This video presents an in-depth discussion featuring Ashutosh Garg, Chief Executive Officer of Eightfold AI, and Varun Kacholia, Chief Technology Officer of Viven, recorded at the New York Stock Exchange. Dave Vellante, co-founder of SiliconANGLE Media, hosts this insightful session as part of the "Mixture of Experts" series, facilitated by theCUBE and NYSE Wired. The focus of this conversation is Viven's emergence from stealth with a $35 million funding round and their innovative approach to integrating AI digital twins into the enterprise.
In this video, Garg and Kacholia explore their motivations for launching Viven, a company focused on creating digital counterparts for individuals within enterprises. They highlight their extensive backgrounds in leading personalization efforts at Google and their past ventures, Eightfold and BloomReach. TheCUBE Research analysts provide critical insights as the hosts explore how Viven's technology harnesses AI to revolutionize enterprise processes.
Key points from this session include Garg and Kacholia's explanation of how Viven's digital twins preserve organizational knowledge and streamline workflows. They emphasize the importance of context-aware models in capturing and utilizing the skills and experiences of individuals within an enterprise. According to Garg and Kacholia, Viven's platform ensures privacy and security while maintaining seamless integration across multiple enterprise systems.
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Ashutosh Garg & Varun Kacholia, Viven
Exploring Process Intelligence and AI with Alex Rinke at theCUBE NYSE Wired
Alexander Rinke, co-CEO and co-founder of Celonis, joins John Furrier from SiliconANGLE Media at theCUBE's NYSE studio to discuss developments in process intelligence and the application of artificial intelligence in modernizing business operations. This conversation is part of theCUBE NYSE Wired series, focusing on developments in AI-driven enterprises.
In this insightful discussion, Rinke shares advancements in process intelligence and explains how organizations leverage AI to streamline their operations. The video introduces Rinke's expertise in the field and provides an overview of how Celonis revolutionizes business processes with AI and process intelligence. John Furrier and analysts from theCUBE Research contribute to the dialogue, enriching viewers' understanding of the topic.
Key takeaways from this discussion emphasize Celonis' role in transforming enterprise operations by "agentifying" core processes and driving business outcomes. According to Rinke, process intelligence provides the foundation for AI integration, enabling companies to operationalize AI efficiently. They highlight Celonis' approach to overcoming conventional challenges by adopting a composable methodology, thereby enhancing automation and enterprise efficiency.
>> Hey, everybody. Welcome back to the New York Stock Exchange. My name is Dave Vellante and you're watching our Mixture of Experts series, NYSE Wired and theCUBE. We're here overlooking the options exchange at the prestigious Buttonwood podium. We've got some big news here. Ashutosh Garg and Varun Kacholia are here. Ashutosh is the CEO, Varun is the CTO, and they are co-founders of a company called Viven. The company is emerging from stealth with a $35 million funding round, and they're bringing, get this, AI digital twins to the enterprise, something that we have talked about incessantly on theCUBE. Super excited to have you guys here. Thanks so much for making some time for us.
Ashutosh Garg
>> Thank you. Thanks, Dave, for this opportunity. Super excited to be here today with you all.
Dave Vellante
>> Yeah, and congratulations. I think this is your third startup that you guys have done. You launched a couple of unicorns, Eightfold and BloomReach. So, I got to ask you, as founders, co-founders, why did you start Viven?
Ashutosh Garg
>> I think we are living in an unprecedented time. The art of possibility with AI is enormous. It's bigger than ever. Both of us started our careers at Google, where we led the personalization efforts, search efforts. And over the years, we have led personalization across a variety of companies. But when the generative AI came in and we look at what is the art of possibility, what struck us is today, the way it is unimaginable to get out of the home without a cell phone, in near future, we will have our digital point with us 24/7, making us smarter, making us better, making sure that each and every one of us have more time to do more things, more important things. And we felt this is the time when we have to come and do this thing.
Dave Vellante
>> I want to bring Varun into the conversation and have you talk about the tech behind Viven, but when we think of the future of agentic, we think of building a digital representation of the enterprise. If I understand it correctly, you're building digital representations of people within the enterprise. What is this new category like? What's the technology behind it? Varun, I wonder if you could describe that.
Varun Kacholia
>> Yes, yes, absolutely. There are a lot of intake approaches that try to take the general-purpose large language model and deploy with an enterprise. But every enterprise, every organization has its own processes, its own values, its own systems, and every individual brings their own skills and strengths as well. Capturing that context, capturing that knowledge, and enabling that across the organization is what we have built via digital twins at Viven.
Dave Vellante
>> So, how do you... Okay, so if I've got... I love this because you're taking the tribal knowledge of the organization and giving it context, giving it memory so when Joe leaves the company, the IP and the knowledge doesn't go with Joe. So, how do you harmonize and do you do this, all that data that's sitting in different places and different buckets, which today sits in applications or data stores, people's heads, there's different things mean different things to different people, how do you harmonize all that? Is that part of the secret sauce? Is that what you're actually doing?
Varun Kacholia
>> Yes, yes, absolutely. If you look at the last 10 years, enterprise SaaS has really digitized a significant amount of data, but all of the data is truly disconnected and accessible in a limited fashion. We have brought it together in a way that not only provides that continuity, but also rethinks the privacy and access control privileges. The example that you gave of when John moves teams or is unavailable for some period of time or long periods of time, how can we provide that knowledge and context in a seamless way that earlier was only accessible to John? We have built an infrastructure that supports it, that truly brings all this across the different services that an organization uses, and we do it in a very highly secure, compliant way, all the way from organizations able to host it on their own VPC in cloud, or even on-prem.
Dave Vellante
>> So Ash, you described Viven's digital twins more than just assistants. They're essentially mirror images, digital counterparts. So, you're capturing how people think, how people make decisions. Are you defining a new category? As CEO, you know it's a challenging thing to do, but it's also an ambitious and exciting thing to do. So, are you defining a new category? And what in your view makes this transformative for enterprises?
Ashutosh Garg
>> It's a great question. Creating a new category is always hard, but this is the third time we will be creating a new category, whether it was at BloomReach digital experience category to talent intelligence platform category at Eightfold. So, familiar with that part. But here are two examples for you. Just before this interview, I went to my own digital twin and asked, "What are the stories I should share with Dave as I'm going on the camera?" And it helped me prepare much better for this conversation. Second example is I was driving in the morning and suddenly it has tracked me, what is happening with this customer? Have we followed up or not? Now, I can go and wake up someone at 7:00 AM or I can just ask their digital twin, "What is going on with this customer? Have we followed up or not? How are the conversations progressing?" And I got the answer in real time from the digital twin of my account representative.
Dave Vellante
>> Okay, so on the former question, I could ask that of an LLM and get an answer. I presume your answer is going to be more in context and more aligned to the way I think, my brain, my digital self. The latter requires a proprietary data set, which is really interesting because now I can unlock that propriety for my competitive advantage. Can you explain that a little bit further?
Ashutosh Garg
>> Excellent. And that is where we are focused on. It's that, how can we make each and every employee in an enterprise accessible 24/7? So, what we do is we build a model of each person by taking all the data that this person has, but then giving them full control over what data about them should be private, what can be shared, with whom, under what context. Privacy is not one size fits all. It is a function of your relationship. It is a function of the context. So, we do that. This person I was asking the question, they have full visibility into what I asked, what answer their digital twin provided back to me so that if they want, they can go modify it. I can follow up with them. If it crosses the boundaries of their personal privacy, it won't answer that. So, if I ask, "What is your compensation?" It will be like, "No, sorry, I can't tell you that."
Dave Vellante
>> Right, so you've built in those guardrails, as they say. Okay. Everybody I think clearly understands the problem that you're solving. I'll call it the enterprise memory problem. Every organization struggles with losing that tribal knowledge when people move on. So, what's the secret sauce, Varun, behind this? How do you actually technically solve that institutional memory gap? And how do you measure success?
Varun Kacholia
>> I would say there are three components of this. The first, as you touched on earlier, is really the massive amount of data that is segregated. They're in every organization. Today, this data is locked in its own ways, so we have brought it together. We have brought it, made it accessible with the right privacy controls and security and compliance as well. The second is really how do you truly understand the work, the way of thinking, the context of every person in the organization, the strengths that they bring in? And as the amount of data is increasing today, many of our meetings are being transcribed as well, so this really helps build a lot more accurate understanding of that individual along with the context that they have. So, that is something that we have uniquely built. And then the final is how do we make this as a platform that is accessible for multiple different use cases? The use cases that Ashutosh shared with you earlier around, hey, I can ask my own digital twin or I can ask an account representative, but there are just so many more things that can just seamlessly in the flow of work. If you're on a vacation, today the auto vacation responders are very simple and vanilla, but can they be a lot more intelligent and done it in a far more easy way? And those are some of the three components of how we have built this digital twin infrastructure.
Dave Vellante
>> Okay, so if I understand it correctly, you're basically blending personalized language models, highly specialized and personalized, with what I'll call agentic workflows. So, my question is how do your digital twins interpret and ingest activities, like processes, and how does that fit in to the whole agentic vision? Ashutosh, maybe you could take this.
Ashutosh Garg
>> Absolutely. Within an enterprise, we indicate with every enterprise system they have, whether it is the Salesforce or CRM, whether it is GFR, bug tracking, ticketing system, whether it is Asana for project management from your email, Teams, Slack, Zoom meeting transcripts, Team transcripts, WebEx, we indicate with all those things. Then we effectively look at the activity of each person over time to build a model around them, what actions they have taken, how they responded to a message, who they have followed up, who they have not followed up, which part of the conversations they are in and they are not. We use that to build a memory of a person, a context of a person, and a language model around that person. So, one very interesting example for you is that for the same question, you might go to your general counsel or you might go to your CFO, and the response you're looking for is different. So, it's not about generic enterprise knowledge. It is about the knowledge and experience of that person. I may go to five people to get five different answers to triangulate, but that is what this enables.
Dave Vellante
>> I want to come back to privacy, security. It's obviously got to be a huge issue for your customer. I'm sure you get this question all the time. You gave a good example before on compensation, but there's IP leaks. And a lot of times, adoption of new tech generally, I'm sure AI specifically, hits speed bumps over privacy, compliance, legal issues. So, you've built your company to run inside of an enterprise's own cloud or even on-prem, I believe. Correct? It's a hybrid model, so I can have more control there. That deployment flexibility is part of the trust model, but you've got this other pairwise privacy concept, this framework that you've developed, and I'm curious as to how that's playing out, how you're proving it out. Because I could see a lot of companies saying, "Well, sounds good, but I'm really afraid that something's going to go wrong." So, how do you deal with that both technically, culturally, and what are you finding in early trials?
Ashutosh Garg
>> So, Dave, great question. You are right. Privacy and security are at the heart of the problem that we are solving. Today, we have customers from small startups to large public companies, financial services institutions using us and seeing great success. And it all starts by building with that trust with the employees of that company. The first and foremost thing we have done is we have a guideline around how you adopt this piece of technology. You start by yourself, you build your own digital twin, you get comfortable with it. Once you are comfortable with it, that is when you start giving access to other people of your twin. And now, you control what all information you're sharing with them. You can take up both a blacklist approach or a whitelist approach. Whitelist approach is this is the part of my knowledge, my information, my experiences that can be shared by others. And this is a part of the knowledge and experiences that I have that should never get shared with anyone. So, another example will be a conversation that I'm having with my spouse could never get shared with anyone. It's just private to me. as an organization, you can set your own guardrails to say that how information should flow within the organization. Then it's also a function of relationship between individuals. My manager will have certain level of access to my data, whereas I will have a different level of access to my manager's data. This is the big IP that we have built, add within, and unlocking for the possibility with digital Twin.
Varun Kacholia
>> I think to add on top of it a little bit more-
Dave Vellante
>> Please....
Varun Kacholia
>> of course you covered the infrastructure pieces, on-prem, more customers' cloud. We also have as part of Eightfold extensive experience. We have achieved IL4 FedRAMP security certification. Those are many of those learnings and best practices we have built in right into the live-in platform. And fundamentally, access control, as it is currently implemented in many organization, it's problematic in its own ways. Often there are certain documents that do not have access that they should, or some which are inadvertently left open and accessible to others. So, this is a way where several of our customers, users have really, really benefited from let's rethink how should knowledge access be provided irrespective of the system where that data or piece of information is coming from. And how do we build audibility into it and visibility into it? And all of this is right there in the infrastructure and platform as well.
Dave Vellante
>> Yeah, the way I look at... Thank you for that. The way I look at this is you're taking human knowledge and you're turning it into AI infrastructure, and I am thinking that through. So, that means that as an organization, I can scale without labor costs, which is a very powerful... That brings in software like marginal economics and learning curve dynamics to all parts of the company, not just IT, and all industries. And you're doing this across teams, across geographies. There's no reason that this has no limits in terms of timezones. And so how do you see digital twins becoming a standard layer inside of what Jensen calls, say, an AI factory? Is that your vision, that it will become a part of the stack?
Varun Kacholia
>> Yes, that is correct. That's the vision, yes. And if we think about it today, the AI factory also to speak, many of the AI agentic platforms are powered by general purpose models, which will be really the same in every single organization, every single team. So, how can every organization, every team bring in what makes them click, what makes them successful? And it is truly the knowledge, the expertise of every individual accumulated through the lifetime of the organization, not just the current snapshot that we have today. So, this is like... Yeah, we absolutely have this vision, and we are set up for that.
Dave Vellante
>> I got to ask you about the future of work. Hot topic. Of course, people are going to be concerned about losing jobs, but let's agree that we're going to actually create more jobs in the fullness of time. We'll align on that front, if that's okay. But the future of work in this era, if every employee and every team has a digital twin, what does that mean for the way work is structured, the way that processes are maybe created on the fly? Are we entering a new era of work? Clearly we are, but how do you see that evolving where essentially knowledge is this sort of durable autonomous asset of an organization? How will that affect future of work in your vision?
Ashutosh Garg
>> As co-founders of Eightfold, we spend a lot of our time thinking about future of work, how organizations are set up. You talk about the AI factory. Today, the way AI is approaching it in enterprises is what is the lowest common denominator of the knowledge and experience? They're not talking about how do we multiply each and every one of us? Most people at work are overworked. They don't have time for things. We are spending our time on mundane tasks versus strategic initiatives. The purpose, the idea with these digital twins is that they are going to unlock that strategic thinking among people. Now, our conversations with each other can be a lot more meaningful. We can take a lot more informed decisions. And all the processes stuff, all the basic stuff can be done by digital twin. So, for example, filing a simple NERA ticket, updating the sales for CRM with some account update, sending an email to someone, all that can be done by my digital twin. But I have the right level of information. I'm better prepared for my meetings. I'm better prepared for customer conversations and just a lot more thoughtful.
Varun Kacholia
>> Another way to think about this is the last 20 years brought us our human processes. They digitized us to different processes and software. And many people feel that they spend a good percent of their time, 20%, 30% of the time working for the software, whether it is... Take any system, entering information in. This, the future of work actually will be how do we free up that time and then have software work for people, giving people the ability to innovate more, be more creative, and spend more time on things that we all are uniquely positioned to do? So, for us, it is less about taking jobs away, more about how can we give time back to people and accelerate the progress of humanity overall?
Dave Vellante
>> Yeah, I want a digital twin. As we were joking earlier, Andrew wants a digital twin, our production lead. I'm interested in how long this is going to take to build out so that everyone has a digital twin, but so help me understand what it takes to build a digital twin. What kind of tools do I need? How do you help do that? What does the process look like?
Ashutosh Garg
>> Depending upon the setup and the infrastructure of the company, today we already have numerous customers using us who are global enterprises with hundreds of thousands of employees. So, we are now ready. The reason to come out of the stealth mode is now we can start bringing customers on board at a scale and delivering the value. For an enterprise, they can be up and running with us in a matter of weeks. These digital twins look at all the data, train themselves, and then a concept that we have introduced is train your own twin, which is an ongoing process. Similar to the humans, as a new employee, when I join a company, I come in with some information, then every day I'm learning, growing, developing myself. That is how these twins are. You deploy them in a matter of weeks, and then they start learning based on each and every action and interaction you are having in the enterprise.
Dave Vellante
>> Well, guys, congratulations. $35 million. COSLA, I think, led the rounds, Foundation Capital, FPV Ventures, the Operator Collective I think is part of this. You've got some angels, some high-profile angels as well. I'll give you the last word. Where do you want to take this company? And how do you see it evolving to become a critical layer of the AI infrastructure wave?
Ashutosh Garg
>> We look at it, this is the new fabric of the enterprises. This is going to fundamentally transform how we interact with each other in the organization. How do we access, analyze things? How do we process information and how we take actions? The future is super bright with Viven. And thank you for this opportunity today. Super excited to work with you all. And Dave, we'll talk about you becoming our customer.
Dave Vellante
>> Okay, great. It's great to have you. I'd love to have you face-to-face at the New York Stock Exchange at some point. Ashutosh and Varun, thank you very much. And congratulations and best of luck to you.
Ashutosh Garg
>> Thank you.
Varun Kacholia
>> Thanks.
Dave Vellante
>> You're very welcome. And thank you for watching our Mixture of Experts series, NYSE Wired plus theCUBE from the New York Stock Exchange. We'll be right back right after this short break.
>> Hey, everybody. Welcome back to the New York Stock Exchange. My name is Dave Vellante and you're watching our Mixture of Experts series, NYSE Wired and theCUBE. We're here overlooking the options exchange at the prestigious Buttonwood podium. We've got some big news here. Ashutosh Garg and Varun Kacholia are here. Ashutosh is the CEO, Varun is the CTO, and they are co-founders of a company called Viven. The company is emerging from stealth with a $35 million funding round, and they're bringing, get this, AI digital twins to the enterprise, something that we have talked about incessantly on theCUBE. Super excited to have you guys here. Thanks so much for making some time for us.
Ashutosh Garg
>> Thank you. Thanks, Dave, for this opportunity. Super excited to be here today with you all.
Dave Vellante
>> Yeah, and congratulations. I think this is your third startup that you guys have done. You launched a couple of unicorns, Eightfold and BloomReach. So, I got to ask you, as founders, co-founders, why did you start Viven?
Ashutosh Garg
>> I think we are living in an unprecedented time. The art of possibility with AI is enormous. It's bigger than ever. Both of us started our careers at Google, where we led the personalization efforts, search efforts. And over the years, we have led personalization across a variety of companies. But when the generative AI came in and we look at what is the art of possibility, what struck us is today, the way it is unimaginable to get out of the home without a cell phone, in near future, we will have our digital point with us 24/7, making us smarter, making us better, making sure that each and every one of us have more time to do more things, more important things. And we felt this is the time when we have to come and do this thing.
Dave Vellante
>> I want to bring Varun into the conversation and have you talk about the tech behind Viven, but when we think of the future of agentic, we think of building a digital representation of the enterprise. If I understand it correctly, you're building digital representations of people within the enterprise. What is this new category like? What's the technology behind it? Varun, I wonder if you could describe that.
Varun Kacholia
>> Yes, yes, absolutely. There are a lot of intake approaches that try to take the general-purpose large language model and deploy with an enterprise. But every enterprise, every organization has its own processes, its own values, its own systems, and every individual brings their own skills and strengths as well. Capturing that context, capturing that knowledge, and enabling that across the organization is what we have built via digital twins at Viven.
Dave Vellante
>> So, how do you... Okay, so if I've got... I love this because you're taking the tribal knowledge of the organization and giving it context, giving it memory so when Joe leaves the company, the IP and the knowledge doesn't go with Joe. So, how do you harmonize and do you do this, all that data that's sitting in different places and different buckets, which today sits in applications or data stores, people's heads, there's different things mean different things to different people, how do you harmonize all that? Is that part of the secret sauce? Is that what you're actually doing?
Varun Kacholia
>> Yes, yes, absolutely. If you look at the last 10 years, enterprise SaaS has really digitized a significant amount of data, but all of the data is truly disconnected and accessible in a limited fashion. We have brought it together in a way that not only provides that continuity, but also rethinks the privacy and access control privileges. The example that you gave of when John moves teams or is unavailable for some period of time or long periods of time, how can we provide that knowledge and context in a seamless way that earlier was only accessible to John? We have built an infrastructure that supports it, that truly brings all this across the different services that an organization uses, and we do it in a very highly secure, compliant way, all the way from organizations able to host it on their own VPC in cloud, or even on-prem.
Dave Vellante
>> So Ash, you described Viven's digital twins more than just assistants. They're essentially mirror images, digital counterparts. So, you're capturing how people think, how people make decisions. Are you defining a new category? As CEO, you know it's a challenging thing to do, but it's also an ambitious and exciting thing to do. So, are you defining a new category? And what in your view makes this transformative for enterprises?
Ashutosh Garg
>> It's a great question. Creating a new category is always hard, but this is the third time we will be creating a new category, whether it was at BloomReach digital experience category to talent intelligence platform category at Eightfold. So, familiar with that part. But here are two examples for you. Just before this interview, I went to my own digital twin and asked, "What are the stories I should share with Dave as I'm going on the camera?" And it helped me prepare much better for this conversation. Second example is I was driving in the morning and suddenly it has tracked me, what is happening with this customer? Have we followed up or not? Now, I can go and wake up someone at 7:00 AM or I can just ask their digital twin, "What is going on with this customer? Have we followed up or not? How are the conversations progressing?" And I got the answer in real time from the digital twin of my account representative.
Dave Vellante
>> Okay, so on the former question, I could ask that of an LLM and get an answer. I presume your answer is going to be more in context and more aligned to the way I think, my brain, my digital self. The latter requires a proprietary data set, which is really interesting because now I can unlock that propriety for my competitive advantage. Can you explain that a little bit further?
Ashutosh Garg
>> Excellent. And that is where we are focused on. It's that, how can we make each and every employee in an enterprise accessible 24/7? So, what we do is we build a model of each person by taking all the data that this person has, but then giving them full control over what data about them should be private, what can be shared, with whom, under what context. Privacy is not one size fits all. It is a function of your relationship. It is a function of the context. So, we do that. This person I was asking the question, they have full visibility into what I asked, what answer their digital twin provided back to me so that if they want, they can go modify it. I can follow up with them. If it crosses the boundaries of their personal privacy, it won't answer that. So, if I ask, "What is your compensation?" It will be like, "No, sorry, I can't tell you that."
Dave Vellante
>> Right, so you've built in those guardrails, as they say. Okay. Everybody I think clearly understands the problem that you're solving. I'll call it the enterprise memory problem. Every organization struggles with losing that tribal knowledge when people move on. So, what's the secret sauce, Varun, behind this? How do you actually technically solve that institutional memory gap? And how do you measure success?
Varun Kacholia
>> I would say there are three components of this. The first, as you touched on earlier, is really the massive amount of data that is segregated. They're in every organization. Today, this data is locked in its own ways, so we have brought it together. We have brought it, made it accessible with the right privacy controls and security and compliance as well. The second is really how do you truly understand the work, the way of thinking, the context of every person in the organization, the strengths that they bring in? And as the amount of data is increasing today, many of our meetings are being transcribed as well, so this really helps build a lot more accurate understanding of that individual along with the context that they have. So, that is something that we have uniquely built. And then the final is how do we make this as a platform that is accessible for multiple different use cases? The use cases that Ashutosh shared with you earlier around, hey, I can ask my own digital twin or I can ask an account representative, but there are just so many more things that can just seamlessly in the flow of work. If you're on a vacation, today the auto vacation responders are very simple and vanilla, but can they be a lot more intelligent and done it in a far more easy way? And those are some of the three components of how we have built this digital twin infrastructure.
Dave Vellante
>> Okay, so if I understand it correctly, you're basically blending personalized language models, highly specialized and personalized, with what I'll call agentic workflows. So, my question is how do your digital twins interpret and ingest activities, like processes, and how does that fit in to the whole agentic vision? Ashutosh, maybe you could take this.
Ashutosh Garg
>> Absolutely. Within an enterprise, we indicate with every enterprise system they have, whether it is the Salesforce or CRM, whether it is GFR, bug tracking, ticketing system, whether it is Asana for project management from your email, Teams, Slack, Zoom meeting transcripts, Team transcripts, WebEx, we indicate with all those things. Then we effectively look at the activity of each person over time to build a model around them, what actions they have taken, how they responded to a message, who they have followed up, who they have not followed up, which part of the conversations they are in and they are not. We use that to build a memory of a person, a context of a person, and a language model around that person. So, one very interesting example for you is that for the same question, you might go to your general counsel or you might go to your CFO, and the response you're looking for is different. So, it's not about generic enterprise knowledge. It is about the knowledge and experience of that person. I may go to five people to get five different answers to triangulate, but that is what this enables.
Dave Vellante
>> I want to come back to privacy, security. It's obviously got to be a huge issue for your customer. I'm sure you get this question all the time. You gave a good example before on compensation, but there's IP leaks. And a lot of times, adoption of new tech generally, I'm sure AI specifically, hits speed bumps over privacy, compliance, legal issues. So, you've built your company to run inside of an enterprise's own cloud or even on-prem, I believe. Correct? It's a hybrid model, so I can have more control there. That deployment flexibility is part of the trust model, but you've got this other pairwise privacy concept, this framework that you've developed, and I'm curious as to how that's playing out, how you're proving it out. Because I could see a lot of companies saying, "Well, sounds good, but I'm really afraid that something's going to go wrong." So, how do you deal with that both technically, culturally, and what are you finding in early trials?
Ashutosh Garg
>> So, Dave, great question. You are right. Privacy and security are at the heart of the problem that we are solving. Today, we have customers from small startups to large public companies, financial services institutions using us and seeing great success. And it all starts by building with that trust with the employees of that company. The first and foremost thing we have done is we have a guideline around how you adopt this piece of technology. You start by yourself, you build your own digital twin, you get comfortable with it. Once you are comfortable with it, that is when you start giving access to other people of your twin. And now, you control what all information you're sharing with them. You can take up both a blacklist approach or a whitelist approach. Whitelist approach is this is the part of my knowledge, my information, my experiences that can be shared by others. And this is a part of the knowledge and experiences that I have that should never get shared with anyone. So, another example will be a conversation that I'm having with my spouse could never get shared with anyone. It's just private to me. as an organization, you can set your own guardrails to say that how information should flow within the organization. Then it's also a function of relationship between individuals. My manager will have certain level of access to my data, whereas I will have a different level of access to my manager's data. This is the big IP that we have built, add within, and unlocking for the possibility with digital Twin.
Varun Kacholia
>> I think to add on top of it a little bit more-
Dave Vellante
>> Please....
Varun Kacholia
>> of course you covered the infrastructure pieces, on-prem, more customers' cloud. We also have as part of Eightfold extensive experience. We have achieved IL4 FedRAMP security certification. Those are many of those learnings and best practices we have built in right into the live-in platform. And fundamentally, access control, as it is currently implemented in many organization, it's problematic in its own ways. Often there are certain documents that do not have access that they should, or some which are inadvertently left open and accessible to others. So, this is a way where several of our customers, users have really, really benefited from let's rethink how should knowledge access be provided irrespective of the system where that data or piece of information is coming from. And how do we build audibility into it and visibility into it? And all of this is right there in the infrastructure and platform as well.
Dave Vellante
>> Yeah, the way I look at... Thank you for that. The way I look at this is you're taking human knowledge and you're turning it into AI infrastructure, and I am thinking that through. So, that means that as an organization, I can scale without labor costs, which is a very powerful... That brings in software like marginal economics and learning curve dynamics to all parts of the company, not just IT, and all industries. And you're doing this across teams, across geographies. There's no reason that this has no limits in terms of timezones. And so how do you see digital twins becoming a standard layer inside of what Jensen calls, say, an AI factory? Is that your vision, that it will become a part of the stack?
Varun Kacholia
>> Yes, that is correct. That's the vision, yes. And if we think about it today, the AI factory also to speak, many of the AI agentic platforms are powered by general purpose models, which will be really the same in every single organization, every single team. So, how can every organization, every team bring in what makes them click, what makes them successful? And it is truly the knowledge, the expertise of every individual accumulated through the lifetime of the organization, not just the current snapshot that we have today. So, this is like... Yeah, we absolutely have this vision, and we are set up for that.
Dave Vellante
>> I got to ask you about the future of work. Hot topic. Of course, people are going to be concerned about losing jobs, but let's agree that we're going to actually create more jobs in the fullness of time. We'll align on that front, if that's okay. But the future of work in this era, if every employee and every team has a digital twin, what does that mean for the way work is structured, the way that processes are maybe created on the fly? Are we entering a new era of work? Clearly we are, but how do you see that evolving where essentially knowledge is this sort of durable autonomous asset of an organization? How will that affect future of work in your vision?
Ashutosh Garg
>> As co-founders of Eightfold, we spend a lot of our time thinking about future of work, how organizations are set up. You talk about the AI factory. Today, the way AI is approaching it in enterprises is what is the lowest common denominator of the knowledge and experience? They're not talking about how do we multiply each and every one of us? Most people at work are overworked. They don't have time for things. We are spending our time on mundane tasks versus strategic initiatives. The purpose, the idea with these digital twins is that they are going to unlock that strategic thinking among people. Now, our conversations with each other can be a lot more meaningful. We can take a lot more informed decisions. And all the processes stuff, all the basic stuff can be done by digital twin. So, for example, filing a simple NERA ticket, updating the sales for CRM with some account update, sending an email to someone, all that can be done by my digital twin. But I have the right level of information. I'm better prepared for my meetings. I'm better prepared for customer conversations and just a lot more thoughtful.
Varun Kacholia
>> Another way to think about this is the last 20 years brought us our human processes. They digitized us to different processes and software. And many people feel that they spend a good percent of their time, 20%, 30% of the time working for the software, whether it is... Take any system, entering information in. This, the future of work actually will be how do we free up that time and then have software work for people, giving people the ability to innovate more, be more creative, and spend more time on things that we all are uniquely positioned to do? So, for us, it is less about taking jobs away, more about how can we give time back to people and accelerate the progress of humanity overall?
Dave Vellante
>> Yeah, I want a digital twin. As we were joking earlier, Andrew wants a digital twin, our production lead. I'm interested in how long this is going to take to build out so that everyone has a digital twin, but so help me understand what it takes to build a digital twin. What kind of tools do I need? How do you help do that? What does the process look like?
Ashutosh Garg
>> Depending upon the setup and the infrastructure of the company, today we already have numerous customers using us who are global enterprises with hundreds of thousands of employees. So, we are now ready. The reason to come out of the stealth mode is now we can start bringing customers on board at a scale and delivering the value. For an enterprise, they can be up and running with us in a matter of weeks. These digital twins look at all the data, train themselves, and then a concept that we have introduced is train your own twin, which is an ongoing process. Similar to the humans, as a new employee, when I join a company, I come in with some information, then every day I'm learning, growing, developing myself. That is how these twins are. You deploy them in a matter of weeks, and then they start learning based on each and every action and interaction you are having in the enterprise.
Dave Vellante
>> Well, guys, congratulations. $35 million. COSLA, I think, led the rounds, Foundation Capital, FPV Ventures, the Operator Collective I think is part of this. You've got some angels, some high-profile angels as well. I'll give you the last word. Where do you want to take this company? And how do you see it evolving to become a critical layer of the AI infrastructure wave?
Ashutosh Garg
>> We look at it, this is the new fabric of the enterprises. This is going to fundamentally transform how we interact with each other in the organization. How do we access, analyze things? How do we process information and how we take actions? The future is super bright with Viven. And thank you for this opportunity today. Super excited to work with you all. And Dave, we'll talk about you becoming our customer.
Dave Vellante
>> Okay, great. It's great to have you. I'd love to have you face-to-face at the New York Stock Exchange at some point. Ashutosh and Varun, thank you very much. And congratulations and best of luck to you.
Ashutosh Garg
>> Thank you.
Varun Kacholia
>> Thanks.
Dave Vellante
>> You're very welcome. And thank you for watching our Mixture of Experts series, NYSE Wired plus theCUBE from the New York Stock Exchange. We'll be right back right after this short break.
Dave Vellante