Revolutionizing AI: A Journey with Ben Gutkovich of Superlinked

And my special guest is Ben Gutkovic. And hopefully I'm pronouncing your surname reasonably accurately. Co founder of superlinked, a company revolutionizing the way organizations build machine learning powered software. So superlinked excels in transforming complex data ranging from text to video into what are called vector embeddings, which hopefully Ben will explain a little bit more about what that means later on. But that's the core of AI data processing at an unprecedented scale. With a nine and a half million dollar seed round led by Index Ventures and Theory Ventures, Superlink is poised to meet rising market demands and expand its product capabilities. So we'll explore Ben's journey, the evolution of superlinked and insights into the future of AI. So Ben, welcome to today's show.

Thank you, Gary, great to be here.

Great to have you. Can you elaborate on the primary value proposition of superlinked? What problems does your product solve for your users?

Absolutely. So we are a platform for machine learning engineers, data scientists at the moment with the vision to support software developers even in building those applications in the future. At the moment the goal of our platform is to help those data scientists and machine learning engineers to launch gen AI powered applications. And not only, but you know, the focus now with all the hype is around generative AI that launch those applications to productions quickly while retaining control over the data they send to those generative AI models and control on the quality of the results, improving that quality based on their understanding of the business and the users. Maybe just to elaborate a little bit on the vector embeddings. So vector embeddings is basically the language of generative AI models, large language models, everyone is familia GPT from OpenAI and other models, from Anthropic and from Mistral and other companies which have been in the news. The way they work and the way they work fast as well, if you experience ChatGPT, is that they churn all the data text into vector embeddings. And what is a vector embedding? It's basically a representation of data as a string of floating point numbers. Now, computers work really well with floating point numbers, much better than with keywords, which traditional search is based on. And so those applications are able to retrieve the right answer very quickly using algorithms such as approximate neighbor search, which is basically an algorithm to find vectors in a large vector space, multidimensional vector space. So where we come in is that we help turn data which is unstructured by using those large language models, but also structured data which the enterprise really relies on in their use cases and their business case to the same vector embedding, helping them actually control what goes into the vector embedding and retrieve the information based on their use case. I can jump into an example. So if we speak about an E commerce website, every product has an image which could be considered unstructured data, a description, again paragraph of text which is also unstructured data. And for that they can use large language models to turn this data into vector embeddings. However, for each of those products there's also a price which is a structured data, there is a level of discount, there is a brand, a category, a season, perhaps some data which is not even visible to the user, like the margin they make on the product, like the stock levels of that product. And they might want to take all this data into account when recommending the right products to the right user.

Can you walk me through your product's evolution from a full stack, real time recommendation or personalization solution to more of a horizontal platform? And how did the launch of ChatGPT Influence or Assist in this transition?

Absolutely. So we started and a lot of that goes to my co founder Daniel, who worked as tech lead on YouTube, working on ad prediction models. So he was helping advertisers on YouTube understand how the ads will perform, how many users will see them, how many will click on them, et cetera. And those models back in, you know, six, seven, eight years ago, they've been using vector embeddings already at Google and they had the whole infrastructure and tooling to use those vector embeddings in this and other context as well. Google Search has been was powered by vector embeddings for since 2017 already. Right. So that's something that the big tech companies have been using for a while now coming out of Google, starting to work on Superlink, building our recommender system. Daniel brought this understanding, or actually understood, that those tools that Google has been using internally are not really widely available to the public and definitely not to enterprise client with limited technical capabilities. You know, the ones that don't employ a thousand machine learning engineers to actually use in their products and make their experience for the users much more attractive. And so we started from building a recommender system using those techniques and started working with clients to implement it in production now. And that was still before the ChatGPT actually came out. That was in 2022. Once ChatGPT came out, the focus and the hype around large language models and generative AI of course exploded right to the level of me speaking with my hairdresser about ChatGPT and how they use it in advertising, et cetera. So everyone is pretty much familiar. It is the application that achieved 100 million signed users, the fastest in history, right? I think just a couple of months. And with that in mind, we actually learned that people are trying to build those systems, but really struggling. So trying to build recommender systems, semantic search analytics system based on large language models, while taking into account the other data which is crucial to provide high quality results. And we learned that actually a problem that we solved in our Full stack recommender system is a big problem that many enterprises actually struggle with. And then talking to investors and talking to executives and large organizations that have been struggling with this challenge, we decided to go on unbundling that solution so that we don't provide a full stack solution for the client where they might already have some pieces of the stacks. For example, our solution had its own database built in, while an enterprise client might have another database they want to use because they already have the contract, because they already have their data in that database. So to do that, we unbuttoned the product, expanded its capabilities in terms of not only serving recommender system use case, but also a bunch of others, and made sure that we can ingest data from multiple sources and store data in multiple, multiple types of databases based on the use case and the desires of the client.

Got it. Now, you recently raised a significant funding round led by Index Ventures and Theory Ventures. What did you and Daniel do to meet the right investors at the right time?

Absolutely. So the timing was right, as we discussed earlier, because of the launch of ChatGPT and the hype of LLMs and us actually building a system based on those print principles which others were trying to use to build their own solutions. Right. And then I think one, one aspect that helped us, we actually raised a precede round in 2022 from investors in the UK, MMC Ventures, episode one and Concept Ventures. And we still had quite a bit of Runway and we were not in a rush to raise, but our investors, especially from mmc, encouraged us to go and meet investors in the Bay Area based on their experience of those meetings and based also on the kind of funding that's been happening around large language models and generative AI at the time. And so we went there, actually, Daniel went there. I was in the middle of integration with a relatively large client of our system. And so the first thing was to make some introductions. And those introductions mainly came from the investors. And then the idea was, okay, you get the first meeting, you impress the person you speak with, you ask for the next introduction. So this was this kind of snowball of introductions, if you like. As long as you make a good impression and watch the vision that you outline makes sense to the investor, you can get other introductions. And that was basically the way we got to Tom Tungusch from Theory Ventures. Now Tom is been a managing director of redpoint, also a VC fund, for 14 years, and then set up his own fund which he called theory ventures in 2022. He's also well respected in the technical circles in the Bay Area. And when Daniel met him, he introduced him to a Stanford professor and several CTOs of those large enterprise companies which were struggling with a similar type of problem. Once they said, yeah, this is a problem that we have and this is a complex problem to solve, he was on board with the investment and then from there on it was very, very easy. Pretty much once he said that he's interested, we started receiving calls from pretty much everyone who wanted to get into the round.

And at that point, was Daniel located on the west coast or was he just there for a short trip?

So he went just for a short trip. Right. The goal was let's chat to some investors, build relationship, let's chat to some companies, potentially get them as clients, not really to fundraise. And I think that that helped us as well in terms of mindset. But he went there for two weeks and eventually as things started to snowball, he extended by another two weeks. And then after we actually raised around, we decided to move the company to.

The U.S. okay, and you've scaled your team in Hungary and Israel, you're based in London, Daniel's based in Silicon Valley. Why have you chosen those disparate geos for scaling? And perhaps just as importantly, how are you maintaining alignment and cohesion within your team working on such advanced technologies over so many different time zones?

Absolutely. So look, we started during the pandemic, right? And that was the era of let's get the best talent wherever they are. Right. And that's how we started working with developers in Hungary. When we were looking for our first developers, we actually discovered Hungary to be one of those undiscovered gems of Eastern Europe. Right. I mean, obviously, you know, Poland, Russia, Ukraine. Russia maybe used to be, but Ukraine is still quite a strong hub for technical talent. Hungary doesn't actually feature that much, but we were able to find great talent there. Actually, our first developer is now our engineering manager in Hungary. So that was a discovery. And once we understood that, we are going to build a deep tech company. And that actually happened with the recent round. We decided that we want to go for a hub type of approach so people are co located and can collaborate, can brainstorm, can get together in front of a whiteboard and can actually get to a solution which is possible in remote, but it's harder. So we decided to go for the hub strategy and then obviously strengthening the Hungarian hub, but also opening the Israeli hub for particular type of talent. There is a certain type of machine learning engineer who is also a software developer which we struggle to find a lot of in Hungary. And so me, being originally from Israel, decided to open Israel as a second hub. Now the way we manage it is that London is our business and operations hub. So salespeople, marketing people, operations we are hiring in London. Obviously London is a great place for talent as well. Hungary became the core engineering team. So they are building the infrastructure or the platform which is generalized for all clients. And Israel is more around services and building kind of very complex, we call it spaces. So for each of the different data types that I mentioned before that you might want to vectorize, we actually build a space which is a model from small to relatively complex that actually converts that type of data into vector embeddings. And so Israel, given the level of talent we are hiring, there is going to be that kind of research hub, but also client services, supporting clients. Also given a lot of companies from Silicon Valley have technical representations in Israel and R and D offices. And in terms of how do we manage this whole remote team, it's not easy, I must say. But one of the things we do is make sure that we write everything down. So we write a lot of documents, we use notion as our knowledge base and everything has to be documented so that anyone in any hub can go and check and make sure they understand what has been discussed and what has been decided.

Now the hubs make sense in terms of building out skills and culture in each location.

But.

And I get, you know, notion and zoom can make a big difference. But how are you building cultural alignment across the whole business? You can't do that just with hubs and just with notion and zoom.

Absolutely. So one of the things we've been doing from the start is actually getting everyone together. And now that the team is bigger, it's obviously getting a bit harder. But every four or five months we gather together in one location. We spend somewhere between four and six days together working, discussing, talking about culture. We've done our latest get together in April in Hungary. So we get everyone together, we talk about culture, we kind of restart the relationship if you like, or reengage, re energize those relationships and then continue and work somewhat independently, each in our own hub. So that's been very important and we make sure to talk about those things. Yes, zoom. But that's also something.

Sure. What are the key components of educating your audience is a highly technical, highly sophisticated audience. And how does Vector Hub play into that strategy?

Absolutely. So from the start, we raised around towards the end of 2023, towards the end of last year, and the first thing we did when we knew that we are going into that more high P and maybe somewhat chaotic large language model and generative AI space, we decided that we want to organize the knowledge around the usage of vector embeddings, the usage of large language models. How do you build AI apps or generative AI apps. And we set up an educational resource and a community around this of basically kind of shaping the way or communicating the way we think to the broader audience. I mean, because of all the hype, everyone is now an expert on generative AI. Everyone is an expert in LLM. There is a lot of content, there's a lot approaches. And so we want to make sure that the way we are thinking about this challenge and this problem is communicated in a structured and clear way, first of all to our audience or to our clients, but also more broadly. And the way we decided to do it is instead of us writing all the content, actually partner with influencers in the space globally. First of all, as a test, can we convince them that our approach makes sense? And I think we did. And then enabling and supporting them with actually creating content around some of those main challenges that people in that space, technical, sophisticated people, deal with at the moment.

And are you doing anything else to kind of build a wider community around Superlinked and around Vector Hub?

Absolutely. So I think one thing that really helps is, is our open source. So in March we decided to open source our basic product and the goal was again to build that community to make sure that people understand that we don't selling them some kind of snake oil, which there is a lot of these days. Right. So people understand what we build, how we build it and why it makes sense. So we are building that community around the open source. We are building the community around the Vector Hub and we are through the influencers, engaging their audiences around, around the topic as well. And so we're still not running a full community because we think this is something that you want to do. Right? And that requires a lot of dedication and someone who is actually dedicated to writing and engaging that community. Otherwise those communities don't really survive and thrive. But. And so we're not still running a fully open community for everyone. But that's something that we definitely have as a plan. But we run a community for our contributors to Vector Hub, which they really value as well, and which helps us also communicate our roadmap and our vision and through them communicate it to the broader audience and of course engage with the people that try out and play or maybe have questions around our open source solutions.

So you have your influencers, you have your open source solution, you have the community that you're beginning to build. Another part of your strategy, as I understand it, is to build out strategic partnerships. So can you discuss some of the partnerships you formed and how that fits into the large ecosystem of AI and machine learning tools?

Of course. And what we did between those two rounds, the precede and the seed round, we actually unbundled our solution, which means that actually we became a relatively narrow part of the modern data stack. Right. We are that vector compute layer that helps organizations turn complex data into a vector embedding, structured and unstructured. However, once you create the vector, you first of all need to store it somewhere. Right? And we worked with RADIUS for the past three years. So Radius is one of those enterprise grade databases that also has a vector search solution. So we've been working with them for about three years already on our recommender system. And so naturally they become one of our first partners. We're also partnered with MongoDB, which is another well known, well respected database with a vector search solution. So that's for storing the vectors. We also work on the ingestion of the data. So how do we bring the data in from multiple types of sources and multiple types of other databases. And so we work with starburst on data ingestion side. They're a kind of abstraction layer so they can take data from all the different sources and they can send it to Superlink platform for vectorization and storing in the vector database. And then Data IQ is another partner who, who is actually working with a lot of our clients, right. So they are this kind of platform for data scientists. And so we want to make sure the data scientists working with Data IQ are able to use Superlinked as well as one of the models. Right. To help them turn data into vectors. Of course we also collaborate with many other vector databases, but those are the four partners that we've prioritized to Start with also based on the relationship and also based on the type of broad appeal they have in the industry.

And let's go on a journey. Let's imagine that it's May, June 2027. So three years from now, what will Superlinked have become? What will you and the team have achieved in those three years?

Absolutely. So what we want to become or what our goal is, first of all, our vision is that vector embeddings, not only for generative AI application, but rather to pretty much, well, maybe not everything, but a lot of things you do with machine learning and AI, vector embeddings are going to be the common language of that. And the reason, again, there is a lot of vector database solutions in the space, there's a lot of models you might want to bring, and computers work better with numbers. We believe that vector embedding will become that common language of machine learning and we want to become the standard for turning data into those vector embeddings. Right. So whatever your use case is, and we currently focus on four use cases, but of course there is dozens of other use cases for using vector embeddings. Whatever the use case is, we want it to be as simple as clicking a button or marking a checkbox of turning your data into vectors through Super Linked, integrated with databases, integrated with data science platforms like DataEqu, et cetera. So that's our goal. Anyone who is turning data into vectors using superlint and who are the three.

People, the three entrepreneurs or technology innovators who've most inspired you to be who you are?

It's a great question. I think Sam Altman will probably be one. I mean with him it's just the clarity of the vision. I attended session with him around 2014 or 2015 when he was talking about those micro nuclear reactors which got me really. Yeah, really excited and I was like, wow, this is like really long term visionary thinking. And we now see with the things he does with OpenAI, that's, that's definitely, that's definitely an interesting person that I, I follow and I listen to the podcast and it's always super, super interesting. The kind of thinking he has, the long term thinking, I would say, you know, Elon Musk, he has some side or something.

They're not exactly best friends today, but carry on.

Yeah, yeah, no, exactly, exactly. But you know, I, I read his biography and I mean just, you know, the vision and the passion and the resilience he showed in his early career and how he kind of went all in on SpaceX when they failed and failed and failed time after time with their launches and eventually made it work and made it to the big and valuable company that it is. Right. We'll not talk what he did to Twitter, which I think maybe is not so great, but I think, you know, early on, definitely one of those figures that was inspiring for me and you know, maybe. Maybe a bit trivial, but, but, you know, Steve Jobs, right. Again, this kind of story of passion and execution and, and kind of going through obstacles again, more, more in the early career and, and something that's. That's definitely inspiring. And I think this focus on design, I think I see it a lot in my co founder as well. I mean, some of those combinations. One of the reasons that we started working together back in 2021, right. Is that he has this kind of vision and idea of how the future will look like and just paints the picture in such detail that you could really believe in it. And the second is this whole attention to detail, attention to design, making sure that things are really well positioned and also as a wording, but also in a graphical way, which is visually appealing.

And talking of your co founder, how are you and Daniel dividing up your responsibilities? Because you. You mentioned they're on about the hubs in Israel and Hungary, what they're responsible for. You walked me through what happens in London in terms of operations, sales and marketing. So beyond investor relations, what. What is kind of Daniel's area of focus? And how do you and he split. Split up your responsibilities?

Yeah, of course. So Daniel is definitely on the product side of things. Him being a practitioner in the space for many years, great understanding of the space and the ability to also design this vision for the space makes him a perfect CPO or Chief Product Officer. So it's definitely an area where he leads the technology side of things. And so kind of how do we take this product vision and translate it to the technology, to the eventual development that we do? That's something also that Daniel is taking the lead on and I'm taking the lead more on the business side of things. So go to market, marketing and sales, operations, of course, finances. I think we're not at the level where we need a CFO yet, but, you know, acting as one for sure. So. So those things are things that I help manage. And again, because. Because we split the team this way, it actually works pretty well because my team is mostly in London and we make sure to meet at least once a week and work together. Daniel, again, a bit of a challenge given that he is now in San Francisco, but he's mostly working with, with the Hungarian team. And for Israeli team, I actually brought in a friend of mine still from the AR in Israel. I mean, you know, most of the people you know in Israel are from the army days because that's, that's kind of this environment where lots of strong relationships create, create. And so I brought, I brought a friend of mine from the army days to be the lead of the Israeli hub. And, and you know, he's relatively senior, so he can be somewhat independent. But of course we all collaborate because, you know, he's working with clients, I'm working with clients. Daniel is, is thinking about the product vision and the roadmap. So there is a lot of that collaboration, which means somewhat early hours for Daniel and somewhat long hours for us to be able to collaborate with him in the evenings.

Well, it's been great getting to know you and to find out more about superlinked and the great team that you and Daniel have been building. Hopefully in 10 years time you'll be spoken about in the same revered tones as Elon Musk and Sam Altman. And I wish you and the team a wonderful second half to 2024.

Thank you very much, Gary. It was lovely speaking with you and you know, I hope what you say will actually happen.