Gilion offers intelligent funding with AI precision: Henrik Landgren, Co-founder & CPTO Gilion

0:00Hello and welcome back to the Scaling Europe show. I'm Seb Johnson. Thank you for following along. Today we've got a great conversation from our time at Slush and the conversation is supported by Databricks, whose unified platform power source on everyone's FY2026 target, becoming a truly AI-driven company, and Harmonic. Harmonic is the startup discovery engine used by leading VCs across the world to find their fun returners. Check them both out. Thank you. Hello and welcome back. I'm here with Henrik from Gileon. super interesting, super different company, which I'm really excited to get into.

0:32Another Stockholm-based founder, which is just amazing. We just had Lucas on, so I want to talk a bit about that, talk about the ecosystem. But for those who don't know who you are or what you've built, can you give a super quick introduction?

0:44Henrik Landgren:Yeah, cool. So my name is Henrik, and I'm co-founder of Gillian, and we're building an AI platform to help companies and investors understand companies better and invest in them better. And we're using a lot of data to make that possible and a lot of AI on top of that. So yeah, that's the first version of the pitch. Yeah, and tell me a bit about where it came from. Where did the idea, the vision come from? Yeah, so all my life I've been into tech, data and business. Started off as developer back in the days and then wanted to learn more about businesses. I went to school, to uni and then to McKinsey.

1:19Henrik Landgren:And then I came to Spotify in 2010 where I kind of discovered how we could use very large data sets to run companies better and faster because all the very granular data sets that we could have got access to revealed a lot of information about how our customers were behaving in the apps and so forth. So that data set, the granular level data set, was new to us at that time. And later after Spotify, I came to start a VC fund, EQ2 Ventures. And then I saw if you're working as an investor, why can't we use all that data that actually exists in a company to help entrepreneurs grow their companies better, but also to help fund the companies.

1:59Henrik Landgren:If someone do that, then we would end up having much more funding available for companies because the data is actually there. Let's use it. So that's what I wanted to start. So after having been at EQT for five and a half years, EQT Ventures, I realized let's build this platform, the tech platform to harvest the data around that actually exists around every company, put it back in the hands of the entrepreneurs, let them get all the metrics that we had at Spotify. that will help them to grow faster with just that insights. And then let's also connect it to banks and investors because then we're now unlocking them to create new financial products to be able to fund the entrepreneurs.

2:35Henrik Landgren:So like kind of bridging the next evolution of data and AI analytics together with finance to actually give real impact, which is funding to entrepreneurs. And so you're using data as the foundational layer to then create or work with other organizations to create new types of financial products. That's it. So it's not just VC funding, but it's also other types of funding, right? That's it. Can you talk a bit about those other types of funding as well? So because I believe, and I think everyone would believe that if we could make that data available and we can build models on top of this, that's actually exactly what everyone who wants to fund a company or fund something needs.

3:13Henrik Landgren:Because everyone here wants to understand what is the risk level here? Is this an equity risk? or is this more of a, I want to understand the downside because I want to give it credit. So in all those financial decisions, they're happening every day, everywhere in the world. And there is a lot of capital out there. But when you don't have access to these tools, you have to take more buffers. You have to increase the rates because you don't know. But we want to change that. Let's make people know more, increase the certain levels of this. And the solutions are already there as you build it. So all this data together with AI on top of this, we can now help to not just do venture investing but also introduce non-dilutive products.

3:55Henrik Landgren:So our first flagship product where we started was our growth loan. So this means that from now and since the last four years founders can come to us and then we can give them non-dilutive growth financing if their underlying metrics are there. And this allows them to stay unprofitable, invest in their growth get up to five ten million euros from us or from our partners to fund their growth come out on the other side and with the profits pay back the loans so we try to create this this long non-diluted growth product similar to a venture backed product or a venture product but without dilution so with this in the market founders can now choose if they want to dilute themselves or not if they have managed to get their underlying unit metrics to work and can Can you touch on what those metrics are?

4:43Is it revenue and revenue growth? Is it gross margin, operating expenses? What are the key things that you look for that kind of gives you the confidence to give them this money?

4:51Henrik Landgren:Yeah, so at the end of the day, it's the same kind of analysis that you want to do with or without this platform. So it's understanding future cash flows, basically. So what is the certainty? Where is the likely scenarios that could happen in the future when we look at cash flow in the future? Yeah. But the difference is the ability to analyze that and project that into the future. And for that, when you look into not just the aggregated levels, but instead look into how often do the customers come back? What are the retention cords? What are the trends? When they launch new products, has that affected or is that affecting these trends into the future or not?

5:28Henrik Landgren:And when you have that level of granularity, we can even look down to clicks and marketing efficiency, all those kind of trends. You can much better understand how solid this is and where the trends are heading into the future. so you can get a much better understanding of what's the range of where this will go. And that kind of clarity and precision makes it possible for banks to basically invest in previously unbankable companies. That's super interesting. And I guess for founders, the rule proposition is you get the cash, but you don't have to give up any of the equity. Exactly. Keep control, keep the equity, and that makes total sense.

6:00And I'm curious because you've got an experience as an ex-VC. You're coming to this with the mindset of a VC. but you're also trying to take a super data-driven approach yeah do you ever find yourself or is there some tension there whereas like a lot of vcs often people focuses relationship focused as well as metrics yeah do you have to try and push all that to a side and you just say look we just look at the financials we just look at the models the metrics how do you balance that and i think that

6:27Henrik Landgren:balance is something that i had to learn as a vc in the previous job at hiketee so i came in really focusing on the data side because that's what i kind of that was my new learning from five at Spotify, we could see and we had learned, we can understand companies much better if we use this data, right? But investing is so much more, to your point, than just the numbers, right? But it helps so much if you're able to get that analysis right. So you can understand the solution space, like these are the different scenarios, this is what we see, so that when you discuss with the team, you can understand how aware are they about these things.

7:04Henrik Landgren:You can have a very sober discussion about different scenarios that could happen. And when you couple this also with predictive models and benchmarks, you can also see like, you know, you can put some sanity in the numbers and assumptions that the founders have. So the discussions that you have with the founders, which is the key, are grounded based on a very detailed analysis. So for me, it feels so much better that our discussions that we have with founders are much more around the future, what they do, the difficulties they have, instead of having to debate the definitions and try to all the time have this feeling that they try to hide something.

7:44Henrik Landgren:So for me, it's like, this is the hygiene thing. It has to become like this in the future for every investment process, I think. And you had a lot of experience as a VC. You probably made a bunch of investments. You're now getting all this amazing data for companies that are looking for funding. Do you ever invest yourself? Do you ever meet companies where you're like, wow, this is a phenomenal company. Take my money. Yeah. No, so we build this platform as a platform. So we work with partners. So we are connecting on the other side then with different partners of different asset classes. Yes. So we have an equity investor network where we have 20, 30 something equity investors hooked up.

8:32Henrik Landgren:We know all their profiles, what they look for. So we can offer matching towards those equity investors. And then we have funds and banks that offer also non-dilutive products. But very well, of course, I'm also like, shit, I'm really, really interesting in these companies, some of them. I really see that this has this super potential. And I do some angel investing on the side as well, but it's very limited. so far, maybe a bit more in the future, who knows. But it's really fun to be, like, stay on top and stay in the market to see what's coming out every day. Yeah, amazing, you know, it seems like, yeah, I started my whole content journey in the financial results of UK startups.

9:15You know, in the UK we've got Companies House, and so the very first tool or product I tried building was a database modeled on the P &Ls of the top 100 UK startups for the past five years. And it's just like the data that you get and the stories that you can tell, I just thought was amazing. So for you to be able to sit in this position and to see the data kind of like right in front of you must be an amazing position to be in. It is, it is. Over the time that you spent building this company, have you seen the type of companies change, the type of financial profiles change? Have you seen different types of companies exploring different types of funding models?

9:48What's changed, I think, over the last few years as the wider tech market has changed?

9:51Henrik Landgren:I mean, it has been a dramatic change, I think, for the whole market. whereas when we started this we thought it was much easier to get access to equity money at that time 2021 and we thought if we're going to introduce a non-dilutive product here, a loan, basically everyone hated loans at that time even with my time at EQT we built this platform called Mother Brain and I remember that we could identify that if the companies have debt it's a very bad sign for a venture investor because that must mean, this is the 2021 version, this must mean that the company hasn't been able to get equity. Oh, interesting.

10:33But that has changed now, right? Yeah, okay.

10:35Henrik Landgren:Because the equity markets have been much more tightened and even equity investors now deem that you need, or they require almost that you have to be not profitable maybe, but close to profitable. Yeah, yeah. We have a very solid plan to become profitable quite soon. Much more risk averse versus what they were before. Yeah. so and then the gap to non-dilutive funding where you also have the same kind of you want we want to be able to see that you can stay unprofitable we want we want to understand that there is an underlying profitability or path to it that you can choose if you want to yeah so the lines are blurring yeah so we can see that that's good for the ecosystem because now we can see it's not as black and white anymore and there is more asset classes out there and some more capital for founders basically interesting okay i also want to talk about the wider ecosystem Sweden's having this crazy moment there's like amazing founders I just had Lucas on we've got Sana acquisition Lovable, Lagora what's it like building in Stockholm at the moment?

11:35Well I think finally that's what I'm feeling

11:39Henrik Landgren:because you know I was at Spotify with Daniel and Martin and the rest of the crew there in 2010 it was so much fun but I always felt so alone because before Spotify I came from McKinsey. And for me, because I had this tech background, I was a developer back in the days, I always wanted to come back to tech. So for me, that was a natural jump. But for all the rest of my friends and my colleagues, they're like, what are you doing? You're throwing away a McKinsey career to go to what is startup? What is that? And for me, it was a very brave decision. Yeah, yeah. At least if you compare it to the common, like the feeling in the market at that time.

12:20Yeah.

12:20Henrik Landgren:So I felt very alone. and I felt like we were in such a big bubble at Spotify. We felt like when we were traveling to New York and San Francisco at that time and came back home, it's like, this is the only place in the town where it feels like San Francisco. We have this belief that we can build something for the entire globe from Stockholm. And you can be that crazy ambitious. You can actually make it happen from here. Everyone else, like, what are you talking about? This is nonsense. It's not for real. and then I felt like that's a shame and that's what I really like now now everybody talks about that and there's really a belief that people think that you can actually build generational big companies for the entire globe from Stockholm and that's really really good and it's natural when you study other markets that you have to have these waves you have to have some successes and then people leave and then start new ones and though the people that have seen that world are then bringing that culture and then like mindset with them to the next one.

13:24Henrik Landgren:So it's natural that it has to be in these waves. And I think now we are in a second or a third wave or something. So I think this is really good, like brewing ground for the ecosystem in Stockholm right now when we've kind of hit critical mass because now also all the investors come here. So yeah, it looks good for the next wave as well. Yeah, and we're starting to see those sort of younger founders, you know, raising their first round on sort of air applications and kind of rising through the ranks. And, you know, one of the things that Lucas mentioned was that how the size right now is perfect.

13:54You know, you have a big thriving ecosystem, but it's still small enough to be quite tight knit.

13:59Henrik Landgren:Yeah. Which I think is a really interesting point because we don't have that in any other European. No. You know, especially Stockholm itself is a relatively small city. Yeah, it is. Because in London, we have probably a bigger ecosystem. Yeah. But completely spread out. Yeah, I agree. And there's no real connections between this, which I think is really interesting. I also want to talk about your career path. Going into McKinsey, that's exactly what Lucas did. Yeah, exactly. We're actually both going to their panel tonight. Oh, really? How funny. Yeah, is that, was that like the common career path?

14:27Is that like the aspirational route for, you know, young Swedish people living in Stockholm? Was that seen as the thing to go for?

14:33Henrik Landgren:It's funny, right? Because as I mentioned, I started coding when I was six years old. So I programmed my whole youth. Nighttime, because at that time, no one thought it was cool to code, right? And then I worked as a developer full-time in the golden.com era back in the days. Built some things then. But I've always also had this business thinking that I'm not going to be the best coder out there, even though I was pretty good, I think. I was probably more interested in what you could do with it. So I wanted to understand businesses, how they work. So I had this entrepreneurial drive already then.

15:11Henrik Landgren:That's when I chose to go to university because I needed to learn that. I couldn't really get, like, where should I learn that? So I went to study. I also love maths. So I have a master's of science. And I chose industrial engineering and management to get, like, a full engineering degree, maths and business. Everything. Everything. All bases covered. But that's interesting because I've been all my life thinking that I'm a generalist. Yeah, yeah. But after years, I've learned that I'm probably more of a specialist in being a generalist. because being a generalist is something that you could be really good at.

15:43Henrik Landgren:So if you are deep but have your specialties actually to connect the dots, that's kind of who I think I am. But after school, at that program, the main path was to go from industrial engineering, master of science, to a strategy consultancy. That was like the main career track that you did. And I felt after five years at university that I still didn't know enough about the business side. I coded all my uni as well. So I knew that. I didn't want to yet leave the business side. So I wanted more, like, how does businesses work? I remember I had that question. I wanted, like, this seems so complicated to run a big company.

16:24Henrik Landgren:How does it work? How does a boardroom work? So that's when I thought, let's go to McKinsey. So I went there and followed the trail. I was lucky to get in. It was such a good school for me, like, the best school to understand businesses. um they threw me out like my second day to to like have an interview and coach the cfo of a very big company wow and i was like oh my god i'm so much out of my comfort zone but given the prep they did to me i could feel already then that i could be helpful right so something's cool um but after three years there uh my i was on a good trajectory but then i was like okay now i know i know how it works i know so i felt like my my marginal uh learning is what i thought was like uh reduced and i i there was i also remember me thinking that this is too predictable so i know that where i will be in 15 years i know the latter and that i realized back then that that's i get bored by that yeah to know what's going to happen yeah so i needed to get back to tech i needed to build things myself i didn't want to be a consulting consultant to consult others what to do with and be frustrated that they don't, I want to get into the weeds and do it myself.

17:36Henrik Landgren:And I wanted to go back to tech. So that's when I joined Spotify. I love it. Yeah. My first job out of university was also consulting, construction consulting. And I did it for three years. I felt the same thing. I always loved tech and I wanted to get into it. So I left to join as early stage company as I could. So I joined a 30 person company here in London. I loved it. I absolutely loved it. Similar tracks then. Yeah, and I interview a lot of consultants who end up becoming great founders. And it's interesting because often people are very critical of consultants for being theoretical as opposed to execution-orientated.

18:10Henrik Landgren:Yeah, but I think there are a lot of those. A lot, but there are so many great founders who've learned the trade as consultants and then left relatively early and then gone to build great businesses. But I think if you can find someone who is intrigued by that model because they want to learn and they want high pace and they're ambitious together with someone who has the craft in them, like someone who builds things on their own, preferably using code, then I think you have a good mix in your profile. And there are similarities between tech and consulting in the sense of the intensity. It may not be like building execution, but you still have so much to do and you're working so hard.

18:53Henrik Landgren:But I think it's also a lot of things that I learned from Hintze back then was around how, because there were always so short projects, you need to be really, really disciplined in what you do, how you spend your time to analyze things. Because you have to come with a conclusion and recommendation in like 24 hours. so you have to be smart where you spend your time yeah yeah don't to don't like know when you should stop yeah and and don't ask more questions and instead it's more worth to jump onto the next thesis yeah yeah uh and that skill is i think the the key that i use so much when um at spotify where we you know it was the time when the whole big data thing was new yeah uh so we could boil the ocean well the ocean wasn't you know that expression right yeah and at at mckinsey even before the big data the bodily ocean was like try to look turn every stone look at everything yeah but it doesn't give you much right you have to think about what do you really need to make the the conclusion um and uh that is so important when you have a lot of data and when you can do much more right and i think that's where a lot of projects have have gone wrong that they're just producing more and more charts for for for what like they kind of lose track of what's the question here and for now what we're building now into the to the platform we're now building a SaaS platform to help investors any investor to actually make those investment decisions amazing just not just provide money but actually use our data analytics platform as an investor and then we build that in into a analysis tree run by agents so we help them to define exactly how they should break down the whole investment analysis question into a mesey tree yeah yeah it's really powerful when you can define every single node in that agentic tree yeah to exactly what you should do in this tree to understand this part of the question and then how it should surface up so i think those learnings from my mckinsey years plus ai is really what can make ai um valuable in in this age amazing well look henry thank you so much for joining me it's been a great conversation

20:58Thank you.