 Hey everyone, welcome back to theCUBE's continuing coverage of Snowflake Summit 22, live from Caesars Forum in Las Vegas. Lisa Martin here, I have three guests here with me. We're going to be talking about Snowflake Ventures and the Snowflake startup challenge that's in its second year. I've got Harry Glazer with me, co-founder and CEO of Modelbit, a startup challenge finalist. Damon Bryan joins us as well, the CTO and co-founder of Hyperfinity, also a startup challenge finalist. And Stefan Williams to my left here, VP of Corporate Development and Snowflake Ventures. Guys, great to have you all on this little mini panel this morning. Yeah, thank you. Thanks for being here. Let's go ahead to Harry and we'll start with you. Talk to the audience about Modelbit. What do you guys do? And then we'll kind of unpack the Snowflake challenge. Modelbit is the easiest way for data scientists to deploy machine learning models directly into Snowflake. We make use of the latest Snowflake functionality called Snowpark for Python that allows those models to run adjacent to the data so that machine learning models can be much more efficient and much more powerful than they were before. Awesome, Damon, give us an overview of Hyperfinity. Yeah, so Hyperfinity were a decision intelligence platform. So we help specifically retailers and brands make intelligent decisions through the use of their own customer data, their product data, and put data science and AI into the heart of the decision makers across their business. Nice, Stefan, tell us about the startup challenge. We talked a little bit about it yesterday with CMO Denise Pearson. But I know it's in its second year. Give us the idea of the impetus for it, what it's all about, and what these companies embody. Yeah, so this is the second year that we've done it. It was really out of, well, it starts with Snowflake Ventures when we started to invest in companies, and we quickly realized that there's a massive opportunity for companies to be building on top of the LEGO blocks of Snowflake. And so, opened up the competition last year. It was the inaugural competition, overlay analytics one. And since then, you've seen a number of different functionalities and features as part of Snowflake, Snowpark being one of them. Native applications is a really exciting one going forward. The companies can really use to accelerate their ability to kind of deliver best-in-class applications using best-in-class technology to deliver real customer outcomes and value. And so we've seen tremendous traction across the globe. 250 applicants across 50, I think 70 countries was mentioned today, so truly global in nature. And it's really exciting to see how some of the startups are taking Snowflake to new and interesting use cases and new personas and new industries. So you had 200 over 250 software companies applied for this. How did you narrow it down to three? We did, yeah. How did you do that? So behind the scenes, we had a sub-judging panel, the ones you didn't see up on stage, which I was a luckily part of. We had kind of very distinct evaluation criteria that we were evaluating every company across. And we kind of took it in tranches, right? We took the first big final and we kind of try to get that down to a top 50 and that top 50, then we really went into the details and we kind of across myself in ventures with some of my venture partners, some of the marketing team, some of the product and engineering team all kind of came together and evaluated all of these different companies to get to a top 10, which was our semifinalists. And then the semifinalists all had a chance to present in front of the group. So we got to meet over Zoom along the way where they did a pitch, a five minute pitch followed by a Q&A. In a similar format, I guess to what we just went through at the startup challenge live, to get to a top three. And then here we are today just coming out of the competition with the folks here on the table. Wow, Harry, talk to us about how did you distill down what ModelBit is doing into five minutes over Zoom and then five minutes this morning in person? I think it was really fun to have that pressure test where we've only been doing this for a short time. In fact, ModelBit's only been a company for four or five months now. And to have this process where we pitch and pitch again and pitch again and pitch again really helped us nail the one sentence value proposition which we hadn't done previously. So in that way, very grateful to Stefan and the team for giving us that opportunity. That helps tremendously. I can imagine being a four to five month young startup and really trying to figure out, I've worked with those young startups before and messaging is challenging the narrative. Who are we? What do we do? How are we changing or chasing the market? Whatever our customers saying we are, that's challenging. So this was a good opportunity for you. Damon, would you say the same as well for hyperfinity? Yeah, I'd definitely concur. It's really helped us to shape our value proposition really and how we speak about that. It's quite complicated stuff. Data science when you're trying to get across what you do, especially in retail that we work in. So part of what our platform does is to help them make sense of data science and AI and implement that into commercial decisions. So you have to be really kind of snappy with how you position things and it's really helped us to do that. We're a little bit further down the line than these guys. We've been going for three years. So we've had the benefit of working with a lot of retailers to this point to actually identify what their problems are and shape our product and our proposition towards that. Are you primarily working with the retail industry? Yes, retail and CPG is our primary use case. We have scenes. Any kind of consumer related industry. Got it. Massive changes, right? And retail and CPG the last couple of years. The rise of consumer expectations, it's not going to go back down, right? We're impatient. We want brands to know who we are. I want you to deliver relevant content to me that if I bought a tent and go back on your website, don't show me more tents. Show me things that go with that. We have this expectation. You've just explained our whole business. It's better than me. But it's so challenging because the brands have to respond to that. How do you, what is the value for retailers working with hyperfinity and snowflake together? What's that powerhouse? Yeah, exactly. So yeah, you're exactly right. The retail landscape is changing massively. There's inflation everywhere. The pandemic really impacted what consumers really value out of shopping with retailers. And those decisions are even harder for retailers to make. So that's kind of what our platform does. It helps them to make those decisions quickly, get the power of data science or democratize it into the hands of those decision makers. So our platform helps to do that. And snowflake really underpins that. The scalability of snowflake means that we can scale the data and the capability of the platform in tangent with that. And snowflake have been innovating a lot of things like Snow Park and then the new announcements Unistore and the native app framework are really helping us to make developments to our product as quick as snowflake are doing it. So it's really beneficial. So you get kind of that tailwind from snowflake's acceleration it sounds like. Exactly that, yeah. So as soon as we hear about new things, we're like, oh, can we use it? And Snow Park in particular was music to our ears. And we actually part of the private preview for that. So we've been using that a while. And again, some of the new developments will be, I'm on the phone to my guys saying, can we use this? Get it implemented pretty quickly. Fantastic. Sounds like a great aligned partnership there. Harry, talk to us a little bit about ModelBit and how it's enabling customers. Maybe you've got a favorite customer example. ModelBit plus snowflake, the power that delivers to the end user customer. Absolutely. I mean, as I said, it allows you to deploy the ML model directly into snowflake. But sometimes you need to use the exact same machine learning model in multiple endpoints simultaneously. For example, one of our customers uses ModelBit to train and deploy a lead scoring model. So you know, when somebody comes into your website and they fill out the form like they want to talk to a sales person, is this going to be a really good customer, do we think? Or maybe not so great. Maybe they won't pay quite as much. And that lead scoring model actually runs on the website using ModelBit so that you can display a custom experience to that customer. We know right away if this is an A, B, C, or D lead. And therefore, do we show them a salesperson contact form? Do we just put them in the marketing funnel based on that lead score? Simultaneously, the business needs to know in the back office the score of the lead so that they can do things like route it to the appropriate salesperson or update their sales forecast for the end of the quarter. That same model also runs in the snowflake warehouse so that those back office systems can be powered directly off of snowflake. The fact that they're able to train and deploy one model into two production environments simultaneously and manage all that is something they can only do with ModelBit. Lead scoring has been traditionally challenging for businesses in every industry. But it's so incredibly important, especially as consumers get pickier and pickier with I don't want to be meshes. I want to opt out. Sounds like what ModelBit is enabling is especially alignment between sales and marketing within companies, which is that's also a big challenge that many companies face. For us, it starts with the data scientist, right? The fact that sales and marketing may not be aligned might be an issue with the source of truth. And do we have a source of truth at this company? And so the idea that we can empower these data scientists who are creating this value in the company by giving them best in class tools and resources, that's our dream, that's our mission. Talk to me a little bit, Harry. You said you're only four to five months old. What were the gaps in the market that you and your co-founders saw and said, guys, we got to solve this. And Snowflake is the right partner to help us do it. Absolutely, this is actually our second startup. And we started previously a data analytics company that was somewhat successful. And it got caught up in this big wave of migration of cloud tools. So all of data tools moved and are moving from on-premise tools to cloud-based tools. This is really a migration that Snowflake catalyzed. Snowflake, of course, is the ultimate in cloud-based data platforms, moving customers from on-premise data warehouses to modern cloud-based data clouds. That dragged and pulled the rest of the industry along with it. Data science is one of the last pieces of the data industry that really hasn't moved to the cloud yet. We were almost surprised when we got done with our last startup and we were thinking about what to do next. The data scientists were still using Jupyter Notebooks locally on their laptops. And we thought this is a big market opportunity and we're almost surprised it hasn't been captured yet and we're going to get in there. The other thing I think is really interesting on your business that we haven't talked about is just the flow of data, right? So the data scientist is usually taking data out of something like Snowflake, a data platform. And the security kind of breaks down because then that's one, it's two, it's three, it's five, it's 20, it's, you know, big companies, this gets really big. And so I think the really interesting thing with what you guys are doing is enabling the data to stay where it's at, not copying it out, keeping that security, that highly governed environment that big companies want, but allowing the data science community to really unlock that value from the data, which is really, really cool. Wonderful for small startups like Modelbit because you talk to a big company, you want them to become a customer, you want them to use your data science technology. They want to see your FedRAMP certification, they want to talk to your CISO. We're two guys in Silicon Valley with a dream, but if we can tell them the data is staying in Snowflake and you have that conversation with Snowflake all the time and you trust them, we're just built on top, that is an easy and very smooth way to have that conversation with a customer. Would you both say that there's credibility, like you got street cred, especially being so early in the stage, Hillary, with the partnership with Snowflake, Damon, we'll start with you. Yeah, absolutely. We've been using Snowflake from day one, really, from when we started our company and it was a little bit of an unknown, I guess, maybe two, three years ago, especially in retail. A lot of retailers using all the legacy kind of enterprise software are really starting to adopt the cloud now with what they're doing and obviously Snowflake really innovating in that area. So what we're finding is we use Snowflake to host our platform and our infrastructure. We're finding a lot of retailers doing that as well, which makes it great for when they want to use products like ours because of the whole data share thing, it just becomes really easy and it really simplifies ETL and data transformation and data sharing. Stefan, talk about the startup challenge, the innovation that you guys have seen in only this second year. I can just hear it from the two of you and I know that the winner is back in India. But tremendous amount of potential, like to me, the last two and a half days, the flywheel that is Snowflake is getting faster and faster and more and more powerful. What are some of the things that excite you about working on the startup challenge and some of the vision going forward that it's driving? I think the incredible thing about Snowflake is that we really focus as a company on the data infrastructure and we're hyper focused on enabling and incubating and encouraging partners to kind of stand on top of a best of breed platform and unlock value across the different, either personas within IT or organizations or industries like Hyperphony is doing. And so it's really incredible to see kind of domain knowledge and subject matter expertise able to kind of plug into best of breed underlying data infrastructure and really drive real meaningful outcomes for our customers in the community. It's just been incredible to see. I mean, we just saw three today. There was 250 incredible applications that passed the initial, do they check all the boxes and then actually, wow, they just take you to these completely different areas you never thought that the technology would go and solve and yet that here we are talking about really interesting use cases that partners are taking us to. 250, did that surprise you and what was it last year? I think it was actually close to 240, 250 as well. I think it was above 250 this year. I think that's the number that is in my head from last year but I think it was actually above that. The momentum is there and again, we're going to be back next year with a full competition too. Awesome, Harry, what are some of the things that are next for ModelBet as it progresses through its early stages? Yeah, one thing I've learned and I think probably everyone at this table has internalized this lesson, product market fit really is everything for a startup and so for us, we're fortunate to have a set of early design partners who will become our customers who we work with every day to build features, get their feedback, make sure they love the product and the most exciting thing that happened to me here this week was one of our early design partner customers wanted us to completely rethink how we integrate with Git so that they can use their CI CD workflows, their continuous integration that they have in their own Git platform which is advanced, they've built it over many years and so can they back all of ModelBet with their Git and it was one of those conversations, I know this is getting a little bit in the weeds but it was one of those conversations that as a founder makes your head explode. If we can have a critical mass of those conversations and get to that product market fit, then the flywheel starts, then the investment money comes, then you're hiring a big team and you're off to the races. Awesome, sounds like there's a lot of potential and momentum there. Damon, last question for you is what's next for Hyperfinity? Obviously you've got, we talked about StraitCrad, what's next for the business? Wow, so yeah, we've got a lot of exciting times coming up so we're about to really fully launch our product so we've been trading for three years with consultancy in retail analytics and data science and actually using our product before it was fully ready to launch so we have the kind of main launch of our product and we're actually starting to onboard some clients now as we speak. I think the climate with regards to trying to find data science resources, a problem across the globe so it really helps companies like ours that allow retailers or whoever it is to democratize the use of data science and perhaps really help them in this current climate where they're struggling to get world-class resource and enable them to do that. Right, so critical. Stefan, take us home with your overall summary of Snowflake Summit 4th Annual, nearly 10,000 people here, huge increase from the last time we were all in person. What's your bumper sticker take away from Summit 22 and the startup challenge? That's a big closing statement. For me it's been just the energy. It's been incredible energy, incredible excitement. I feel the products that have been unveiled just unlock a ton more value and a ton more interesting things for companies like Model Bear, Hyperfinity and all the other startups here to go and think about. So there's just this incredible energy, incredible excitement, both internally, our products and engineering teams, the partners that we've spoken here with at the event, the portfolio companies that we've invested in and so there's just this incredible momentum and excitement around what we're able to do with data in today's world powered by an underlying platform like Snowflake. Right, and we've heard that energy, I think through all 30 plus guests we've had on the show since Tuesday and certainly from the two of you as well. Congratulations on being finalists. We wish you the best of luck. You'll have to come back next year and talk about some of the more great things. Hopefully we'll be exhibiting next year. There you go. That's a good thing to look for. Guys, really appreciate your time and your insights. Congratulations on another successful startup challenge. Thank you so much. For Harry, Damon and Stefan, I'm Lisa Martin. You're watching theCUBE's continuing coverage of Snowflake Summit 22, live from Vegas. Stick around, I'll be right back with Dave Vellante and our final guest of the day.