In The Arena by TechArena - IONOS on AI, Cloud, and Data Strategy for the Enterprise

Episode Date: June 24, 2026

In this Data Insights episode, co-hosts Allyson Klein and Jeniece Wnorowski sit down with Isayah Young-Burke from IONOS to explore how AI is evolving from experimentation to enterprise-scale deployment.

Welcome to Tech Arena, featuring authentic discussions between tech's leading innovators and our host, Allison Klein. Now, let's step into the arena. Welcome in the arena. My name is Allison Klein. Today is the Data Insights episode, which means Janice Norowski from Solidim is with me. Janice, how are you doing today? Hi, Allison. I'm doing great. Happy Friday. Happy Friday to you. We have a really cool episode today. So why don't you go ahead. and just introduce who we're talking to and what the topic is. Yeah, we always have a cool episode, but I've learned a lot in my earlier discussion today.

So I'm excited for everybody else to hear this. But today we have Isaiah from Ionos. And Isaiah is going to get a big perspective on AI cloud infrastructure. He has a pretty rich background. So with that, welcome to the program, Isaiah. Yeah, Allison, Janice, thank you so much for having me today on the tech arena. Again, hi, everybody. My name is Isaiah Imburg. I work at Izionis at the intersection of cloud infrastructure and AI, as well as go-to-market strategy. If you're not really familiar with Ionis, just a little bit about us, Ionis is part of the United Internet Group, which is a publicly traded European technology group, with roughly over 28 million customer contracts globally. So my role specifically, it sits at the, A really interesting crossroad, I would say part of my time is outward facing, working with

the end users, so like MSPs, SaaS partners on how to actually integrate cloud and AI into their digital operations in a way that drives real return on investment. And the other part is somewhat internal. So AI enablement, data analytics, and supporting marketing and sales operations. What I really love about both sides is that they really feed from each other and I learned so much. Externally, I spend a lot of time consulting with businesses, understanding how their systems work and identifying where there's real room for cloud and AI optimization, not theoretical optimization. So my conversations are typically, here's really where you're bleeding time or money and here's a con-free fix from. a cloud and AI perspective. I would say internally, if I could share a little bit more about

some of the things I've done. One recent project I can touch on was, if you guys are familiar with NAN, it's a workflow automation platform, and I got to take part in our beta testing of it for our cloud. For this project, we set up our own cloud infrastructure and built workflows that actually changed how teams operate. There were two workflows. I was more proud of, and I want to start by asking the question. I don't know if either of you two were off for Easter holiday, but if you were, how many emails did you come back to when you were back in the office or I guess back online for you guys?

Too many. Yeah, hundreds. Too many. So that's interesting. So I did a survey and I asked the decent amount of employees at Ionis. They said the minimum was actually 40, which I thought was kind of low. But the average tech employee said it was over 100 due to Jira tickets. They got me thinking.

I was like, oh, okay, maybe I can create something to reduce that friction. So with that, I developed two workflows. The first one was a trigger that worked off with all of the emails I missed on vacation and gave me a warning brief. And it catches me up on the time I was away. The second one was an end-of-day logging workflow that tracks activity and fees into a database. So you can later use that for RAG, which is retrieval augmented generation, which pretty much helps large language model chatbots leverage and use their own data. So I want to share those projects because, again, I work a little bit on the external and internal side.

And that project was useful because it forced me to think about the full stack, not just the AI layer, but the storage, the orchestration, the workflows underneath it, and which is exactly the lens I bring when I'm working with partners as well. So whether it's externally with partners or internally with a specific business unit, the focus to me is the same. We're moving from experimentation right now to things that actually stick at scale. Yeah, that's super awesome. And I love the examples.

And it really relates to my next question. which is about Addis as a company, because you guys sit in this really interesting landscape between domain registration and cloud service delivery, which means that you have to really understand your customer base of how they're utilizing technology in an acute level. When you're describing the workflow that you just talked about, I could imagine that this gives you an opportunity with your clients

to really be working up the stack in terms of how they're utilizing AI and then building in delivering the right cloud services to, support that. Can you talk about that a little bit? Yeah, I think you mentioned a couple great points. And I think the best part about my experience at Ionis, I got to work on the domain and hosting side, as well as the cloud computing AI side. And what makes Ionis genuinely unique is that we see the full life cycle of a business's digital operations. And I don't just mean that as a big marketing line. I mean it literally. We actually just had a conversation yesterday with my colleague Kelly, who's a subject matter expert in cloud and a prospective partner we're looking to onboard.

And it was Kelly and myself who were sitting with the CEO of a managed service provider. And I think they work out of Connecticut. And they run a very serious operation that is very Microsoft Azure Heavy, they were exploring a reseller partnership with us where he would use our infrastructure, our public cloud infrastructure at a discounted rate, and he would be able to create subcontracts for his individual clients that he's providing these managed services for. So real enterprise conversation was very exciting. As we were talking through the marginal advantages of Azure versus AWS versus Ionis, he started opening. up about his chain points. And one of them was unpredictable billing, paying for compute he wasn't

utilizing. And I was like, oh my God, this is the perfect conversation where we can introduce them to our public cloud and maybe get a new partner. It came to the conversation at this point where he told us how we first came across Ionis. And this is where things got exciting because it wasn't like a sales pitch or a LinkedIn ad. He told us that his daughter needed to set up a website for a school assignment and she needed a dot-com domain and a content management word hosting setup to go with her assignment. So she got with our customer service team on the domain hosting side and told her dad about this smooth and positive experience she had. She started poking around on our website and got very curious. You start digging into our full stack

And that's ended us up into a partner conversation. So for me, hearing that story and thinking about what she just said, it really reminds me what position we have in the market. You know, we're a company that when someone needs their first domain for a class project, we got that. But we're also the infrastructure partner for someone running a multi-client managed service provider business. That range from startup to scale is genuinely.

rare. And the thread connecting it all, it's that customer service and that experience we carry behind our brand. It has to be good at every level, regardless if it's a dot com or we're talking about virtual machines. Because just like this instance, you really never know who you're watching. Everybody uses email. Everybody uses cloud storage. And with that, AI adoption follows the same art. It doesn't really just start with AI. It starts with that digital footprint. that grows into infrastructure. AI becomes that natural next step, just like after you get a website,

you start thinking about cloud storage and caught infrastructure. So we get to see that whole journey. And for me, that's one of the best parts of working here. Awesome. Now, Isaiah, you told me you've been dabbling in AI since you were 15.

But for the majority of the world, we've really only seen a lot with AI in the past couple of years, right? At least experimentation. real experimentation with it. And many expect 2026 to make the real mark and transition to enterprise scale. What signals are you seeing that suggest that this shift is really happening now? Great question. Around the, I would say December to January, I spent some time writing my thesis and it was studying the cloud computing industry between a domestic provider and an

international provider. So I chose AWS because they're a huge hyperscaler and Alibaba just to understand the Asian and Chinese landscape of cloud computing. And what I noticed is that data center and cloud spends over 700 billion last year. So some of the biggest ships we're seeing now is that organizations are moving away from just curiosity. Like what is chat, GBT? They're really moving towards capacity. I would say the past couple of years. Most teams were running specific pilots. So we saw a lot of people making their own chatbots and really testing use case and asking what can AI do for us. And again, now we're seeing that real transition. I would say at I onus, what I see in my day to day that stands up clearly,

that's kind of painting and forecasting 2026 is a rising demand for GPU power compute. I would say almost every day to every week I have someone asking for GPUs, where can we get GPUs to host this model? Because that's the backbone of both training models and running the inference for it. I would say secondly, the massive investment going into data infrastructure is huge, specifically scalable storage like object storage,

which I'll get into a little bit later. I would say customers are realizing that the model isn't exactly the bottleneck in AI. It's actually the data and it's how it's stored, how it's accessed, how it's managed at scale, because when it's at a startup phase, how these things happen is very different at a scale level. We're also seeing tools like NAN, which I mentioned earlier for automation, which is very similar to people who may know Zapier or make. We're seeing tools like NAN as well as hugging face and Anthropics API actually being adopted,

not just for these one-off experiments. This is actually being adopted into enterprise production and workflows. And the industry-specific AI applications, they're multiplying from health care, logistics, finance. And this really tells us moving past that experimentation, phase I was mentioning, and we're really trying to double down on how we can scale with these business cases. The turning point I always listened to for my customers or we're having these conversation is when they stop asking, okay, what can we do with AI? And they start asking, how can we scale this without breaking the bank? How can we scale this without breaking any reliability

here? And I think that's when it becomes real to me. Isaiah, you talked about AI integrating into core business systems. When you look at what you're delivering from a cloud service perspective, what does that change in terms of what your customers need from the core capabilities of the infrastructure you're providing? That's a great question. And I might repeat myself a little bit just because talking about like the enterprise stack and expectations, but definitely something to be aware of because 2026 things have been

moving super, super fast. As AI moves into core business systems, I would say the expectations for infrastructure changes pretty dramatic. It's no longer just give me access to these GPUs I was just mentioning. It's, can your environment hold up to this when it actually matters, again, with reliability and costs? So I can give you a better example so you can picture what I'm talking about. Because enterprises now need infrastructure to be stable, scalable, predictable. And that means dynamic scaling with AI workloads, high performance data access, low latency, real-time applications. But again, what people don't talk about is what it costs to have something like that running

at scale. So do you guys know the company OpenAI, right? So are you familiar with SORA AI? Yes. Okay, I think Janice's. So that was their text to image social media platform they had. So you can not only create these videos from text, you could also share and comment on people's videos as well. So the operating costs spent for Open AI daily was about 20 million to run this platform. And it's rumored that they are reportedly shutting down. And I bring that up not to really pile. on their business and their gossip, but to really make it concrete about what I'm saying, you know, AI workloads they scale very fast. And without clear pricing controls, costs, or tokens can really spiral without you even noticing. And for me, you know, that's very important understanding the operational and the cost behind it. I would also say the security and

compliance concerns are super important to the moment AI gets integrated into business, it's very critical. What can happen is you'll have tenant access given to an AI, and without the right rules and regulations behind that, it can be a disaster. So I always try to focus my customers and partners on just understanding the security aspect of it, as well as the cost behind scaling something like this, as we really shift from providing tools to really providing these environments, infrastructure that can support AI reliably, securely, and at scale, that's the new bar. That's what you should expect for enterprises in 26. Amazing. And you've talked a little bit about looking at AI across the entire value chain,

not just the mall here. Where do you see kind of the biggest gaps emerging as organizations try to operationalize AI. Most organizations focus on the model, which I don't think is always the best thing to be focusing on because model benchmarks, they change as new people join these companies and as new tech is discovered. I try to really focus on different layers. And I was talking to Janice a little earlier about a panel. I was speaking on for IT Expo, in February and it was all focused on AI and R.O. And I was like, oh, this will be fun. And one of the most valuable perspectives I actually got came from this guy named, because he was Ivan Reyes. She is either the head of cybersecurity or the CIO of

Embertoon Solutions in Miami, Florida. And he raised the most interesting perspectives and points that don't get talked about enough. And it was strictly the security concerns, around the level of tenant access these agents have when in the enterprise intranet. So when an agent gets connected to your internal systems, it's not just reading the data. It has potential to write and delete. The ability to trigger workflows across your entire environment has a very different risk profile than a website chat bot answering questions. Coming back from that conference, it made me be more aware of the security concerns.

and those gaps enterprises are not looking at because they're so interested on the return on investment. And we saw a lot of real world problems going on with similar situations. I don't mean to beat the horse on OpenAI. But another story I remember around this time when I was this conference was, I'm not sure if you guys, are you guys familiar with OpenClaw? Rubin? Yeah. Open AIs, one of their security leaders,

was using OpenClaw connected with Gmail and M4 Mac Mini, which is an awesome setup, by the way, especially if you're trying to do continuous development. But she was doing a sorting experiment where the agent ended up deleting her entire inbox and kept going even after she prodded it stopped. And what was really interesting about this is it was the online backlash against the security head on Twitter. Because he was just trying, this is why I love tech.

Because, you know, people who are serious engineers, they're always trying to keep feeding the documentation, keep feeding the open source, making sure everybody knows so they can learn from that experience and then provide a better product. And for me, it was a little funny, but also, like, disappointing to see online people giving this engineer.

It was very high up and open the eye a hard time. one because of her position as security and two because of where she works and I thought the reaction really missed the point. Everybody was like, how could you have such a breach or such a failure as security had? And any real engineer understands, you know, bugs happen. What matters is how you prevent them from happening again and how fast can you recover from these bugs. So that's the real engineering question. So beyond security, I would say the biggest gaps we see. the data management preparing and managing the data, AI depends on, integrating into these

real business systems like CRMs, emails, internal workflows, the skill gaps across the full AI cycles because not every employee is trained on how to use and interact with stuff. I would say that's another really great thing about IONIS is they build, you know, initiatives like IONIS Momentum Cloud and the AI Model Hub and the AI Model Hub studio, which are meant to bridge the gap for any type of business, more focused on the SMEs and SMEs and public sector organizations that need practical and trustworthy solutions, not just raw infrastructure. So the bottom line for me, most AI, it challenges at scale. And it's not really a capability problems. It's more a system problem, not model. And increasingly, they are a trust and

access problem, like I was mentioning that Ivan was mentioning and talking about at that conference. So that's where the real work is when it comes to filling these enterprise and the enterprise gaps. And I don't have much to say other than that, but I can't emphasize more the importance on that. Now, one thing that I wanted to talk to you about is I know that Ionis also operates across geographies. And do you have any sense of where you're seeing differentiating? in terms of AI deployments across the landscape that you're operating in. Different regions are generally optimizing for different things. And it's not just cultural as structural sometimes.

In Europe, data protection, digital sovereignty aren't optional conversations. They're backed into their regulatory environment. So you guys might be familiar with GDR and initiatives like GIA or GaiAX. They've made it so my data, actually, it lives as a front of mind question from day one. It's not an afterthought. Companies want to ensure that their data doesn't leave the EU and it shapes every infrastructure decision they make. So that's the European lens.

And when I look at the U.S., because we're headquartered out of Philadelphia, in the U.S., adoption tends to move faster. There's more emphasis on speed to market innovation and iteration. But what's really interesting here in the U.S. as I'm starting to notice is as AI becomes more embedded into business operation, U.S. companies are operating globally, starting to care a lot more about where the data governance is to. So more people are being concerned about that terms and condition box, they're just checking off. And you can't sell in Europe without taking this seriously at all. So the starting points are different, but the long-term direction I'm starting to see it converge towards a balance of innovation control and trust. I own this as a company with data centers in the U.S. and Europe, I would say is well positioned

to serve both sides of this regulatory equation. So cloud service delivery itself is, evolving across markets. How are customer expectations kind of changing and how has Ionos adapted its cloud offerings in response? Great question, Janice. When I look at how customers, their expectations have shifted, it really comes down to three things. We're consistently hearing from the end user as well as the partner level. And I would say that's flexibility, transparency, and I would say ease of use. And if you're, you're not delivering on all three of these things, I think you're losing out on the deal conversation.

So to touch over all three of them, flexibility, nobody really wants to be locked in. Multi-cloud is definitely the new normal. Customers want to run workloads where it makes sense, whether that's on-prem, private cloud, public cloud, and they want the ability to move without there being a six-month project. Vendor Lofkin, it used to be tolerated. Now it can really be a deal breaker we're seeing. So from that, we've learned to be very versatile offering pay-as-you-go for compute, but also long-term cost-saving plans for people who have their scope and dedicated projects ready.

On the second point, I would say transparency is actually a very big piece as well. And it's deeply tied to trust. a lot of organizations, they've been burnt by unpredictable cloud bills. And that revolves around data egress costs that weren't really visible up front, scaling charges that hit without warning. So when customers come to us, one of the first things they ask is, what am I actually going to pay? And that's always interesting when I see one of our reps give the demo because they'll show

them a cost and usage tab. And I'll see like the customer or the partner. I'll see their eyes wide up. And they're like, no way. This isn't real. I thought this was a norm. I guess in the hyperscalor lens, nebulous billing works in their favor. So when our partners and end users can predict transparent pricing, it doesn't just become a feature. It becomes a signal that we're going to start a very good partnership here. And then lastly, easy use. Again, another obvious one, but you would be surprised by how many software companies deploy applications with very garbage-like user interfaces or garbage-like U-Xs and with features that don't even work. I think either use, especially when it comes to the cloud computing vertical and using those products, it is very

important. If you look at the salaries for AWS practitioners, they are 100K plus. And not everybody has a headcount to support that, but like I said, digital operations is expanding and is becoming more mandatory. So the fact that we provide something that is so easy to use that if you train like an L1 or L2 with our documentation to do it and they can do it, that saves you on headcount. And that's something our higher-ups, I think, understood when creating our data center designer and our public cloud offering, because these are the pains that a lot of the hyperscalers were ignoring. So to tie it all together, I would really say ultimately, these customers are looking for that flexibility, transparency, and ease of use. When you think about multi-cloud, and that's a model that I really agree with, that we're moving into that multi-cloud era,

There's the workflow management, but there's also the data management. What do you see from customers in terms of trends there? Another great question. You guys got amazing ones today. Yeah, data, multi-cloud. I would say that's the highlight, 2025 and 2026, those two words. It's like, what are you doing with your data and how are you posting? Is it with AWS?

Is it with Azure? And the reality is you can't have all your eggs in basket. it. That's what I'm really starting to learn with these outages because the cost of an operation when it's down can be immense to the minute, especially with these large enterprises. So I think the best way to answer this is this is where a lot of organizations are going. They're showing that weight of complexity, especially as AI starts to get heavier and they start to scale it. I want to say is really no longer about running these specific, like, what's the right model. It's more about managing the data behind these work loans across distributed work environments.

So we're seeing a clear shift away from monolithic architectures towards distributed approaches. And now we're seeing more sharding, more hybrid, multi-cloud setup. where workloads are spread across on-premise, private cloud, and in public cloud. So to make that really work, organizations are relying on, if you guys are familiar with Docker, so containerization, orchestration, but not on the AI later, but more on the compute layer for Kubernetes. If you know what Kubernetes are, think of it as more like an air traffic controller, airplanes, but more for your applications.

And it really ensures everything is portable, scalable, and lands where it needs to be. So Iona's being a one-stop cloud shop, we offer managed Kubernetes for that exact reason. And also, one of the hardest challenge isn't really the technology. It's just keeping this consistently working day to day, managing the data access, performance, and governance across all these environments,

without losing your mind. So again, not putting all your eggs in one basket is definitely something people are seeing and why they're adopting towards the hybrid cloud. But also, when these outages happen, if you're able to get back a couple seconds and if that's just spinning up Backup Environment B with Azure or Backup Environment C with Ionis, the fact that you have that failsafe in mind makes you a more complete business in your competitors. Data strategy, though, right? It's really becoming central to AI infrastructure and maybe it always has been. But how are enterprise rethinking storage and whether that's object storage,

distributed systems, or kind of other approaches? Oh, awesome. I'm glad we got to go back to storage. I love talking about that. Me too. You get it. Yeah, everybody's got ICloud, Google Drive. The best part about working at Ionis in the U.S. is we have a strong economy to scale when it comes to S3, and we have the best price for object storage. And I believe it's about $4.99 per terabyte. So storage itself, just like my example, has quietly become one of the most strategic layers in AI. And I say quietly because if you're paying attention to like stocks in the news, you understand it. But a lot of people are still sleeping on it. As organizations scale, I think that's like the word of the day scale. As they scale AI, they're dealing with massive amounts of unstructured data from text,

images, logs, embeddings, and far beyond the traditional way storage systems were set up and designed to store data. So what we're seeing is a strong shift towards object storage because it's very versatile, and especially as a foundation for AI workloads for training, as well as many organizations using, I'm not sure if you guys are familiar with the medallion architecture. So that's pretty much breaking up data into like three tiers, one being like bronze, silver, and gold, and that's enrichment pipeline. So a lot of people are using different versions of object storage to further enrich their pipeline of data. So we've been seeing a lot of partners manage that and a lot of end users using object storage for these use cases. These practices

are definitely the backbone of what people call data lakes if you're not too familiar. And that is pretty much a central repository where all this raw data lives before it gets processed for training and inference. So S3 compatible object storage has become the de facto standard because it's highly scalable, cost-effective, especially if you're going with IONIS, and accessible via API. Now, Isaiah, we've talked about a lot of topics, but one of the ones that I really wanted to see is we're talking about a global landscape. We're talking about data distributed across this landscape. Where does policy come in? And how do you, as a service provider, ensure that your clients are understanding policies in different geographies?

That's another great question. And we touch a little bit about regulation, you know, Europe versus the U.S. sovereignty. It's really moved from a compliance checkbox to a genuine strategic decision. And the angle that doesn't get talked enough about, I would say, is the U.S. Cloud Act, if you're familiar with that. Here, that's something people don't realize I was mentioning how a lot of people just check that box just to get into the application. But if you're using a U.S.-based cloud provider, the U.S. government has the legal ability under the Cloud Act to request an auction to ask us that data, even if it's restored in Europe. So that's not just some conspiracy that's the law. And I noticed this difference because we're a subsidiary of a German company.

So that means we operate under a fundamentally different legal framework when it comes to who can get access to our customers' data. So for companies in very regulated industries, especially with European customers, really distinctly, it matters to them. Just to share a story that I think will make this hit a little more at home is we were going through a vendor compliance audit where that exercise was so eye-opening to me. And I was speaking with some of our HR staff. And the whole point they were trying to get to me was they're doing this vendor audit. to make sure all of our vendors, in turn, are selling or exposing our data that HR has access to and that they're actually GDPR compliant. And I thought about that for a second.

I thought about how many companies have I worked with that weren't. And at the end of the day, what it comes down to is sovereignty is starting to stay in people's head a little bit more. It means different things depend on where you are geographically and which legal framework covers your operations, but I am seeing a common thread where knowing where your data lives and who has access to it under what conditions really matters. Providers who can give answers to those questions and can give that reassurance to their customers, I'm seeing a real advantage to them. And it used to be more in the European landscape, but even in specific industries in the U.S., we're starting to care a little bit more about it. So, hopefully that answers your question.

So Isaiah, looking ahead as AI systems become autonomous with agents interacting across platforms and services, how might that change the way background infrastructure needs to be designed? Oh, great question. Anthony, subject matter expert in cloud and AI. We always like to joke around because he's a back end guy and I'm a front end guy. I like to design the way things looks. And he likes to get into the nitty-gritty problems. solving of it. So to really answer your question, as AI becomes more autonomous, infrastructure has to be more deliberate. And I think most organizations aren't fully ready for what that means. And we're really moving into a phase where AI agents aren't just generating outputs. They're interacting with these back-end systems. They're triggering workflows from different applications, different software's making decisions across platforms in real time. So they give an agent that boasts a meeting updates a CRM sends a slack message and files a report without Janice or Allison in the loop.

That's not science fiction anymore. It's real life in 2026. That's NAN with a large language model attached to it running in a cloud infrastructure. So this layer introduces new infrastructure requirements. You need decentralized architecture to support low latency interaction. Very important. Nobody likes to wait. And especially across your edge and cloud environments, really from a back-in perspective, this is where interoperability becomes critical. Agents need to be able to communicate securely across different platforms, which means open standards and strong APIs are essential. I've touched upon this multiple times, but I'm going to say it again.

Overall, the backend has to have very strong security procedures and requirements around it. That is increasingly going to grow as AI becomes stronger. I'm not sure if you guys are aware of some of the work Meta and Anthropic are doing and some of their superintelligence and models. But recently, Meta released Muse, which is their superintelligence model, and Anthropic released, I think it's called Mythos. And two days ago, they released an article discussing a sandbox experiment where they were testing the model offensively.

And Anthropic, after this test, made a choice not to release the model, but they would release, you know, this study. and pretty much in this sandbox environment that the Anthropic tested this model, it said, okay, do whatever you want, we're going to test you offensively. Your goal is to escape. The model found all of the vulnerabilities in the environment and escaped with East. And Anthropic concluded this tech will only be used for defense, and they're partnered with like Google and Nvidia to do some testing with it.

And I wouldn't just release it out into the public like Claude. But this brings a big point. You know, without fail safes, they know it's otherwise to just release this model into the public. And these are the types of stories that scare people around AI usage. But it's honestly, to me, very, very fascinating. For somebody like Aonis, we have 30 years of infrastructure experience. And our particular vision for AI is to give it this trustworthy and practical branding. You know, we want to make it accessible, like I said, to,

SMBs and even mom and pops shops. So the foundation for these automated systems, it has to be solid. As autonomy increases, especially the value, it's going to be super, super important that the back end infrastructure is reliable and supports both sides. Now, Isaiah, this conversation was fantastic. I just want to thank you for all of your insights today. It's so exciting to to see what Ionos is doing out in the marketplace and you're an inspiration. For those of us who are listening online, they want to engage with you further. Where would you send them information about Ianos and then also to engage with you? Oh, thank you so much, I want to say, and you nice for having me.

You guys are wonderful and I think the tech arena is an accessible grade set people. If you would want to find more information, you can always visit our website. I think one of our SMAS service reps Paul and they'll help you out. Also, who messages me on LinkedIn, I say Albert. Thank you so much.

It was so great to talk to both of you today. What a great conversation, Denise. Thanks so much for both of you. Amazing. Thank you. Thank you, Isaiah. Thank you, Allison. Thanks for joining Tech Arena.

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