How Do Regulated Enterprises Deploy Private, Secure AI?

Aaron interviews Ivan Lee, founder and CEO of Datasaur, about what it takes to build private, secure AI for regulated enterprises. Lee traces the shift away from third-party model reliance to compliance pressure, IP protection, and cost, and frames the core decision as buy versus rent: enterprises historically rented frontier models by default, but Western and Chinese open-weight models have matured enough that companies like AT&T now run a growing share of workloads on owned infrastructure. He breaks the stack into three layers (infrastructure, model, and harness), argues models have become commoditized enough to be swapped like building materials, and calls the harness, the connective layer to internal data and tools, the least solved but highest-leverage piece. Lee describes agentic AI's shift from opt-in tools to opt-out, event-triggered workflows as the real 2025-2026 adoption unlock, while flagging FinOps trade-offs (agentic tasks can run 3 million tokens versus 2,000 for a chatbot query) and the custom benchmarking his team uses to earn CISO trust before production rollout.

SHOW: 1065 

SHOW TRANSCRIPT: The Enterprise AI Show #1065

SHOW VIDEO: https://youtu.be/PkLSI2pRZs8

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SHOW LINKS

• Datasaur homepage
• Ivan Lee on LinkedIn
• Datasaur Launches Forge: AI Native Services to Deploy Private, Model-Agnostic AI Inside Regulated Enterprises
• Datasaur's Private LLM overview

GUEST BIO

Ivan Lee is the founder and CEO of Datasaur, which builds private, model-agnostic AI agents that deploy entirely inside a regulated enterprise's own infrastructure. He previously built AI products at Yahoo and Apple after Yahoo acquired his first company, Loki Studios, and holds a computer science degree from Stanford. Datasaur's clients include a leading GSIB, federal agencies, and Am Law 100 firms, and its backers include Initialized Capital and OpenAI president Greg Brockman.

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Transcript

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0:08

Good morning, good evening, wherever you are, and welcome back to the Enterprise AI Show. This is your host, Aaron, and super quick intro today. We have Ivan Lee, founder and CEO of Datasaur, to talk about what it takes to build private, secure AI for regulated enterprises. We talk a bit about the buy versus rent debate and dig into control and ownership of AI and how that is critical to the enterprise. And all of that is coming up right after this quick break What does the outside world already know about your company? Cybercriminals could be seeing leaked credentials, compromised session cookies, exposed infrastructure, and even impersonations of your brand and executives. NordLayer Intelligence by NordStellar gives security teams visibility into those threats across the deep and dark web, data breaches, attack surfaces, and brand impersonation all in one platform. Find out what attackers know before they can use it. Visit nordlayer.com/intelligence/enterpriseai and use code enterprise10 for 10% off your NordStellar plan Today's show is sponsored by Nasuni. There's a growing gap in AI right now between what's possible in theory and what successfully works at scale inside an enterprise. The difference comes down to unstructured file data. Many AI initiatives struggle because the file data they depend on is scattered, unstructured, and disconnected from where and how work actually happens. Nasuni changes that. It brings your unstructured file data into a single secure foundation, so AI, both generative and agentic, can access it with the context, governance, and performance it needs in production. Bring AI to where your unstructured data lives. See what it takes to activate your data for AI and request a demo at nasuni.com/ai.

Aaron: 2:15

And we're back, and for today's topic, it's actually something Brian and I have talked about on the podcast a n- a number of times now, but enterprise AI, but more specifically secure and sovereign enterprise AI. And for that, we have Ivan Lee. Ivan is the founder and CEO of Datasaur. Am I saying that correctly, first of all, Ivan?

Ivan: 2:41

That's correct. Thanks

Aaron: 2:43

cool. I just wanna make sure. And what we're gonna do is we're gonna jump into a bunch of topics here, but before we do that, why don't you give us, Ivan, a little bit about your background? You've got a really interesting background from, you had an early exit under your belt and then went over to Apple and now kinda doing your own thing. But, I jokingly refer to everyone that's been doing, AI before LLMs, you did AI before it was cool. So give everyone a little bit about the background

Ivan: 3:10

absolutely. My name is Ivan. I'm the founder and CEO here at Datastore.ai. My own background is that I studied computer science at Stanford. So fun fact my very first AI class was with Dr. Andrew Ng, and my first computer vision class was with Dr. Fei-Fei Li. And so I got to learn from these global superstars before, their stardom. As you mentioned, my first company was a very different space. It was mobile gaming. Ended up selling that to Yahoo where I began working on machine learning for the first time. This is 2013, the last AI wave. And I worked on search and machine learning algorithms at Yahoo, then Apple before realizing that I really enjoyed the world of NLP, natural language processing, and decided to found this company.

Aaron: 3:58

Very nice. And tell everyone a little bit about, And we'll talk about enterprise AI here in a second, but I'm just really fascinated about the background here. What was it like doing NLP before NLP really was a big part of generative AI, right? Like what was the fit and what was the use case, and why were people doing, NLP and even data labeling as well back then?

Ivan: 4:23

Yeah. I think it was just this pursuit of being able to teach machines about our language and how we operate as society. And it felt like such this such a fundamental part of how we communicate, and it was just really fun watching machines learn more about us and be able to communicate. So I worked on products like Siri and it felt like we were on the cusp of something, but even I could not have foreseen the, big ChatGPT moment.

Aaron: 4:51

That's fair. That's fair. All right, so let's jump in here. So what I want, what I, I wanna do is start at a high level, and then we'll kinda keep going lower with each of these topics here. Let's kinda start with data sovereignty and secure AI. The reason why I wanna start here is in particular we tend to be a little m- less timely on the podcast. We don't like to introduce all the news because it tends to, really okay, it might age the podcast a little quickly, especially with the speed of AI news these days. But there has been recently the whole idea of, I'm gonna use the, the term forced training, meaning, Fable and some of the others where it's like, okay, you-- there's no opting out of training the data. And I've seen that as one big aspect for data sovereignty, and certainly has made this almost like a, a board level and C-level issue extremely quickly. But even before that, this is something that was super concerning, and so why don't we start there? Tell everyone, Ivan, when you're talking to customers and they say,"I want my own AI," and I wanna talk to you about it what's the drivers behind that, first of all?

Ivan: 6:06

Yeah. There are several drivers. I think for some of our customers, it just begins with a compliance story. Their security, their own internal legal teams are turning off chat.com and disallowing some of these external tools because they need to understand how employees are using them, right? And there may be compliance and regulations in place where you're just not allowed to send certain types of internal data to third-party vendors like OpenAI and Anthropic. For others it may be a little bit more forward-thinking. They realize that AI needs to be a strategic asset, something that continues, you know, developing in value over time. And whenever they're just sending all of these prompts and all of these use cases off to a third-party vendor, they're not really capturing that value internally. And then, of course, there's the age-old just straight up price and value, right? We have people saying, "Hey, my, my token bills are too high. Are there cheaper alternatives that are, going to have better price performance?" And it turns out there have been a lot of alternatives released.

Aaron: 7:11

Yeah. And that's a really good segue into the next part here because I really wanna… You and I both have talked about this and by the way too, the way w- we were introduced speaking together at, at a few events here recently. But there's almost like this big, like what is the first top level deci- decision is architecturally are you gonna buy, are you gonna rent? And I w- I wanna talk about that, but then I also wanna break it down even more and go, okay, why buy, why rent? Is it as simple as it's the private cloud and public cloud argument all over again and it's OpEx versus CapEx, or is it more to it that in the whole buy versus rent?

Ivan: 7:58

Absolutely. So I think, first of all for many years now, the default decision has been to rent because there was no alternative solution. You couldn't build and pre-train one of these on your own. Kind of the open weights models that were available just were not nearly good enough. So it made sense and everybody defaulted to this concept of renting. But the landscape has matured and evolved significantly, and in 2026, there are many alternatives now to simply renting from these third-party vendors. And as the concept implies, whenever you're renting, you're always on borrowed resources from someone else. You're on their roadmap, and so that limits your own capabilities to drive what direction it goes in. Again, here in 2026, there are many different open weights models that you can use as a foundation that may be 90%, 95% as good, but now you use that as your own internal foundation. You can continue customizing it, developing it and building out your own strategic capabilities over the next couple of years. And we're seeing more and more stories, companies like AT&T come out openly and say, "You know what? We've moved 40% of our workloads to open weights models." And their goal is to eventually get to 70%. Now, one thing I wanna call out there is the goal isn't 100%. It doesn't have to be, right? It's this mixture. The, the most advanced clients are starting to realize there's certain workloads that we want to send off and continue renting because we need top-tier capabilities. But for many of these, where you're just summarizing a document, where you're just generating some basic email concepts, you really don't have to be using frontier top dollar options.

Aaron: 9:53

Yeah. And I wanna dig into the m-model at agnostic in a little bit more, but a, a kind of follow on to that. You-- What's the m- I'm sure it's, it's very varied at times, but what is the top level decision? A, a lot of times does it come down to simply, is it more like this is our proprietary data and proprietary workflow kind of thing, and I wanna keep that, I'm gonna, I'm gonna buy that in my terminology here, and I'm gonna, build that out because it is, crown jewels, keys to the kingdom, whatever analogy you wanna use, and then you rent out or go use cloud services or basically pay by the token or pay by the API for the non-differentiated

Ivan: 10:37

Yeah. Yeah, absolutely. So let's take a step back for a moment here. How, how is this technology trained to begin with? OpenAI, Anthropic, they scraped everything available on the public web, and that's a lot of knowledge. That's why these models are impressive. But it turns out that everything that's on the public web only represents 2% of human knowledge that we have as, as a society have created. Where's the remaining 98%? In long lost books, in your personal Gmail inbox, and yes, the proprietary data that these enterprises are holding close to their chest, right? So my silly example here is if you've got the KFC 11 spices, I sure hope some employee isn't like typing that into ChatGPT and asking questions with it, because that's how you let go of your secret sauce, in that case, literally. And so as enterprises and organizations are becoming more aware of this, they're realizing why should I continue taking my hard-earned expertise in how to assess an insurance claim and working with OpenAI FDEs and teaching them how to do this? Why am I not just turning that into my own internal AI that I own and nobody else will be able to ever train from?" Remember, ChatGPT and Anthropic need to continue evolving. They need to continue fueling their IPO, and GPT-6 Fable 2.0 will be trained on all these specialized finance workflows, these insurance and legal workflows that they're out there trying to learn about as actively as possible.

Aaron: 12:25

Thank you for that, first of all. And I wanna kinda move on to the next… What I wanted to do is, and I wanna break down when it comes to like making sovereign AI or secure enterprise AI how do you see the stack? Because I wanna talk about models and and agnostic models in a second, but I also wanna break down the stack. Like NVIDIA famously has the five-layer cake. Like in your conversations and in you building customized agents and workflows for customers, how do you approach it? What are the layers that you see?

Ivan: 13:03

So I think the important layers are what infrastructure you're going to deploy on. So we start with is, does this need to be on public cloud, on your private cloud, on your AWS or Azure instance? Or does this need to go the distance? And we have some clients who insist on bare metal machines, right? So we start with where will all of this lie? The next layer will be the model itself. What models are available to you and your team? We have some clients who insist on Western-trained models where we would consider models like Google's Gemma Mistral models, or the new Meta Spark models. We have others that are more open to which we would be able to consider a lot more of the, the Chinese open-weights models as well. So once you have the model layer, the next is this is where things get a little bit ambiguous, but I'll refer to it hol- holistically as the harness, the context. That is the ability for this model to plug into your internal databases and data sources. The ability to call tools and APIs. So for example, if it needs to connect to your CRM or if it needs to connect to your EHR is that available as context for each individual call or agent? So at a oversimplified level, perhaps we see those three layers.

Aaron: 14:25

Yeah, I like that. So when-- first of all, I'll ask the question: Do, do a lot of your customers then want this to be actually agnostic top to bottom? There's always the, we famously talk about it on the show of like customers don't wanna be locked in, and every vendor on the planet wants to lock you in. And so is agnostic AI even possible, or is it like only maybe at the model level at best? And tell me a little bit about how you think about agnostic AI

Ivan: 15:06

Yeah, absolutely. So I think it was unfathomable just a couple of years ago when it felt like OpenAI and perhaps Anthropic at the time were way ahead of the pack. But we have to accept that in this day and age, models are largely commoditized. Yes, one may be ahead for a m- couple of months or so but by and large, if, I share with you a prompt and the resulting answer from any of these models, you and I probably wouldn't be able to tell the difference. Or maybe we're very, experts in, very much experts in the space and we can tell the nuances, but if we prompt engineered it to speak in proper British format, then we really wouldn't be able to tell the difference anymore, right? And so that's what I can communicate to my clients. We have to be able to train this in such a way that it can easily swap out the model itself from one model family to another, and the rest of the, the harness will still work. Because we don't know what's happening with these third-party vendors. We don't know when one gets sued by"The New York Times" and has to retrain their model family, and suddenly it's lacking certain kind of knowledge and we have to swap to something else. We don't know when something's going to be deprecated. I had a client recently who built everything on top of GPT-4o, and it was deprecated four or five months ahead of schedule, and they were scrambling to retrain everything. So for these reasons, it can be dangerous to be overly dependent on any particular vendor

Aaron: 16:38

Yeah, and I-- this reminds me of for our listeners out there with cloud backgrounds, this reminds me of the days of Kubernetes when Kubernetes was doing a release like every six weeks, like three to four months. And if you're an enterprise, y-you're not doing that. If you're an enterprise, you'll be lucky to do big upgrades, twice a year sometimes, three times a year sometimes. And so I think what you're also telling me is it's a way for enterprises to protect themselves from churn because everything is moving so fast. But let me ask you the, the, the flip side to that. So is… So the industry is gonna continue to move fast, if not even faster going forward. But we're trying to build an AI stack that almost has hot-swappable pieces, if I'm using those terms, and also we wanna protect our IP. We don't give, wanna give it out to the vendors as well. So what does that mean when it comes to, okay, I I've built something I've built it, I'm gonna use that t-term hot-swappable. Like everything's hot-swappable. I can, I have my infrastructure pieces, I can do it inside and out, but there's almost this difference between like the tools and the tool adoption and the infrastructure adoption. So tell me a little bit about how you go about convincing someone to that, because for every piece, there's a time trade-off, there's a cost trade-off, like there's lots of trade-offs here in all of this.

Ivan: 18:07

Yeah. So I wanna take a moment here and empathize with all the employees out there that are being asked to adopt AI or being forced to do so, right? I've been to, to conferences where a lawyer will come up to me and say, "You know what? I have access to 11 different AI tools and five more POCs, and it's really confusing to know which model or which tool to use at any given point." So I want to acknowledge we're in this kind of like frenzy where everybody's testing out a dozen different tools at any given point, and it gets real confusing to the end user, right? So I think there is this need, this pressure from the market, from the board, from investors to adopt AI. But the way that we're all approaching it can be a little disorganized. It's, whoever we hear from first, a headline that we see in "The Wall Street Journal." People are just picking things up and seeing how it works. One of the things that has become a trend again in Silicon Valley over the last couple of years has been this concept of change management, just human change management. How do we make sure that it's clear to the end user how to use each of these tools? When's the appropriate time to use them? And we need to stabilize that as an industry.

Aaron: 19:27

Yeah. And I'm gonna take it one step further. I'm gonna make the job of complexity even harder. L- let's talk about regulated industries, like banks, hospitals, law firms, you know, manufacturing, government, all these s- different ones. They have different regulatory environments and certainly globally even differently within the same verticals. But they kinda all share this common bar of okay, you gotta be able to trust the AI with sensitive data. That's kinda the core fundamental principle around all of this. What does that actually look like from where you sit? Like, how do you approach navigating these waters, if you will, of regulatory concerns and convincing that, that CISO that is an AI hater that this is gonna be okay?

Ivan: 20:19

It's a tough question. And and again it's the, the CISO's role is not easy these days with new cybersecurity threat fronts opening up on every dimension regulations constantly changing around the world, having to pay attention to what the EU is requiring versus what the state of California or the state of New York is enacting, right? It can be a lot just to keep up with the regulatory front. One of the big advantages of private and sovereign AI is that it does just sidestep a lot of those issues. Because a lot of the data and the models themselves now live entirely on your infrastructure, on your environment, you no longer have to vet nearly as many terms of service and ongoing changes to which data c- is allowed to be sent to a vendor

Aaron: 21:09

Makes sense. That makes sense. Let me ask you this, 'cause I, I feel like agents is its own different beast right now in the industry. There's AI and then there's AI agents. And I assume this, but I'm gonna ask this, y- with the customers you talk to, how much of this is create me enterprise AI, and how much of this is create me enterprise AI agent workflows? And break down the differences of how you consider or scope or what you even think about, what's top of mind when somebody goes down one of these, these paths with agents?

Ivan: 21:46

I'm really glad you asked, Aaron. I think that's actually been one of the most important developments of the past year. A year ago, if you and I had been talking about agents, I would say that was, these are overhyped and agents don't actually work, because in reality, they didn't. There was a breakthrough around December 2025 with around Claude 4.5, where agents went from an R&D theoretical kind of promise to something that actually started working. There was this up-leveling where the reasoning actually worked. Why is that important? It comes back actually to what we were talking about earlier with change management. A lot of these AI tools for the past couple of years have really been opt-in. You had to learn when to open a tool, how to use it, how to prompt it a certain way to get the answer that you wanted, and for many, that was quite confusing. The benefit to agents is that they're the exact opposite. They're opt-out. So with an opt-out technology like this they get triggered by external events, a customer support ticket being filed, an invoice being sent from a business partner. They get triggered by these events, and then they alert the employee what action needs to be taken and when. This flip from opt-in to flip-- to opt-out makes all the difference. If you look at these market reports, the chatbots, the AI tools of the past were 15% adoption rate. With these agents, suddenly it's 100% adoption rate, right? And so what we're trying to push as many of our customers to now is agentic flows because it makes it the adoption curve a lot easier for the end user

Aaron: 23:32

First of all, I love that analogy, and I'm also gonna take it one step further though of, okay, let's say they do that. Let's say we, you get that first use case and we get into production. One of the big things I'm seeing is there's three big concerns around agents today. Number one that trust, and what I mean by trust, 'cause I wanna break that down slightly, is predictability. Like it's going to get to the answer, it's going to get to the answer in the same way, and it's going to get there in a way that's accountable. So if I ever do need to troubleshoot it, I know what's going on. So that's one area. The second area is security. And the third area is really this idea of FinOps and the finances behind it. How many tokens is it gonna consume, if that's how you're metering your environment? And so I feel like there's a lot of folks that go, "Wow, this is amazing." But then they, somebody comes in and puts the, on the, the enterprise hat and goes what about this, and what about this, and what about this?" And so how do you handle the after the fact of like the trust and the security and the ROI of these?

Ivan: 24:38

Right. So good questions. Those are, let me break that down from different angles. Let's start with the FinOps and the ROI. On the one hand, the ROI is abundantly more clear because now you are getting that 100% adoption rate, right? And that for a lot of our customers has been delightful to see that, like everything that was promised, they don't have to worry about the adoption curve. It just immediately starts delivering. There is one big caveat to this. The average chatbot query you and I put into ChatGPT, that takes about 2,000 tokens. The standard agentic task does take on average 3 million tokens. That's a lot more tokens. That's three orders of magnitude more tokens. And I'm not here trying to like dupe you into using up more tokens. That is a real trade-off, right? And so that kind of ties back into earlier parts of our conversation where it may be important to consider smaller models that are more token efficient, that will have less cost per token, making the ROI still fully viable. Let me pause there before I move on to security and

Aaron: 25:52

No, please. That's perfect. Go ahead. Makes perfect

Ivan: 25:55

All right.

Aaron: 25:55

it

Ivan: 25:56

So on the security and trust part, that's something that we handle very carefully as well. We create custom benchmarks for every single customer, and we start with,"Okay, what does your workload, workflow need to accomplish?" We work with their subject matter expertise to establish a baseline of 50, 100 questions that kind of set that evaluation level. And we won't stop until the agent is able to deliver 95%, 98% accuracy, whatever you deem appropriate here, right? And so that is how we establish the trust in the broader system, and we continue running that even after it's in production, even as we continue updating the model and connecting to new data sources, that benchmark always has to be met.

Aaron: 26:49

Yep. And the one big thing I go to though, this kind of goes back to our original thesis of buy versus rent. One thing I've heard, I'm gonna devil's advocate this for a second. In the buy model yes, you get I'm gonna own everything all the way down to my data and my embeddings. I'm gonna own my models. I'm gonna own my harness. I'm gonna own everything. But now you have to run it, and you have to have the expertise in more particular to be able to run these things. And so this is where, of course, the rise of FDEs has come into play and folks coming in, and I don't have the expertise to build it, so I'm gonna have somebody come in. Makes perfect sense. But what happens over the life cycle o- of all of that? Do you have to educate your customers that, "Oh, by the way, when you build this, this is what day 122 looks or day, 500 looks like of owning and operating this." I jokingly refer to it as care and feeding. What is the care and feeding of all of this over time? And the rent scenario, a lot of people just go look, I don't need to do any of that." So how do you handle that trade-off, and how do you advise customers on that?

Ivan: 28:09

It's a great question. And part of it is this is something that only really gets solved with time, right? Just as companies over the last 30, 40 years have developed IT as a critical function perhaps more recently kind of cloud maintenance specialists as a critical function of an organization, so too will it be important to have an AI infrastructure team in the future. Maybe by 2040 this will be a moot point and everybody knows how to maintain this. The question is, it's a matter of timing. When should I begin to invest, right? When does it feel important? And for many, 2026 is that inflection point. It's this kind of, back to the very beginning of our conversation, it's this perfect storm of I don't want to be fully dependent on a vendor like Fable that can be turned off by the US government at a moment's notice, right? I don't want to be tied to a vendor that is constantly training on my data and using this to promote their own financial interests over my own. Or it could just be a matter of, look, it is now financially viable for me to start building and owning my own AI at this point

Aaron: 29:22

Yeah. Now, I actually wanna ask you what you're seeing in the field because one thing I've seen is up until now it was like, oh, if I wanna get quote unquote "custom AI" or, my AI, y- you could do a certain amount of context engineering or prompt engineering. You could do, RAG and do RAG retrieval. And I felt like it was always the, the bazooka, the, the big one was, fine-tuning your own models kind of thing. I feel like this fourth option has come along now, which is harness and being able to steer it with a harness. Where do the customers you speak to sit in that y- almost like a spectrum of like easiest to hardest, and which ones are… What's your opinions on which ones are the most effective these days?

Ivan: 30:13

Yeah. Of course the answer is it depends on each individual project, but at a high level I would stack rank those as, we try to go-- We have all these tools in our tool belt, and we will approach it from lowest hanging fruit to the most difficult one. So we'll start with things like prompt and system engineering. RAG is probably the next easiest, and then Harness has really become the largest way for us to close that kind of evaluation benchmark gap. And then last but not least, I'm still a big fan of fine-tuning, but it is the tool of last resort for us. Because once you do that, you're going to have to continue to fine-tune all future versions of the model as well. So that's my loose hierarchy, but of course we will evaluate each project on a case-by-case basis.

Aaron: 31:06

It makes perfect sense. Let me ask you this 'cause I feel like we've been digging into that. The one other thing I wanted to ask you is how do you handle data? Because obviously, if you're talking to customers about their AI and customization of it, there's going to be data, their data, but there's gonna be PII information in there. There's going to be sometimes cases where maybe they don't have enough data and they're doing a certain amount of synthetic generation on top of their data, or maybe there is regulatory concerns of things they can and can't do. How do you approach I'm looking for a term here. I want to say data scrubbing, but it's almost like a, a data audit or a data analysis of, and advising customers on what they can and can't do, and then where and how they store that data

Ivan: 32:00

Great question. So that is something that we take very seriously. Perhaps it's because of my background at Apple but we are privacy first, right? And so we start with the customer's constraints around where data is allowed to sit, who's allowed to touch it, what form it takes and we'll accommodate. Remember, just because the model is on your infrastructure, that doesn't necessarily determine where the data sits. So if it's already housed in AWS S3 bucket somewhere, we can still have the model access that. If the data also needs to join on the machine itself, we can accommodate that as well, right? And so we will always work hand in hand with the security and the legal teams to work backwards from, okay, where is the data existing? How can we safely access it? If we need to tune or do we have to use, as you've mentioned de-identified or synthetic data, or are we allowed to use the actual data just to tune the model weights themselves?

Aaron: 33:03

Makes sense. Makes sense. All right, so I think that's a great place for us to stop. I think we did a good job at starting at the very top and getting all the way to the very bottom through the episode. So Ivan, thank you very much for that. For anyone out there that is interested in this or wants to reach out to you or just wants to get started in general what's the best way they could get started, Ivan?

Ivan: 33:24

We can start with a conversation. Please reach out to me on LinkedIn. My, my tag is IYLee. I'm happy to talk about sovereign AI and just schedule a, strategic consultation and see what's the best path for you

Aaron: 33:38

Fantastic. I appreciate your time, Ivan. And everyone out there, thank you very much for listening this week. We certainly appreciate your time. On behalf of Brian and myself, thank you everyone for listening. We certainly appreciate it. And wherever you get your podcasts, if you don't mind, if you could leave us a review if your outlets allows reviews, we would certainly love that. And we're always interested in feedback as well. Feedback details are in the show notes. So again, thank you everyone for listening, and we will talk to everyone next week. Thanks for listening. Check us out at theenterpriseaishow.com for past shows, newsletters, and all things enterprise AI