Olivier Collart, founder and Chief Executive Officer of INTANGIA, joins John Furrier of theCUBE at the New York Stock Exchange as part of the NYSE Wired MedTech Unplugged series. In this enlightening session, Collart shares insights on the role of data in transforming the pharmaceutical industry.
Collart discusses work at INTANGIA, a London-based pharmaceutical technology platform. They explain how the innovative use of data, particularly intangible asset data, revolutionizes the way pharmaceutical companies manage their assets. The discussion is moderated by Furrier, co-founder of SiliconANGLE Media, and includes insights from theCUBE Research.
Key takeaways from the conversation include the significant potential of intangible assets and data-driven strategies. According to Collart, the integration of artificial intelligence in identifying trends and potential breakthroughs is crucial for gaining competitive advantages in the pharmaceutical sector. Their insights into the Asian markets as emerging hubs for innovation provide viewers with a glimpse into future growth opportunities.
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>> Hello, I'm John Furrier with theCUBE are here at theCUBE Studios at The New York Stock Exchange for our Med Tech series. This is an ongoing series featuring the leaders in med tech, life sciences technology, powering the next generation applications as generative AI and as large-scale AI factories start to emerge. The benefit of data, the value of systems are going to enable new kinds of business models and new kinds of capabilities. And ultimately, the competitive advantage of the future will be based upon it. Olivier Collart here, founder and CEO of Intangia, doing something amazing, technology work around pharma tech specifically using data. Olivier, thanks for coming on theCUBE. Appreciate you coming in.
Olivier Collart
>> Welcome. Nice to see you, John.>> Yeah, you're part of the NYSE Wired program and community. We were just talking before we came on camera, some of the work you've done. You have an interesting startup you're launched and building out and growing, using data to change how pharma builds their assets, their products, uses technology to identify opportunities, a unique approach to help people go faster, to see things early and get a competitive advantage. And I was just interviewing an AI expert. He says "Inference is where the money's being made, and inference is just reasoning faster than the next guy." Okay. That's what you've essentially built, in my opinion. Explain what you guys are doing. I think this is super compelling, and a sign of where the market's going.
Olivier Collart
>> Right. Thank you, John. So we are a pharma tech platform based out of London, and so my background is looking at IP data, so intangible asset data and most specifically IP scientific literature, etc. So we are looking at finding trends and trigger within those intangible assets to help the end user, now the Pharma company, to act on some actionable insight to basically identify the next big thing as early as possible. So what we do is a lot of landscaping, trend analysis, packaging that into a workflow platform that can be leveraged by BD teams more specifically within pharma. So we work with small biotech, mid-cap companies and also large pharma companies. And right now, we see a lot of activity, especially in Asia and China. And so we have been spending some time in China trying to see how we can uncover data and package those data and insights for the end->> You say things in China, you mean like new IP being developed?
Olivier Collart
>> Yes, new IP.>> And new data signaling.
Olivier Collart
>> New molecules. Yeah, new assets basically. And so the whole industry is now looking very carefully at China. So we try to see how we can get a unique data set so we have some collaboration ongoing in China to help them to uncover insights.>> I have to ask because I'm sure people are probably asking this question as they're watching. I think of, and I hear intangible assets, I think of when I was in business school, accounting class. Tangible and intangible assets in pharma, and define intangible, because we've seen this on crypto too, and also the submissions on how it was classified. What does intangible assets mean in your context versus tangible? I mean, is it just the process? Explain what intangible asset is.
Olivier Collart
>> Yeah, so I mean, there are a lot of different types of intangible assets, so IP, so patent is a big one in there, where it's pretty hard, there's no real metrics around it. So my work over the last few years has been to try to build metrics around how to value an intangible patent portfolio because a lot of the value today of companies is coming out of intangible assets. And this is even more the case for biopharma companies because they pretty much have nothing. They're only losing money, but they're building a huge value, an IP or patent portfolio of intangible asset portfolio, and suddenly they get acquired for a couple of billions. So the question is how do you uncover and find those intangible assets as early as possible?>> I really wanted to get that out there because I think this is a huge point. The old expression, beauty's in the eye of the beholder, and intangible assets, it can be anything. But if someone sees something valuable as an ingredient or a piece of something bigger like say in a portfolio or even in a product, you see that with software algorithms. You see that with a lot of these things where how do you value that work? Well, we know we put money in developing it, so that's cash in. In these large scale systems in pharma and all these drug discoveries and all these life sciences, one little thing could make a huge difference and I think you're surfacing this. So if I'm a pharma company, I'm always looking for a competitive advantage. Is this how your system works? Would that help me answer that question?
Olivier Collart
>> Yeah, no, exactly. So we are looking for triggers and trends that allow you to start looking at a space and prioritize the space. So we are looking at what's happening, for example, in the scientific literature, what's happening in the patent world, and then the outcome on the clinical space. And we're trying to find an algorithm and predictive model to tell us, okay, now it's the time to look at some mechanism of action for example, because we see this and this and this. So this helps you to prioritize areas in which to look at.>> I go to the money-making side of it of building a product, but I know there's other use cases because you have a data strategy. What are the use cases? Because companies probably have a lot of other things going on. If I'm a pharma company, what else is going through my mind right now? Because again, the world's going from old way to new way. Old way was throw a bunch of researchers at it, go dig and do some research, squint through things, sort through things, benchmarking. Are we doing the right thing? So there's always operational things I could see you disrupting. Take me through some of the use cases in your vision.
Olivier Collart
>> Yeah, so for example, right now when you search for certain asset, it's a hard work. You need to look in a lot of different data sources. You want to look funding activity, patent activities, scholarly activity. We try to combine all of that and then benchmark it in a UI and a UX that is friendly because most of the current players are old school, kind of difficult to navigate software. So we try to bring all of that together, and it's the best time now, with LLM, we can clean a lot of the data quite easily and much more quickly. So the cost to actually produce those data source and clean those data source has come down drastically over the last two years I would say. So the time to disrupt the market is perfect right now.>> Yeah, pharma-like tech has a bottoms-up innovation and a top-down kind of business logic. Are you seeing any activity with the platform opportunity to see, hey, the people who are close to all the action, hey, I want to buy that company or go license this technology, then have to go to the C-suite and say, "Hey, can we license this?" And they're like, okay, how do we buy it? What's it cost? Is there a connection there with your software between kind of identifying the opportunity, say the organic area, and then the mechanism, the lawyers, everyone gets involved, how do you price it?
Olivier Collart
>> Very good question. So the goal of our software is to be a end-to-end platform for the entire BD downwards and upwards. So we help to identify, to search and evaluate, so very early on, but then all the way down to the valuation. So that's where we want to be. We're not there yet. So we are going to spend a lot of time now working valuation and benchmarking to basically help you to price the licensing deal. And so if that's happening, then we can work with the strategy team or the most senior team. We also would like to connect the BD team with the R&D team because there's a lack of communication and sometimes it's harder to prioritize what BD is looking at, what R&D is looking at. So we would like to enable a flawless communication between R&D and BD based on the trends that we are seeing. Also, there's a huge opportunity to look at the IP team within the R&D group. So those are the teams we're targeting and trying to build a crossover platform.>> Olivier, this is why I love this market, because all AI-dative ventures have that same pattern. They shortcut and accelerate the two worlds. If you think about, I'm just oversimplifying it, an M&A department or some sort of licensing departments, business-oriented and say a technical person, that process is happening faster, but it also connects the worlds. So one, transactions happen faster, they're more accurate. The old way was set up a meeting, do some due diligence, educate, give a presentation, and then may or may not get there. So you're bringing those worlds together.
Olivier Collart
>> Right. We're trying to make it more efficient, and where we are trying to go is to enable a data moat where companies are starting to share data onto a company. So that decreases the number of meeting you as a BD person in pharma needs to take, so the whole process become more efficient.>> I am gathering, you probably have a lot of other customers too. Investors probably would love this. So if I'm an investor, I'm like competitive advantage also is on the money-making side, who to invest in, where's the diamond in the rough?
Olivier Collart
>> Yeah, yeah. So actually some of the use cases also identify intelligence or deal intelligence around who's investing where, et cetera. So we have been working with a large biotech investor, helping them to source investment idea. And now we also are scaling into the more pharma space and investors within pharma.>> All right. You've got a great-
Olivier Collart
>> Hedge funds as well, by the way.>> Of course. They'll buy anything because they need to get an edge. They're so high-frequency-oriented, they want the best data as fast as possible. I want to ask about the origination story. Talk about how this all came together. You gave a little bit of an overview of your background. How big is the company? Where are you in your stage? What are you looking to do? Who's involved? How did this come together?
Olivier Collart
>> Yeah, good. So I started to look at IP data in correlating IP portfolio with stock price performance. And more recently, we've built a software, so that was more on the public equity side, trading. And then now I've been focusing more on the private side of and the venture side, working closely with venture capital fund. So now we're a small, lean team of four, very efficient, shaping product very well. So we are now starting to get some good customer traction. And so now the next big milestone is to look at the best distribution channel. So how can we reach all the biotech, mid-cap pharma? So now we exploring a few different paths. Also going to raise some large funding round. So talking to some investors right now. We have some plan to expand the team in Asia, but also have some people in the US and also in Europe, where we are HQ.>> And you're targeting pharma companies. This is a pharma tech macro trend you're going after, right?
Olivier Collart
>> Yes, but the long-term plan is to also look at different type of intangible assets outside of pharma. So first, we want to nail pharma, and then expand outside of pharma, so med tech, health tech. We also have all deep tech, renewable tech, a lot of different tech.>> Crypto? A lot of intangible assets on crypto.
Olivier Collart
>> Yeah, yeah. Eventually. And the goal would be, my dream would be to start launching like ETS trading on intangible assets. So I would like to come back to the market where I started.>> So you are building a platform, taking advantage of the big trend of data's available, synthesizing the data, making use of the data, and targeting companies who want to accelerate IP?
Olivier Collart
>> Yes, that's right. And so we also work with ecosystem enablers like IP law firm for example, consultant, different type of users.>> All right, final question for you while I got you here, because you're an expert. Describe the pharma tech or just life sciences in general, the opportunity around these intangible assets. And with AI coming in, what's your view or opinion of the disruption? Scope the opportunity, because we're seeing large scale systems and platforms like what you're doing, AI-native platforms as a major enabler for how they do business. Discovery, monetization, tons of movement. Scope the opportunity and kind of peg where we are in that.
Olivier Collart
>> Yeah, yeah, yeah. I think we are now starting to realize more and more the value of intangible assets, and that's only going to grow going forward. I think some estimates are looking at 1 trillion market. So we are in really the beginning of building metrics, building a benchmark, indices, et cetera. So I think the next five years will be very interesting in the intangible asset world.>> All right, what are you most excited about right now?
Olivier Collart
>> I mean, what's going on in Asia, I think it's a pretty unique opportunity. I think we need to understand how to position a company between the Asia world and the western world, how to protect the data, how to generate the best insight, and how to partner with the best people there.>> Well, put a plug in for the company. You looking to hire? Obviously you're doing a round of funding, you mentioned, so I'm sure investors watching. Hiring? Areas that you're looking to put people in and go to market? Any kind of operational goals you have? Put a plug in for what's going on.
Olivier Collart
>> Yeah, so we are looking for ideally a distribution partner. So we're speaking to a couple at the moment, international platform that can help us just reaching as many clients as possible as quick as possible.>> Olivier, thanks for coming on in theCUBE at our Med Tech series. Life science is a big part of the AI enablement. Thanks for coming on.
Olivier Collart
>> Thank you, John. Pleasure.>> I'm John Furrier with theCUBE. We are here at the New York Stock Exchange, the NYSE Wired program with theCUBE and the community. Bringing you all the data, doing our part to bring all those assets, intangible data to you. I'm John Furrier, your host. Thanks for watching.
>> Hello, I'm John Furrier with theCUBE are here at theCUBE Studios at The New York Stock Exchange for our Med Tech series. This is an ongoing series featuring the leaders in med tech, life sciences technology, powering the next generation applications as generative AI and as large-scale AI factories start to emerge. The benefit of data, the value of systems are going to enable new kinds of business models and new kinds of capabilities. And ultimately, the competitive advantage of the future will be based upon it. Olivier Collart here, founder and CEO of Intangia, doing something amazing, technology work around pharma tech specifically using data. Olivier, thanks for coming on theCUBE. Appreciate you coming in.
Olivier Collart
>> Welcome. Nice to see you, John.>> Yeah, you're part of the NYSE Wired program and community. We were just talking before we came on camera, some of the work you've done. You have an interesting startup you're launched and building out and growing, using data to change how pharma builds their assets, their products, uses technology to identify opportunities, a unique approach to help people go faster, to see things early and get a competitive advantage. And I was just interviewing an AI expert. He says "Inference is where the money's being made, and inference is just reasoning faster than the next guy." Okay. That's what you've essentially built, in my opinion. Explain what you guys are doing. I think this is super compelling, and a sign of where the market's going.
Olivier Collart
>> Right. Thank you, John. So we are a pharma tech platform based out of London, and so my background is looking at IP data, so intangible asset data and most specifically IP scientific literature, etc. So we are looking at finding trends and trigger within those intangible assets to help the end user, now the Pharma company, to act on some actionable insight to basically identify the next big thing as early as possible. So what we do is a lot of landscaping, trend analysis, packaging that into a workflow platform that can be leveraged by BD teams more specifically within pharma. So we work with small biotech, mid-cap companies and also large pharma companies. And right now, we see a lot of activity, especially in Asia and China. And so we have been spending some time in China trying to see how we can uncover data and package those data and insights for the end->> You say things in China, you mean like new IP being developed?
Olivier Collart
>> Yes, new IP.>> And new data signaling.
Olivier Collart
>> New molecules. Yeah, new assets basically. And so the whole industry is now looking very carefully at China. So we try to see how we can get a unique data set so we have some collaboration ongoing in China to help them to uncover insights.>> I have to ask because I'm sure people are probably asking this question as they're watching. I think of, and I hear intangible assets, I think of when I was in business school, accounting class. Tangible and intangible assets in pharma, and define intangible, because we've seen this on crypto too, and also the submissions on how it was classified. What does intangible assets mean in your context versus tangible? I mean, is it just the process? Explain what intangible asset is.
Olivier Collart
>> Yeah, so I mean, there are a lot of different types of intangible assets, so IP, so patent is a big one in there, where it's pretty hard, there's no real metrics around it. So my work over the last few years has been to try to build metrics around how to value an intangible patent portfolio because a lot of the value today of companies is coming out of intangible assets. And this is even more the case for biopharma companies because they pretty much have nothing. They're only losing money, but they're building a huge value, an IP or patent portfolio of intangible asset portfolio, and suddenly they get acquired for a couple of billions. So the question is how do you uncover and find those intangible assets as early as possible?>> I really wanted to get that out there because I think this is a huge point. The old expression, beauty's in the eye of the beholder, and intangible assets, it can be anything. But if someone sees something valuable as an ingredient or a piece of something bigger like say in a portfolio or even in a product, you see that with software algorithms. You see that with a lot of these things where how do you value that work? Well, we know we put money in developing it, so that's cash in. In these large scale systems in pharma and all these drug discoveries and all these life sciences, one little thing could make a huge difference and I think you're surfacing this. So if I'm a pharma company, I'm always looking for a competitive advantage. Is this how your system works? Would that help me answer that question?
Olivier Collart
>> Yeah, no, exactly. So we are looking for triggers and trends that allow you to start looking at a space and prioritize the space. So we are looking at what's happening, for example, in the scientific literature, what's happening in the patent world, and then the outcome on the clinical space. And we're trying to find an algorithm and predictive model to tell us, okay, now it's the time to look at some mechanism of action for example, because we see this and this and this. So this helps you to prioritize areas in which to look at.>> I go to the money-making side of it of building a product, but I know there's other use cases because you have a data strategy. What are the use cases? Because companies probably have a lot of other things going on. If I'm a pharma company, what else is going through my mind right now? Because again, the world's going from old way to new way. Old way was throw a bunch of researchers at it, go dig and do some research, squint through things, sort through things, benchmarking. Are we doing the right thing? So there's always operational things I could see you disrupting. Take me through some of the use cases in your vision.
Olivier Collart
>> Yeah, so for example, right now when you search for certain asset, it's a hard work. You need to look in a lot of different data sources. You want to look funding activity, patent activities, scholarly activity. We try to combine all of that and then benchmark it in a UI and a UX that is friendly because most of the current players are old school, kind of difficult to navigate software. So we try to bring all of that together, and it's the best time now, with LLM, we can clean a lot of the data quite easily and much more quickly. So the cost to actually produce those data source and clean those data source has come down drastically over the last two years I would say. So the time to disrupt the market is perfect right now.>> Yeah, pharma-like tech has a bottoms-up innovation and a top-down kind of business logic. Are you seeing any activity with the platform opportunity to see, hey, the people who are close to all the action, hey, I want to buy that company or go license this technology, then have to go to the C-suite and say, "Hey, can we license this?" And they're like, okay, how do we buy it? What's it cost? Is there a connection there with your software between kind of identifying the opportunity, say the organic area, and then the mechanism, the lawyers, everyone gets involved, how do you price it?
Olivier Collart
>> Very good question. So the goal of our software is to be a end-to-end platform for the entire BD downwards and upwards. So we help to identify, to search and evaluate, so very early on, but then all the way down to the valuation. So that's where we want to be. We're not there yet. So we are going to spend a lot of time now working valuation and benchmarking to basically help you to price the licensing deal. And so if that's happening, then we can work with the strategy team or the most senior team. We also would like to connect the BD team with the R&D team because there's a lack of communication and sometimes it's harder to prioritize what BD is looking at, what R&D is looking at. So we would like to enable a flawless communication between R&D and BD based on the trends that we are seeing. Also, there's a huge opportunity to look at the IP team within the R&D group. So those are the teams we're targeting and trying to build a crossover platform.>> Olivier, this is why I love this market, because all AI-dative ventures have that same pattern. They shortcut and accelerate the two worlds. If you think about, I'm just oversimplifying it, an M&A department or some sort of licensing departments, business-oriented and say a technical person, that process is happening faster, but it also connects the worlds. So one, transactions happen faster, they're more accurate. The old way was set up a meeting, do some due diligence, educate, give a presentation, and then may or may not get there. So you're bringing those worlds together.
Olivier Collart
>> Right. We're trying to make it more efficient, and where we are trying to go is to enable a data moat where companies are starting to share data onto a company. So that decreases the number of meeting you as a BD person in pharma needs to take, so the whole process become more efficient.>> I am gathering, you probably have a lot of other customers too. Investors probably would love this. So if I'm an investor, I'm like competitive advantage also is on the money-making side, who to invest in, where's the diamond in the rough?
Olivier Collart
>> Yeah, yeah. So actually some of the use cases also identify intelligence or deal intelligence around who's investing where, et cetera. So we have been working with a large biotech investor, helping them to source investment idea. And now we also are scaling into the more pharma space and investors within pharma.>> All right. You've got a great-
Olivier Collart
>> Hedge funds as well, by the way.>> Of course. They'll buy anything because they need to get an edge. They're so high-frequency-oriented, they want the best data as fast as possible. I want to ask about the origination story. Talk about how this all came together. You gave a little bit of an overview of your background. How big is the company? Where are you in your stage? What are you looking to do? Who's involved? How did this come together?
Olivier Collart
>> Yeah, good. So I started to look at IP data in correlating IP portfolio with stock price performance. And more recently, we've built a software, so that was more on the public equity side, trading. And then now I've been focusing more on the private side of and the venture side, working closely with venture capital fund. So now we're a small, lean team of four, very efficient, shaping product very well. So we are now starting to get some good customer traction. And so now the next big milestone is to look at the best distribution channel. So how can we reach all the biotech, mid-cap pharma? So now we exploring a few different paths. Also going to raise some large funding round. So talking to some investors right now. We have some plan to expand the team in Asia, but also have some people in the US and also in Europe, where we are HQ.>> And you're targeting pharma companies. This is a pharma tech macro trend you're going after, right?
Olivier Collart
>> Yes, but the long-term plan is to also look at different type of intangible assets outside of pharma. So first, we want to nail pharma, and then expand outside of pharma, so med tech, health tech. We also have all deep tech, renewable tech, a lot of different tech.>> Crypto? A lot of intangible assets on crypto.
Olivier Collart
>> Yeah, yeah. Eventually. And the goal would be, my dream would be to start launching like ETS trading on intangible assets. So I would like to come back to the market where I started.>> So you are building a platform, taking advantage of the big trend of data's available, synthesizing the data, making use of the data, and targeting companies who want to accelerate IP?
Olivier Collart
>> Yes, that's right. And so we also work with ecosystem enablers like IP law firm for example, consultant, different type of users.>> All right, final question for you while I got you here, because you're an expert. Describe the pharma tech or just life sciences in general, the opportunity around these intangible assets. And with AI coming in, what's your view or opinion of the disruption? Scope the opportunity, because we're seeing large scale systems and platforms like what you're doing, AI-native platforms as a major enabler for how they do business. Discovery, monetization, tons of movement. Scope the opportunity and kind of peg where we are in that.
Olivier Collart
>> Yeah, yeah, yeah. I think we are now starting to realize more and more the value of intangible assets, and that's only going to grow going forward. I think some estimates are looking at 1 trillion market. So we are in really the beginning of building metrics, building a benchmark, indices, et cetera. So I think the next five years will be very interesting in the intangible asset world.>> All right, what are you most excited about right now?
Olivier Collart
>> I mean, what's going on in Asia, I think it's a pretty unique opportunity. I think we need to understand how to position a company between the Asia world and the western world, how to protect the data, how to generate the best insight, and how to partner with the best people there.>> Well, put a plug in for the company. You looking to hire? Obviously you're doing a round of funding, you mentioned, so I'm sure investors watching. Hiring? Areas that you're looking to put people in and go to market? Any kind of operational goals you have? Put a plug in for what's going on.
Olivier Collart
>> Yeah, so we are looking for ideally a distribution partner. So we're speaking to a couple at the moment, international platform that can help us just reaching as many clients as possible as quick as possible.>> Olivier, thanks for coming on in theCUBE at our Med Tech series. Life science is a big part of the AI enablement. Thanks for coming on.
Olivier Collart
>> Thank you, John. Pleasure.>> I'm John Furrier with theCUBE. We are here at the New York Stock Exchange, the NYSE Wired program with theCUBE and the community. Bringing you all the data, doing our part to bring all those assets, intangible data to you. I'm John Furrier, your host. Thanks for watching.
