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Today on the podcast, I want to talk about a really interesting startup called Datumo that just raised $15.5 million to take on scale AI. The reason why I think they're interesting is basically what they're doing. I think the industry and all the drama behind scale AI's recent meta aqua Higher acquisition investment thing makes it a really interesting company, but also how they were able to get their investment, they actually got it from Salesforce Ventures in I think maybe like a very unique way. So I want to dive into everything this company is doing and where the market is going to going in this area because I think it is an absolutely massive market as we've seen with Scale and Meta doing their, you know, $14 billion deal there. There's a lot going on here, so let's dive into it. Before we do, I wanted to mention if you want to try any of the AI models I talk about on the show, I'd love for you to check out my startup, which is called AI box. On AI box, I have the top 40 AI models. You're able to try all of them in one playground and event event, essentially try them side by side to see which model is the best. We have audio, text, image. It's $20 a month. So instead of paying that for an individual platform, you get access to over 40 different models and we're adding new ones all the time. So if you want to try that out, it is AI Box AI. I'd love to have you try it out. Let's get into the show today. So this company is based out of Seoul, South Korea. It's called Deumo, and they're taking on scale AI. What's interesting is I think most companies are basically from surveys that have been done, say that they're not prepared to use AI in a safe and responsible way. I think this is kind of a funny statistic. It's also, this is done by like a report out of McKinsey basically that kind of had these findings. I will say there's gotta be some, some biases in there on who is pulled inside of this. I think there's plenty of companies that say that they, they know how to use AI safely, but probably also a lot of executives at companies that just don't feel like they, they don't know what they don't know. And so maybe this is kind of the result. In any case, one of the big concerns a lot of people, regardless of that data, that data. I think one concern a lot of people have is basically understanding how an AI will make a decision. 40% of the people that took that McKinsey survey said that they view it as a significant risk. About 17% said that they are actively addressing it and trying to like basically figure out this problem. So this is where we are today and this is basically where the company Datumo comes in. They started as a data, a data labeling company and now they want to also help companies basically do like a safety benchmark for AI models so that you're able to monitor and improve your models, you're able to test what kind of responses come out. And that's kind of a new thing that they've been doing. They've been a data labeling similar to scale AI. That's kind of been their core product up until this point. So on Monday they just announced another $15.5 million. Um, they've raised about $28 million total. And so this round their investors were Salesforce, they had KB Investments, AC VC Partners and SBI Investments. There's a bunch of other people, but their CEO, his name's David Kim and he is a former AI researcher at Korea's Agency for Defense Development. Basically he was really frustrated. The reason he started the company is it just takes a really long time and it's very time consuming to label data for any sort of AI training. So he came up with an idea which is basically he built this reward based app. I think this is super, super funny, but basically it's reward based app. Anyone can get on there and you basically, if you have free time, you can sit there and label data in your spare time and you get paid for it. So I thought this was kind of funny, but it's obviously a huge business. It needs a lot of humans to do this. And so they basically crowdsource this data labeling on this app. So this is kind of cool. They did like a startup competition that they won, yada yada. But I think overall great idea. Before the app was fully built, they actually had tens of thousands of dollars in pre contract sales during their customer discovery phase. So obviously they're going to talk to customers like, hey, would you use this? A ton of people are like, yes. And they are, you know, signing pre contracts. So basically they're like, all right, this is a great idea. In their first year they actually passed a million dollars in revenue. So a ton of people wanted this well labeled data. Today they have a whole bunch of really big companies that use them. Mostly it's Korean companies. So they have Samsung, LG Electronics, LG cnc, Hyundai Naver, there's, you know, the Seoul based telecom giant, which is SK Telecom. So a lot of big companies in Korea are basically their main clientele and I imagine they'll try to push and expand a little bit more to America in the coming years or over. Over the last couple years their clients also started asking them for other things other than just data labeling. I talked about this in the intro, but basically they're going to start doing, they're going to start helping companies to benchmark models. Right now they're seven years old, they have over 300 clients and they generated about 6 million in revenue last year. So growing really well. Here's a quote from one of the co founders, Michael Huang. He said, and basically this is talking about the new service that they're going to be offering, which is kind of this benchmark thing. He said they wanted us to score AI model outputs to compare them to other models. That's when we realized we were already doing model evaluation without even knowing it. We started in data annotation and then expanded into pre training data sets and evaluations as the LLM ecosystem matured. So they're growing, they're adding these new features. I think basically the drama in the industry is that meta just spent $14.3 billion. They did this kind of like acquisition investment into scale AI. I think they bought like they got like 50% of the company. I mean honestly everyone like says like, oh my gosh, Meta bought scaly, but it's kind of the exact same thing that Microsoft did with OpenAI for $10 billion to get 50% of the company, so. Or 50% of one of the shell companies that owns shares in the blah, blah, blah. I know it's all convoluted for OpenAI, but in any case, I don't see it as being super far, super far off. But when this happened, a lot of the big customers of Scale AI pulled out, OpenAI being one of them. OpenAI actually stopped using Scale AI after the whole Meta deal because they're like, well, Meta's our competitor and you know, how can we trust it? All this stuff, right? So there's a whole bunch of similarities between these companies, between Datumo and Scale AI. I think basically they have this, this like pre trained data that they're labeling. One thing I do think that's interesting that they're doing it differentiates themselves, but I think this is a great business model for a lot of these types of companies today is that they actually have some license data sets. So beyond just, you know, labeling data Sets for companies. They, they have their own license data sets that they'll also license to people. One of theirs that's really interesting is it is a whole bunch of data crawled from published books and the company actually says that it's a really good they say a rich structured human reasoning. So they said it's notoriously difficult to clean this kind of data but it's actually, it helps with the reasoning. So all these reasoning models, apparently reading books is a good way for them to like reason through, learn how to reason through problems which I thought was absolutely fascinating. Unlike other companies, they have a full stack evaluation platform. So that's their Dadu Mo eval which they basically have recently launched. One of their main products is kind of a no code evaluation tool. It's for non developers. So people that are on policy trust, safety compliance teams will use this. You don't have to be a developer and really know how any of that works. I, I thought that the, I thought the story of how they actually got their the most recent investor which was Salesforce Ventures was interesting. Their CEO said that they previously hosted a fireside chat with Andrew Nagy who's the founder of DeepLearning AI. So obviously very famous person in the AI space. And this was hosted at an event in South Korea. But afterwards they shared that event on LinkedIn and from that someone at Salesforce Ventures actually saw it and then they said they had a whole bunch of zoom calls and they got like a soft commitment but the whole funding process took about eight months to actually roll out and then they were able to close that. But I thought it was so interesting. It's like hosting some big famous person and then posting it on LinkedIn is like the best way for a startup to raise money. Thought that was very interesting. I may have to take a page out of their book for my startup AI box. This new round of funding that they're going to be using is basically used to accelerate their R and D. Specifically they said in developing automation automated evaluation tools. They're doing this for enterprise AI and also to help them scale their global go to market strategy as well as talked about a little earlier, they're very focused right now in Korea. I think that they, they say they want to expand to Japan and the US. They have about 150 employees in Korea and they also have a presence in Silicon Valley. I think they might have opened like a little office or hired some people there in March so starting to expand. Very interesting. I'm really excited about this company to be, to be honest, very bullish. The data labeling side of things. Love kind of the journey where they're coming from and the industry they're going into. Right scale AI taking on $14 billion. This is obviously a very big industry. So excited to see what they do with it and how they're able to grow. Thanks so much for tuning into the podcast. If you enjoyed the episode, make sure to leave a rating review, leave a comment, or like the video on YouTube if you're watching it there. Really appreciate every single one of you. And if you want to check out AI Box AI, it's an awesome way to save money on tonight. Tons of different AI models and get access to everything all in one place. So I'll leave a link in the description to that as well. AI Box AI. Catch you next time.