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Unlocking Opportunities with AI Startups: A Dive into NVIDIA's Inception Games
As AI continues to transform industries, startups have become pivotal players in pioneering new solutions. Recently, a dynamic competition known as the "Inception Games" showcased eight promising AI startups from the NVIDIA Inception Program. These companies presented innovative solutions that address specific business challenges, providing invaluable opportunities for decision-makers looking to leverage AI for growth.
From Generative AI to Business Efficiency
The startups featured in the Inception Games offer diverse applications of AI, each tailored to enhance business performance. One standout, DeepChex, focuses on ensuring that generative AI systems function optimally. It provides tools to evaluate machine learning models, ensuring they produce reliable outputs without hallucinations or inaccuracies. Their solutions are particularly beneficial for healthcare, defense, and financial sectors.
Another notable entrant, Expander AI, simplifies the creation of customized AI agents. By enabling organizations to build sophisticated AI workflows without intensive technical expertise, Expander AI accelerates time-to-market for businesses keen on harnessing AI capabilities.
Revolutionizing Video and Data Management
Beamer, an innovator in video optimization, addresses the challenges faced by media-heavy industries. By enhancing video encoding efficiency on NVIDIA GPUs, Beamer reduces video handling costs significantly for sectors like autonomous vehicles and media entertainment.
In a similar vein, PlyOps targets data management inefficiencies, enhancing GPU performance and facilitating better data storage and retrieval processes. Their solutions are invaluable for businesses dealing with large data volumes, helping to optimize operational costs effectively.
Simplifying AI for Creative and Analytical Processes
Startups like Glia Cloud and Contextual AI are transforming how businesses approach creative and analytical tasks. Glia Cloud uses generative AI to automate the creation of video ads, offering a cost-effective solution for companies with limited creative resources.
Contextual AI, on the other hand, specializes in retrieval-augmented generation (RAG), connecting businesses' unique data with LLMs (large language models) to deliver precise and informed outputs. This is essential for enterprises aiming to enhance decision-making based on their proprietary data.
Fashion and Data Analytics: A New AI-Driven Future
Innovations like Democratize and Illumix are redefining their respective domains with AI. Democratize leverages AI to create digital twins for personalized apparel, addressing issues of overproduction in the fashion industry by enabling bespoke clothing production.
Illumix provides an analytics platform that ensures trustworthy and explainable AI-driven insights. Their focus on data quality and integration allows business users to make informed decisions confidently.
Empowering Business Growth with AI Innovation
The diverse applications and solutions presented by these startups underscore the immense potential of AI to solve real-world business problems. The Inception Games serve as a reminder of the innovative spirit thriving within startups today, paving the way for businesses to adopt AI efficiently and effectively. By keeping an eye on such rising companies, decision-makers can identify the right opportunities to enhance business operations and drive future growth.
For businesses aiming to integrate AI, these startups offer not just solutions, but new avenues for achieving operational excellence and competitive edge. Through careful adoption and integration of AI technologies, companies can unlock new possibilities and reach new heights in the ever-evolving business landscape.
Topics Covered in This Episode:
- Inception Games AI Startup Competition
- DeepChex Generative AI Systems Evaluation
- Expander AI's Multi-Agent AI Platform
- Beamer's Video Compression Technology
- PlyOps' GenAI Application Accelerator
- Glia Cloud's Automated Ad Creation
- Contextual AI's Augmented Retrieval System
- Democratize's Custom Apparel Intelligence
- Illumix's Enterprise Data Analytics Platform
Episode Keywords:
NVIDIA inception program, AI startups, generative AI systems, machine learning, LLM based system, generative AI projects, prompt engineering, version comparison, NVIDIA GTC conference, DeepChex, Expander AI, multi AI agents, PYT configurations, video optimization, Beamer, PlyOps, data handling efficiency, Glia Cloud, generative AI video, Contextual AI, retrieval augmented generation, enterprise clients, democratize apparel intelligence, digital twins, textile AI, Lumix, data analytics for enterprises, token optimization, flash fashion, sustainable fashion solutions, professional athletes' apparel, AI integration in business, innovation in GenAI applications, deep learning, AI-driven video solutions, structured and unstructured data, futuristic AI applications, GenAI business adaptation, AI agent advancements, AI trust and explainability, agentic AI.
Podcast Transcript
Jordan Wilson [00:00:16]:
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 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 gonna quickly on today's episode give you eight fast pitches from our awesome eight from NVIDIA inception. And you, dear listener and livestream viewer, are going to decide which one moves on. So, maybe your team is out of the big tournament and they weren't dancing this year in March.
Jordan Wilson [00:01:16]:
Don't worry. Maybe one of your favorite startups is in this very competition. Alright. I'm excited for this one. It's gonna be a fun time. What's going on y'all? My name is Jordan Wilson, and welcome to Everyday AI. This is your daily livestream 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 and our careers. So if that sounds like you, welcome.
Jordan Wilson [00:01:42]:
You're in the right place. We do this every single weekday, you know, on our website, which is where you need to go, youreverydayai.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 are super advanced and, you know, they have great fantastic products and sometimes they're not. But, with the NVIDIA 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 livestream audience, I hope this one's gonna be a lot of fun, but if you are listening to this on the podcast, you can still get in on the voting action.
Jordan Wilson [00:02:43]:
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. Alright. So, you're gonna hear who the pitch is from. You're going to hear, a little bit about their company, and, you know, I'm gonna 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.
Jordan Wilson [00:03:17]:
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 audience is. I need to hear from you. Alright? So you can come and vote in two different ways. So pay attention and, hey, live stream audience, you know, doctor doctor Scott, Michael, good to see you back. Big bogey, Brian, everyone else, Sandra. You you actually get two votes.
Jordan Wilson [00:03:54]:
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. Alright. So if after all eight, you're like, yes.
Jordan Wilson [00:04:24]:
Deep checks is the one I wanna hear more of. 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 gonna 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, the the startup scene, very closely. When I was at the NVIDIA GTC, Inception pavilion, yes, they have so many, startups in the, NVIDIA inception program that they had their own essentially dedicated expo hall. I had only heard of maybe 10% of them.
Jordan Wilson [00:05:04]:
Right? And as I'm going around doing these interviews, I'm like, wait. This this software is amazing. This startup is amazing. It's gonna 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.
Jordan Wilson [00:05:23]:
That's number one. Number two, you can vote in our newsletter. Okay? So if you haven't already, go to youreverydayai.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.youreverydayai.com. So in today's newsletter for Friday, April 4, we're gonna have all eight, our 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.
Jordan Wilson [00:06:07]:
And we're gonna 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 gonna give them your questions as well because the two that move on, we're probably gonna do a quick, I don't know, like eight minute, secondary pitch. Right? So all these questions that you have still get them in. Alright? So, you know, as an example, deep checks is up here first. It would be helpful for me, livestream audience, if you say, hey.
Jordan Wilson [00:06:43]:
What is, you know, deep checks, you know, ideal client or, you know, if you wanna know how much does, you you know, deep checks cost or whatever. Right? Get those questions in as well, but that's why for your vote, use the hashtag. Alright? It's gonna 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. Alright. 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. Alright? So I'm gonna, like, essentially be sitting back, listening to these pitches again as well at the same time
David Twizer [00:07:22]:
as you. But I wanna make sure that you all can hear them. Alright? So, here here we go. Let me know, livestream audience, if you can hear this audio, and then we're gonna start it over. Don't worry.
Jordan Wilson [00:07:35]:
Got it. Alright. So I am here with Philip from DeepChex, another NVIDIA inception company. Philip, tell us a little bit about DeepChex.
Philip Tannor [00:07:45]:
Hi there. So thank you. First of all, thanks for having me. I'm
Jordan Wilson [00:07:48]:
Alright. Live stream audience. I just started. Played about ten seconds. Yeah. Alright. Alright. So I am Hopefully, Phil, hopefully y'all can hear.
Jordan Wilson [00:07:55]:
Alright. Thank you, thank you to a couple of our, YouTube audience. This is also how I find out, which live stream platform is the best because, you know, we we we just got confirmation from our live stream audience, on YouTube, but I'm guessing the, there we go. Alright. Our our LinkedIn audience, just just chimed in as well. Alright. Here we go. I'm excited.
Jordan Wilson [00:08:17]:
So, we're gonna hear first, three quick three to five minute pitches from our first group. Then I'm gonna come back on, you you know, ask questions of you all. Make sure to see what you guys are liking, what you're not, and then we're gonna get the second group on. Alright. Here we go. I'm excited. Let's kick off the inception games. You're gonna hear real quick, quick pitches from eight amazing, companies that were at the Nvidia Inception Pavilion.
Jordan Wilson [00:08:48]:
Here we go, Inception games round one. Let's get it.
David Twizer [00:08:55]:
Go.
Jordan Wilson [00:08:56]:
Alright. So I am here with Philip from DeepChex, another NVIDIA inception company. Philip, tell us a little bit about DeepChex.
Philip Tannor [00:09:04]:
Hi there. So thank you. First of all, thanks for having me. I'm, Philip, cofounder, CEO of DeepChex at DeepChex. 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 LMM based system, it's really hard to know how they're doing. There's no like, in the traditional machine learning systems, there's something called a test set. You heard of a test set before? No test set.
Philip Tannor [00:09:26]:
Nothing of this 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 prompt better. You're changing the setting of your React system. You're having a better you know, you wanna change the different model from, let's say, Gemini to, Sonnet 3.5. Then you don't know if you're doing better or less because that's 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.
Philip Tannor [00:10:02]:
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 don't we're kind of starting off by saying, did the system work or did it not work? Did it have a hallucination? Did it give irrelevant information? Did you know what you're talking to, you know, you're talking to Chachi Buffini. It says, I'm sorry. I'm an AI chatbot. Yes. 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.
Philip Tannor [00:10:40]:
So it's really end to end evaluation of, generative value systems.
Jordan Wilson [00:10:44]:
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 Deepgrams? So first of all, any company that's building, generative system, any company that's using OpenAI in the background,
Philip Tannor [00:11:01]:
for for some sort of textual, interface, they're a potential client. The three verticals I'd say that we're working the the closest with are health care, government, including, defense, and financial institutions. So those are, I'd say, the three largest verticals, but there's really a long tail. Any startup could be using it. Any any, Fortune 500 company, could be using it. And we and we're proud to say we started with startups, and now now more of our, new business is coming in from the some of the larger enterprises.
Jordan Wilson [00:11:30]:
Very cool. So you kind of told us, some of the features. What are the benefits? Right? What do, you know, your customers or clients have to gain, by using DeepChats versus if they didn't use it? So I think the number one benefit you're getting is a higher probability of success for
Philip Tannor [00:11:46]:
the entire Gen AI project. And Gen AI projects today, when you start them, their chances of success are well under fifty percent. And I think just by having something of our sort where you can actually iterate quickly, check the next versions, understand what we call the AI progress. Are you actually improving your system? So by having that in place, it's kind of like test driven development. You you're raising the chances of deploying. And then number the second best benefit is you're improving the timeline. You're gonna release more projects and so forth. So there's there's no real way around it.
Philip Tannor [00:12:18]:
You it's just the question, are you gonna be doing evaluation with, you know, you know, hand labeling using CSVs, sending them by Slack or Teams, or or you're gonna have, like, our 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 and production. We have a whole flow of end testing of checking, you know, kind of checking of all the different, you know, risks that happen, within these types of systems for the malicious prompts. So we kinda try to give everything you need in one place. But if I have to if I have to talk about the the number one benefit, it's actually shipping, raising the ship of shipping and shipping faster.
Jordan Wilson [00:13:01]:
Perfect. And then real quick, what value has the NVIDIA inception program, provided to your organization?
Philip Tannor [00:13:09]:
So first of all, the NVIDIA Inception team is amazing. I I really don't know what it's like in other hubs, but we're we have a lot of working with the Israel with the Israeli hub, and it's really in almost every aspect aspect, we're getting some sort of assistance from them. So they'll give us feedback. And even if they see an event of ours that's not related, they'll give me feedback. They'll say, hey. You should have changed this. Join blog post, working together on, on, integrating within, you know, NAMS, within the Nexmo ecosystem, helping us figure out how to reach out to, let's say, new verticals like the telco, sectors, one that we had less experience with, and they really give us a lot of know how, the specific connections. So at the beginning, actually, when we signed up, it was just like, oh, cool.
Philip Tannor [00:13:45]:
You know, some other program we could join, but it turned out to be a a really good choice.
Jordan Wilson [00:13:49]:
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 to
Jac Hsieh [00:13:57]:
get them to sign up?
Philip Tannor [00:13:58]:
I think the 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 LMM application, then you can see which one's better. That's usually the first hook, not always. But the the 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 set, like in the classic machine learning. It's really once once you try it, it's really hard to go back
Jordan Wilson [00:14:26]:
to having this kind of voodoo and kind of manual, CSV thing. So Awesome. Philip, thank you. If you wanna hear more from DeepChux, let us know. Alright. Here we go with our next startup. Yep. Alright.
Jordan Wilson [00:14:44]:
I am here with David from Expander AI. Davis, tell tell us a little bit about Expander AI.
David Twizer [00:14:51]:
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 AI agent.
Jordan Wilson [00:15:00]:
So I mean I mean, what's this best business use case, for expander AI? Because I know, you know, agentic AI is is all the buzz right now. Right? So what's kind of, easy to understand use case, for that, scenario?
David Twizer [00:15:16]:
So customers are now looking to build custom AI agents instead of buying AI agents that are already configured. So, for example, support AI agent, the tech scale of a support escalation and, answering instead of you to a ticket, that's only that now customers are now building. So instead of them building and investing in fine tuning and connecting to different AI, application, we give them an, a a platform that can they can easily, choose the connectors and design the workflow that they want to build and then run it. Okay.
Jordan Wilson [00:15:48]:
So is it I mean, is this for, you know, technical clients, nontechnical? Do you work with big enterprises, startups? Like, walk us through kinda like what your average client looks like and also kind of the benefit that they get.
David Twizer [00:16:01]:
So the average client is, with the developer team that, would more than 100 developer team. So, we're talking about small medium enterprises, but we're also working with startups. We're gonna release open source really soon. For any developer that are building AI agents, they can just use the platform. But our EDL customer is on
Jordan Wilson [00:16:20]:
someone that is focusing on building internally 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?
David Twizer [00:16:40]:
So it's engineering time. Right now, building AI agents is so expensive and requires a lot of skills that non not many a lot 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 in one platform, and then they're losing the other capabilities of the second platform. So we we give them a platform that, can automatically, use only good words of all the platforms. So we work with NVIDIA, we work with EnPrope, we work with, OpenAI, and developers don't need to compromise about how they choose the technology. So, organization that choose to use expander, 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.
Jordan Wilson [00:17:33]:
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?
David Twizer [00:17:47]:
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 con our augment technologies to generate connectors. Center by this, like, thousand systems. Yeah. So we built the technology that generates connectors to private APIs, and the inception program help us to do benchmark, with NVIDIA, experts.
David Twizer [00:18:11]:
So we do the we did the benchmark, with NVIDIA and Teril, and then they benchmark our connectors and they publish public facing, article about how good the connector is. And they prove that using expander connectors, AI can do the job three times better without its spender. So that's like a game changer for us as a startup. It's an acknowledge from from Nvidia that he do something that with the with the value, and it's all thanks to the inception partner.
Jordan Wilson [00:18:40]:
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?
David Twizer [00:18:52]:
Yeah. So, now we're gonna talk about the multi AI agent that are doing multistad. That's like the next big thing. Everyone knows or should should be able to know how to build the 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. Mhmm. And that's a very noble 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, very complex task that goes into human level, complexity. Right.
David Twizer [00:19:36]:
So that's like, the the the very focused area that we are focusing on and that we are reading from customers, but we want they want a little choice to solve.
Jordan Wilson [00:19:44]:
Alright. Last 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?
David Twizer [00:19:53]:
Yeah. Sure. So any organization that they have internal systems, why is 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 frameworks that you have currently available in the market. So instead of going all in one framework, langchain, core AI, Nvidia, often AI in Tropic, we give you the ability to, to really use all the frameworks, in your private API and design a step machine that works close on those frame. So this, this technology allows developers to really focus on business challenges instead of technical challenges. And you as a business leader, not don't need to do, investment in one specific, framework. You can enjoy all of them.
Jordan Wilson [00:20:43]:
Alright. David, thank you so much. So if you wanna see more from Expander, let us know. And now let's take a look at another startup. Yeah. These are these are some good ones. Yeah. Alright.
Jordan Wilson [00:21:01]:
We have our next NVIDIA inception startup. We have, Sharone here from Beamer. Can you tell us
Sharon Carmel [00:21:06]:
a little bit about Beamer? Sure. Beamer built a technology to optimize video at large scale and deploys on Nvidia GPU's. So we gain acceleration, for video encoding from Nvidia GPU, a component called NVENC, 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 GPU's at our scale.
Jordan Wilson [00:21:43]:
Okay. What would you say is the one biggest problem that you solve for your, customers or clients?
Sharon Carmel [00:21:50]:
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 tonnage, with the amount of videos. So think about that cost, half size.
Jordan Wilson [00:22:04]:
Okay.
Sharon Carmel [00:22:05]:
Okay. You know, that's, that's a huge benefit.
Jordan Wilson [00:22:07]:
Okay. So, who is your average, customer or client at Beamer?
Sharon Carmel [00:22:12]:
So we are approaching three different markets. One of them is the traditional markets that we've been there forever. It's 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 to train an average, model for autonomous vehicles, you need a hundred petabytes of video. Mhmm. Nothing 50 petabytes.
Sharon Carmel [00:22:43]:
Okay. That's a big deal. Yeah. Right? With user generated content, we're talking about about eighty to a hundred many years of video captured every day. These are hundreds of millions of video clicks, short video clips. Thinking about that, you know, distribution, the amount of networking that it requires, 50% of the internet out there is occupied with video. Mhmm. So Think about that half the size.
Jordan Wilson [00:23:10]:
Yeah. So, so who is your, your average client? And then are you mainly working with large enterprise, smaller startups, a little bit of both? Who is your your average, client
Sharon Carmel [00:23:19]:
or customer? Traditionally, larger enterprises. But, as of a year ago, we launched what is called BeamerCloud. So we have a cloud service available on the AWS and post CI, Oracle Cloud, the infrastructure. And that means that everybody, you know, can 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.
Jordan Wilson [00:23:43]:
Very cool. You yeah. You gotta love no code and and saving time. Right? So the NVIDIA inception program, how has that helped your success so far at Beaver? Cool.
Sharon Carmel [00:23:54]:
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 being here because, you are actually coming to a place where everybody comes to see what's new. Yeah. And it is pretty much, you know, 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.
Sharon Carmel [00:24:35]:
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 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 new platform, launched just to date, and we're part of that announcement yet another, you know, big thumbs up to inception.
Jordan Wilson [00:25:15]:
Love it. Alright. And, last question here. If if someone listening out there is like, oh, wait. I think I need Beamer. How do you convince them? What's your kinda one sentence pitch?
Sharon Carmel [00:25:27]:
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.
Jordan Wilson [00:25:41]:
Alright. Love it. So if you wanna see more of Beamer, let us know. Alright? And here we are with another startup.
Jac Hsieh [00:25:48]:
Love that.
Jordan Wilson [00:25:53]:
Alright. What are you guys thinking so far? So that is three down. We have five more to go. So for our livestream 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.
Jordan Wilson [00:26:18]:
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 gonna 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.
Jordan Wilson [00:26:45]:
So let me know if you can hear our second, video here. If you could y'all, here we go. Alright. Let's, now talk to our next inception startup. This is PlyOps. Tony, tell us about PlyOps.
Tony Afshary [00:27:02]:
Absolutely. PlyOps is a solution accelerator for GenAI applications, very complimentary to help people maximize what they can get out of their GPUs and their investments in NVIDIA GPUs.
Jordan Wilson [00:27:13]:
Alright. So tell me about who is your, you know, client or customer, your your average client or customer?
Tony Afshary [00:27:19]:
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 OPEX.
Jordan Wilson [00:27:29]:
So, like, tell me maybe, you know, you can pick out one actual customer, but, you know, what is kind of the the before and after benefit? You know, are these companies that that have mountains of data and 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?
Tony Afshary [00:27:47]:
Absolutely. So, as Jensen mentioned, biggest, driver of data nowadays is after the model is trained, how do you wanna, 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 that's the reason people are deploying PlyOps.
Jordan Wilson [00:28:21]:
What do you think, you know, so far? Well, first of all, how how long has, PlyOps been in business?
Tony Afshary [00:28:27]:
PlyOps, 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 IP or IP, which is also a match with what NVIDIA is envisioning for, your data sets.
Jordan Wilson [00:28:46]:
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 Plitelops?
Tony Afshary [00:28:56]:
Completely. %. Just like it had is changing the world now and it's changing the data centers, how people are deploying, gear in their data center, how what peep people are employing. All of that is, we are very, a reflection of that. We have completely changed our, product road map 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 do they're doing now, but really just the start of what they will be doing more, which is mind blowing.
Jordan Wilson [00:29:35]:
So, you 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 playoffs? Well, for one thing, we're very thankful for NVIDIA to giving us
Tony Afshary [00:29:48]:
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, with participants, solution providers, potential customers, and really ordinary people that are their lives are impacted by by what NVIDIA is doing is been instrumental.
Jordan Wilson [00:30:17]:
What would you say is the biggest problem right now for, you know, everyday businesses that PlyOps solves. Right? If you had to say, here's our number one solution that we provide. What is that?
Tony Afshary [00:30:31]:
Not for you to having to hire PhDs to figure out how AI works. A simple plug and play solution that is, that will give you the boost that you need and avoids having to have the the kinds of, services that, that are required, unfortunately, now to to bring people up to where they need to be with them.
Jordan Wilson [00:30:56]:
What is next for playoffs? What are those next big, problems that you're hoping to solve? Right? And so maybe I come back in a year and then ask you what's that big, you know, next problem that you're looking to solve for your customers. Right.
Tony Afshary [00:31:10]:
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 Gen AI applications. And we will be, offering it as a service in multiple classes.
Jordan Wilson [00:31:25]:
Alright. Our our our last question for you, for all of our listeners out there, maybe you you piqued their their interest, caught their attention. Why should they, you know, work with you or engage with clients?
Tony Afshary [00:31:38]:
Simply because we, improve your dollar per token cost. We improve your margins. We bring we let you 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 GenAI AI data center better for you.
Jordan Wilson [00:32:00]:
Alright. Sounds good, Tony. Thank you so much 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 gonna go into our next one. Alright. I'm loving this so far, y'all.
Jordan Wilson [00:32:17]:
That was playoffs. And here we go with our next. Got it. Alright. So I am here with Billy from Glia Cloud. Billy, tell us what Glia Cloud is.
Billy (Tsung Han) Ho [00:32:26]:
So we're a video surfacing process, and we're we're doing automizing the video app creation. And mostly, we serve late late adapters within so, like, think like golf moon sectors. And your regional, let's say, tourist sectors are a little bit of, I'd say small businesses, which they're selling physical objects, and we help them create automized ads. Mhmm. Being fifteen to thirty second YouTube PROs or the ones you get in, let's say, news, ad slots, stuff like that.
Jordan Wilson [00:33:02]:
Okay. So yeah. And, like and I know we kind of have it going on here in the background. So, you know, this is for, you 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,
Billy (Tsung Han) Ho [00:33:20]:
the early I think it's around 2016. Oh, wow. Okay. Yeah. I think, like, GBT two times. Okay. So at the time, we have to make a lot more infrastructure. So, like, so so I get around the the little, like, gen AI that was going on at the time.
Billy (Tsung Han) Ho [00:33:34]:
And we actually did our own, video sort of, like, render engine. And a lot of the work is there, but it's figuring out how we build this engine.
Jordan Wilson [00:33:44]:
Right. So yeah. Yeah. How how has the product changed? So first of all, 2016, the very early days, I love it. I mean, how has the product changed over the years as generative AI just gets more and more powerful?
Billy (Tsung Han) Ho [00:33:56]:
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 sense. Sure.
Billy (Tsung Han) Ho [00:34:18]:
But within the time, we also we're trying to make ourselves, like, really, like, ahead of a curve. We have Yeah. So, like, a really timely task force. So, like, staying ahead of, like, what's new and looking at what you're doing in the new model. So what you're seeing if, you can see on big screen right there What I've seen is one of our experiments. K. So talking about Gen AI, we're trying to leverage what's happening, like, what's so exciting. We're in just announced yesterday about, like, cosmos and how we can actually leverage the controls for, like, three d environments.
Billy (Tsung Han) Ho [00:34:47]:
The models actually understand three d scenes that we can retain, let's say, camera controls. Sure. We can initiate camera control within the first stage. Right. We can output them, like, three d accurate vehicle. Right. And we put it into video to video models that would retain that information. And we can actually create more like live action looking transcripts.
Billy (Tsung Han) Ho [00:35:10]:
This is where we will head.
Jordan Wilson [00:35:11]:
Oh, that's that's that's awesome. So real quick, I know we kinda 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?
Billy (Tsung Han) Ho [00:35:21]:
That's a big so average customer, I think, legacy media. I think it's a it's a really good one, and they want to boost their, let's say, traffic through their articles. So we have find out with some publishers, and then we 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 of the CTR.
Jordan Wilson [00:35:49]:
Wow. Okay. So real quick, with the inception program from NVIDIA, how has that helped your success?
Billy (Tsung Han) Ho [00:35:57]:
It it has been really helpful in terms of the resources we're getting, from I think we actually started contacted by NVIDIA one invite to the from NVIDIA, and it told us about this program. And a big part is attending this and connecting with so many other startups and then sharing a lot of because, like, both of us are no. No. We're technical. We got a guy in the area. So the big part of us is just, like, sharing experiences, working on this kind of, like, all all over the place. So it's like new clients.
Jordan Wilson [00:36:28]:
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 Glia Cloud?
Billy (Tsung Han) Ho [00:36:38]:
I guess, things maybe AI could be a little bit intimidating for maybe someone who's looking into this this kind of product, but think of us as a agency. As an agency who's 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 third of the cost of a normal, like, human led agents.
Jordan Wilson [00:37:01]:
Alright. Great. Thank you, Billy. So if you want more from GleeCloud, make sure to let us know. Alright. A couple more. You know? Here we go. Give me the little free two one, Amy.
Tony Afshary [00:37:16]:
Come.
Jordan Wilson [00:37:17]:
Also, I have to I have to shout out, you know, Amy from NVIDIA helped 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. Alright. Here we go. Contextual AI. Alright. So here we are with our next startup, Contextual AI. John, tell us a little bit about, Contextual AI.
John Marini [00:37:40]:
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.
Jordan Wilson [00:37:56]:
Alright. That's awesome. You you just hit in all the buzzwords people are talking about. Right? You know, agents, rags. So maybe just explain, for maybe some more, nontechnical people, you know, why do they need retrieval augmented generation, in their company, And then how do you all make that happen?
John Marini [00:38:13]:
Yeah. Yeah. Yeah. At a simple level, you know, we wanna have LLMs have access to current relevant information. And, of course, the 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. 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.
John Marini [00:38:37]:
We've taken the top of the, text to SQL benchmarking recently.
Jordan Wilson [00:38:40]:
Yeah. That's that that's huge. Right? Being able to, you know, make use of, you know, both structured and unstructured data. You know, tell me a little bit. Who are your average, you know, your customers? Is it just enterprise clients? Is it more medium size? Like, who all do you serve?
John Marini [00:38:54]:
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. It's getting that QR code, and get started and try out our component APIs or even our form full platform. You can do a thirty 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 production.
John Marini [00:39:19]:
So that's really where we come into play is getting that RAG project into production. If you're struggling with quality, then we'd be the one to talk to you.
Jordan Wilson [00:39:26]:
So you you 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?
John Marini [00:39:36]:
Biggest problem we solve. I mean, I think RAG is a very big problem. So I 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 wanna have knowledge at the core of that, agent experience. So that's where we can really come into play, as well. So it's it's connecting that data, connecting that, you know, valuable resource in your organization to your your LLMs. And then 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 gonna get better.
John Marini [00:40:08]:
Your agents are gonna get better over time. So it's not just a static thing. It's something that's gonna improve over time. And I like to think about that as you're like, institutional knowledge is being re encoded back into 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 or agents.
Jordan Wilson [00:40:23]:
Speaking of improving over time, what if, you know, if 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?
John Marini [00:40:34]:
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 and really thinking about how RAG works relative to this agentic future we're all heading to. Alright.
Jordan Wilson [00:40:46]:
Real quick. We're at the, inception kind of pavilion here. What has the inception program from NVIDIA, meant to contextual AI?
John Marini [00:40:56]:
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 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.
Jordan Wilson [00:41:20]:
Awesome. Last question. If you caught someone's attention, what's your pitch to them? Why do they need to check you out?
John Marini [00:41:26]:
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, you know, we really work on very domain specific knowledge for our 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 wanna solve with AI and bring those problems to us. That's where we're gonna excel.
Jordan Wilson [00:41:48]:
Alright. Thank you so much, John. So if you wanna see more of contextual AI, let us know. Thanks.
Jac Hsieh [00:41:53]:
Get ready for the next one.
Jordan Wilson [00:41:57]:
Alright, y'all. It's 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 livestream, on LinkedIn or on YouTube, as well as vote on, vote in our newsletter, for, Friday, April newsletter. Alright. Here we go. Our last two. Again, this is our awesome eight of the inception games.
Jordan Wilson [00:42:28]:
Only two are gonna move on, and they're gonna answer more of your questions. And we're gonna determine one winner next week. Alright. Here we go. We have two last pitches, some great ones here. Again, livestream audience. If you could, let me know, if we can hear the audio on this last group. Two more, great inception startups.
Jordan Wilson [00:42:50]:
Here we go. Alright. Now I am here with Jack from Democratize. Jack, can
Jac Hsieh [00:42:55]:
you tell us a little bit about your company? Okay. So democratize is actually also AI company, but the a stand for apparel intelligence. So we do have provided body scanning and also textile AI to turn body human body and also to textile into digital twins. And then we use this zero freeze to create your personalized, apparel. And and right now, we focus on purple purpose wear. So we help all the, professional athletes to create really, precise and tailor made clothes for them to enhance their, sports performance.
Jordan Wilson [00:43:29]:
Very, very cool. I love it. So, aside from maybe professional athletes, who are some of your other, customers or clients for democratize?
Jac Hsieh [00:43:38]:
Alright. So, Ashwin, this is a a a new topic that we have. Previously, we are 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, underarmored Yeah. They use our textile digital digital solutions to detox all the material they have and then, and and use this material to the, digital design workflow. So previously, we have we would have customers from fashion brands, from, supply chains, like apparel supply chains or things on the supply chains. And right now, for this new top beef this new projects, we are actually working with the cycling, fashion athletes.
Jac Hsieh [00:44:16]:
So we are making the very, tailor made, and very precise, cycling jersey for this. Very cool. What would
Jordan Wilson [00:44:25]:
you say is the biggest, problem that democratize solves? K.
Jac Hsieh [00:44:31]:
So we try to kind of reengineer this entire ecosystem. Right? Because right now, because of fast fashion, and people love new things. And and so it's actually caused the overproduction issue there 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 onboard the momentum, to actually have we try to kind of kind of reengineer this process. So if you are the consumer, if you you say, alright. If you push you place order and we can get the dealer made, close within five days.
Jac Hsieh [00:45:09]:
Why don't you do that? Because you can get you you you don't need to care about any sizing. You just tell me, no. You wanna slim fit. You wanna lose fit, and I tell them it for you. So if we can kind of build this process, then we can re convert the the whole ecosystem. Right? You you decide you don't have to go buy this clothes, and we tailor it for you, and then we should be able to you. So we don't do the, production update to reduce all the overproduction niche. So
Jordan Wilson [00:45:34]:
so, yeah, it's it's get getting loud in here in the, inception program about to open, but, maybe real quick, tell me a little bit about what the NVIDIA inception program, has meant to your company so far. I
Jac Hsieh [00:45:48]:
think NVIDIA, they are looking for a lot of, AI new start up, and they we are pretty, as as you understand, we are pretty, focused on the fashion technology. So it's pretty niche for this then, and they they 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 liking this area and also is also related to, like, sustainable, prop to issue that civil problems. So I think they gave us a lot of resources to use their, SDK and the software to help build this digital twins, and and and models.
Jordan Wilson [00:46:20]:
So out outside of, you know, maybe is there a next iteration? Are you looking to bring, this concept to to bigger or wider markets in the future?
Jac Hsieh [00:46:30]:
Yeah. So as I as I mentioned, we just pivot to this b two c direction. So we are going to launch our first proof of 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, both on cycling, maybe next, next one will be the running and yeah. So gradually to expand the old flashings.
Jordan Wilson [00:46:53]:
Very cool. Alright. So if someone just heard you and they're like, wait. I need democratize. What's your quick pitch to get them on board?
Jac Hsieh [00:47:03]:
Good question. Alright. So I I think we just tried to, introduce a new way to purchase a close. So we give consumer Atlanta a choice to be more sustainable and choose the right fit for your own. So you are the meet you are the brand, not you try to fit into the brand's, clothes, but you are the brand. So we design for you. You are you are you can decide whatever you wanna wear and you don't need to care about all the size of the product.
Jordan Wilson [00:47:29]:
Alright. Yeah. Jack, thank you. If you want more from democratize, let us know. Alright. And our last one. Go. Alright.
Jordan Wilson [00:47:43]:
So we are here with, Ina from Illumix. Tell us a little bit about Illumix.
Inna Tokarev [00:47:49]:
Illumix is a 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 properties. Unumax enabled that in less than seven days with 80% savings on the top.
Jordan Wilson [00:48:07]:
Okay. So yeah. Okay. So it's about token optimization or, like, what's the actual what's the benefit? It's just, just more efficient, tokenization. Like, walk us through what that looks like.
Inna Tokarev [00:48:17]:
The best the most exciting benefit about Elumex is actually trust. So, business users from our perspective have hard time to actually understand the agenda answers, especially whether it's black box and make decisions based on that. So, what Elamax does is also handles the quality of underlying data, make sure that the data which is going into Authentic is a high quality and compatibility from one side. On the other side, you prize a full explainability about the answers so any users can understand how the question was interpreted, which data is mapped to, and what logic is implement. This is like ultimate on the black box.
Jordan Wilson [00:48:52]:
Awesome. Walk walk me through. Who is your average, customer or client for your platform?
Inna Tokarev [00:48:59]:
So buyer would be chief data officer Fortune 500 company. Also, lately we see lots of giants from Silicon Valley, super excited about the solution as well, but the users are every support center or marketing or a product analyst. So so basically any business user in enterprise or any organization really can ask that data related question and have explained and hallucination free answer.
Jordan Wilson [00:49:25]:
That's great. So essentially, it's just, providing, more confidence in the answers you get out of agentic systems?
Inna Tokarev [00:49:32]:
It's full stack solution. So we handle data quality, we handle governance and trust, and we also handle interruption in within system that you would like to use. So Air Max is not a new interface. We all embedded into your CRM, your Power BI, your Slack, or your Teams whenever you already are, and we provide this access to the web.
Jordan Wilson [00:49:52]:
Very cool. Talk a little bit about the, NVIDIA Inception program. How has this, helped your organization?
Inna Tokarev [00:50:00]:
NVIDIA inception program was exciting so far. So we recently had PR just yesterday
Sharon Carmel [00:50:05]:
Yeah.
Inna Tokarev [00:50:06]:
About how we use an Indian names and other underlying technologies to basically scale for those enterprises that we serve. Right now, we have systems which has hundreds of thousands of tables and millions of ways, and there is nothing like NVIDIA to to enable us to have seven days set up other than running for those massive companies.
Jordan Wilson [00:50:28]:
Awesome. And then, so if 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?
Inna Tokarev [00:50:40]:
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 Genpix that you can trust, contact Illumax and we'll make you happen for 80% less cost in seven days.
Jordan Wilson [00:50:54]:
Awesome. What great pitch. Alright. So if you want more of a Lumix, let us know, and, let's dive into another startup.
David Twizer [00:51:05]:
Wow.
Jordan Wilson [00:51:06]:
Alright. So that's it. That is our awesome eight. That was wild, y'all. That that was a lot of, fantastic startups. Right? 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 you know, heard something you liked, maybe we'll expand the field, from eight to sixteen for the inception games.
Jordan Wilson [00:51:47]:
But that is a wrap y'all. So, a couple questions that kept coming up. I'm gonna go through all of the questions that came in from our livestream audience, and, you know, for our two finalists, I'm gonna make sure to ask some variations of those questions, to our two final, that are gonna 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 gonna have a recap in the newsletter.
Jordan Wilson [00:52:17]:
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 newsletter's 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 careers. So, you know, maybe, maybe the the the startup that you like most, from the, inception program isn't gonna be the one that makes it to the finals, and that's okay, because we're gonna have links, to all of the startups in the newsletter so you can go find out. Maybe they're gonna 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. Alright? I'm not gonna repitch them, but, as a reminder, we had deep checks, we had expander AI, we had Beamer, we had PlyOps, Glia Cloud, Contextual AI, Democratize, and IllumX. So, make sure you get two votes.
Jordan Wilson [00:53:15]:
Use them wisely. Right? 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 wanna vote twice, maybe you don't just wanna vote once in the newsletter, we always put the link to the livestream in the show notes for today's podcast. You don't have a lot of time. We are saying voting ends Sunday night at 11:59PM Central Standard Time.
Jordan Wilson [00:54:01]:
So you got a little bit more than forty eight hours, until we go from our, awesome eight in the inception games to our final two. And we're gonna 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. Like, this is the first time we've done something like this. You you know, 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.
Jordan Wilson [00:54:46]:
Like, as an example, did you know eleven 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 in 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 you know, this basketball format of, you you know, the brackets and the games and all that.
Jordan Wilson [00:55:34]:
So make sure to get your vote in. Go to youreverydayai.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 4. So thank you so much for tuning in. Hope to see you back later for more everyday AI. Thanks, y'all.