Exploring the Advancements and Challenges in AI Agent Deployment
John Nay, founder and Chief Executive Officer of Norm Ai, joins theCUBE's special presentation with NYSE Wired, focusing on the upcoming Artificial Intelligence Agent Conference 2025. Hosted by John Furrier, co-founder and co-Chief Executive Officer of SiliconANGLE Media, this insightful discussion covers the pivotal developments in AI infrastructure and the regulatory complexities faced by enterprises.
In this episode, Nay shares their expertise in regulatory AI infrastructure, particularly as it pertains to AI agent deployment in highly regulated sectors. The conversation, hosted by Furrier, delves into the evolving landscape of AI technology, compliance challenges, and the strategic initiatives underway at Norm Ai to address the pressing issues surrounding AI deployment. The discussion provides valuable insights for both technology and policy influencers.
Key takeaways from the discussion include the emphasis on the need for dynamic, real-time compliance frameworks that align with regulatory standards, as emphasized by Nay. Furthermore, the episode highlights how enterprises can leverage existing compliance structures to integrate AI technologies more effectively, offering a glimpse into the future of AI agent scalability and regulation. The conversation underscores the importance of bridging the gap between engineering, policy, and technology for sustainable AI innovation.
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Aman Gour, FurtherAI
Exploring the Advancements and Challenges in AI Agent Deployment
John Nay, founder and Chief Executive Officer of Norm Ai, joins theCUBE's special presentation with NYSE Wired, focusing on the upcoming Artificial Intelligence Agent Conference 2025. Hosted by John Furrier, co-founder and co-Chief Executive Officer of SiliconANGLE Media, this insightful discussion covers the pivotal developments in AI infrastructure and the regulatory complexities faced by enterprises.
In this episode, Nay shares their expertise in regulatory AI infrastructure, particularly as it pertains to AI agent deployment in highly regulated sectors. The conversation, hosted by Furrier, delves into the evolving landscape of AI technology, compliance challenges, and the strategic initiatives underway at Norm Ai to address the pressing issues surrounding AI deployment. The discussion provides valuable insights for both technology and policy influencers.
Key takeaways from the discussion include the emphasis on the need for dynamic, real-time compliance frameworks that align with regulatory standards, as emphasized by Nay. Furthermore, the episode highlights how enterprises can leverage existing compliance structures to integrate AI technologies more effectively, offering a glimpse into the future of AI agent scalability and regulation. The conversation underscores the importance of bridging the gap between engineering, policy, and technology for sustainable AI innovation.
>> Welcome back to theCube Studio here at the New York Stock Exchange. This is NYSE Wired and theCUBE connecting Silicon Valley to Wall Street. And this May, theCUBE and NYSE Wired are going on the road not too far, we're going to the AI Agent Conference here in New York, early May. Joining me now is one of the speakers who will be attending and taking to the stage that day. Aman Gour, CEO and co-founder of FurtherAI. Welcome, Aman.
Aman Gour
>> Thank you so much for having me.
Gemma Allen
>> So you have an AI startup that wants to disrupt the insurance industry. Two things we know about insurance industry is it's extremely boring. Am I okay to say that? And quite rigid, right?
Aman Gour
>> Yeah.
Gemma Allen
>> Talk me through, it seems as though in some respects though, both of those things make it right for AI disruption, automation.
Aman Gour
>> Yeah.
Gemma Allen
>> Talk to me a little bit about the company, how this came to be. Give me the 101.
Aman Gour
>> Absolutely. I think traditionally insurance is labeled as slow, boring, and unsexy. I would actually counter that. I would say that insurance wasn't slow to adopt technology. The technology wasn't there to solve the core need of insurance. If you think about insurance value chain, it deals with a lot of unstructured data. And I've built my career in AI and NLP and NLP was not there to process these complex documents. With LLMs, that's changing. And I would say that our goal is to evolve insurance rather than disrupt it. So evolution over revolution there. And insurance is adopting AI at the pace that nobody imagined it would.
Gemma Allen
>> So let's talk about the before and after, the as is and the to be. Okay?
Aman Gour
>> Yeah.
Gemma Allen
>> So insurance, yes, very tedious industry. A lot of manual interception and workflows. A lot of manual, I guess, transactional style labor.
Aman Gour
>> Absolutely.
Gemma Allen
>> A lot of folks doing roles that have relative repeatability from the perspective of automation, right? Talk me through the product. Are you trying to solve for specific parts of the workflow, particular silos within the industry broadly? Is this customer facing? Is this ensuring that things move in a more fluid manner? What specifically are you looking to solve?
Aman Gour
>> Yeah. So when we started insurance, we were two people who did not know anything about insurance.
Gemma Allen
>> Wow. What a great reason to start a company. Love it.
Aman Gour
>> In the early days we started because we saw an opportunity that the industry would be rewired with LLMs at the core of it, with all the busy work getting automated. We started because of the opportunity we saw. We stayed because of people. I really feel that we had some of the greatest relationship in the industry with the partners that we work with. And the industry is very forward leaning on our adoption of AI tools. What I would say is also, over the course of time, we realized that world is getting riskier and riskier and insurance access are a fundamental backstop. So that's more of an idealistic reason to build for insurance.
Gemma Allen
>> Yeah.
Aman Gour
>> But what we are doing is pretty simple. We looked at the value chain. We looked at the value chain deals with some of the core documentation at every step of the way. It could be a policy form, it could be a loss run, it could be an SOV and so on. And most of the workflows are about reading, understanding, and reasoning on top of that. So what we did was we built on top of the existing models like Claude, GPT, LAMA, we created a model router that gets better and better at understanding insurance context. So it understands that CAT is not a full like furry animal, it's a catastrophe. But also more complex examples, it can sort of generate code on the fly to process a loss run, which is known for being very difficult to process. But we don't sell that AI as like ChatGPT or Cloud, we actually automate end-to-end work that humans did manually.
Gemma Allen
>> Wow.
Aman Gour
>> So these could be 20 step processes, like a standard operating procedure, which somebody was performing manually, obviously not their highlight of the day, but had to do that because there was no tech behind it. So one example could be underwriting. When an underwriter gets a request for a proposal or a quote, they get these documents, some could be handwritten, formatted like an Excel sheet or maybe an inverted document. And then previously they would process it one by one and extract data, research the account. They would research if, let's say, a property which is a restaurant converts into a bar over the weekend or a warehouse converts into a nightclub. So they would research all of that. But now an LLM agent can really help with all of that research.
Gemma Allen
>> I really want you to get into the tech in a second, but from the perspective of product use, it's like mass discoverability, scope, but does this product also actually make decisions? Does it intercept workflows and change directions of tasks? Like is it actually instructing systems to perhaps green light approvals or is it more, I guess, on the orchestration layer around administration?
Aman Gour
>> It's more of the orchestration layer around administration.
Gemma Allen
>> Okay.
Aman Gour
>> So insurance being regulated industry, we are not making decisions for the end users. Let's say you have to be a trained underwriter or a certified adjuster to take those decisions. But before you take that decision, there are 20 steps that lead to that.
Gemma Allen
>> Yes.
Aman Gour
>> So we automate that busy work behind that decision making and highlight nuances which maybe a human, I might miss.
Gemma Allen
>> A lot of tedious reading, understanding.
Aman Gour
>> Busy work. Yeah. Yeah.
Gemma Allen
>> Painful work, I'm sure you might agree, right?
Aman Gour
>> Actually true. Imagine like you have an Excel sheet and you have to copy paste data one sell at a time into another Excel sheet, that nobody's highlight out of the day.
Gemma Allen
>> Yes. And I was an intern once, so I know.
Aman Gour
>> Yeah.
Gemma Allen
>> Much like yourself.
Aman Gour
>> Yeah.
Gemma Allen
>> Okay. So let's talk about the tech for a second because it is a very interesting time from the perspective of building on LLMs, right? Who are also competing in their own space, in their own direct to client way, B2B way, with some of these large FinTech and insurers. So you're building on top of all of these LLMs. You have like kind of preferred elements of the stack that you use more regularly? How do you continue to assess, because it seems to me as though there are new releases like-
Aman Gour
>> Every two weeks. Yeah.
Gemma Allen
>> Yeah. It's impossible to keep up, right?
Aman Gour
>> Yeah, yeah, yeah.
Gemma Allen
>> Even from an R&D perspective or from an engineering perspective-
Aman Gour
>> That's true....
Gemma Allen
>> how do you monitor this?
Aman Gour
>> Yeah. So it's interesting. I was having a chat with my co-founder and CTO, like generally for human being, the smartest person I've met, but he was saying that the maintenance stacks when it comes to AI applications is much, much, much more than the SaaS applications before, because now you are refactoring the entire orchestration around when the model changes and ensuring that it does not regress. But parking that aside for a bit, as the core models evolve, so if you think about insurance and the insurance workflows, they are here, which is like very specific to what an underwriter does.
Gemma Allen
>> And also a lot of compliance, right?
Aman Gour
>> A lot of compliance.
Gemma Allen
>> There is compliance.
Aman Gour
>> Yes.
Gemma Allen
>> So my assumption would be, does that mean Anthropic tends to be more aligned to the space based on their kind of winning enterprise, their governance rails, or is that just a marketing narrative that we believe?
Aman Gour
>> Yeah. So what we did is actually we use all the models. We use Gemini, we use Claude, we use OpenAI, but we use it through Azure, so there is no data being used for training of the base model. So all of that remains in a single tenant which is specific to that particular partner. But on top of that, what we have seen is some of these models have their own edge. Like right now Claude is really, really good. The latest model of Claude are really, really good with processing documents because it generates code on the fly and does things. So we use that too. But where we add value is we create these workflows which are specific to insurance, which are auditable, repeatable, and that can be governed by the insurance companies, so that if they get regulated in terms of what went in making this decision, they can see the entire decision trace of what was processed by the AI agents, like the system of AI agents at different steps. That is like super critical because you don't want it to be a black box where it produces different decision every time. So that's where we spend a lot of value and time where take these models and ensure that they could be audited, like auditable, repeatable, and create these processes, which the end user does today.
Gemma Allen
>> So talk me through a scenario here. Say I'm AIG and I don't know what size insurers you're targeting, but let's just say for the sake of it's a brand everyone knows, right? Yeah. I might have Microsoft deployed across various elements of my stack. I have Teams, I have Azure, I'm using Power BI visualization, and I know I ask this because you worked at Microsoft too at one time, much like I did, right? It makes it easy. So I now have Copilot, right? I'm saying, "Hey, I have a discoverability issue. I want to automate these workflows. I want to speed up processing time in this part of my business." You have a company like that, like a hyperscaler who says, "Well, we're fully integrated. We're already here, there and everywhere, right? We can connect these dots for you." AI has made everything suddenly like that, like a hyperscaler who says, "Well, we're fully integrated. We're already here, there and everywhere." We can connect these dots for you. AI has made everything suddenly extremely accessible."
Aman Gour
>> 100%, yeah.
Gemma Allen
>> How do you compete with that? How do you guys, as any AI startup, and by the way, I love startups, and I love to see startups, right? So I am always backing the underdog.
Aman Gour
>> By building those integrations as well ourselves, that's what we have been doing from early days. We have built integration, not with the Microsoft Suite, with Outlook and SharePoint and Excel, but with the core products that insurance companies use day in, day out, be it the policy administration system, agency management systems, CRMs and so on, and be that orchestration layer on top of that. Because at the end of the day, these agents, the way we are building it, they're not replacing anything that exists. They're working with it. So very much like Microsoft approach where you partner with everyone rather than trying to be more disruptive. You try to fit into the ecosystem the way it exists and then grow from there. So that's been our approach and it's been successful so far. So if you take an example of AIG, they might be using a different platform for different lines of business and some lines might be completely manual. So we will start there and we'll prove ourselves that we are faster, better, and more economical than maybe a company that name starts with a P and ends with an R.
Gemma Allen
>> Love it. So there is a great line that a lot of the most successful companies in the world don't necessarily have great customers, they have great hostages, right?
Aman Gour
>> Yeah.
Gemma Allen
>> Vendor lock has been a long term challenge in tech. I mean, I worked in tech since 2006 and there is certainly just a rhythm to it, right? In terms of whether it previously has been a rhythm whereby you do what you always did, you work with the same players and then your systems are somewhat locked in. We saw some interesting events last week with IBM stock and the result of the Anthropic code announcement for Cobalt, right? And I think it kind of opened up this whole kind of maybe reckoning to the industry to say, wow, like these models and AI broadly can truly disrupt what was a long and somewhat secure problem.
Aman Gour
>> That is true. Yeah.
Gemma Allen
>> Right? Do you think that these conversations are that that as a premise is weighing in for buyers and decision makers? Do you think folks are more aware in this next wave of tech to not perhaps lean in a way that get results in lock-in?
Aman Gour
>> That is true. Yeah, absolutely. And actually, I am against that keeping your customers hostage because in that way that's like a balance of power that you're creating, which ends up blowing in anybody's face over a course of time. If you look at five or 10 years timeline. So we, on our side, we don't push our partners and customers to have like 10-year contracts. We're okay with a three-year contract with a price lock in or even a one-year contract, but with an understanding that price might increase next year. And we are promoting that and we see that the end users on the other side do not want to get logged into long term contracts, especially that with the pace that AI is evolving, they would want to bet on companies that are actually at the cutting edge. That allows us to really put our best game on every sort of new release and we ensure that we are playing from the front foot.
Gemma Allen
>> And is this price per seat? Is it a per seat model?
Aman Gour
>> No, we avoid that. It's consumption based model because when they are consuming this, they're getting the value and we are generating that value. So it aligns the incentive really well. So it's a consumption based model. We keep it fixed minimum, so that we can have a predictability in our business model, but at the same time, it aligns incentives really well for both sides.
Gemma Allen
>> Talk to me about the consultancy services side of this business, because again, in FinTech and insurance predominantly, there has always been a heavy crutch around leaning on service providers or consultancy firms to help kind of direct your product roadmap, right, to help deliver on strategy. Is AI disrupting that? When you win a piece of work or a new contract, are you coming in and is there like an envision stage, a scoping phase where people are suddenly in the place by which they can think differently about product from speed, agility, and even just like integration?
Aman Gour
>> It's very interesting. So I do think that like the consulting space is evolving because the knowledge was powered before, but knowledge is now a prompt. So that-
Gemma Allen
>> Well said.
Aman Gour
>> I came up with on the fly shit, the coffee is working. So that was the case before, but I don't think anymore that's the case. You can actually create a report that maybe big four would create by just writing a prompt. What is changing is now making that a reality, like what does it take from that vision? What platform can support that idea to reality? That's where we think of ourselves as an AI transformation partner, but with a product at the core of it. And we get involved in the processes that this is your workflow currently, this is made for the human doing the job. Is this the right workflow when AI agents are doing the job and a human is monitoring a group of AI agents and what changes does that entail? What volume can you process and so on?
Gemma Allen
>> And what are your own sprint cycles like? How often are you releasing new features? Are you feeling the pressure of an industry that's moving at the speed of sound in the background, I guess? Is that the broad thesis that everyone's kind of now moving at that three month phase?
Aman Gour
>> Yeah. I think we are releasing honestly every day, not even every week. So that release is there at a very rapid pace. But at the same time, we are trying to be rational and responsible in deploying these two odd partners who might need more stability that, okay, we don't want the best model that is coming in every day. We just want that our workflows are accurate, they're auditable, and we're not going to get in trouble. So we act as the layer in middle, which ensures that the evaluation set and the orchestration work like it should be working every day for our partners. Yeah.
Gemma Allen
>> You just raised 25 million in October.
Aman Gour
>> Yeah.
Gemma Allen
>> A16Z led the rounds, great credibility from the first sector of them at the firm. You mentioned that it was, you closed in seven days?
Aman Gour
>> It's closed in seven days. So shout out to Joe Schmidt to make that possible.
Gemma Allen
>> That's impressive.
Aman Gour
>> Yeah.
Gemma Allen
>> And that brings you to a 30 million raises overall?
Aman Gour
>> That's right. Yeah.
Gemma Allen
>> Okay. What's ahead? What are you doing with this 30 million? What does the next six, 12 months look like? Are you going to move towards a B or what are you thinking?
Aman Gour
>> Yeah. So if I think about a longer term goal, the goal for us to build a public company and maybe go public here-
Gemma Allen
>> Here at the New York Stock Exchange.
Aman Gour
>> We'll see. But that's the goal. From like the early days, I built a company before which did not go public, so that has been a chip on my shoulder, that this time around I want to do things right. And in my head, what that means is building a company that goes public. Now, that's a milestone. We are at series A. A lot will happen between that. Maybe series B could happen in the next few quarters or a few years. We'll see about that, but right now we are heavily invested in making our customers super, super successful. We have a very land and expand business. So what that basically means is we have to really make initial use cases with our partners successful, that then only they would expand, which is actually the right way of building business in my opinion, because then you're putting delivery at the core of everything that you do. So in that way, we are truly customer obsessed. I know that term gets thrown around easily.
Gemma Allen
>> That's trusted partnership, I guess, right?
Aman Gour
>> It has to be trusted partnership. That is actually the headline of our website as well, that we are a trusted partner for insurance AI.
Gemma Allen
>> Great. And Aman, you moved to San Francisco from India-
Aman Gour
>> Yeah....
Gemma Allen
>> and I hear in other interviews, you talk about coming from a small town, founded a startup in India, sold it. I guess it's kind of the dream, right? I'm sure for lots of folks watching you from afar and from home, from small town to Silicon Valley, it's the North Star. Talk to me a little bit about that experience and that journey and I guess what you've learned along the way?
Aman Gour
>> Yeah. I think the two things that I try to be, like one, I'm super hungry to do more. So this is just the start of a lot of things that I want to do. As a kid, I used to have a dream to become a prime minister of India, so we'll see how that goes, but for now-
Gemma Allen
>> Narendra Modi.
Aman Gour
>> Yeah. I don't know. I don't want to say that out loud, but right now the goal is to build a successful company, but do that with a lot of heart, without taking shortcuts in the process and a lot of humility. So I'm trying to be hungry, but humble. These are the two terms that I'm leaning a lot on as an individual. But the story has been interesting. I think I've taken a lot of leaps in my life, grateful to my family. I'm very close to them as well. I was raised by two gentlemen who were two very different personalities, my grandfather and my father. One was a teacher, so he said that, "Life is long, you have time." My father was a businessman, so he's always like, "You have one day, get the best out of it." So two very different individuals, and that has served me well. I plan for macro, live in micro, so execute on a daily basis, but plan on the longer run. I invest a lot in relationships, and that is where insurance is really good, because it's a very relationship driven industry, and we're building the relationships and making our partners super successful. So more than anything else, that will compound.
Gemma Allen
>> Sure.
Aman Gour
>> Technology will change, but that's something that will compound.
Gemma Allen
>> Well, I'm sure those two men are very proud of you. Look at you at the here at-
Aman Gour
>> Hopefully....
Gemma Allen
>> NYSE today, and I'm looking forward to seeing you on stage in May at the AI Agent Conference.
Aman Gour
>> And also going public at New York Stock Exchange.
Gemma Allen
>> Well, yes, you better invite me to that party. I expect an invite.
Aman Gour
>> Thank you so much, Gemma.
Gemma Allen
>> Thanks for coming on theCUBE.
Aman Gour
>> Yeah. Awesome.
Gemma Allen
>> I'm Gemma Allen coming to you from the New York Stock Exchange. This is NYSE Wired, and we are partnering with the AI Agent Conference happening here in May in New York. Stay tuned. Thanks for watching.
>> Welcome back to theCube Studio here at the New York Stock Exchange. This is NYSE Wired and theCUBE connecting Silicon Valley to Wall Street. And this May, theCUBE and NYSE Wired are going on the road not too far, we're going to the AI Agent Conference here in New York, early May. Joining me now is one of the speakers who will be attending and taking to the stage that day. Aman Gour, CEO and co-founder of FurtherAI. Welcome, Aman.
Aman Gour
>> Thank you so much for having me.
Gemma Allen
>> So you have an AI startup that wants to disrupt the insurance industry. Two things we know about insurance industry is it's extremely boring. Am I okay to say that? And quite rigid, right?
Aman Gour
>> Yeah.
Gemma Allen
>> Talk me through, it seems as though in some respects though, both of those things make it right for AI disruption, automation.
Aman Gour
>> Yeah.
Gemma Allen
>> Talk to me a little bit about the company, how this came to be. Give me the 101.
Aman Gour
>> Absolutely. I think traditionally insurance is labeled as slow, boring, and unsexy. I would actually counter that. I would say that insurance wasn't slow to adopt technology. The technology wasn't there to solve the core need of insurance. If you think about insurance value chain, it deals with a lot of unstructured data. And I've built my career in AI and NLP and NLP was not there to process these complex documents. With LLMs, that's changing. And I would say that our goal is to evolve insurance rather than disrupt it. So evolution over revolution there. And insurance is adopting AI at the pace that nobody imagined it would.
Gemma Allen
>> So let's talk about the before and after, the as is and the to be. Okay?
Aman Gour
>> Yeah.
Gemma Allen
>> So insurance, yes, very tedious industry. A lot of manual interception and workflows. A lot of manual, I guess, transactional style labor.
Aman Gour
>> Absolutely.
Gemma Allen
>> A lot of folks doing roles that have relative repeatability from the perspective of automation, right? Talk me through the product. Are you trying to solve for specific parts of the workflow, particular silos within the industry broadly? Is this customer facing? Is this ensuring that things move in a more fluid manner? What specifically are you looking to solve?
Aman Gour
>> Yeah. So when we started insurance, we were two people who did not know anything about insurance.
Gemma Allen
>> Wow. What a great reason to start a company. Love it.
Aman Gour
>> In the early days we started because we saw an opportunity that the industry would be rewired with LLMs at the core of it, with all the busy work getting automated. We started because of the opportunity we saw. We stayed because of people. I really feel that we had some of the greatest relationship in the industry with the partners that we work with. And the industry is very forward leaning on our adoption of AI tools. What I would say is also, over the course of time, we realized that world is getting riskier and riskier and insurance access are a fundamental backstop. So that's more of an idealistic reason to build for insurance.
Gemma Allen
>> Yeah.
Aman Gour
>> But what we are doing is pretty simple. We looked at the value chain. We looked at the value chain deals with some of the core documentation at every step of the way. It could be a policy form, it could be a loss run, it could be an SOV and so on. And most of the workflows are about reading, understanding, and reasoning on top of that. So what we did was we built on top of the existing models like Claude, GPT, LAMA, we created a model router that gets better and better at understanding insurance context. So it understands that CAT is not a full like furry animal, it's a catastrophe. But also more complex examples, it can sort of generate code on the fly to process a loss run, which is known for being very difficult to process. But we don't sell that AI as like ChatGPT or Cloud, we actually automate end-to-end work that humans did manually.
Gemma Allen
>> Wow.
Aman Gour
>> So these could be 20 step processes, like a standard operating procedure, which somebody was performing manually, obviously not their highlight of the day, but had to do that because there was no tech behind it. So one example could be underwriting. When an underwriter gets a request for a proposal or a quote, they get these documents, some could be handwritten, formatted like an Excel sheet or maybe an inverted document. And then previously they would process it one by one and extract data, research the account. They would research if, let's say, a property which is a restaurant converts into a bar over the weekend or a warehouse converts into a nightclub. So they would research all of that. But now an LLM agent can really help with all of that research.
Gemma Allen
>> I really want you to get into the tech in a second, but from the perspective of product use, it's like mass discoverability, scope, but does this product also actually make decisions? Does it intercept workflows and change directions of tasks? Like is it actually instructing systems to perhaps green light approvals or is it more, I guess, on the orchestration layer around administration?
Aman Gour
>> It's more of the orchestration layer around administration.
Gemma Allen
>> Okay.
Aman Gour
>> So insurance being regulated industry, we are not making decisions for the end users. Let's say you have to be a trained underwriter or a certified adjuster to take those decisions. But before you take that decision, there are 20 steps that lead to that.
Gemma Allen
>> Yes.
Aman Gour
>> So we automate that busy work behind that decision making and highlight nuances which maybe a human, I might miss.
Gemma Allen
>> A lot of tedious reading, understanding.
Aman Gour
>> Busy work. Yeah. Yeah.
Gemma Allen
>> Painful work, I'm sure you might agree, right?
Aman Gour
>> Actually true. Imagine like you have an Excel sheet and you have to copy paste data one sell at a time into another Excel sheet, that nobody's highlight out of the day.
Gemma Allen
>> Yes. And I was an intern once, so I know.
Aman Gour
>> Yeah.
Gemma Allen
>> Much like yourself.
Aman Gour
>> Yeah.
Gemma Allen
>> Okay. So let's talk about the tech for a second because it is a very interesting time from the perspective of building on LLMs, right? Who are also competing in their own space, in their own direct to client way, B2B way, with some of these large FinTech and insurers. So you're building on top of all of these LLMs. You have like kind of preferred elements of the stack that you use more regularly? How do you continue to assess, because it seems to me as though there are new releases like-
Aman Gour
>> Every two weeks. Yeah.
Gemma Allen
>> Yeah. It's impossible to keep up, right?
Aman Gour
>> Yeah, yeah, yeah.
Gemma Allen
>> Even from an R&D perspective or from an engineering perspective-
Aman Gour
>> That's true....
Gemma Allen
>> how do you monitor this?
Aman Gour
>> Yeah. So it's interesting. I was having a chat with my co-founder and CTO, like generally for human being, the smartest person I've met, but he was saying that the maintenance stacks when it comes to AI applications is much, much, much more than the SaaS applications before, because now you are refactoring the entire orchestration around when the model changes and ensuring that it does not regress. But parking that aside for a bit, as the core models evolve, so if you think about insurance and the insurance workflows, they are here, which is like very specific to what an underwriter does.
Gemma Allen
>> And also a lot of compliance, right?
Aman Gour
>> A lot of compliance.
Gemma Allen
>> There is compliance.
Aman Gour
>> Yes.
Gemma Allen
>> So my assumption would be, does that mean Anthropic tends to be more aligned to the space based on their kind of winning enterprise, their governance rails, or is that just a marketing narrative that we believe?
Aman Gour
>> Yeah. So what we did is actually we use all the models. We use Gemini, we use Claude, we use OpenAI, but we use it through Azure, so there is no data being used for training of the base model. So all of that remains in a single tenant which is specific to that particular partner. But on top of that, what we have seen is some of these models have their own edge. Like right now Claude is really, really good. The latest model of Claude are really, really good with processing documents because it generates code on the fly and does things. So we use that too. But where we add value is we create these workflows which are specific to insurance, which are auditable, repeatable, and that can be governed by the insurance companies, so that if they get regulated in terms of what went in making this decision, they can see the entire decision trace of what was processed by the AI agents, like the system of AI agents at different steps. That is like super critical because you don't want it to be a black box where it produces different decision every time. So that's where we spend a lot of value and time where take these models and ensure that they could be audited, like auditable, repeatable, and create these processes, which the end user does today.
Gemma Allen
>> So talk me through a scenario here. Say I'm AIG and I don't know what size insurers you're targeting, but let's just say for the sake of it's a brand everyone knows, right? Yeah. I might have Microsoft deployed across various elements of my stack. I have Teams, I have Azure, I'm using Power BI visualization, and I know I ask this because you worked at Microsoft too at one time, much like I did, right? It makes it easy. So I now have Copilot, right? I'm saying, "Hey, I have a discoverability issue. I want to automate these workflows. I want to speed up processing time in this part of my business." You have a company like that, like a hyperscaler who says, "Well, we're fully integrated. We're already here, there and everywhere, right? We can connect these dots for you." AI has made everything suddenly like that, like a hyperscaler who says, "Well, we're fully integrated. We're already here, there and everywhere." We can connect these dots for you. AI has made everything suddenly extremely accessible."
Aman Gour
>> 100%, yeah.
Gemma Allen
>> How do you compete with that? How do you guys, as any AI startup, and by the way, I love startups, and I love to see startups, right? So I am always backing the underdog.
Aman Gour
>> By building those integrations as well ourselves, that's what we have been doing from early days. We have built integration, not with the Microsoft Suite, with Outlook and SharePoint and Excel, but with the core products that insurance companies use day in, day out, be it the policy administration system, agency management systems, CRMs and so on, and be that orchestration layer on top of that. Because at the end of the day, these agents, the way we are building it, they're not replacing anything that exists. They're working with it. So very much like Microsoft approach where you partner with everyone rather than trying to be more disruptive. You try to fit into the ecosystem the way it exists and then grow from there. So that's been our approach and it's been successful so far. So if you take an example of AIG, they might be using a different platform for different lines of business and some lines might be completely manual. So we will start there and we'll prove ourselves that we are faster, better, and more economical than maybe a company that name starts with a P and ends with an R.
Gemma Allen
>> Love it. So there is a great line that a lot of the most successful companies in the world don't necessarily have great customers, they have great hostages, right?
Aman Gour
>> Yeah.
Gemma Allen
>> Vendor lock has been a long term challenge in tech. I mean, I worked in tech since 2006 and there is certainly just a rhythm to it, right? In terms of whether it previously has been a rhythm whereby you do what you always did, you work with the same players and then your systems are somewhat locked in. We saw some interesting events last week with IBM stock and the result of the Anthropic code announcement for Cobalt, right? And I think it kind of opened up this whole kind of maybe reckoning to the industry to say, wow, like these models and AI broadly can truly disrupt what was a long and somewhat secure problem.
Aman Gour
>> That is true. Yeah.
Gemma Allen
>> Right? Do you think that these conversations are that that as a premise is weighing in for buyers and decision makers? Do you think folks are more aware in this next wave of tech to not perhaps lean in a way that get results in lock-in?
Aman Gour
>> That is true. Yeah, absolutely. And actually, I am against that keeping your customers hostage because in that way that's like a balance of power that you're creating, which ends up blowing in anybody's face over a course of time. If you look at five or 10 years timeline. So we, on our side, we don't push our partners and customers to have like 10-year contracts. We're okay with a three-year contract with a price lock in or even a one-year contract, but with an understanding that price might increase next year. And we are promoting that and we see that the end users on the other side do not want to get logged into long term contracts, especially that with the pace that AI is evolving, they would want to bet on companies that are actually at the cutting edge. That allows us to really put our best game on every sort of new release and we ensure that we are playing from the front foot.
Gemma Allen
>> And is this price per seat? Is it a per seat model?
Aman Gour
>> No, we avoid that. It's consumption based model because when they are consuming this, they're getting the value and we are generating that value. So it aligns the incentive really well. So it's a consumption based model. We keep it fixed minimum, so that we can have a predictability in our business model, but at the same time, it aligns incentives really well for both sides.
Gemma Allen
>> Talk to me about the consultancy services side of this business, because again, in FinTech and insurance predominantly, there has always been a heavy crutch around leaning on service providers or consultancy firms to help kind of direct your product roadmap, right, to help deliver on strategy. Is AI disrupting that? When you win a piece of work or a new contract, are you coming in and is there like an envision stage, a scoping phase where people are suddenly in the place by which they can think differently about product from speed, agility, and even just like integration?
Aman Gour
>> It's very interesting. So I do think that like the consulting space is evolving because the knowledge was powered before, but knowledge is now a prompt. So that-
Gemma Allen
>> Well said.
Aman Gour
>> I came up with on the fly shit, the coffee is working. So that was the case before, but I don't think anymore that's the case. You can actually create a report that maybe big four would create by just writing a prompt. What is changing is now making that a reality, like what does it take from that vision? What platform can support that idea to reality? That's where we think of ourselves as an AI transformation partner, but with a product at the core of it. And we get involved in the processes that this is your workflow currently, this is made for the human doing the job. Is this the right workflow when AI agents are doing the job and a human is monitoring a group of AI agents and what changes does that entail? What volume can you process and so on?
Gemma Allen
>> And what are your own sprint cycles like? How often are you releasing new features? Are you feeling the pressure of an industry that's moving at the speed of sound in the background, I guess? Is that the broad thesis that everyone's kind of now moving at that three month phase?
Aman Gour
>> Yeah. I think we are releasing honestly every day, not even every week. So that release is there at a very rapid pace. But at the same time, we are trying to be rational and responsible in deploying these two odd partners who might need more stability that, okay, we don't want the best model that is coming in every day. We just want that our workflows are accurate, they're auditable, and we're not going to get in trouble. So we act as the layer in middle, which ensures that the evaluation set and the orchestration work like it should be working every day for our partners. Yeah.
Gemma Allen
>> You just raised 25 million in October.
Aman Gour
>> Yeah.
Gemma Allen
>> A16Z led the rounds, great credibility from the first sector of them at the firm. You mentioned that it was, you closed in seven days?
Aman Gour
>> It's closed in seven days. So shout out to Joe Schmidt to make that possible.
Gemma Allen
>> That's impressive.
Aman Gour
>> Yeah.
Gemma Allen
>> And that brings you to a 30 million raises overall?
Aman Gour
>> That's right. Yeah.
Gemma Allen
>> Okay. What's ahead? What are you doing with this 30 million? What does the next six, 12 months look like? Are you going to move towards a B or what are you thinking?
Aman Gour
>> Yeah. So if I think about a longer term goal, the goal for us to build a public company and maybe go public here-
Gemma Allen
>> Here at the New York Stock Exchange.
Aman Gour
>> We'll see. But that's the goal. From like the early days, I built a company before which did not go public, so that has been a chip on my shoulder, that this time around I want to do things right. And in my head, what that means is building a company that goes public. Now, that's a milestone. We are at series A. A lot will happen between that. Maybe series B could happen in the next few quarters or a few years. We'll see about that, but right now we are heavily invested in making our customers super, super successful. We have a very land and expand business. So what that basically means is we have to really make initial use cases with our partners successful, that then only they would expand, which is actually the right way of building business in my opinion, because then you're putting delivery at the core of everything that you do. So in that way, we are truly customer obsessed. I know that term gets thrown around easily.
Gemma Allen
>> That's trusted partnership, I guess, right?
Aman Gour
>> It has to be trusted partnership. That is actually the headline of our website as well, that we are a trusted partner for insurance AI.
Gemma Allen
>> Great. And Aman, you moved to San Francisco from India-
Aman Gour
>> Yeah....
Gemma Allen
>> and I hear in other interviews, you talk about coming from a small town, founded a startup in India, sold it. I guess it's kind of the dream, right? I'm sure for lots of folks watching you from afar and from home, from small town to Silicon Valley, it's the North Star. Talk to me a little bit about that experience and that journey and I guess what you've learned along the way?
Aman Gour
>> Yeah. I think the two things that I try to be, like one, I'm super hungry to do more. So this is just the start of a lot of things that I want to do. As a kid, I used to have a dream to become a prime minister of India, so we'll see how that goes, but for now-
Gemma Allen
>> Narendra Modi.
Aman Gour
>> Yeah. I don't know. I don't want to say that out loud, but right now the goal is to build a successful company, but do that with a lot of heart, without taking shortcuts in the process and a lot of humility. So I'm trying to be hungry, but humble. These are the two terms that I'm leaning a lot on as an individual. But the story has been interesting. I think I've taken a lot of leaps in my life, grateful to my family. I'm very close to them as well. I was raised by two gentlemen who were two very different personalities, my grandfather and my father. One was a teacher, so he said that, "Life is long, you have time." My father was a businessman, so he's always like, "You have one day, get the best out of it." So two very different individuals, and that has served me well. I plan for macro, live in micro, so execute on a daily basis, but plan on the longer run. I invest a lot in relationships, and that is where insurance is really good, because it's a very relationship driven industry, and we're building the relationships and making our partners super successful. So more than anything else, that will compound.
Gemma Allen
>> Sure.
Aman Gour
>> Technology will change, but that's something that will compound.
Gemma Allen
>> Well, I'm sure those two men are very proud of you. Look at you at the here at-
Aman Gour
>> Hopefully....
Gemma Allen
>> NYSE today, and I'm looking forward to seeing you on stage in May at the AI Agent Conference.
Aman Gour
>> And also going public at New York Stock Exchange.
Gemma Allen
>> Well, yes, you better invite me to that party. I expect an invite.
Aman Gour
>> Thank you so much, Gemma.
Gemma Allen
>> Thanks for coming on theCUBE.
Aman Gour
>> Yeah. Awesome.
Gemma Allen
>> I'm Gemma Allen coming to you from the New York Stock Exchange. This is NYSE Wired, and we are partnering with the AI Agent Conference happening here in May in New York. Stay tuned. Thanks for watching.
Gemma Allen