how do we think the, and before you even get to a solution, it starts to figure out who’s the persona and it starts to use already known information out in the internet, right? It’s like creating these, it creates a persona. Who is the customer?

Why are they the customer? What’s their age, backstory, education, everything that you normally try to figure out and it will rapidly interview them virtually. And so what does that mean? It uses our tools to say, if I was going to interview somebody with this, what do I know about these types of people that can help me to provide like a real answer and will actually create a whole story and interview. And then that interview, you have agents that will look at those interviews and start to figure out what would be a solution. So like it figures out what are the problems that they have in their day to day lives? What really pains them? Because again, that day, nobody buys a product unless there’s a solution that solves a real pain point, right? Like people don’t want to spend money unless it creates value for them. So what the agent is doing is looking at those pain points and trying to figure out what are those technologies that could solve the problems and it will create a whole list of them, right?

Anthony Codispoti : You’re basically creating a focus group out of thin air.

Greg Geehan : That’s it. That’s it. Now, what we figured out is our first version of it, we’re like, yeah, maybe this is like 50% there. Like, you know, there’s some good things that’s really basic. Like I could have figured that out on the back of my nappy. Yeah. And then we’ve solely built it.

We think we’re closer like 70, 80% there. But what that gives you is that rapidly within three to five minutes, I now have new business ideas that solve really potential problems. And we can use that internally to say, Hey, now, now let’s go do our own research things that we know. And we’re going to figure out if it makes sense or not, we keep feeding that information into our into our agents, and they’re going to keep getting better and better and better. So, as everybody knows, you actually have agents that can go and create a code really fast. So for example, we actually created some of this code within like, you know, you know, a week, three days, that would have normally taken months and months and months to code. So now what we can do is very rapidly go out and create a create a workable solution of a minimum viable product that we can bring out to people that we think are the real stakeholders and get their reaction to it. And if it doesn’t work, what do we spend a few hours instead of months and maybe millions of dollars.

Anthony Codispoti : So where does the original seed of an idea come from? Are you guys feeding that in or are you just sort of pointing one of the agents in the direction of we’re interested in healthcare based software, like you’ve got all these different personas go and come up with some ideas for us.

Greg Geehan : Right. Well, remember that a lot of this technology is still very new. A lot of agent that AI is still like, it’s still being figured out. So for us, it’s the same thing, right? We have a lot of this IP that’s been developed, but we’re continuing to build more over time, we’re building these building blocks so that we can continue to use them as we spin them out. So there’s still a lot of manual piece up front for us, which is why we’re very focused on energy advanced manufacturing and healthcare because we know the space really well. And when we talk to corporations, so we actually physically go out and talk to corporations, we generally understand where there’s major pain points. And what we can do is feed that in to start to figure out what are the what are the underlying technologies that might be able to solve some of those pain points. So there’s some manual work up front. But what’s nice is that we can enter in just a high level. Like this is we think we need to solve this and our agents will actually completely refine it.

it within seconds. Say, okay, this is what I think you’re saying. Here’s how I reconstitute that to be really concise on what the problem is we’re trying to solve, and then it picks it up from there. So we sell some manual prompts in between. We can’t do everything. I mean, that’d be incredible. We’ll get there, hopefully someday, but a lot of the inputs are still manual.

Anthony Codispoti : So how many ideas are you guys kind of tinkering with, I don’t know, on a weekly or monthly basis? I mean, given this rapid prototyping process, I mean, I’m an entrepreneur, like, I know how our minds are wired, like you walk down the street and you see three opportunities, and it’s like, well, one thing an entrepreneur has to learn to do up until this point in history is to focus. I can’t go after all those three new opportunities I just saw in the last 10 minutes. I got to stay focused. That’s right. Now what you’re talking about is, oh,

Greg Geehan : man, I got all these ideas, like, let’s get the agent started, I’ll come back tomorrow morning and we’ll have a prototype that we can go and show people. So like, how many are you guys kind of kicking around on a regular basis? There’s always ideas. So what’s really great about this process is that when you get a whole bunch of these ideas that come out is that in reality, you’re going to try to distill it into one major solution. So like, our goal right now is six to seven new companies a year.

It’s not huge numbers. In order to get there, we need to create a lot of interest. And so we have internally have processes to help us to figure out, like, which ones do we want to go after? Your traditional investment committees, but for us, it’s more like from a business perspective, is the problem big enough?

Is the solution strong enough? And we kind of go through these processes and that helps us to really start to focus in. So we may have, you know, 10, 12 ideas a year that we really want to pursue. That can start off with many, many more ideas that are input into our platform. For example, I’m working on one right now that I really like in the therapist space, it’s the referral space.

And there’s a company that we know that’s created kind of a front end for it, but doesn’t have any AI infrastructure. And I started looking into this through one of our, you know, some people on the team who brought that to our attention. And, you know, talking to like my chiropractor, he’s like, Oh, we need something like that too. And so all of a sudden, it takes on a life of its own because you start to socialize it. And people give you feedback that helps you really start to define it. And you can take all these multiple ideas that are kind of floating out there and start to really crystallize it into a real, real potential problem and solution. And we create decks around it, like a pitch deck has to be created. Otherwise, we can’t tell our story.

If we can’t tell a story around it, then there’s probably not a lot of value in it. So we go through the same thing as a traditional startup entrepreneur would do. And, you know, like, let’s figure out the financial model, let’s figure out the pitch, like who’s going to buy it, who are the stakeholders. And so it needs to like, to get to that six, we’re going to have to do that a bunch of times every year.

Anthony Codispoti : What is the, I don’t know, sort of the leading key indicators that, okay, we’ve got something here, this one doesn’t have to go on the garbage heap. This one is going to be one of the six or seven that we push forward with this year.

Greg Geehan : So clear stakeholders and potential first buyers. Right. So like we talk to corporations all the time, small, small, all the way up to multinationals. And one thing that, you know, becomes clear pretty quickly is when we start socializing, it will somebody actually buy it. So we don’t want to just go create and put a lot of cash into creating something that doesn’t have like your first, your first early adopters already built in. So that’s really where we, where we like the pivot starts to happen. You can have as many ideas as you want.

But if nobody’s going to buy it, then there’s no value in it. And so for example, we work with like some industry organizations that work with small to medium sized manufacturing companies in Massachusetts and more nationally now as well. We think right now that small to medium sized businesses are an underserved market. If you think about most of these big, big tech startups, they, most of them end up pivoting to some sort of enterprise solution because there’s a lot of money there. But it turns out that only 6% we think, because what the industry says, about 6% of US manufacturers are using technology on a day to day basis.

Most, most manufacturing companies are still using paper or no data, like paper travelers, they go around. Traditionally, you know, like non AI types, technology companies, you need the big dollars coming in from the big, big manufacturing companies in order to justify the cost of development to all the development work. Well, we can rapidly create AI that is a lot cheaper. And all of a sudden we have this massive market that we can go into and help to solve some of their most pressing needs. So this gives us like this direct feedback loop. And what we do is we narrow it down and we say, okay, this is here’s a bunch of ideas that we think can solve the problem. We heard that they’ll actually buy this. Let’s move that into the into the top of Donald, right?

Anthony Codispoti : So you’ve got a lot of relationships where you’re going to show kind of these decks and these like early prototypes to say, is this helpful? Is this something you would use? No, no, no. Oh, yeah, we want that one. Okay, we’ve heard a few yeses. We’ve got some commitments are going to move forward. What what does the process look like from there? I mean, you’ve had the AI, you know, help you spin up the coding, you know, kind of build the tool. How long before like, I don’t know, the company is launched? Is it ready as soon as you start getting some yeses?

Greg Geehan : Yeah, I mean, everyone is a little different, right? So like, we need to find a management team for it, right? We can’t do everything with a small set of resources that we have, we run very lean. And so we have to go out and find a team to actually turn it into a real business. And so that could take a little while. It could be as soon as we get our first customer in, we need to start doing rapid prototyping around it.

And therefore, we’re going to spin it off. So like right now, we have a couple of companies that have been incorporated. But we have a bunch more that have not been incorporated, but are still there’s like a MVP within within the structure of our lab, that can allow us to go out and make sales. And eventually, when we think there’s enough critical mass, we’ll start to think about the management team. So everyone is a little different because each company is a little bit different. Our goal is to create lasting companies that can we can get an exit in 24 or 36 months, if we can get some sort of M &A, or continue to grow it over a longer period of time. In either case, it needs to be a real business that has real real management around it.

Anthony Codispoti : You guys started this scale up labs, not quite three years ago. So I’m going to guess you haven’t had anybody sort of graduate to that stage of an exit or not yet.

Greg Geehan : No, no, no, we have some that are, you know, like HealthQ is one of them, that that we think is starting to get enough traction where that might happen in the next, you know, 12, 14 months. It’s really extraordinary how we’ve seen health systems just need and want what this is.

And what is the point somebody’s going to go up. So we focus on denied claims. You know, in the news, health care claims in the news, I’m sure you’ve seen with United HealthCare, some of the some of the other issues that happened.

It’s a massive it’s hundreds of billions of dollars in waste. And on one side, you have the insurance companies that are leveraging in AI in order to deny claims. In many cases, they’re they’re denying them, but they’re not very successful at having a real reason why it’s just it just does the work. On the other side is that you have health systems that are in the verge of bankruptcy. And they have something like 100 days of operating capital sitting in these, you know, they do unbelievable amounts of revenue in the door, but they don’t get paid out on a lot of it, because they get they get denied.

And so because of that, they’re always on the verge of bankruptcy. And so what we’re doing is, you know, one of the one of the issues that happens is that when it comes back as a denied claim is usually no information on why it’s just a generic this was denied because of, and you need to have somebody go in and work on each of those claims to figure out if they’re going to resubmit them. As a result, it’s like $54 per claim and overhead. And the vast majority of them, especially the small dollar amounts never get resubmitted because they don’t have the resources to do it. On the AI side, it’s very, very you can take a lot of complexity and start to figure out what are the anomalies that are causing these to happen. And from that, we’re able to start clustering and figuring out where why these were denied so that they can be adjusted and sent back very, very affordably and effectively. Longer term, we’re going to become almost a copilot where we’re saying, hey, we know based on the information that you put into this claim, this is going to get denied likely because of this, this and this make the change now so it never even comes back as a denied claim.

Anthony Codispoti : That’s pretty exciting. And I’m going to guess your target customers and maybe even target target acquirers would be like these health systems that are they’re cash strapped, right? That’s right. Yeah. And they need the extra revenue and they need to not have the collection process be so labor intensive.

Greg Geehan : That’s right. We have some major, major health systems like brand name ones that are talking to us right now and we’re doing MVPs and we’re proof of concepts and it’s, you know, there’s a major need for it.

Anthony Codispoti : Where do you see Scalop Labs going? I would ask two to three years, but that seems like an eternity. I’m just going to say six to 12 months. How do you see things kind of unfolding here?

Greg Geehan : Well, so I mean, our goal is six companies a year. Within a couple of years, we’d like to see something like a billion dollars assets under management from the companies we’ve created. You know, there are a lot remains to be seen to do that, right? But if we can create rapid, rapid opportunities and rapid value creation, then that is foreseeable within the next couple years.

In order to get that within the next six to 12 months, we need to raise more capital into the company itself. So we’ve been bootstrapping most of this for years and for the last year and a half or so. And we’ve had some really good success. We’ve been in the middle of a fundraise selling equity in the lab itself in order to really put a lot of fuel behind these companies that are coming out of the labs. And we’ve been pretty successful on it. We have the first couple investors in and we’ve been talking to some family offices to close the rest of it.

We still have a little ways to go. So we are looking for family offices that want to seat at the table in the forefront of AI, but really specifically strategic partnerships that can provide input to us and then also be interested in the outcome of the types of companies we create, not just from an economic perspective, but the value that it’s creating within the industries. The reason we’re linking our family offices is we’re not looking to sell our company in seven to 10 years, which is what venture needs. They need an exit, traditional venture. Family offices, so what we’re doing is we want to continue the capitalization, the equity that they own in the business to continue to grow. But along the way, as we have exits from the companies that are coming out of the lab, we’re going to be providing some of that back to our investors to make sure that there’s cash flow back to them as well.

Anthony Codispoti : What does a potential strategic partner look like? What’s that profile?

Greg Geehan : So family offices or corporations that are in advanced manufacturing, energy or healthcare or care about those spaces? It could be cybersecurity or other like Vintech that hit those particular industries. And one of the things that I think a lot of corporations or family offices or investors realize is that AI is going to completely change industries. Since 1954, 88% of all Fortune 500 companies have gone out of business. 54% of those are since 2000.

The velocity is increasing. And so what you’re going to see is AI is going to be the next big thing to really drive businesses that don’t figure this out. You’re going to see them being disrupted by the new businesses that come out of it. Think about as like a CRM.

If you no longer need to have these databases, there’s some major, major corporations that are in the CRM space. If they’re not embracing that, they’re going to have problems. In the supply chain space, so manufacturing, in the supply chain space, the companies that are not utilizing AI in order to figure out how to better utilize the materials that are coming in to produce better output are going to have a disadvantage against the ones that do. And so if you don’t realize and have a seat at the table and understand how these changes are coming, you’re going to potentially have some issues. So when I think about strategic, I want groups that care about that and not only coming in to give us money because we need cash as fuel, rocket fuel, but are actually going to participate. It doesn’t have to be all the time, but like sit in our roundtables, give us their feedback. What are they hearing? Potentially become first customers, potentially become acquirers down the road.

That’s fine. Co-invest in the companies that they think are the best companies coming out of the studio. But the ones that are going to participate along the way has the most value to us.

Anthony Codispoti : Could you see like an existing CRM company coming in with your idea and saying, hey, we see where this is going. We don’t want to be left behind. We understand that our old business model setup arrangement is probably going to die off at some point. Let’s get involved here. Yeah.

Greg Geehan : I mean, that could easily be it. Like a lot of companies have some sort of CVC corporate venture capital already set up and some of them are willing to come in and pay a little money every year to have a seat at the table or do an investment into form of equity. So we’re actually talking with a number of multinationals now that want to see the table. And we’re happy to talk to anybody and see if there’s a fit.

Anthony Codispoti : Is it a fit for like an existing fund to come and invest in you guys or is that not really a thing that you’re looking for?

Greg Geehan : It could, private equity potentially. So from a venture perspective, we’re not looking to exit our company in six to 10 years. In some cases, if it’s an evergreen fund that can wait it out and get returns like revenue in or cash flow in from the sales of companies, that could be a fit. It needs to be the right thesis though. Oftentimes, there’s really, really specific thesis in the funds on what they’re allowed to invest in or not.

We’re willing to talk to them, but we’re also not going to compromise and say, hey, we’re going to be exiting your IPO in seven to 10 years. Maybe that happens. You never know. Hopefully, we get big enough that that’s a thing. Eventually, we’re going to switch more to a fund model similar to like what you see with flagship pioneering. Originally, they were funding things themselves.

They got multiple billion dollar funds that they’re spinning up in order to support the companies that are coming out of their venture studio. We would probably do something very similar to that as we get bigger.

Anthony Codispoti : In that case, that would be more venture class. Yeah, I get what you’re saying. What is your gears, Greg, and talk about a serious challenge that you’ve overcome in your life, personal or professional? Maybe it’s both. What was it? What did you learn? How did you get through it?

Greg Geehan : There’s challenges in every part of business. My biggest personal challenge is, I’m not 100% sure I have this, but I’m undiagnosed ADHD. Everybody in my family has it. I’ve never technically been, but all the characteristics are there. This has been a big, big challenge for me over the years because focus can be a big challenge. I can be very, very focused on things for a short period, but I also have this shiny object syndrome, which is very indicative of where every time I see something new that looks cool, I want to drop everything and go after that. Finishing things can always be a big, big challenge. For a long time, I was starting before Scale-Up Labs, I would go and I’d start, I would put together pitch decks around this idea and that idea, and then my wife would say, why are you going after that? That doesn’t make any sense.

Finish the one you already created. This would also challenge me a lot when I was in the corporate world, because in the corporate world, it’s much more by the books. I always thought different. Somebody would say, we need to do this incremental change in order to get this small incremental increase. I’m like, oh, let’s just go invest in this company, or let’s go start this new line of business.

It just clashed. For a long time, I was always trying to figure out how do I focus myself in a way that allows me to create maximum value without stopping my creativity or my interest level. As you looked on my LinkedIn, you said, I have all these different businesses. It’s taken over the years a while for me to narrow it down to one business, which is Scale-Up Labs. The cool thing about Scale-Up Labs is it allows me to go and explore a bunch of different opportunities and other people to come in and help me focus. Having a great support system around you is really, really important. Going out and finding a great mentor, somebody who can help you to figure out your next steps, your next path, is really important. Figuring out what’s important to you that you can actually stick with and have goals that you can go after. If it’s like, I want to be a millionaire by this time, that sounds like a cool goal, but how do you do it? You need to come up with an actual plan, step back and figure out where you’re creating the value creation and then how do you stick with it.

It has to be something that’s interesting to you and creates a value for those around you in order to get you to that end goal. If you don’t do that, you’re going to be distracted. That’s been my personal challenge that I’ve struggled with since the beginning of my career. If I’m really honest with myself, it was all through school as well. I could only do concentrated short spurts. There’s a lot of great things with that too. It’s also my superpower, I guess.

Anthony Codispoti : I’m glad that you mentioned that because I hear this from entrepreneurs all the time. Most entrepreneurs, I would guess, have some level of ADHD and it is, at times, a curse and other times, it is exactly your superpower. It’s a huge blessing. We had a son diagnosed with ADHD and somebody turned us on to this book called ADHD is awesome.

Greg Geehan : I’ve heard that I haven’t read it. Well, I’ll tell you what, for guys like us, the audiobook might be better for you. That’s how I consume most of my books now. Yeah, I figured. The audiobook is very entertaining because one of the co-authors, very talented,

Anthony Codispoti : ADHD off the charts and his energy and sort of ping-ponging all over the place really comes through. What I’ve seen both in my own experiences as well as other folks that I’ve got to meet over the years is when you do have a founder person who is like that, lots of ideas, all kinds of directions we can go.

If they do have a partner or a support system like you’re talking about that is not wired that way. They’re not the idea of people, but they’re great from an operational perspective and they can kind of help put some guardrails around things so that it’s not, oh, Greg came up with four new ideas today. He’s going to have us chasing all of them at the same time. The problem is though, Greg, with the technology that you’ve built, now you can chase so many of these ideas at once. Probably all the corralling that you’ve done with yourself and your massive brain power over the years.

Now you’re like, no, now I can just go free because the tech can help us chase all of those. One thing that I found, and I’m curious if you’ve ever implemented anything like this for yourself is when I come up with a new idea before I bring it to my team, I have a one-week waiting period. What I do, what I make myself do is I write down the idea, type it out, whatever. Because as I do that, I start to see some of the holes.

I can go back and fill them in. And sometimes I never finish with that document before I’m like, yeah, this isn’t going. Sometimes I get done with that document and I’m like super excited about the idea, but I still make myself sit on it for a week. If I come back to it a week later and I’m still just as excited, okay, that’s when I bring it to the team.

Greg Geehan : I like that actually. I have not implemented something like that for myself, like mentally thinking that I need to do that. I probably have forced myself to do that just share necessity in the past. And it’s a really great strategy because a number of things that I’ve started and then just put down and never picked up again, it’s probably better I never tried to bring them to the team. Yeah.

Anthony Codispoti : What else have you tried that has kind of been helpful with you and corraling your superpower?

Greg Geehan : So I’ve got to say, I’ve actually become a lot more operational over time force and necessity, right? Because there’s always things that have to happen. Being able to put something down in writing to your point, but something that puts a process in place is really, really important to me. So lists, lists, lists, lists. I have, unfortunately, sometimes I have five or six running lists and I can’t remember where my lists are. But as long as, so like every morning I’ll wake up and I’ll take my old list if I can find it from the day before and start to write down again.

Here’s the things that I need to accomplish today. And then I go through that throughout the day. I refer back to it and say, did I actually set up this meeting? Did I send out this presentation? Did I do this?

Did I do that? And this drives some of my business partners crazy a little bit. But I’ve actually taken to putting things in my calendar as well. So as one of my partners says, he goes, if I want to know what’s on Greg’s mind, I look at his calendar and see what he’s put on it, a blocker on to take care of for that day. Because I’ll do it like two in the morning, I wake up and I’m like, oh, I need to do this. And I’ll open up my phone, go to my calendar, put a blocker in it to do it. You go, okay, this is what’s on Greg’s calendar.

This is what he has to do. And then, you know, maybe I can help maybe I maybe I can help. But I have a as long as I’m writing it down and tracking it, I feel like that can help me to reset myself and say, am I accomplishing what I set out to accomplish?

Anthony Codispoti : I like it. I use that calendar one myself, especially. And it’s super helpful. Because even if I end up getting distracted, and I don’t get to it in the moment, I’ve still got a running list of these alarms that popped up for my calendar. Yeah, sort of like my constant to do list that follows me around, you know, before I dismiss it. How about some daily habits, goals, rituals, things that help keep you on track, get you started, keep you focused?

Greg Geehan : So for me, it’s exercise. So, you know, they always say, you know, when you’re an entrepreneur, I have two kids, one’s a teenager, but to drive the other one’s almost a teenager. And, you know, so family is extremely important and things get really, really busy. And so you have, you have your family, you need to make sure you take care of your friends, you have your business, you have your health, all these things in, it’s really hard to be able to focus on all of them.

So I actually have built things into my life in order to make sure that I take care of myself, my body. And so really specific things like I drive to the train station, I take the train station in, but I get I could take it to right to my front door of my office. I’d have to switch a train, but instead, I get off and I walk 28 minutes from a different stop to my office.

Clear my head, listen to audio books, think. When I leave, I walk 28 minutes, 28 minutes back. And then when I get home every night, I either go for a walk with my wife or I go to the gym with my son. And does it help? Yeah, I mean, it doubly keeps me in check. But what I really find is that exercise really stimulates a lot of things in my brain that allows me to really think a lot clearer. When I walk into the office after exercising for 28 minutes, I’m like ready to go.

Where if I sat on the train and just, you know, close my eyes for a few minutes, I would not have the same way of doing that. And plus, I feel better at the end of the day.

Anthony Codispoti : I love that. And I do something a little bit similar. I live about a 15 minute walk from my house. There’s a garage here at my office. I could drive and I could park. But even in the worst of weather, I am walking to and from every day. Some days, that’s the only physical exercise that I’m able to squeeze in.

That’s right. But I also really like that transition time where now I’m here, I’m in work mode. When I leave here at the end of the day, I sort of have that decompressed time where I kind of let it go and I get ready to go be a dad and a husband. And then I do that kind of in reverse on the way back in. So yeah.

Greg Geehan : That’s great. And when you get it out of the way as part of your day to day, when you get home, you have time for your family. No, it’s if you’re if you’re going home and just spending all your time now focusing on exercise and forgetting about your family because it’s outside of work, then then you miss out on that. Right. So like I try to bring everything into my day and structured in so that I don’t have an excuse.

Anthony Codispoti : Yeah. Greg, as you look back on your career up to this point, what do you think is the best decision that you made for yourself along the way?

Greg Geehan : So there was there was a defining point where I was working for a company while starting a self hub and then moved transitioning that into a scale up lab. So again, my shiny objects, I was trying to do too many things. And I noticed my output and the value I was trying to create was suffering. And one of the best things I did was start to really focus. So got left the company, it was a tough decision.

It’s nice to have a steady paycheck and healthcare paid for and all that. But it’s another thing when you actually all you want to do is start a company and you need to focus on it. You’re not going to make the traction unless you’re focused. And so I moving, moving into this full time into scale of labs a few years ago was probably one of the most important things I ever did, because we went from like slowly growing it to like getting it out to steroids and being able to sit side by side with my partners every day all day, really just allowed us to really do that.

And this is the focus, the focus thing. I forced myself to focus where shiny objects had always kind of been, I built an interesting career around it. I did a lot of cool things.

I’ve been involved with a lot of great things. It helped them in Boston, where we have this ecosystem, right? That helps. But I was not focusing myself was holding me back. And when I finally realized that it, that was kind of the next, the next opportunity for me to to progress in my career and in the company.

Anthony Codispoti : Nice. Greg, I’ve just got one more question for you. But before I ask it, I want to do two things. First of all, everyone listening today, go ahead and hit the follow button on your favorite podcast app. So you can continue to get more great interviews, great content like we’ve had here today with Greg from scale up labs. Greg, I also want to let people know the best way to either get in touch with you or to follow your story of that of scale up labs, they’ve heard something today and it’s intriguing to them, they want to learn more, maybe they want to be a part. What’s what’s the best way to make that happen? Yeah.

Greg Geehan : So the best way, you know, check out our website, scale up labs, dog VC is interesting. But if you want to connect in and discuss more, we’re always open to that. And I’m a big, big proponent of LinkedIn. We use LinkedIn for so much. And we post a lot of what we do on LinkedIn. And so if you wanted to follow us on on LinkedIn at scale up labs, and you can find us there. But also if you want to be personally connected, I’m just let me know that you listened, listened to the podcast, I get a lot of a lot of requests every day, and I don’t necessarily take all of them. But anybody who’s listened to the podcast and wants to connect, just mention in a little note. And I’ll know that it’s that it’s interesting that that you want to hear more about what I’m doing.

Anthony Codispoti : And I’m happy to say that. Bonus value for those folks who made it to the end of the interview.

Greg Geehan : You get that connection. I’m under Gregory Guy and I look just like this on the picture.

Anthony Codispoti : And again, since I slaughtered the pronunciation of it, let’s give the spelling, which is G E E H A N and we’ll include a link in the show notes. But for those that may be driving and listening to this, there you go. Okay, so last question for you, Greg, you and I reconnect in a year, and you’re celebrating one thing super excited, very happy about it. What do you what do you hope that is?

Greg Geehan : One, the one thing, okay, so it’s going to be tied to some successful exit of a company that comes out of our studio. That’s that’s what we’re going to consider a success, you know, we’re building it because there’s value that can be created. But the end of the day is, you know, we’re trying to build this also to make sure that we can provide for our families and provide value to the ecosystem and provide value to our stakeholders. And if we’ve successfully launched and sold the company in a year, I’m going to I’m going to be very, very happy with that. Clicking your heels at that point.

Anthony Codispoti : That’s it. I get it. Greg, I want to be the first one to thank you for sharing both your time and your story with us today. I really appreciate it. Welcome. Thanks for having me. Folks, that’s a wrap on another episode of the inspired stories podcast. Thanks for learning with us today.