Building AI Teams That Scale with Kumar Velugula Xnode

bigger enterprises they're giving internal employees to access to open AI or other models and they're concerned about the data privacy and security of the IP of the company and the data. So because of this regulated companies are holding back using AI in their company at scale. So what we figured out is the control plane we built is now allowing them to sleep peacefully right oh my data is now guarded I know who is accessing the model I know what kind of permissions they have. So we kind of built very robust enterprisegrade entitlements and be able to you know allow the many departments to build agents consistently on a single framework. >> Hi my name is Dmitri Bonichi and I'm a content creator agency owner and AI enthusiast. You're listening to the AI agents podcast brought to you by Jot Form and featuring our very own

CEO and founder Idakin Tank. This is the show where artificial intelligence meets innovation, productivity and the tools shaping the future of work. Enjoy the show. Hello and welcome back to another episode of the AI agents podcast. In this episode, we're interviewing Kumar Belugula from XNode AI. He is the founder and CEO and we're excited to have him on. How you doing, Kumar? >> Thank you, Liberty. I'm glad to be joining here. >> I appreciate you for making the time today. Uh, you know, first and foremost, there's a lot of background I feel like we always got to get into with any founder that kind of comes on the show. Would you be I would be pleased if you could give us a little bit of background on yourself, how you got started in uh not only what you're doing at X node but just you

know AI in general. >> Sounds good. I'm a software engineer by profession. I've been there in the last 20 plus years. >> I worked at the major uh investment bank, hedge fund, a wealth management company, recently a fintech. Um so I was fascinated by this uh communication war overhead that happens in a an organization where business engages technology to solve their problems and um there are a lot of times I found myself in the critical path of communication between the key stakeholders and the technology IT teams. So I the generative AI kind of opened up a very interesting you know opportunity to see how do we reduce that communication overhead to zero from like going from strategy to execution right how do we go efficiently so that's a premises on which we started XNOD yeah >> very cool um and what was your background like

kind of I mean I know you said by like what other products or companies did you work in uh before this >> I worked at UBS investment bank um and then 72 previously known as SCSC capital and then I was at AQR um as a hedge fund and then I was another wealthtech company as a CTO before and then I started X node inc 203 May >> nice and uh what wanted you what made you want to start Xnode >> uh the journey of techn as a technologist right I I have grown from individual contributor to the manager overall planning technology strategy for the company advising other startups I've learned learned quite a few while in the last decade uh on the yeah >> a strategy for a company then I realized no I need to build something very horizontal capabilities that I can actually

put in the hands of other businesses >> the only way to do that is to start your own company and actually build an IP and uh what is it exactly that yall do for companies >> um the what I have done with the companies is people have um an idea or a vision for delivering a service or a good for their company and how do you really enable with technology to deliver that good or service right >> so from ideiation to creating the core technology platform what takes a company to do that >> you know in terms of build versus buy and the team to hire and how do you build high performing teams all of these things right I was involved in strategizing for a few fintexs and of course at my companies I worked in the past You know, I I I I

think every time somebody starts a new company, it's interesting. I just interviewed somebody else this morning and and it's interesting to me that there's so many companies out there that are building these new uh AI tools, these new AI products and they're they're basically pitching like we'll make you more efficient, you know, through uh our different AI and how or orchestration. In your your case, you're an AI operating system, right? Could you explain a little bit about what that means and and how you came up with the idea? >> Sounds great. So, typically if you open a laptop, right, you find yourself interacting with an operating system. What does it do for us? It gives us some compute, some memory, some storage, right? Just operate your file system whatnot. If if you look at it in you know the current world of agentic AI the

agents are nothing but they have certain memory and uh you know storage and how do you operate uh with different points of access to different things in the system. So what we are actually focused on is how do you effect effectively delegate the resources to the agent so that they actually deliver more value to the human right when when engaged in a meaningful way in a workflow with human beings to serve the organizational need and deliver better ROI. >> Okay. And you know I I'm just taking a look at everything that that you guys have done in the last little while. What are some of the like examples concretely that you could say you've you know you've can give examples of actual companies or not >> where you've you've implemented this and and how has it kind of helped improve their uh you know their

company from an efficiency standpoint a cost savings standpoint all that type of stuff. >> Yep. Um if you go back to the I mean 2023 is is the year around everybody is building prototyping PC's right. we kind of raised to the point where hey can I find a use case that a company is willing to you know do a design partnership or maybe pilot with us right so the the the initial phases of X node we focused heavily on uh product management space where the requirements are being captured by multiple personas using different modes of communication whether it is email chat >> or other integrations project management so we built a uh a specific platform around product management that allows uh people to go uh transcribe your meetings. It's like around the copilot timing, right? When Microsoft released copilot, it can join meetings, transcribe them

and create a high detailed BRD requirement documents for the business guys to sign off and technology team to define what they need to do in the workspace. That that is how we kind of started >> and then the company was engaging consultants at that time to do the same work before. So we kind of added this agents to help them. It kind of brought down like you know maybe 3 months of writing work after you attended all the meetings into probably less than uh two days to actually synthesize all of that work in different formats right some of them are transcripts some of them are emails some of them are PDF documents so this happens over time and we were able to show them productivity by getting them 70% of uh written material in a short time frame right that is autogenerated by the system

by taking all the inputs that's one thing Second thing is you know they wanted a workflow where um it's more like a KYC know your customer. Uh typically what happens is um the asset management companies who are in the lending business they need to comply with certain regulations. Uh they have to provide documentation to the governing bodies. So it takes a lot of operational intensive when humans to follow up the things that they are not receiving from the customers for know your customer angle. This is more like documents and periodically going out. Um we kind of automated some of the pilot proof of uh concept we showed to their customer is can we scale like 250,000 requests a month and then get them acknowledge these KYC requests submit the documents and close loop and get them more efficient right operational people who are on the

company side that's another one and then we looked at uh collaborative space where we extended this product management suite bit more where can I actually enable the IT department uh or the engineering team to actually efficiently gather requirements, right? Uh another case study is around conversations happen in Slack with end customers internally employees discuss on the teams. How do you bring them together with again template based uh you know requirement and spec generation? This is more oriented about engineering space. How to make IT teams more productive? Uh but at the same time give visibility for the business teams right uh what are the uh pieces of work getting prioritized what are they delivering lot more things involved in that that is a recent phase where we played around and now this thing evolved into human in the loop maker checker process where we can actually

allow users to generate workflows with the help of the agents. >> Sure. If you look at N8 and other companies out there, they're giving you white canvas to start assembling the widgets and then create workflow. We made it more conversational. You can talk to you like a consultant, right? And then you figure out what the problem is and then let the agent help you build the workflow. So that's what we have been doing like you know that is another thing we have done recently. What happened was bigger enterprises they're they're giving internal employees to access to open AAI or other models and they're concerned about the data privacy and security of the uh IP of the company and the data. So because of this regulated companies are holding back using AI in their company at scale. So what we figured out is the control plane

we built is now allowing them to you know sleep peacefully right. Oh my data is now guarded. I know who is accessing the model. I know what kind of built a very robust enterprise grade entitlements arbbacks and be able to you know allow the many departments to build framework right it kind of makes the sea level people go at ease when they are selling telling the board hey I'm investing this much in AI and this is how I see the product we are in the early phases of you know generating the KPIs on top of you know what is outcome they're seeing maybe it'll be clear in in shortly in a month or two to get >> yeah for sure Right. That Yeah, that's that's fair. And and how many um or not how many, how have you found that there have been some, you

know, like hurdles you had to overcome early on with this? Cuz I'd imagine, right, like you found your companies you're going after, if I'm not wrong, it was finance, right? Like as the primary, right? What was kind of some of the hurdles that you originally had to to overcome in order to make this a consistent and effective process? So the early on when we go in as a point solution um the feedback we we we were getting were is it all you can do or what else can we do right besides that one solution you're selling us for example the pro product management program management and when we became a very horizontal then they said what exactly is the problem you can solve for us right you're a very such a horizontal platform can you give us use cases the challenge was actually trying to

get them on board hey this is a platform you can build your vertical solution Later on we focused on you know on the ver on the horizontal platform we built vertical solution people bought that and they saw more on the platform now they are saying hey I can do this I can do that right that's aspect of onboarding the um platform as a solution to the business but the next thing is uh safety guardrails hey you are a SAS system that's how we started right we built a SAS platform on a cloud and then we were say hey can you log into our system we'll segregate all your data we'll give you a secure access That was not an easy sell and people wanted no you have to host it on our uh VPC or our cloud and then we were able to do that

and now they are saying hey no not only just our VPC we feel like now the cloud is more expensive to host all the LLMs and whatn not we are doing can you put it on our onrem right that's a you know transitions we went through over time >> yeah and you know like at this point you're you're in a position where you've uh kind of you're focusing on an industry with finance and stuff like that. How how as a business owner, right, did you manage to figure out that this is the the route to to go? >> Yeah, generally uh as a a very small company startup, right, the way to figure out the road ahead is with the help of the senior advisers who can help us. So I was able to I mean I was fortunate enough to have my previous mentors

who are CIOS, CTOs at the previous companies and hedge funds and the banking. They were able to assist me to see the path a little wider than what is now and be able to understand what is a sticky point for a bank or an asset management or a fund what will they see to buy or how to get there right we I was able to get more guidance uh from that angle so we have a very good uh advisory board I would say >> you know it's uh it's pretty it it is really awesome when you have uh great advisor like that and you have that insight because um going it alone I probably be difficult to kind of figure out where to where to approach and I'm very happy that you know you had that uh opportunity to to have those connections and and

build that out like how did you know these people prior was there any like previous relationships you had built in other companies or like how did that kind of come to or are you I forget are you bootstrapped or you funded >> it's bootstramped self-funded wow >> wow yeah okay very cool so how did you build those kind of relationships >> so I think um what I realized is when we when we try to become a better humans around us, people will love us and it goes a long way right it's not like you suddenly build a network like you can just go to the event and conference network them I rarely believe that it'll go somewhere but what what has happened to me by know of good fortune I was able to build a relations over time at least three decades right all my

senior leadership is at least I know them 25 plus years >> that's amazing wow yeah >> yeah yeah >> you know I Uh there's this you know community is a big thing. Knowing people is obviously so important in business. Um there's actually a book uh I'm forgetting the name. Influence is the name of the book uh uh by Robert Keani Keelini. Um there's a there's a section in there called the law of reciprocity. And um you know long story short do good onto others they actually want to do good back onto you, right? So >> uh so I think that's that's amazing. and you know no funding and getting that adisement is still is still amazing. So kudos. Um you know what have been some of the more interesting aspects of building this company out that kind of threw you for for a curveball so

to speak and you like weren't expecting when you you've been building this out. Um I think kurbox is more like you know in the beginning when we start out where it sticks right for example I know I'm I came from finance backgrounds but I also saw a lot of opportunities in education healthcare it was an interesting game hey should we go and uh put a pilot there because they're very interested because everybody's trying to see what they can do with AI right I mean the companies interested to invest in hiring but they're interested to work with few vendors to see hey how can I really see a value in my company so We we got few opportunities in other verticals. It was very tempting. We kind of experimented a little bit to just know right and then we kind of doubled down on finance because

we built things that are very easy to tell a story around what value we can bring to a business with our background and knowing the domain well. So the the challenges are around you know it's very easy to get distracted early on in the startup life when we see here is a customer potentially in a industry A B C but rather I think it's better to stick to one industry and show the value traction and then see what are adjacent verticles out there. >> Yeah. No that's that that's fair. Yeah. Um it's it's interesting uh as I've seen so many different people in these uh spaces kind of grow their company that everyone has a unique selling point. Even though I've interviewed at this point it feels like I think it's like 40 something AI Asian companies. Everyone's got this unique selling point and maybe

they're trying to solve the same problem >> but they have their unique selling point. What would you say is your unique selling point if you had to like really distill it down? >> Excellent. I the answer may not be sounding so um unique but I would say every AI company out there uh that is coming out they're solving one of these five verticals. One is in the productivity space and someone is focused on data protection right and someone is focused on automating the workflows and someone is protect protect uh focused on unlocking the data in a company through MCP and other things and someone is focused on u more observability standpoint if you combine all of these five that is what is X node so you're getting in one place what is needed to for an enterprise if you look at the cycles in the

past right how the VCs invested in uh startups, they're focused on a very niche uh offering of the company. So they are trying to enrich that area and if you look at an organization using tools, they might end up using 20 different tools to run their company, right? So with agentic what happened here is all of those SAS layers becomes like glorified databases. You might have heard all these uh reputed companies talking about right how this is changing the world. Now the agentic solutions need not be fragmented, right? you can have one roof under which you're able to interact with your existing tools. So we are able to build that five pillar uh you know value proposal to the company which they can actually build horizontally across their organization. >> Do you find I I don't know if you've heard this phrase. It's one of

my favorite quotes and I think it fits pretty well. >> Yeah. >> Are you familiar with the Steve Jobs quote about how he was trying to build a bicycle a bicycle for the mind when he was making a computer? Hey, maybe we can expand more here. >> Yeah. No, he he basically when he was building I think it was the Mac I don't think it was the Mac 2. I think it was pre- Macintosh which is kind of funny cuz Macintosh was a massive failure. >> But like he basically stated that the goal of you know this the personal computer. >> He said in the 80s so like he said that >> computers are a bicycle for the mind. >> Right. What are your thoughts on how this like revolution in AI and what you're doing as a as a product has really been able

to speed that bike up and maybe even turn it into let's say a motorcycle cuz I do feel like it's uh it's a lot easier now to to do things right we went from knowledge work with very man like you know how computers used to be before they were personal computers then personal computers became pointandclick and then you had automation and now you have whatever this is How have you felt the world has changed in the workspace with the implementation of companies like yours just in general like >> sorry >> how do you feel like work has really changed and that that that coming from the root of do you feel like it truly has become that bicycle for the mind uh whereas maybe previously it was like it was on training wheels >> I think it's still in training wheels uh >> okay all

right it is getting there but uh >> it It is not like you saw in 2024 right what's happening there but right now I think people have experienced the experimentation nature of AI how they can see and feel and what will happen if they use AI now the the challenge is now to have this control over how they can deploy AI within the company because you know it takes one or two bad incidents right to completely shut down the company if you look at in regulated industries so they're very careful to take this path of you know how do you put AI in the desk of every employee or the core employees. So what I'm trying to figure out is yes it is on training wheels but companies like us will really you know uh speed boost the adoption because we are focused on bringing

the discipline right as a business you don't need to distract yourself from what you're selling to your customer right so people like us are seeing okay um agents are you know are hallucinating or maybe they don't have a deterministic uh you know capability to arrive at a decision but there is research happening in the space and I think that is where we bring to the table down the road, right? As a if this is not your core competency of your business, why invest in the space yourself, right? >> Yeah. Interesting. Yeah. Now, you know, it's funny. Usually when I ask that question, people feel like it's further along than it is, but you said that we're still on training wheels. So, I guess what what do you feel like if if we're still at that level, where do you think this whole thing can go?

Right? like a lot of people have different opinions on the end not the end point but the the future thinking type stuff next 10 years what do you think it looks like >> yeah I think you know I am coming from the regulated industry background maybe this strategy is much more opened up in the retail and other spaces but when it comes to banking as >> finance yeah >> in finance right it is still very locked down right because of the nature of security of the data and and the IP and whatnot right they want to still hold as a business right what they want to do with AI Right. So in terms of where it goes, yes, of course, if you look at the intelligent document processing automation that was early on, right? What all the banks and everybody else latched on to. How

do you process client statements? How do you process these PDF documents within the company? How do you give access to your employees about your HR policies? How do you give access to you know various departmental knowledge to your com you know engineers for example now they have cursor they play all these tools. How do you access I mean understand code at scale like if I'm a CTO the chances of me understanding a line of code written by remote developer is very less likely. Now I can do that because I have a code understanding tool set. I can I can simply ask hey how is the requirement implemented right? It gives me a lot more confidence in when I'm facing my business. Hey this is how developers have been doing this is how it is scoped this it is becoming more deterministic in in my own

delivery to my business right because I'm able to scale myself and look at it broadly what's happening in the organization and the outcomes. Of course, I'm going to review myself, right? If I even if I hire a junior analyst, I can think of an agent as junior analyst, right? It is able to grab things what I need. Just like, you know, it is making my life easy to recall from memory, recall from things, right? Or store things easily without writing a deterministic software pipelines to store and retrieve data every time, right? That requires engineering. Now, that becomes much easier, a much, you know, lower lift compared to before. So if I have to spend five developers to manage let's say 10 projects I can use this five developer and get more capacity right to deliver more value to the business. >> Yeah that's an interesting

point that you make and going back to the whole thing you were talking about as the CTO >> how if you were to find one line of code like you'd be able to get an answer about that whereas previously maybe you wouldn't. Um, do you feel like this is changing the way that companies work to where while we've probably had increasing amounts of data, so to speak, >> right, at companies over time as you know, knowledge work continues to expand, we're now finally able to synthesize information top down a lot better because of whether it be AI summaries or, you know, the organization that AIs can bring with a with some a company like yours where it's like a layer across the whole system as an operating system. >> Yeah, I think the traditional technology that evolved if if you look at the likes of

big data, look at likes of what happened with ML how things are being evolved, right? >> Everything is valuable. I I don't think AI is necessarily replacing the things that were there. >> When I say in banking, in investment management, the companies have built a very rigid structured pipelines. They are not going away in the next day, right? It's more like uh can we improve our day-to-day human work in dealing with knowledge right that has improved uh quite a bit right because ability to find things relevant to my current context changes every day and I can actually use the AI help to put me in the direction where this data is located or the things that are relevant to my current day-to-day work right it has significantly I would say improved right from before right but I still need to rely on the systematic uh

pipelines that are because of the nature of you know being probabilistic models right these are all like you won't you will never be 100% unless you know you have a a software that actually passes out A to B to C that's what you need versus an agent passing A to B to C which may be different at every time it tries right so until we finish that deterministic nature to actually you know work like a human but also I don't know if regulatory uh things change where accountability also falls on where and who is responsible for an action is taken right that space is also evolving once the these are clear I think we'll have lot more ROI harvested out of the AI >> are you familiar with there was a recent I'm trying to remember MIT uh report that said like there was like

90 you're familiar with the report >> yeah would you like to speak on that a little bit because everyone's had an interesting take on it and I I just kind of want to maybe break down what it was and then kind of talk about what your thoughts are on what that means especially in the context of what you're doing >> there So I I think you know whenever there is a technology wave right of course we want to be the first experience experiment so that we understand how things are evolving especially tech first companies they do that very well and if the business like trying to hire AI AI experts in the company they first see the overhead of the you know human capital expenditure because they're hiring the top talent and then you need to start seeing ROI on top of it so there

is a heavy cost on the human side right human capital invested >> sure >> and the time it takes to let's say break even the cost from ROI point of view to the business. It takes time. We can't decide that in a year or two where the the break even point may occur maybe 5 years uh from the time they invest it because the heavy investment is on human capital right how much time it takes to get see the real gains in the business ops or whatever the things that they are trying to improve it will take time and also the company is it a small company or is it a large company that is playing with AI let's say you have 100 users company or 100 employees company uh what is the net new investment in AI headon right and then What's a net

new gain in the business hops they're serving to their customers? These also play a role in how do you see that 95% or whatever the thing that stats came out of study. Yeah. >> Yeah. It was 95 it was the it was 95%. So yeah, I mean >> yeah it's it's always an interesting thing because I I feel like personally I'm in a bubble in like in regards to the context that I have of everyday workspaces >> implementing and utilizing AI. Do you feel like >> and it's probably the case for you too. I mean, when you run one of these companies, I feel like it's just natural like that you're going to be in that bubble. Or maybe not, cuz you deal with finance where I'd imagine adoption might be slow actually, like you said. So, so for a given business to really gain

headwind into the AI advancements on a day-to-day basis, it's kind of hard unless you have a a spec specialized group focusing on the you know, let me bring AI good things into the company. But adoption is still difficult, right? Like you know if you look at existing business users in the company they have certain tools they use and and you are asking one more tool to be tried out right and taking away from their day-to-day habits of what they have been doing for almost two decades or three decades of working in our company right using other SAS tools. So what uh I think is going to happen is uh what is the switching cost to take a user away from let's say using tool A to tool B or maybe X node whatever that is right the the the the question goes back to the

companies which enable the users to have less switching cost and enable them to use their tools what they are today and gradually become the power users of AI it will happen over time but if we are to do that that is also one of the things that at XO we focus How do we make regular user life better in where they are rather than making them switch from what they are doing? For example, if you're a heavy uh communication user on the teams or Slack, why would you need to go and open another tool to do your things, right? Can we build natively within that space so that you're able to still use AI agents and everything from where you are, right? That is where we are focused on. So we want to uh let the regular users operate within their space using AI and

allow them to use agents right to as a knowledge workflows without switching the environment where they're operating. >> Yeah, I think that's the these are all interesting and fair points you know I just to kind of put it out there because it's a question everyone seems to be asking. >> Yeah. Where do you feel like companies are going to move in the job market regarding this? Right? Like even with as we're recording this on the 16th, there's the concept of the uh September surge going on, right? Well, where whereas still people who are in our industry or outside of our industry just questioning like are we going to have a a job market like shift to where a lot of entry- level work is going to be removed? what is your stance on that? Is it going to happen? How long will it take? And

and just in general, what are your thoughts? >> Yeah, I think technology always uh challenged human to learn uh and be current with the current trend. For example, if I go back maybe 20 years ago, how many engineers were there writing software? I guess there is some and they saw value in writing software. Maybe they found oh I can make a living by just by writing software code. They need to learn a steep uh um you know learning curve right to become a good software. >> Absolutely. >> Now what's happening here is it will challenge us to learn how to use AI but I think the more and more it gets embedded within the daily job routine of the person whether you're operating a robot or whether you're operating you know I don't know logistics in somewhere or you're writing code all of these people

will be boosted by X amount by AI. I think honestly it creates capacity for the company right so and then people will uh uh you know learn up you know they will improve themselves by learning additional skills I think AI can help solve that right what's a difficulty for you to let's say become a layman in AI to an expert in AI it can help you to get there right I think companies will actually boost their uh you know developing employees right significantly faster compared before they get more insights into where the gaps are, skill gaps, right? Training them and then getting them onto the level what they need for operating their business. It may operate leaner but uh people will tremendously get benefit by using the tools of AI in the right way. Right. I >> and what over timeline do you feel like

what timeline do you feel like things will start to get leaner then? Like when do you think that would actually happen? Yeah, I I think we're already seeing the bigger companies realizing the you know they are actually you know laying off a lot of people but I see people will start focusing on uh maybe launching a specific sol business solutions to different areas. Look at the largest consulting companies out there like McKenzie Bane right they have a lot of knowledge and harnessing the knowledge is of course within their capacity but can small shop come out in a geography where they can start serving a customer with the lower cost than McKenzie maybe they can right I'm just talking about consulting with the help of the AI now there's an expert expert is available as long as you know how to harness AI for a given

problem now you're able to know get uh more reach to smaller people who are otherwise can dream of competing with Mckenzie or competing with other companies right that's another area I I would think it it creates more net new businesses where they'll go after opportunities more rapidly which was a kind of you know otherwise a hurdle to go and compete with like so Mckenzie >> yeah I think a lot of big companies could could see you know as they're laying off these people there are going to be more agile startups that come up because those people with that more interest in doing that, they have the ability now to do so with agents, whereas previously I feel like, you know, a big issue with starting a company is staffing, right? Like you just you're doing it all yourself. But honestly, like a lot of solarreneurs,

a lot of two to five, 10 employee companies now can be the equivalent of a 100 person company if they really know how they're they're organized, >> right? >> And and how to to get these agents taking action. And so I think that that's a fair point cuz you know in development I think you're probably aware of this more than more than anyone or people who aren't in the development space like there have been conversations I've had with founders of companies or heads of companies that you know they'll bring in people from a Microsoft from a Google. >> Yep. >> That were hire management like at these big companies to try to help their startup grow. And what they'll see is like to be quite frank not a lot of value there. The timelines on these projects are a lot slower than what they were

experiencing when they were hiring like these hungry people who are like in that startup culture. So there is a lot of like I don't want to say it but >> dead weight that's going to exist in enterprises because that's just the nature of enterprise and these hungry fastmoving people will think of specific solutions to problems and act on it way quicker because they're just able to be more agile. >> Right. Right. Y >> yeah, it's very it's very interesting how this is going to all play out. So, you know, as as a company yourself, you know, trying to to solve problems for primarily enterprise companies, I'm imagining, right? >> Is that fair? Uh what do you find is the different issues that AI solves on the enterprise level versus like the small business level? because I feel like small business is kind of obvious like

you know like production of outputs that are easy to do back and forth whether it be content or whether it be email or like project management like the basic stuff. >> We kind of get that intuitively from the little anybody has seen about AI agents. What on an enterprise level consistently does a enterprise have to deal with more than a small business in order to cut out that that extra fluff? >> Yeah. I I think if if you're asking like what would the enterprise do to evaluate companies like us? Is that how you're looking at this? >> Yeah. Yeah. Pretty much I'm trying to trying to let you pitch yourself so to speak. >> Yeah. Excellent. So typically the enterprises right they scale is everything right. You want to build a solution bring a solution in house. Can it scale across uh the the people

and departments and different things I have? Can I bring consistency of uh how the things are being used right with AI and how can I monitor them right this is becoming a bigger challenge for the enterprise whatever tool they build or bring in from outside they want to make sure that there's a consistency on how this is being deployed and then access controls entire this is all very typical expectations on any software with AI it becomes more even um interesting so can we build one piece of solution that can be used across like the department or the organization and allowing them to build at scale more from nontechnical if I bring let's say you have to deploy another 10 engineers to use your solution that's not going to fly well right can you actually have less time spent by my technical staff and let the

business or nontech nontechnical people to harness the you know tooling right you on themselves right making more intuitive when it comes to small business it is more like can I can you give me holistic solution that I can run point with less cost it it's again the same thing can they be a nontechnical user right to be able to use this AI agents or platform or workflows can they build with a smaller help so they might be looking for you know le more of a managed service model can you manage some of my AI needs so that I will just focus on a business when it comes to enterprise we'll manage it by ourselves you just give us the keys to the entire box so we'll we'll do it ourselves right Yeah. No, that that that makes a lot of sense. And I it's interesting

you mentioned earlier that, you know, companies aren't really going to have the ability to take advantage of of AI or agents without like having even specialized departments around. So, I mean, to be honest, it is probably better that they just hire an outside solution that are more specialized in it because it is pretty difficult. like I have someone on staff that helps out with automations um and stuff like that and I have for a little bit um and it's really helpful. But if you don't do something like that, you really aren't going to take advantage of the of the new tech, right? It doesn't just magically happen that someone at your company is working in whatever department's like, "Hey, we got to I I know how to do complete full company optimization of everything." It's like, no, that's no, they're they're they're making LinkedIn posts

or whatever. So, it's uh it's very difficult to to get that. So I think you know you provide a a really great solution. Um you know just couple of last things to kind of close close it out. What what would be your one message to to every company right now with the um improvements in tech in regards to how they could keep up to date with the improvements in AI and AI agents to take advantage of it for their companies. I I think you know la by by now in the last two years companies have embarked on different u you know prototyping or trying to see value out of the AI at this time I think it they're challenged by the spend that happened already or is happening now and it's very important for all of us to look at uh you know how do

I get clarity on failing this solution whatever I build right uh I don't want to have point solutions in the company I want to have a holistic solution that I can sleep peacefully that that my data is protected and access controls are in place and human is in the loop I'm able to enable business users or nontechnical users to build their solutions much faster they I think it challenges the CIO CTO's to look at this angle right I don't want to bring another vendor or I I don't want to sponsor a project in my company that is solving a need in a specific vertical versus the completeness of what I can bring uh to the company from the AI point of view and keep it upgraded constantly so that I'm not spending my dollar on just you know not sure where it'll you know realize

the ROI for the company but rather if a vendor is in the place they are challenged right their their delivery is what is going to get them you know dollars back right to the company so it's always important to partner with the right vendors and upskill their talent inhouse you have to have your own internal uh experts but you don't need so many of them right to manage the solution and focus on where business is actually driving value. As you see value, then of course you expand your internal expertise. Hey, this this is how this guy is helping us. So let's build a fullfledged AI team now, right? And take >> very smart. >> No, I think that's that's that's a totally fair fair point. Um is there anything that you'd like to plug obviously to to kind of end this episode? I'm sure it's

I'm sure we know what it is, but just uh giving you the floor before we uh close the show. Yeah, thank you Nemiti for uh you know talking to us and giving us an opportunity to explain what we're doing at X nodding and very short cycles of you know deploying our product and solution and we are able to consult as a partner uh to understand the problem and get them the right solution. It's not necessarily we have to sell X node but can we actually help you understand how you guys can potentially you know transform your area in your business. We're happy to listen to and be a partner. Please reach out to us on XO.ai. A yeah, >> absolutely. Sounds like a plan. Well, thank you so much for uh joining us today, Kamar, and thank you everyone to you for listening to this

point in the episode. We appreciate it. Please make sure to leave a like on the YouTube video, subscribe, and don't forget to leave us reviews on Apple Podcast and Spotify. Thank you so much for watching, and we'll see you in the next one. Bye.