 Okay, welcome everyone to this CUBE Conversation here in Palo Alto, California. I'm John Furrier, host of theCUBE. We're here to talk about the state of MarTech and AI. We're here with the co-founder and head of AI for BlueShift, Menya Malaya. Welcome to theCUBE, thanks for coming on. Thank you John, thank you for having me. Excited to chat with you. BlueShift is a company you've co-founded with a couple of other co-founders and you guys have a stellar pedigree going in data AI back before it was fashionable in the old days. Web 1.0, if you want to call it that. So, you know, we know what you guys have been doing in your careers. Now you got a company on the cutting edge solving problems for customers as they transition from this new way of doing things where users have data and power and control, customers are trying to be more authentic, you got walled gardens emerging everywhere but we're supposed to be away from walled gardens so there's a whole set of new patterns, new expectations and new behaviors. So all of this is challenging but yet it's an opportunity. So I want to get into it. What is your vision and what's your view on the mark tech today in AI and how do you guys fit into that story? Yeah, great question John. We're still in the very early innings of where every digital experience is informed both, you know, creatively from the marketing side of the organization as well as the AI doing the heavy lifting under the hood to be able to create those experiences at scale. And I think today every digital customer and every user out there are leaving a trail of very rich, very frequent interaction data with their brands and organizations that they interact with. You know, if you look at each of us, you know, many, many moments and hours of our digital lives are with these interactions that we do on screens and devices and that leaves a rich trail of data. And brands that are winning, brands that we want to interact with more have user privacy and user safety at the center of it. And then they build that authentic connection from there on. And, you know, just like when, you know we log into our favorite streaming shows or streaming applications, we want to see things that are relevant to us. They in some sense knowing, you know kind of intimately our preferences or changing tastes and how does a brand or organization react to that but still make room for that authentic connection? It's an awesome opportunity and it's a lot of challenges and it's just starting. I totally agree. Let me ask you a question, Manny, if you don't mind how did you guys come up with BlueShift? I know you guys have been in this game before it was fashionable so to speak with solving web 1.0, 2.0 problems and then Walmart labs, everyone knows the history of Walmart and how fast they were with inventory and how they use data. You have that kind of trajectory. When you saw this opportunity, was it like the team was saying, wow, look at this, it's right in our wheelhouse or how did you guys get here and then how did it all come together? Yeah, thanks for, you know, offering me an opportunity to share our personal journey. You know, I think prior to starting BlueShift with my co-founders who I've worked with for almost the past 20 years of my life, we were at a company called Cosmix which was a Silicon Valley early AI pioneer. We were doing semantic search and in 2011, Walmart started their Silicon Valley Innovation Hub, Walmart Labs with the acquisition of Cosmix and we went into Walmart Labs and until then they were already an e-commerce leader. They had been practicing e-commerce for a better part of 12 years prior to that but they're certainly behind in compared to their peers, right? And the peers to be named. But they saw this lack of, what it is that they were doing so well in brick-and-mortar that they're not able to fully get there on the digital side. And, you know, this was almost a decade ago and when they brought in our team with a lot of AI and data systems at scale building things at the cutting edge, you know, we went into it a little bit naively thinking, you know, hey, we're gonna solve this problem for Walmart scale in three months. But it took us three years to build those systems of engagement despite Walmart having an enormous amount of resources being the number one retailer in the world and the data and the resources at their disposal. We had to rethink a lot of assumptions and the trends that were converging were, you know, users for interacting with them across multiple formats and channels and, you know, both offline and online, the velocity and complexity of the data was increasing. All the marketing and merchandising teams that even a millisecond delay for me is unconscionable. And how do you get fresh data and activated at the moment of experience without delay is a significant challenge at scale. And that's what we solve for our organizations. It really is the data problem, it's the scale problem, it's all that. And then having the software to have that AI predictive and, you know, it's omnichannel when you think about it in that retail and that brick and mortar term used for physical space and digital converging. And we saw the pandemic pull forward this same dynamic where events and group behaviors and just interactions were all converging. So this line between physical and digital is now blurred, completely blended. The line between customer experience and marketing has been erased. And you guys are the center of this. What does it mean for the customer? Because the customers out there, your customers or potential customers, they got problems of self. They're going all digital, cloud native applications, the digital transformation. This is the new normal and some are on it, are starting it, some are way behind. What are they, what's the situation with the customers? Yeah, that's certainly the maturity of, you know, the each brand and organization along that, you know, both transformation and from transformation to actually thriving in that ecosystem and how do we actually win, you know, share of mind and then share of like that market that they're looking to does take a while. And many are, you know, kind of midway through their journey. I think there was initially, there is a lot of, you know, push towards, let's collect all the data that we can, but then, you know, how does the actually data becomes something useful that changes experience for Monium versus John is really that critical moment. And that moment is when, you know, a lot of things come into place. And if I look at like the broader landscape, there are certainly lines of hours, like discovery, like audacity and lending tree and jumper, car pods across all these industries who would have thought like, you know, all these industries who you would not think of actually as solving a digital engagement problem are now saying that's the key to our success and outlook. Yeah, it's absolutely the number one problem. This is the number one opportunity for all businesses, not just verticals here and there, all verticals. So walk me through your typical customer scenario. You know, what are the challenges that they face? You're in the middle of it, you're solving these problems. What are their challenges that they face and how do you guys solve them? Absolutely. So I'll talk through two examples, you know, one from a finance industry, one from online learning. You know, one of our, you know, great customers that we partnered with is lending tree. They offer tens of millions of customers, finance products that span from home loans, students loans, order loans, credits, all of that and let these people come into their website and collect information that is relevant to the loan that they're considering, but engage them in a way for the next period of time. So if you typically think about engagement, it's not just a one interaction. Usually that follows a series of steps an organization has to take to be able to explain all their offerings in a way that is digestible and relevant and personalized to each of those millions of customers and actually have them through the funnel and measure it and report on it and make sure that that is the most relevant to them. So in a finance setting that is about consuming credit products, consuming loan products, consuming, you know, reporting products in an online context, I'll give you an example of one of our customers, you're asking, imagine you are a marketing team of two people and you're in challenge with how do you engage 20 million students? You're not going to write 20 million communications that are different for each of those students, certainly. I think you need a system to say what did actually all these students come for? How do I learn what they want at this moment in time? What do they want next? If they actually finish something they started two months ago, would they be eligible for the right course? Maybe today we are talking about self-driving cars. That's the course that I should put in front of them and that's only a small segment of the students. But someone else may be on the media and the production side. How do I personalize their experience so that every single step of the way for that student is, you know, created and delivered at scale and that's kind of the problem that we solve for our brands, which is they have these millions of touch points that they have. How do they bring all their data very fresh and activated at the moment of action? So you guys are creating the 10X marketer. I mean, kind of a riff on the 10X engineer, the famous quote, oh, you 10X engineer. You guys are bringing a lot of heavy lifting to short staffs or folks that don't have a data science team or data engineering team. You're kind of bringing that 10X marketing capability. Absolutely. I think that's a great way to put it. I call it the mission impossible, which is, you know, you're signing up for the mission impossible for every marketing team is like, you know, now they're like, you know, they are the product managers, they are the data scientists, they are the analysts, they are the creative, you know, author, all of that combined into a role. And now you're interested with this really massive challenge and how do you actually get there? And it's that 10X marketer who are embracing these technologies to get there. Well, I'm looking forward to challenging that because I imagine you get a lot of skeptics out there. I don't believe you, it sounds too good to be true. And I want to get to that in the next segment, but I want to ask you about the state of Martek and AI, specifically, Martek traditionally has been on a web 2.0 standards, DNS, URLs. It's a naming system of the internet, it's the internet infrastructure. So what needs to change to make that scale higher? Is there any new abstraction or any kind of opportunities for doing things and just managing tokens that need to be translated? It's hard to do cross talk. I mean, there's a lot of problems with web 2.0 legacy that kind of holds back the promise of high availability of data, privacy, AI, more machine learning, more exposure of data. Can you share your vision on this next layer? Yeah, I think there's a lot of excitement about what web three would bring us there in the very earnings of that possibility. But the challenge of, you know, data that leads to authentic experience still remains the same, whichever metaverse we might actually interact with the brand name. Like, you know, if I go to a Nike store, in the metaverse, I still need to understand what that customer really prefers and keep up with that customer as they change their preferences. And AI is the key to be able to help a marketer, I call it the, you know, our own brew, call it like IPA, you know, which is ingest all possible data, even from metaverse, you know, the protocols might change, the formats might change, but then you have to not only have a sense of what happened in the past, I think there are more than enough tools to know what happened. There are only emerging tools to tell you what might happen. How do I predict? So ingest, predict, and the next step is activate. Actually you have to do something with it. How do I activate that experience for you, whether it's web three or web two changes? And that IPA, kind of our own brew of AI marketing that we're taking to market. And that's an enablement piece. What's so, how does this relate to the customer's data? You guys are storing all the data, are they coming in? Is there a huge data lake involved? Can I bring in third party data? Does it have to be all be first party? How is that platform level enabling this new form of customer engagement? Absolutely. There's a lot of heavy lifting that the data systems that one has to bring to bear upon the problem. Data systems ranging from distributed search, distributed indexing, low latency systems, data lakes that are built for high velocity AI, machine learning, training model inference, that validation pipeline. And we certainly leverage a lot of data lake systems out there, including many of the components that are provided by our preferred partner AWS and open source tools. And these data systems are certainly very complex to manage. And for an organization that with a five to 10 people team of marketers, they're usually short staffed on the amount of attention that they get from the rest of the organization. And what we have made is that you can ingest a lot more raw data. We do the heavy lifting for both data management, identity resolution, segmentation, audience building, predictions, recommendations, and then give you also the delivery piece, which is, can I actually send you something? Can I put something in front of the user and measure it and report on it and tell you that this is the ROI? How do, if all this would be for nothing, if actually you go through all this and there's no real ROI and we have kind of our own forester did a total economic impact study with us and they found 781% ROI for implementing nutrition. And it's a tremendous amount of ROI you get once you are able to reorient your organizations towards that. You know, Menya, one of the problems of being a visionary and a pioneer, like you guys are, you're early a lot. And so you must be scratching your head going, oh, the hot buzzword these days is the semantic layer in cotton, you see snowflake and a bunch of other people kind of pushing this semantic layer. It's basically a data plane, essentially for data. And you guys have done that, been there, done that. But now that's in play, you guys have this. You got all this semantic search built in into the system, all this data ingestion, it's a full platform. And so I need to ask you how you see this vectoring into the future state of customer engagement. Where do you see this intersecting with the organizations you're trying to bring this to? Are they putting more investment in? Are they pulling back? Where are they and where are you guys relative to this technology? And first of all, let's get your reaction to the semantic layer first. Right, right. It's fantastic. As a technologist, I love kind of the ontology and semantic differences. You know, how data planes, data meshes, data fabrics are put together. And I saw this kind of dichotomy between a CIO org and a CMO org, right? The CIO says like, I have the best data plane, the data mesh, data fabric. And the CMO says like, but I'm actually trying to accomplish something for this campaign. And they're like, oh, that doesn't actually connect the both of pieces. I think the CMO org suddenly will need purpose built applications on top of the data fabric, on top of the data lakes, on top of the data meshes to be able to help marketing teams both technical and semi-technical to be able to accomplish that. Yeah, and the new personas, they want turnkey, they want to have it self-service. Again, the 10x marketer is someone with a small step that can do the staff of 100 people, right? So this is where it's going. I mean, this is the new normal. So we call them AI marketers. And I think it's like you're calling it 10x marketer. I think, you know, over time, we didn't have, you know, this word business intelligent analyst, but then once the tool are there, then they become business intelligence analysts. I think like guys, once these tools are available, then we'll have AI marketers out in the market. Well, Menya, I'd love to do a full, like one hour podcast with you. You can go for a long time with these topics, given what you guys are working on, how relevant it is, how cool it is right now, and with what you guys have as a team and solution. I really appreciate you coming on theCUBE to chat. For the last minute we have here, give a quick plug for the company. What you guys are up to, size, funding, revenues, what you're looking for, what should people pay attention to? Give the plug. Yeah, we are a global team spanning multiple time zones. We have raised $65 million to date to build out our vision. And over the last eight years of our funding, we have served hundreds of customers and continuing to take on more. I think our hope is that over time, the next 10,000 organizations see this as a very much an approachable problem to solve for themselves, which I think is where we are. AI marketing is real, doable, proven, ROI. Can we get the next 10,000 customers to embrace that? You know, as we always used to say in the kind of web business and search, it's the contextual and the behavioral. You had to bring them together here. You got all that technology for the sites and applications for the behavior and converting that contextually into value. Really compelling solution. Thanks for sharing your insight. Yeah, thank you John, really appreciate it. Okay, this is CUBE Conversation. I'm John Furrier here in Palo Alto. Thanks for watching.