Everyday AI Podcast – An AI and ChatGPT Podcast - EP 497: Inception Games Round 1: Who's the Top NVIDIA AI Startup?

Episode Date: April 4, 2025

Missing the Madness between games? 🏀It's not over – especially if your as big a fan of AI startups as your are basketball. Recently at NVIDIA's big GTC conference, we chatted with an Awesome 8 group of startups in NVIDIA's Inception program, which powers startups with cutting-edge tools, training, and global connections.Join us for the first part of a two-part series, and help us vote who's going to move on to the finals!You can vote twice. Voting ends Sunday, April 6th at 11:59 PM CST. 1. Leave a comment on the LinkedIn livestream show with the company you want to vote for and a hashtag like this: #companyname2. Vote on the April 4 edition of daily newsletter, which you can read here. Inception Games Round 1: Who's the Top NVIDIA AI Startup? – An Everyday AI Chat with Jordan Wilson Check out NVIDIA for StartupsNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Want to get in on the action and cast your vote? Do so here! Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: [e-mail] with Jordan on LinkedInTopics Covered in This Episode:Inception Games AI Startup CompetitionDeepChex Generative AI Systems EvaluationExpander AI's Multi-Agent AI PlatformBeamer's Video Compression TechnologyPlyOps' GenAI Application AcceleratorGlia Cloud's Automated Ad CreationContextual AI's Augmented Retrieval SystemDemocratize's Custom Apparel IntelligenceIllumix's Enterprise Data Analytics PlatformTimestamps:00:00 AI Startup Showcase Series06:43 Live Stream Engagement Instructions10:02 Agent Interaction Evaluation System12:18 Streamlined Evaluation and Deployment System16:40 Streamlined AI Development Platform19:53 Unified Framework Connector Platform21:06 Beamer: Enhanced Video Optimization Technology23:54 Inception Program GraduSend Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and all episodes: StartHereSeries.com Also, here's a link to the entire series on a Spotify playlist. 

This is the Everyday AI Show, the everyday podcast where we simplify AI and bring its power to your fingertips. Listen daily for practical advice to boost your career, business, and everyday life. Meet Firefly AI Assistant, now live and Adobe Firefly, the all-in-one creative AI studio. Just describe what you want to create and the assistant handles the rest, orchestrating multi-step workflows across Photoshop, Premiere Express, and more in one conversational interface. You direct the outcome. The assistant accelerates execution. The madness isn't over.

Actually, if you're an AI fan or a fan of startups, the madness is just getting started. Welcome to a special edition of Everyday AI. This is the Inception Games. We have a tournament, a fantastic lineup. of eight awesome startups out of the Nvidia Inception program, and we're going to be handing it over to you all. We're going to quickly on today's episode give you eight fast pitches from our awesome eight from Nvidia Inception, and you, dear listener and live stream viewer are going to

decide which one moves on. So maybe your team is out of the best. big tournament and they weren't dancing this year in March. Don't worry, maybe one of your favorite startups is in this very competition. All right, I'm excited for this one. It's going to be a fun time. What's going on, y'all? My name is Jordan Wilson and welcome to Everyday AI. This is your daily live stream podcast and free daily newsletter, helping everyday people like me and you, not just learn AI, but how we can leverage it to get ahead and to grow our companies in our career. So if that sounds like you, welcome, you're in the right place. We do this every single weekday, you know, on our website, which is where you need to go, your everyday AI.com. On there,

you can listen to now more than like 500 episodes or almost 500 episodes anyways from some of the world's leading experts on generative AI. And, you know, one thing I noticed is a lot of times we don't bring a lot of startups on the show because sometimes I'm, like, hey, you know, sometimes startups are super advanced and, you know, they have great, fantastic products and sometimes they're not. But with the InVIDIA Inception program, which I partnered with for this series of shows, it's a legit startup, some great ones. So when I was at the NVIDIA GTC conference, I was lucky enough, like I said, to partner with NVIDIA and to be able to go interview eight awesome AI startups. So for our live stream audience, I hope this one's going to be a lot of

fun. But if you are listening to this on the podcast, you can still get in on the voting action. So real quick, here's how it's going to work. I have kind of on my screen here eight different quick video pitches. They're between three to five minutes long. All right. So you're going to hear who the pitch is from. You're going to hear a little bit about their company. And I'm going to be interviewing them, you know, on the GTC floor here. So we recorded these about about a week and a half ago. And I want you, you know, for our podcast audience, to listen in. Which one is the best? Which one would you want to use? Or which one do you want to hear from again? Because essentially, we're starting with eight. It's an elite group. But only two are going to move on to our final

show next week. And like I said, for our podcast audience, I know that's where our bigger audiences, I need to hear from you. All right. So you can come and vote in two different ways. So pay attention. And hey, live stream audience, you know, Dr. Scott, Michael, good to see you back. Big bogey, Brian, everyone else, Sandra. You actually get two votes. Okay. So hear me out. You can vote in two different ways. Vote number one is you can vote once on the live stream. So on either LinkedIn or on YouTube. So what you are going to need to do to officially cast your vote is you are going to put hashtag and then followed by the company name. Okay. So as an example, the first company in our pitch, the first of eight is called deep checks. All right. So if after all

eight, you're like, yes, deep checks is the one I want to hear more up. Maybe they're the type of company, you know, that would be great for your business, right? I think a lot of these companies that we're going to be going over today from the Nvidia Inception program are probably great solutions that your company has been looking for. Because let me be honest, I talk about AI every day. I love startups. I follow the startup scene very closely. When I was at the Nvidia GTC Inception Pavilion, yes, they have so many startups in the

Invita Inception program that they had their own essentially dedicated Expo Hall. I had only heard of maybe 10% of them, right? And as I'm going around doing these interviews, I'm like, wait, this software is amazing. This startup is amazing. It's going to solve so many people's business problems. Okay. So number one, you can vote on this on this live stream. So podcast audience, I always leave the link to the live stream.

Okay, that's number one. Number two, you can vote in our newsletter. Okay. So if you haven't already, go to Your EverydayaI.com. In our newsletter at the very top of today's newsletter, which you can always access actually on the web as well at read. Your EverydayAI.com. So in today's newsletter for Friday, April 4th, we're going to have all eight, our awesome eight group. And you can vote on there as well. Okay.

So again, two votes, one in the live stream. use the hashtag and then two in our newsletter. And then the two companies with the most votes move on to the finale, which is next week. And we're going to hear some updated, hopefully pitches from them, answering a lot of your questions. Okay, so that's the other thing. Live stream audience, as we go along, ask questions.

What more do you want to know from these startups? I'll probably have some additional questions, but I'm going to give them your questions as well, because the two that move on, we're probably going to do a quick, I don't know, like eight minute secondary pitch, right? So all these questions that you have still get them in. All right. So, you know, as an example, deep checks is up here first. It would be helpful for me, live stream audience, if you say, hey, what is, you know, deep checks, you know, ideal client? Or, you know, if you want to know, how much does, you know, deep checks cost or whatever, right?

get those questions in as well, but that's why for your vote, use the hashtag. It's going to be a little bit easier for us to tally them, you know, in case there ends up being, you know, dozens of comments in the live stream. All right. I hope that makes sense. Real quick, I need a little help from the live stream audience. Let me know if you can hear the audio real quick, all right? So I'm going to like essentially be sitting back listening to these pitches again as

well at the same time as you. But I want to make sure that you all can hear them. All right. So here we go. Let me know live stream audience. If you can hear this audio and then we're going to start it over. Don't worry. All right. So I am here with Philip from Deep Checks, another Nvidia inception company. Philip, tell us a little bit about deep checks. Hi there. So thank you. First of all, thanks for having me. All right. Live stream audience. I just started, played about 10 seconds. All right. All right. So hopefully, uh, hopefully y'all can hear. All right. Thank you. Uh, thank you to a couple of our, uh, YouTube audience. This is also how I found out, uh, which live stream platform is the best.

Because, uh, you know, we, we, we just got confirmation from our live stream audience, uh, on YouTube, but I'm guessing the, uh, there we go. All right, our LinkedIn audience, uh, just, just chimed in as well. All right. Here we go. I'm excited. So, uh, we're going to hear first, three quick three to five minute pitches from our first group. Then I'm going to come back on, you know, ask questions of you all. Make sure to see what you guys are like and what you're not. And then we're going to get the second group on. All right, here we go.

I'm excited. Let's kick off the Inception Games. You're going to hear real quick, quick pitches from eight amazing companies that were at the Nvidia Inception Pavilion. Here we go. games, round one. Let's get it. Go.

All right. So I am here with Philip from Deep Checks, another Nvidia Inception company. Philip, tell us a little bit about Deep Checks. Hi there. So thank you, first of all, thanks for having me. I'm Philip Co-Feller CEO at Deep Checks at Deep Checks. What we do is we're giving you everything you need to make sure generative AI systems are doing what they should be doing.

So the main thing, when you're building an LLM-based system, it's really hard to know how There's no, like in the traditional machine learning systems, there's something called the test set. You heard of a test set before? No test set, nothing of the sort exists for generative AI systems. So what we're trying to do is enable the finding, measuring and validating the progress of systems like this. So as you're building it, you're making the prompts better, you're changing the setting of your React system. You're having better, you know, you want to change the different model from, like, say, Gemini to Sonnet 3.5. And you don't know if you're doing better or less, because that's how we help.

So what do we do? We have a suite of proprietary models, small language models, that we combine with a kind of no-code option for creating LLM judges. And then we orchestrate all this together. We call it the swarm of agents to determine per individual interaction. Was this a good interaction or did it fail at one of the different criteria? So we're kind of starting off by saying, did the system work or did it not work? Did it have an hallucination? Did it give a relevant? information that you know when you're talking to you know you're talking to chatchipina it says I'm sorry I'm an AI chat yet so so it takes all those into account and then it tells you did you manage or not manage and that way you can score a version so you could say you know sonnet was better than sonnet was better than Gemini and this and here's why and it shows you examples so it's really end-to-end evaluation of generative AI systems very cool you hit all of my favorite

words, all my favorite things, right? But tell me a little bit, who are your average customers or clients at deep chants? So, first of all, any company that's building a generative bank system, any company that's using opening eye in the background for some sort of textual interface, there are potential clients. The three verticals I'd say that we're working the closest with are healthcare, government, including the defense, and financial institutions. So those are, I'd say, the three largest verticals.

There's really a long deal. Any startup could be using it. Any Fortune 500 company could be using it. And we're proud to say we started with startups and now more of our new business is coming in from the larger enterprises. Very cool. So you kind of told us some of the features. What are the benefits, right? What do your customers or clients have to gain by using deep checks versus if they didn't use it? So I think the number one benefit you're getting is a higher probability of success for this. the entire Gen. A.I. Projects today when you start them, their chances of success are well under 50%. And I think just by having something of our store where you can actually iterate quickly, check the next versions, understand what we call it the AI progress. Are you actually

improving your system? So by having that in place, it's kind of like test-driven development, you're raising the chances of deploying. And then the second best benefit is you're improving the timeline. You're going to release more projects and so forth. So there's, There's no real way around it. It's just the question, are you going to be doing evaluation with, you know, hand labeling, using CSV, sending them by Slack or teams? Or are you going to have, like, a kind of more robust automatic system that's helping you do that. There are many, many different side benefits. So we said, talked about version comparison, giving the go, no go per version.

We can use our AI to assist human annotators. We also have monitoring in production. We have a whole flow of test, you know, kind of checking all, you know, kind of checking all, the different, you know, risks that happen within these types of systems for the malicious prompts. So we kind of try to give everything you need in one place. But if I have to, if I have to talk about the number one benefit, it's actually shipping, raising the shift up shipping and shipping faster. Perfect. And then real quick, what value has the Nvidia Inception program provided to your organization? So first of all, the Invity Inception team is amazing. I really don't know what

it's like in other hubs, but we have the luck of working with it with, with, the Israeli hub, and it's really in almost every aspect we're getting some sort of assistance for them. So they'll give us feedback. Even if they see an event of ours, it's not related, they'll give me feedback. We'll say, hey, you should have changed this, join blog post, working together on integrating within, you know, NIMS within the Nemo ecosystem, helping us figure out how to reach out to, let's say, new verticals, like the telco sectors, when we had less experience with, and they really gave us a lot of know-how, these specific connections. So at the beginning, actually, when we signed up, was just like,

oh, cool, you know, some other program we could join, but it turned out to be a really good choice. Awesome. And real quick, if someone, a viewer, listener, they're like, wait, I need deep checks. What's your quick pitch to them to give them to sign up? I think the first use case almost always is if you're, you have one, at least one person that's trying to, a few different versions for how to have an LLM application, then you can see which one's better. That's usually the first hook, not always. But basically the main idea is, wouldn't it be amazing if you're building a generative AI system and then you could

get a score for every version like a test that like in the classic machinery. It's really, once you try it, it's really hard to go back to having this kind of voodoo and kind of manual SESP thing. Awesome. Philip, thank you. If you want to hear more from deep checks, let us know.

All right. Here we go with our next startup. All right. I am here with David from Expander AI. Davis, tell us a little bit about Expander AI? Sure. Love to be here.

My name is David. I'm from Expander AI. Expander helps the organization to connect their internal system and build sophisticated multi-EI agents. So, I mean, what's this best business use case for Expander AI? Because I know, you know, agentic AI is all the buzz right now, right?

So what's kind of an easy to understand use case for that scenario? So customers are now looking to build custom AI agents. instead of buying AI agents that are already configured. So, for example, the support AI agent, the tech scale of support escalation, and answering instead of fuel to a ticket, that's something that now customers are not busy. So instead of them building and investing and fine-tuning and connecting to different AI applications, we give them a platform that they can easily choose the connectors

and design the workflow that they want to build and then want it. Okay, so is it, I mean, is this for, you know, technical clients, non-technical, do you work with big enterprises, startups, like walk us through kind of like what your average client looks like and also kind of the benefit that they get. So the average client is with the developer team that would work in 100th developer team. So we're talking about small, medium enterprises, but we're also working with startups. We're going to release open source really soon. For any developer that are building AI agents, they can just use the platform. But our ideal customer is focusing on building internal AI agents. What is, you say, the biggest value that you can add to companies?

Is it more of, you know, less time, more potential revenue, right? Like, what's that one big value from using Expander AI? So it's engineering time. Right now, building AI agents is so expensive and requires a lot of skills that non-sunami development of developers has, an organization that want to move fast and build internal AI agents, they need to invest a lot in bridging the knowledge gap and or going all in one platform, and then they're losing the other capabilities of the second platform. So we give them a platform that can automatically use only good words of all the platforms.

We work with Nvidia, we work with Empropriate, we work with Open AI, and developers don't need to compromised about how they choose the technology. So organizations that choose to use expender, they move much faster. They're focusing on business challenges instead of technical challenges. And their developers are able to complete AI agents much more quickly. Yeah, speed especially right, when you're trying to take advantage of everything agentic AI has to offer. I think speed is huge, right?

Let's talk a little bit about the Nvidia Inception program. How has this helped your company? Wow, a lot. So we worked with Inception since we started the company. One of the first use cases that we did together was to release a benchmark. So our main technologies to generate connectors, enterprise is like thousands of systems. Yeah. So we built a technology that generates connectors to private APIs.

And the Inception program helped us to do benchmark with Nvidia experts. So we did the benchmark with Nvidia and Therile. And they benchmark our connectors and they published public-facing article about how good the connector is. And they proved that using expender connectors, AI can do the job three times better without expender. So that's like a game changer for us in a startup. It's an acknowledge for LVIDIA that he do something that with value. And it's all thanks to the Inception program. I love it. I love it.

What would you say is kind of the next big challenge that, that you're working on to help clients or maybe the next opportunity that Expander is working on. Yeah, so now we're going to talk about multi-EI agents that are doing multi-set. That's like the next big thing. Everyone knows or should be able to know how to build AI agent that perform up to 10 to 20 operations,

but it becomes very strategic when you have an AI agent that can perform a human level task. And that's a very, very strategic. And that's a very novel challenge to do right now. And we are focusing on that, as we speak, we have an MVP for just this problem on how to build a multi-AI agent with a graph system that can do a very complex task that goes into a human level complexity. So that's like the very focused area that we are focusing on and that we are reading from customers that they want to try to solve. All right. question. If someone in our audience heard this and they're like, I need

Expander, right? What's your what's your kind of sales pitch to them on why they should use it? Yeah, sure. So any organization that have internal systems, why spending the time generating connectors instead of just using a platform that can generate connectors and gives you the ability to design graph and a state machine with all the framework that you have currently available in the market? So instead of going all in on one framework, Langchain, Kauai, NVIDIA, Ophania, and Tropic, we give you the ability to really use all the frameworks in your private API and design a state machine that works close all those frameworks.

So this technology allows developers to really focus on business challenges instead of technical challenges and you as a business leader don't need to do investment in one specific framework. You can enjoy all of them. All right. David, thank you so much. So if you want to see more from Expander, let us know. And now let's take a look at another startup. Y'all, these are some good ones. Go.

All right, we have our next Nvidia Inception. Startup, we have Sharon here from Beamer. Can you tell us a little bit about Beaver? Sure. Beamer built a technology to optimize video at large scale and it plans on Nvidia GPUs. So we gain acceleration for video encoding from Nvidia GPU,

a component called NVNC, the Nvidia Encoder, and we make it so much better. We make it efficient by about 40% on average, or 30 to 50%, depends on the use case. And what you get is that you can take this huge video repositories for autonomous vehicles or for user-generated content or media and entertainment and make them so much more efficient running on Nvidia GPUs, that's our case.

Okay. What would you say is the one biggest, biggest problem that you solve for your customers or clients? I think that every customer that has large volume of video, then all of the associated cost of the video handling has to do with the tonage, with the amount of videos. So think about that cost, half-side. Okay. You know, that's a huge benefit. Okay, so who is your average customer or client at Beamer? So we are approaching three different markets. One of them is the traditional markets that we've been there forever,

is the media and entertainment market. And now thanks to the Nvidia accelerated platform, we can also actually approach markets that much larger volume. One of them is autonomous vehicles, which you can see over here. And in order to train an average model for autonomous vehicles, you need 100 petabytes of video. Now think 50 petabytes. Okay. That's a big deal.

Yeah. Right? With user generated contents, we're talking about 80 to 100, many years of video captured every day. These are hundreds of millions of video clicks, short video clips. I think about that distribution,

the amount of networking that it required, 50% of the internet out there is occupied with video. So, because of that, half the size. Yeah, so who is your average client? Are you mainly working with large enterprise, smaller startups, a little bit of both. Who is your average client or customer? Traditionally, larger enterprises.

But as of a year ago, we launched what is called Beamer Cloud. So we have a cloud service available on AWS and OCI, Oracle Cloud infrastructure. And that means that everybody, you know, can use it with a very low friction. You open an account with your email and, you know, you can start working. Very easy. No code. Very cool. Yeah, you got to love no code.

and saving time, right? So the Nvidia Inception program, how has that helped your success so far at Beaver? I don't know where to start. So last year, we've been a part of the Inception program, and we are now graduate of the Inception Program. So at the very beginning, I think, you know, having the envelope of the Inception program was huge for Beamer because you are actually coming to a place where everybody comes to see what's new. And it is pretty much hosted by Nvidia. So everybody comes to see Nvidia and what's new. And then you're right there.

So the opportunity is mind-blowing. So that's the initial benefit. But also, you know, helping us to take the word out there. And helping us with introduction to prospective customers. Okay, Nvidia is an amazing ecosystem. It works like a huge startup. So so many opportunities are coming our way and every inception member way. And this is amazing.

And now, you know, being a graduate of the inception program, you know, we are also benefiting, you know, from, for example, being, getting additional exposure for inception when they are now offering, you know, all of their partners to get promoted if we are offering discounts to newcomers to the platform. So there was a platform launch just to date. And we're part of that announcement. yet another big thumbs up to Inception. Love it. All right. And last question here, if someone listening out there is like, oh, wait, I think I need Beamer, how do you convince them?

What's your kind of one sentence pitch? If you have a lot of video and you want it to move faster for one place to another, if you want to save on your cost, if you want to have better user experience, and marry that with AI, that's Beamer running on Nvidia GPUs. All right. it. So if you want to see more of Beamer, let us know. All right. And here we are with another startup of that. All right. What are you guys thinking so far? So that is three down. We have five more to go. So for our live stream audience, if you joined us halfway through, we have eight, our awesome

eight group in the Inception Games. You're hearing their quick pitches. We are three down. We have five more to go. Y'all, each time like, obviously I did these interviews, about a week and a half ago, the GTC show. And now I'm listening back to them and I'm like, oh, wait, I'm thinking of so many amazing questions that I should have followed up on. So two are going to move on to the finals. So make sure to get your vote in and also get your questions in for these companies as well, hoping to pull out another quick interview for our two finalists.

Enough with that. Let's go ahead and listen to our next round of startups because I'm switching tabs here. Doing this live is always tricky, y'all. So let me know if you can hear our second video here, if you could, y'all. Here we go. All right. Let's now talk to our next inception startup. This is Pliops.

Tony, tell us about Pliops. Absolutely. Pliops is a solution accelerator for JNAI applications, very complimentary to help people maximize what they can get out of their GPUs and their investments in VyDIA GPUs. All right. So tell me about who is your client? or customer, your average client or customer. Yes.

Anybody who's putting infrastructure together using GPUs and is looking to maximize what they can get out of those GPUs and overall lower their optics. So like tell me maybe you know, you can pick out one actual customer, but you know, what is kind of the before and after benefit? You know, are these companies that have mountains of data and they're just trying to figure out what to do with it or what does it actually look like before they come to you and then after they come to you. Absolutely. So as Jensen mentioned, the biggest driver of data nowadays is after the model is trained, how do you want to extract data from it and interact with it? So that's

creating a ton of data that is not normally saved and we help do that. And what does that do is allows these extra cycles as a result of these savings to be used to serve new users and new new applications. So precisely, that's the reason if people are deploying PliOS. What do you think, you know, so far? Well, first of all, how long has PlyOps been in business? Pliops has been around over five years. And we have been into Gen AI business in the last year and a half, two years. So we've really accelerated that program. It was a perfect match with our IP4 IP, which is also a match with what NVIDIA is envisioned. for your data sets.

So, yeah, even let's talk about that. So, you know, generative AI, you know, our audience, that's really what they care about. How has generative AI changed what you do at Plymouths? Completely 100%. Just like it is changing the world now and it's changing the data centers, how people are deploying gear in their data center, how what people are employing. All of that is a very reflection of that. We have completely changed our product.

roadmap to match what Gen. AI means. And so that means being very much aligned with what GPUs are doing, which are becoming the center of the compute now in our world, and all the good things that are doing they're doing now, but really just the start of what they will be doing more, which is mind-blowing. So, you know, talk a little bit about the Nvidia Inception program. How has that helped, you know, what you've been able to accomplish so far with Play-Ops? Well, for one thing, we're very thankful for Invidio to giving us a space here and letting us our ship showcase our solutions. It's amazing to be part of this community. This show has just been so amazing over years, but especially now with where we are with the AI era.

So the fact that we are here and we can interact with participants, solution providers, potential customers, and really ordinary people that are, their lives are impacted by what you. and video is doing is been tremendous. What would you say is the biggest problem right now for, you know, everyday businesses that Plyop solves, right? If you had to say, here's our number one solution that we provide. What is that? Not for you to having to hire PhDs to figure out how AI works. A simple plug-in play solution that is, that will give you the boost that you need

and avoids having to have the kinds of services that are required. Unfortunately, now to bring people up to where they need to be with day. What is next for pliops? One of those next big problems that you're hoping to solve, right? So maybe I come back in a year and then ask you, what's that big next problem that you're looking to solve for your customers? Right. So right now, we're showcasing maybe.

two Gen. AI applications. I think next time, next year this time, we would be showing 10 different J.I applications and we will be offering it as a service in multiple classes. Adobe just introduced an entirely new way to create, bringing the power and precision of its creative suite into one conversational experience. Meet Firefly AI Assistant, now live in the Adobe Firefly app, the all-in-one creative AI studio. Powered by Adobe's creative agent, Firefly AI assistant lets you start with your vision, just describe what you want, and shape the outcome as it takes form with the assistant. The assistant orchestrates multi-step workflows, drawing on 60 plus pro-grade tools across Adobe Creative Cloud apps, including Photoshop, Illustrator, Premiere, Lightroom Express, and more to help bring your ideas to life. You can also get started with creative skills, a growing library of pre-built workflows for common creative tasks, like batch editing.

photos, creating mood boards, portrait retouching, and creating social variations. Every step the assistant takes is visible so you can refine, redirect, or take over at any time. You stay in the driver's seat as the creative director. Adobe Firefly AI assistant now in public beta. See it today at firefly.adobie.com. All right. Our last question for you, for all of our listeners out there, maybe you piqued their interest, caught their attention. why should they, you know, work with you or engage with plyups?

Simply because we improve your dollar per token cost, we improve your margins. We bring, we actually maintain more money out of your AI operations and your GPU operations. And so that's really what's driving things. And so if you come to us, we'll help you make your Gen. AI data center better for you. All right. Sounds good. Tony, thank you so much for introducing.

for introducing us to playoffs. And make sure if you want to see more out of the playoffs team, let us know both in the comments and in the newsletter. And here we go. We're going to go into our next one. All right. I'm loving this so far, y'all. That was plyups.

And here we go with our next. Got it. All right. So I am here with Billy from Glea Cloud. Billy, tell us what Glea Cloud is. So we're a video surface impulses. And we're doing, like, automizing the video. the ad creation and mostly we serve late adapters within sort of like think like government sectors

and your regional let's say tourist sectors or a little bit of i'd say small businesses which they're selling physical objects and we help them create automised ads being 15 to 30 second youtube pre-rolls all the ones you get in let's say news ads slots stuff like that okay so you Yeah, and I know we kind of have it going on here in the background. So, you know, this is for, you know, companies that maybe don't have access to huge creative teams. So you're using generative AI video to help them create ads online. Is that right? Absolutely.

We actually started pretty early on. I think it's around 2016. Oh, wow. Okay. Yeah, I think like GVG2 times. So ad design, we have to make a lot more infrastructure. So like to get around the little like, and AI that was going on at the time.

And we actually did our own video, sort of like Renner Engine, and a lot of the work at the time was figuring out we write this. Right. So, yeah, how has the product changed? So first of all, 2016, the very early days and love it. I mean, how has the product changed over the years as generative AI just gets more and more power?

Yeah, it's definitely having a great impact on the content side. Like maybe just a precursor. Because so like our time base are less sort of like knowledgeable on more like technical side of things. So we don't really have a high standard. Not to say it's bad. They're looking at our videos more like a product. They just boost their selves. Sure.

But within the time, we're also, we're trying to make ourselves like really like ahead of a curve. We have. So like a really tiny task force. So like staying ahead of like what's new and looking at what the name of. So what you're seeing if you can see on the big screen. right there is one of our experiments. So talking about Gen. AI, we're trying to leverage what's happening,

like what's so exciting. We're just announced yesterday about like cosmos and how we can actually leverage the controls for like 3D environments. The models actually understand three scenes that we can retain, let's say, camera controls. We can initiate camera control within the first stage. You can output them like 3D accurate video.

And we put it into video to video, models that would retain that information. And we can actually create more like live action looking from scratch. This is where we will have. Oh, that's awesome. So real quick, I know we kind of touched on this, but who is your average customer? And if you had to say it in like one sentence, what is that problem that you solve? That's a big.

So average customer, I think legacy media. I think it's a really good one. And they want to boost their, let's say traffic through their articles. So we have finding out with some publishers. and it would create automatic video for them. And big part is their engagement rate, also there's also tourism sectors within governments. Those we have a really great case study,

which ended up like boosting their CTR is for, I think, four times. Okay. Wow. Okay, so real quick, the Inception program from NVIDIA, how has that helped your success? It has been really helpful in terms of the resources we're getting from, I think we actually started contacted by NVIDA one, by two, the FBrials from NVIDIA,

and it told us about this program. And the big part is attending this and connecting with so many other startups and then sharing a lot of it. Because like both of us are no more techies or kind of guys yet. So the big part of us is just like sharing experiences, working on this kind of like all over the little places like in New Clams. Okay. And then if someone in our audience, if they heard what you said, they're like, that's us, we need this.

What would you say to them? Why do they need Gleic Cloud? I guess maybe AI could be a little bit intimidating for maybe someone who's looking into this kind of product. But think of us as an agency. As an agency was focusing on, let's say, the volume of that video you can create within a short time. Also think of the cost. We're genuinely operating within one thing. of the cost of a normal, like, human-led ages. All right.

Great. Thank you, Billy. So if you want more from Glee Cloud, make sure to let us know. All right. A couple more. Here we go. Give me the little free to one, Amy. Go on.

Also, I have to shout out, you know, Amy from Nvidia, help me film all these. So if you hear me say, you know, if you hear someone say go, shout out, Amy. And thanks, thanks Danny as well for helping us, you know, hunt down some of these startups. All right. Here we go. Contextual AI. All right. So here we are with our next startup contextual AI.

John, tell us a little bit about contextual AI. Yeah, contextual AI. We're the world leaders in RAG. So we help large enterprises and fast-growing teams build specialized rag agents for knowledge-intensive tasks. So anyone who's building RAG should be looking at contextual AI. Our CEO, co-invented RAG at Meta, and then left meta to start contextual AI. All right, that's awesome.

You just hit in all the buzzwords people are talking about, right? You know, agents, rag. So maybe just explain for maybe some more non-technical people, you know, why do they need retrieval augmented generation in their company? And then how do you all make that happen? Yeah, yeah. At a simple level, you know, we want to have LLMs, have access to current relevant information. And of course, a major differentiator in the enterprise or for any business is their data.

So being able to connect that data to LLMs and get very grounded responses, that's where we excel. And these can be very complex tasks like we work with Qualcomm for their support engineering team, other large enterprise tasks. And we also work on both structured and unstructured data, which is actually a really hard problem. We've taken the top of the text SQL benchmark recently. Yeah, that's huge, right? Being able to make use of both structured and unstructured data.

Tell me a little bit, who are your average customers? Is it just enterprise clients? Is it more medium size? Like, who all do you serve? Yeah, so we typically work with large companies or even fast-growing teams. We do have a free trial offer. So if you want, you can do a free trial here and scan that QR code and get started and try out our component APIs or even our full platform. You can do a 30-day free trial.

And so we really try to make it accessible for developers, but also working with those large enterprise teams. We have forward-deployed engineers that could help teams get their rag projects into productions. That's really where we come into play is getting that rag project into production. If you're struggling with quality, then we don't want to talk to you. So you kind of, you know, answer this in. one way, but if you were to say directly, what is the one biggest problem that you all solve? What would that be? Big a problem we solved.

I mean, I think RAG is a very big problem. So I hope that we're solving that problem for any team that's thinking about RAG. But then as you're starting to think about agents, you know, moving towards more agentic experiences, you want to have knowledge at the core of that agent experience. That's where we can really come into play as well. So it's connecting that data, connecting that, you know, valuable resource in your organization to your LLMs. And I think the other thing

that often gets overlooked with contextual is we actually tune based on feedback and based on ongoing kind of behaviors. So your model's going to get better. Your agents are going to get better over time. So it's not just a static thing. It's something that's going to improve over time.

And I like to think about that is your institutional knowledge is being re-encoded back into the AI. So that's how I would talk about it. But I think there's a lot of value for anyone who's thinking about RAG or agents. Speaking of improving over time, what if we're having the same conversation

next year, what's that next big thing that you all are looking to solve or improve upon? Yeah, I think for for next year, I think, you know, this is the year of agents. So we're very focused on 2025 being the year of agents. So I think that will be our focus and really thinking about how RAG works relative to this agentic fusion we're all heading to. All right, real quick. We're at the inception kind of pavilion here. What has the inception program from Nvidia meant to contextual AI?

Yeah, it's been huge. I mean, it's given us presence here in the pavilion. So, you know, this is an amazing group of care companies to be with. Additionally, being able to market through the NVIDIA team and presence on the blog. So, you know, for us, we just went GA in January. So this has been a huge kind of boost to our ability to go to market. And that's the team I work on. So it's been wonderful having NVIDIA as a partner and working with them as part of the inception program.

Awesome. Last question. If you caught someone's attention, what's your pitch to them? Why do they need to check you out? where other AIs are like your intern, we're like your best analyst or your best researcher. So that's where the knowledge intensive tasks come into play. And that's where we really work on very domain specific knowledge for hard problems. So as my CEO would say, we want you to be ambitious.

Think about those hard problems. Those really high ROI problems that you want to solve with AI and bring those problems to us. That's where we're going to excel. All right. Thank you so much, John. So if you want to see more of contextual AI, let us know. Thanks. Get ready for the next one.

All right. y'all six down we have two more to go again only two are going to move on to the final so you can vote by leaving a hashtag in the name of the company in the live stream on lincoln or on youtube as well as vote on vote in our newsletter for friday april fourth's newsletter all right here we go our last two. Again, this is our awesome eight of the Inception games. Only two are going to move on and they're going to answer more of your questions. And we're going to determine one winner next week. All right, here we go. We have two last pitches. Some great ones here. Again, live stream audience, if you could, let me know if we can hear the audio on this last group. Two more great

inception startups. Here we go. All right. Now I am here with Jack from Democratize. Jack, can you tell us a little bit about your company? Okay. So, Demargetyce is actually also an AI company, but the A is seen for apparel intelligence. So we do have provided body scanning and also text out AI to turn body, human body, and also the text out into digital things. And then we use these digital trees to create your personalized arrow.

And right now we focus on purple people. So it helps all the professional outlets to create really precise and tailor-made clothes for them to enhance their sports provosturbiness. Very cool. I love it. So aside from maybe professional athletes, who are some of your other customers or clients for democratized? So actually, this is the new topic that we have. Previously, we are a tech company, fashion tech company.

So most of our customer comes from fashion brand or like fashion supply chains. So our bigger customers like under the network, They use our textile digital solutions to design all the material they have and use this material to the digital design workflow. So previously we would have customer brand fashion brands from high chains like the apparel surprise or text on the price. And right now for this new project, we are actually working with a cycling fashion athletes. So we are making the very tailor-made and very precise. site in Jersey for this.

This is very cool. What would you say is the biggest problem that democratize solves? So we try to kind of re-engineer this imperial ecosystem, right? Because right now, because of fascination, and people love new things. And so it's actually caused the over-profession issue

in this industry. So it's pretty, I don't know, this is a huge problem, but no one really wanna address this. So by leveraging AI and also the motivation, we have, we try to kind of re-engineer this process. So if you are at the consumer, if you say, all right, if you place older,

and they can get the Miller made close within five days. Why don't you do that? Because you can get, you don't need to care about any sizing. You just tell me, you know, you want to slim fit, you want to lose fit, and then I tell me it with you. So if we can kind of build this process, then we can, can revert the whole ecosystem, right? You decide you're not to buy this close and we can't make for you and then we

should be it to you. So we don't do the production up to be made to reduce all the production issues. So yeah, it's getting loud in here in the Inception program. I'm not to open, but maybe real quick, tell me a little bit about what the Invidia Inception program has meant to your company so far. I think anything that they are looking forward. a lot of AI new startup, and then we are pretty, as you understand, we are pretty focused on the fashion technology. So it's pretty neat for this, and they like to this kind of vertical end-to-end solution. So I think they, when they listen to our pitch, and they are really like

in this area, and also it's also related to, like, sustainable problems. So I think they gave us a lot of resources to use their SDK and software to help build this digital twins and air bottles. So outside of maybe is there a next iteration are you looking to bring this concept to bigger or wider markets in the future? Yeah. So

as I mentioned, we just pivot to this B2C direction. So we are going to launch our first ProVoC concept. So we're going to release our first collections by end of this year. And then next year we are moving forward to like extend to different kind of closed types. Like

right now we've got cycling, maybe next Next one will be the running and yeah, so greatly to expand the old collections. Very cool. All right. So if someone just heard you and they're like, wait, I need democratize, what's your quick pitch to get them on board? Good question. All right. So I think we just try to introduce a new way to purchase as a close. So we give consumer a lot of choice to be more sustainable and choose the right fit for your own. So you are the brain, not you try to fit into the brand's clothes, but you are the brain. So we designed for you. You can decide whatever you want to wear and you don't need to care about all the size and problems.

All right. Jack, thank you. If you want more from democratize, let us know. All right. And our last one. Got me. All right. So we are here with Ena from Lumex. Tell us a little bit about Alunex. You know Max is self-service access for data analytics for business users and enterprise. Think about banks, pharma, financial services. All of them would like to have business intelligence as their daily practice. Intermax enabled that in less than seven days with 80% savings on the top. Okay, so it's about token optimization or like what's the actual, what's the benefit?

It's just more efficient tokenization. Like, walk us through what that looks like. The most exciting benefit about Illumax is actually trust. So business users from our perspective have hard time to actually understand that Jentic answers, especially when it's black box, and make decisions based on them. So what Iromax does is also handle the quality of underlying data, make sure that data which is going into Argentic is of high quality and compatibility from one side. On the other side, we provide the full explainability about the answers.

So any users can understand how the question was interfered, which data is mapped to and what logic is implemented. This is like ultimate from the blade box. Awesome. Walk me through. Who is your average customer or client for your platform? The buyer would be chief data officer, Fortune 100 company.

Also, lately we see lots of giants from Silicon Valley. Super excited about the solution as well. But the users are an average support center on marketing or a product analyst. So basically any business user in enterprise or any organization really can ask that data-related question and have explained and hallucination-free answer. That's great. So essentially, it's just providing more confidence in the answers you get out of agetic systems.

It's full-sex solutions. So we handle data quality, we handle governance and trust, and we also handle interaction in this system that you like to use. So it makes it not a new interface. We all embedded into your CRM. your power, VI, your Slack or your teams, whenever you already are, and we provide this answer to be able to. Very cool. Talk a little bit about the

Nvidia Inception program. How has this helped your organization? Invidian inception program was exciting so far. So we recently had PR just yesterday about how we use the Indian names and other underlying technologies to basically scale for those enterprises as we serve.

Right now, we have systems which, There's hundreds of thousands of tables and millions of ways, and there's nothing like Nvidia to enable us to have seven days set up other than running for those massive companies. Awesome. And then, so if one of our listeners or viewers, if they heard that and they're like, wait, I need this exact thing. What's your quick pitch to get them to sign up for your platform?

If you have silent data sources in your system, you have your SAP, your BI tools, your Azure, And you would like to have a GENTI that you can trust, contact Elymex, and we'll make you happen for 80% less cost in seven days. Awesome. Great pitch. All right. So if you want more of a Lumex, let us know.

And let's dive into another startup. Wow. All right. So that's it. That is our awesome eight. That was wild, y'all. That was a lot of fantastic startups. Like I wish, in all honesty, I wish I had more time, you know, at the Nvidia GTC conference.

If you've been listening to the show, you know I've already had like six interviews with some of the brightest minds in AI, both from Nvidia and other companies. So maybe next year, if you all like this format, if you, you know, heard something you liked, maybe we'll expand the field from 8 to 16 for the Inception games. But that is a wrap, y'all. So a couple of questions that kept coming up. I'm going to go through all of the questions that came in from our live stream audience. And for our two finalists, I'm going to make sure to ask some variations of those questions to our two finals that are going to come back for another round, the final round. So here we are. We have our awesome eight of the Inception games.

Yes, a couple of questions that came up. We are going to have a recap in the newsletter. Okay, so, you know, in case you're sitting there jotting down, you know, notes with your pencil on what they all do, we got that. That's what the newsletters for. That's why I always say we learn on the podcast and the live stream and we leverage it in the newsletter, right, to grow our companies and our career. So, you know, maybe the startup that you like most from the inception program isn't going to be the one that makes it to the finals. And that's okay, because we're going to have links to all of the startups in the newsletters. You can go find out.

maybe they're going to solve a huge pain point for your company. So as a reminder, you know, because a lot of people are like, hey, I need a quick recap at the end. All right. I'm not going to repitch them. But as a reminder, we had deep checks. We had Expander AI. We had Beamer. We had PlyOps.

Glea Cloud contextual AI democratize in Illumex. So make sure you get two votes. Use them wisely. So maybe you're torn between two. You can vote for one in the live stream and then a different one in the newsletter. You can vote for the same company once on the live stream, once in the newsletter as well. But we're only counting, you know, one vote on the live stream and, you know, on the newsletter. You can only vote once anyways.

So get them in now if you, you know, kind of were sitting on your vote and waiting until the very end, again, for the podcast audience. Maybe you want to vote twice. Maybe you don't just want to vote once in the newsletter. We always put the link to the live stream in the show notes for today's podcast. You don't have a lot of time. We are saying voting ends Sunday night at 11.59 p.m. Central Standard time. So you got a little bit more than 48 hours until we go from our awesome eight in the Inception games to our final two. And we're going to be bringing them to you all to answer your questions.

so I can't wait. So I hope this was a fun one, y'all. This is the first time we've done something like this. Actually, it was last year at GTC when I partnered with Nvidia. I went through the inception area of the GTC conference and I'm like, wait, our audience needs to hear more about some of these startups because I can tell you, I can tell you already. There's been a lot of startups in the inception program that have gone on to become literal household names.

Like as an example, did you know 11 Labs, the leader in text to speech? They're in the inception program. Right. So this is where tomorrow's biggest AI players are starting out today. So I can

almost guarantee it a couple years a lot of these, maybe you heard about them for the first time, but a lot of these companies that we just talked about, I think they're going to continue to grow, continue to change how we all do business. So I hope this was a fun one. Again, shout out to our partners at NVIDIA at the Inception program. This was a great one. I love startups. I love AI. And I love,

you know, kind of, you know, this basketball format of, you know, the brackets and the games and all that. So make sure to get your vote in. Go to your everyday AI.com. If you're looking to sign up for the newsletter just to vote, that's where you can do it. So make sure. you go look at today's newsletter April 4th. So thank you so much for tuning in. Hope to see you back later for more everyday AI. Thanks y'all. Meet Firefly AI assistant. Now live in Adobe Firefly, the Allman One Creative AI Studio. Just describe what you want to create in your own words and the assistant handles the rest, orchestrating multi-step workflows across Adobe Creative Cloud apps, including Photoshop, Premiere Express, and more in one conversational interface. You direct the outcome

while the assistant accelerates execution. Stand control with the ability to step in and refine at any time. See it today at firefly.adobie.com. And that's a wrap for today's edition of Everyday AI. Thanks for joining us. If you enjoyed this episode, please subscribe and leave us a rating. It helps keep us going. For a little more AI magic, visit Your EverydayAI.com

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