Jon Krohn: 00:00 It’s the perennial question, should you build or buy your AI solution? Well, what if you could have your cake and eat it too? Welcome to episode number 932 of the SuperDataScience Podcast. I’m your host, Jon Krohn . Today’s guest is Larissa Schneider, co-founder and COO of Unframe, a startup that has raised $50 million in venture capital to bring you AI solutions as fast as when you buy them, but also as tailored as when you build them yourself with returns on investment in weeks instead of years. Sound too good to be true? Larissa spills the beans on how she and her team do it. In today’s episode. Enjoy.
00:37 Larissa, welcome to the SuperDataScience Podcast. Great to have you on. Where are you calling in from?
Larissa Schneid…: 00:41 Thanks for having me, Jon. I’m in Berlin today.
Jon Krohn: 00:44 Excellent. And Berlin is the home of frame ai, isn’t it?
Larissa Schneid…: 00:48 Well, we have a few homes actually. We’re kind of pretty global wherever the customers are officially headquartered in California. We have a pretty large r and d team in Israel and in Tel Aviv. We have our operations team in Berlin and then the go-to market sales teams between Florida and New York, London all around.
Jon Krohn: 01:15 Tell us a bit more about Unframe because it’s a business model that I don’t think I’ve seen before and it seems like it’s working really well for you. You recently raised, well, I guess the total of the raises, the venture capital raises you’ve done so far comes out to $50 million, including I think a relatively recent announcement. You can correct me on these timings and exact numbers, but this unique model that you have seems to be working out for you, so fill us in on what it’s
Larissa Schneid…: 01:42 Yeah, sounds good. Yeah, we started the company in roughly March last year, raised a seed round, then raised another round, so a round in March this year. And I think from day one we actually did everything against the book, so really not following the typical playbook. If I go back to the very first VC pitches we did and we came up with this crazy business model and everyone’s like, but you need to focus. You can’t start with doing something for multiple personas and multiple industries and multiple products from day one. And we set challenges we can because with ai it’s kind of, everything has been reset. We were rethinking everything that we’ve done like the same way forever, and we’re doing it again and we’re doing it better and more efficient and we are really pushing the boundaries in that regard. So when we came out with our out of stealth announcement at the beginning of April this year, we actually came out with, we call it a managed AI delivery platform.
02:41 And in very simple terms, we often actually refer to this metaphor of Lego bricks. So we built an AI platform that is made up of hundreds of different building blocks. So we looked at all of the most complex, the most challenging, the most time consuming problems that enterprise leaders face when building and deploying AI solutions. We packaged it and we use it hundreds of times over for all kinds of enterprise use cases. So someone gets something that is super tailored to their specific environment without having to prepay or no commitment, no cost involved until they actually feel business value. And that’s what we came out with from day one. And yeah, it’s been working well,
Jon Krohn: 03:26 No commitment, no cost involved until they feel business value.
Larissa Schneid…: 03:31 Yeah, absolutely. That’s how confident we feel about it. And it’s funny, sometimes people are like, you sure A POC is no cost? And we’re like, yes, it really is not because that’s how we build the business and that’s how efficient we made the platform. And it’s really in Tech Unframe seems to be the only one doing it that and the comparable that Chime, my co-founder always mentions, imagine you are getting a new home and you want a sofa, your custom sofa that fits your specific space and your style and your angles and whatnot, your measurements. Well try to find a sofa bill that says, sure, I’ll build it for you, totally custom to your measurements and then you can try it and if you like it, you’ll pay me. Otherwise, no problem, I’ll take it back for free. You won’t find that, but at timeframe you can.
Jon Krohn: 04:17 That is wild. And so then how do you know that they’re not getting business value and not telling you?
Larissa Schneid…: 04:24 Well, yeah, I mean that’s always a challenging area I would say because what we’ve seen a lot in AI specifically now is there’s been so much board level pressure, so much executive visibility on the topic of AI that a lot of people are like, let’s just execute on it. What can we do? What can we build? Let’s just do something. And what we are really pushing for is for them to start with the ROI and the KPIs in mind. So what are you actually trying to achieve? Not just like which tech do you have at your fingertips that you could use? And so we really work, we call it a business impact analysis that we do with the customers upfront and say, we want to build one or two or three different POCs with you, but let’s try to find the one that actually moves the needle and moving the needle for you means X, and if we hit that, then let’s move to licensing.
Jon Krohn: 05:15 I see, I see. So you’re kind of with them from the beginning on some metric that they’re looking to hit with this particular feature or aspect of their product, their platform. And so it sounds like, correct me if I’m getting this completely wrong, but it sounds like Unframe is kind of mixing both services and SaaS together. It sounds like you’re able to have lots of different ai, AI platform options for your clients that are kind of ready to go, but then you customize them. So there’s some services, some adaptation to make the couch say fit perfectly into their space. Exactly the color and the fabric that they want. So it is a blend.
Larissa Schneid…: 06:04 It is a blend because we think that is very important right now because we’ve moved so far beyond this moment of generic software. It’s like one size fits none, and so we really want to make sure that we offer that, but we don’t charge for it. So all of our services and our AI product leaders that work on the specific tailoring off the solution, everything is included in our subscription. So you don’t have any hidden costs, no additional charges that just pop up that you never planned on having.
Jon Krohn: 06:35 And now the subscription that’s got to be also bespoke. Presumably some of your clients are using lots of functionality they might add over time. A big client of yours might have lots of different pieces of functionality within their enterprise that depend on you. And so presumably there’s different kind of tiers of subscription.
Larissa Schneid…: 06:55 Yeah, we do, yes, but we try to make it as simple as possible as well. It’s really fast. It’s all about simplicity. We do t-shirt size pricing, so depending on the complexity of your use case, small, medium, large, extra large. But yeah, we do it per solution per year. And some of our customers, as you say, they started maybe with one or two use cases, but now they realized how important Unframe is for their strategy and now we’ve moved to 5, 6, 15 different type of solutions that they’re running Unframe at this stage, but they know how much they’ll be paying.
Jon Krohn: 07:30 Cool. Congrats. And it sounds like you co-founded this business, this novel, this completely new kind of business model based on the idea that we’re now in a completely new kind of era because of ai, and so we’re going to need to have the businesses of the future are going to have different needs. The kinds of company that you can be building is different than ever before. And so let’s try this completely new kind of business model, see how it goes, and you guys are crushing it. Congrats.
Larissa Schneid…: 08:02 Thanks. Yeah, actually there was three of us, or there’s three of us as founders and all three of us were working at No name Security before, which was Shai, who’s our CEO. He co-founded that business before and we’re just seeing this hype around AI everywhere. Chad, GBT came in the world, changed all of our personal lives, but then we’re like, how are we still working with business software that does not really give us the same experience and doesn’t really, there’s some AI tools that everyone was trying, but it didn’t give that impact. And so we said this is a good time or the time to do it, and we jumped on it and have been building ever since.
Jon Krohn: 08:43 Excellent. Very cool. Are you able to go into maybe one or two case studies that you might not be able to because maybe everything that you do is proprietary for your customers, but it just occurs to me that we’ve kind of been speaking in hand wavy ways about you have some platform offerings that are somewhat off the shelf, then they’re customized. If you had one or two examples you could talk us through, we might be able to really visualize this.
Larissa Schneid…: 09:11 Yeah, totally. So there’s three main areas that we work with customers on and that’s usually where we start with customers. The first one is all about observability and reporting. So think about BI analytics and search type use cases. I’ll give you some examples for that. The other category is extraction abstraction. So working with a lot of unstructured data, we are really enterprise focused. So as you can imagine with some legacy large global corporations, you have a lot of types of spreadsheets, financial statement reports, contracts and so on. And then the other category are all of the AI workflows and automation areas. So all of the actions and decision making that can be assisted with ai, and let’s say for example, one of the top use cases we see a lot is around IT operations. So we work with one of the Fortune 10 insurers in the US and they approached us with their IT operations team having an observability department, 26 people, and all they do day in day out is have six tabs open on their screens with ServiceNow, JIRA, confluence, Dynatrace, Splunk, the whole stack.
10:25 They’re trying to manually correlate with these logs and all of the different tickets that they have and they’re like, that’s not efficient anymore in the day of ai. So what we gave them is like a one pane of glass where they can see all of the correlation between the different tools made by ai. So for them a lot less manual work, AI being able to see patterns at a much faster level, reducing time to resolution of all their tickets and just a lot more customer experience that they can offer. So that’s a very common use case for us as well as contract management. So we do a lot of that and think about contracts and real estate. It might be a lease agreement in HR businesses, it might be an employment contract or a legal MSAs, all kinds of contracts where we abstract data because with many businesses that we see right now, they want to see the trend of their agreements, their expiration dates at scale, but everything just sits in a scan, PDF, somewhere in a SharePoint folder. And so we come in and we take everything that they already have and make sure that we provide them insights and value of everything that’s hidden there.
Jon Krohn: 11:32 Really cool. I’m crystal clear now in what you do that was so helpful. And so does this mean that you end up being in a fair number of situations where a client comes to you and says, we have this long list of needs that we think can be solved with ai, and you say, okay, from all of these or maybe even some additional ones that you haven’t thought of, these are the ones where we can make a big impact. These are the ones where we have some kind of ready-made solutions ready to go. We can customize ’em just for you, but then there are these other needs that you have where that isn’t our core expertise. Is that right?
Larissa Schneid…: 12:10 Yeah, that can happen. Obviously while we want to tackle a lot of problems, there are some great other companies out there and we don’t have to do every single thing. Even if we can, we really focus on the business systems, the information type systems. So if someone comes to us and says, I don’t want to pay for my Slack license anymore because it’s gotten expensive, we’re probably not going to build them another Slack. But there are actually a lot of use cases where we see a lot of repeatability and that’s where those Lego blocks the building blocks of our platform come back in. So think about the first time we ever saw unstructured data, which was a lease agreement and commercial real estate. So think about 80, 90 page documents. We had not gotten that building block as part of our platform, but we build it and that’s where we build it so generically that now when a financial statement in Excel came in or a CV or image type use case, we already have 95% of those capabilities and we just need to change the last few and then put it into the library of building blocks that we have available and can use it again tomorrow and the day after for any other enterprise customer.
Jon Krohn: 13:16 So this partly explains to me why it’s so important that you and your co-founder Shy are involved in founder-led sales still today. Even though you’ve grown so much, even though you’ve raised $50 million, it sounds like one or both of you are still involved in some way or other with the sales cycle to all of your clients. And now I can see why that helps you identify what are the next Lego building blocks that Unframe need to build? Because it sounds like, oh wow, actually three times this week I heard a client say that there’s this new kind of thing that they need and so maybe that’s a good place for us to move next.
Larissa Schneid…: 13:52 Yeah, absolutely. I mean, while we have an amazing team now in the company has grown crazy, we’re close to a hundred people in the next couple of months here, so we really have an amazing team around the world, so we can’t be everywhere all the time, but we try to be in as many as possible because for a lot of our customers now, and as I said, we work with large enterprises and sometimes when I meet people a few weeks ago, we had a conversation with say, an AI leader of a top three Wall Street investment bank. And usually in my normal pitch, I always say, you probably have 200 to 300 AI use cases mapped already. And she’s like, I wish Larissa. And I was like, what do you mean? I was like, I didn’t know what to expect. And she was like, my backlog currently is 1,670 use cases that the business has brought to me that I have to execute on by the end of 2026, so I could have as many internal engineers as I possibly wanted. I will never get them done. So I need a scalable approach. And so that’s how with our pricing model and the business value that we are showing them, the idea at Frame is really not just try to maximize returns on a single use case, but them feeling value and coming back for use case number two and three and four and so on. And that’s the partnership we want to build. We don’t want to be just a vendor. We really want to be there to execute on AI strategy with our customers.
Jon Krohn: 15:18 It makes perfect sense. I feel like if I was in your situation, I might be scared because there’s reports. So I did last month, I did a five minute Friday episode, episode 9, 2 4 on this report out of MIT. You probably know about this, Larissa, it’s from Amanda group that said that 95% of AI projects fail to show value in production or never make it to production. So it’s only 5% of AI projects that get into production and then provide business value. So if that was the case with Unframe, of course your whole business wouldn’t be tenable because 19 out of 20 projects you’d be giving free POCs, the customer doesn’t get value, and of course then you wouldn’t have a business either. So somehow you’ve found a way to get a return on investment from the vast majority of the AI projects that Unframed does, how do you do
Larissa Schneid…: 16:16 It? But actually when that report came out for us, it was like finally someone is seeing it because that’s what we’ve been preaching the whole time, and that is what it really is, the scientific proof of why our model is so superior in this space, to be honest. So think about the other type of AI platforms that you have right now. You have the prompt to app generator. I want a tool that does X, it’s not enterprise ready, but everyone loves it, everyone loves playing with it, or you have all these agent builders, but then you end up, and actually I just had a conversation with a large insurance company on the west coast just this week. He’s like, well, our business leaders, they come to us and they say they want to build agents. And I was like, that is fantastic, but for what’s like it doesn’t matter.
17:08 We just need to give them something as the IT department and they can build their own agents. It’s like, do you really want to give them something where you don’t know what they’re going to do and you’re going to have hundreds or thousands of agents that are not maintained that are doing autonomous actions inside your business? You’re highly regulated. He’s like, actually, no one has ever pushed back on me like that. I was like, that’s why the business impact is actually important for us and we don’t want you to be ending up with all of these resources like whether that’s time or money or both spend on POCs, but you actually don’t know what you’re working towards. And so that’s actually for us, that report was gold.
Jon Krohn: 17:45 So yeah. So you’ve commented on the report, but then how to answer my question, how do you pick projects or how do you pick KPIs business indicators that are going to be a success? How do you get in that 5%, Larissa?
Larissa Schneid…: 18:00 Yeah, no, absolutely. So usually when we talk to a customer, they have a few use cases that they have in mind, and what we do is we do a bit of a deep dive on those. So what are you trying to achieve? And usually there are some areas either like you’re trying to reduce headcount, you want to be more efficient, you want to improve the response time to customers, to tickets, or you want to reduce costs. And so we really try to drill down with our AI strategists and which one is business impact? Where does the budget, where does the team actually buy into the idea? Because you can have as much business impact if you’re the team that you’re building towards is reluctant to change in that regard, it’s not going to adopt the solution, you’re not going to move the needle for your business. So that’s about five, six different factors that we look at. We do a half an hour hour brainstorming session and usually at the end of it you have a pretty good idea just asking the questions and seeing their responses, which one you should start with, and that’s usually what we would tackle. Then if possible, we get a few data samples that they share with us and if they’re receptive to that and the business user actually works with us on getting that POC stood up, then you’re on a good path.
Jon Krohn: 19:13 Cool, great tips. So when any of our listeners are out there considering building some AI functionality, how do they decide whether to build or buy or do both? What are the kinds of trade-offs?
Larissa Schneid…: 19:27 Interesting one, because also conversation obviously we see on the news all the time, but also realistically in conversations, especially early ones that we have, we see this a lot. Usually they tell us, well, we’re currently in the process of deciding for ai. Do we build or do we buy realistically an enterprise level? It’s probably going to be both. You can’t possibly be building everything not possible, but also maybe you shouldn’t be buying everything either. I think most cases like this probably 80 20 rule by the things that you need to get quick impact on your business, stuff that is simple use cases, maybe quicker wins those things. But what you could consider building, and I totally understand when you want to build that in houses, anything that has IP ownership concerns, you want to increase your strategic positioning, things like that. So I mean think about you are bank, your core banking system, I understand if you want to build that or customer interaction type things, but if you’re talking about your IT ticketing system or knowledge based search marketing content generation, do you really need to build that and have this backlog and have your internal developers maintain that for the rest of their lives?
20:44 It’s probably not necessary and you would be significantly more cost effective and faster to just buy it, in my opinion.
Jon Krohn: 20:52 Gotcha. So when you say 80 20, 80% roughly should be bought, use cases should be bought for speed and then the 20 a smaller set can be built for strategic advantage in some core business areas?
Larissa Schneid…: 21:06 Yeah, yeah, I would say that’s a fair assessment and I think then you have this hybrid, the not black and white type approach, which is where we fall in a lot because you could consider us being on both sides of the coin here or somewhere in a perfect middle I guess because you are getting all of the advantages of the build without the trade-offs of having to manage and govern and keep up to date because reality, I mean we all see it. AI moves so fast by the time that you launch something house, it’s best in class on that day, but the following week I can promise you there’s a new model, there’s new AI capability, new research that has come out, it’s probably already outdated again.
Jon Krohn: 21:54 Right. So it sounds like a lot of listeners should be thinking to reach out to Unframe.
Larissa Schneid…: 22:00 Well, I’m always happy to chat with anyone for sure, and we will be also pretty transparent if we say this is a use case that we saw another really cool startup out there that does really well in that regard.
Jon Krohn: 22:11 Great. Yeah, so I have this quote from you. I’m taking this from an email that you sent me, but it sounds like with Unframe they can get the best of both worlds in a way because working with Unframe can be as fast as buying, but as tailored is built, getting the outcome, getting that ROI in weeks instead of years on some particular functionality.
Larissa Schneid…: 22:32 Yeah, absolutely. Usually the turnaround time in our process that we work on with customers, so we do this deep dive, the workshop on specific use case and usually the following week we already meet again and show them the complete production ready solution that is built on our platform and tailored completely to their use case, some caveats. If customers like we work a lot with regulated industries like bank, healthcare, insurance, finance, obviously sometimes they don’t want to hook up in their production systems from day one. Totally understand we can work with Dami data, we can mimic integrations so they can actually see how everything works and only at a later point try the full integration. We’re flexible in that regard, but it really is a matter days and yeah, that’s the cycle we operate in.
Jon Krohn: 23:18 Really cool, amazing business you have there. Larissa, as one last kind of general technical question for you, it seems like the common thread that I’m getting across this conversation is that if people want to have successful AI projects in their organizations, they need to be clear on what the KPIs are upfront. So whether that’s cost or time or accuracy, they need to have that clearly structured so that whether they’re working with Unframe or whether they’re doing this on their own, how do they know the project is a success unless those KPIs are defined upfront?
Larissa Schneid…: 23:58 Yeah, totally agree. Initially when we first started, there was not much out there around KPIs to be honest. So people were just experimenting and then in some stage we met a company and they were so crystal clear with us. They said, here are a thousand data samples When we go through the testing for this POC, we want 96% accuracy at a completeness rate of responses of over 90%. I’m like, okay, wow, this is like the first time and now this is over a year ago, but the first time that we saw someone was so prepared and they really knew what they were working towards and that really changed everything for us because finally there was Tructure and they knew, okay, this is what good looks like. Because in the past when maybe two years ago they started experimenting with AI and you’ve probably seen it in chat GBT and so on as well.
24:50 You try to get something but you iterate and you’re like, actually the idea was great, but did I really get the response I wanted to? No, maybe not. So you try a few other, maybe you try your Gemini and whatnot, some other models and the same way that they do it as well, they are probably not only going to evaluate Unframe, they’re probably going to look at others as well and having those benchmarks and knowing what you are comparing towards super important more so than this general idea of let’s see what we could possibly do with ai.
Jon Krohn: 25:20 Fantastic. Larissa, a really helpful episode for us. You’ve provided so much useful knowledge in such a short period of time and you express all these concepts so clearly it doesn’t surprise me that you built such a successful company when you’re so good yourself at communicating ideas. So crisply and quickly before I let you go, Larissa, we always ask our guests for a book recommendation. Do you have anything for us?
Larissa Schneid…: 25:49 Yes, actually, and I am glad I got a two minute heads up before, so I looked at something that I had on my bookshelf and I’ll show it to you, is this book called Fact Full by Hans Rosling. I really love it. I often go back to it because you can really see so much information and data that we have out there. You can interpret it from multiple angles and really make up your own story along the way and just keeping our mind open, seeing the other side of the coin when it comes to analytics and data, always an important one. So highly recommend.
Jon Krohn: 26:22 Very nice, great recommendation and for people who want to follow you and get more of your thoughts after this episode, where should they do that?
Larissa Schneid…: 26:30 Amazing. All kinds of places. Unframe AI is our website. My email address is Lars at Unframe. AI can find me on LinkedIn. Happy to chat. As I told you before, currently we try as many customer meetings to be one of us founders on there at any given time, so lots of learnings and really cool to see how fast the space has moved in the last couple of years.
Jon Krohn: 26:53 Great. Larissa, thank you for taking the time out of your busy schedule to speak with us today and maybe we can check in again in the future and see how the Unframe story is developing then.
Larissa Schneid…: 27:03 Sounds great. Thanks for having me, John.
Jon Krohn: 27:08 Thoroughly impressed by Larissa Schneider’s Brilliance Drive and her ability to crisply communicate. It is not at all surprising that her company Unframe is enjoying so much success. In today’s episode, Larissa covered went to buy AI solutions, which is most of the time versus when to build them, which is only when it’s differentiating for your business. She also talked about how the key to AI project success is to always start with return on investment, what’s it going to be and KPIs, key performance indicators. The winners define success upfront, be it cost, time, accuracy, metrics rather than just asking can we build it. Alright, I hope you enjoyed today’s episode to be sure not to miss any of our exciting upcoming episodes. Subscribe to this podcast if you haven’t already, but most importantly, I hope you’ll just keep on listening. Until next time, keep on rocking it out there and I’m looking forward to enjoying another round of the SuperDataScience podcast with you very soon.
