Charles Cormier hosts six AI startup pitches with VC feedback. Axosuite fixes LLM visibility gaps for B2B companies. Editorial turns articles into branded videos. Scalys built an AI recruitment loop. Brandon and David stood out as the strongest founders.
Chapters
Welcome to the 6th edition of Spark Tank. Today we have, uh, 6 or 7 founders with us, a lot of promising founders with, uh, very cool startup ideas and pitch decks that we're gonna go over today. Um, yeah, Spark Tank, what it's about, it's, uh, 3 minutes for founders to pitch their company, share their screen with their pitch deck, and then we have VCs and advisors giving them feedback, giving them Checks or Sells. That's the, the full concept. 6th edition already is passing pretty quick today. I promise we have the highest quality of founders. So without further ado, I'll start introducing folks to the stage here. So let's start with Dana. If you can unmute yourself, tell us a bit more about yourself.
Hi, I'm Dana Billingsley. I'm just outside of Dallas, Texas, and I'm the founder of Axosuite that I built to help B2B companies understand and improve how AI models recommend them so they show up when buyers ask who to select.
Jeremy, a quick intro about yourself.
I'm Jeremy Robb. Uh, I'm the founder of Robb Clan, and we built Wayfinder, which is a, uh, learning management platform that also builds, uh, context and, uh, training for AI agents along with their humans.
Paulo, a quick introduction about yourself, what you're building.
Hi everyone, I'm Paulo Martins, founder and CEO of Vitruvian Shield. We are, uh, we have headquarters in Switzerland and we developed the only platform for decentralized clinical studies or clinical trials at home.
David, quick intro about yourself.
Uh, how's it going guys? My name is David Roden. I am a junior at USC and the co-founder of Editorial, where we help journalists and newsrooms create authentic human-first content.
Brandon, quick intro.
Hi, I'm Brandon, co-founder and CEO of Scalys. We're AI-native recruitment infrastructure for organizations to facilitate sourcing candidates all the way through offer letter sign.
And last but not least, Effy on the founder side. You're muted.
Hi guys, my name is Effy Showentoski. I'm the CEO and co-founder of Tuba Media. We are IBM Watson X generative AI.
Send me a quick intro about yourself as a VC.
Oh, I think Effy was still finishing. Yeah, do you want to finish up, Effy?
No, I'm good.
Okay, Sunny Ray, my name. I'm one of the founders of RazRocket, also founded UnoCoin, India's first Bitcoin exchange. I'm also a director of Humanoid Global.
All right, so I'm gonna bring everyone off stage and we're gonna start with our first founder, which is, uh, David. Uh, so David, I'm gonna help you share your screen here and start, uh, pitching. So I have your screen here. Oh, actually myself, just, uh, share the deck and you have this.
Am I good to go?
Yes, you are.
Can you guys hear me? Can you hear me?
Yes.
I'm gonna take that as a yes. Yeah. How are you guys doing today? My name is David Roden and I'm the co-founder and CEO of Editorial, where we help journalists, newsrooms, really anyone who wants to create authentic human-first video, make their content better and quicker than they would using AI. So let's get right into it. Right now there's a huge shift in the media space. Last year, 65% of Americans under the age of 50 got their news from social media. And that matters because there's a huge gap in this news industry. News is still primarily written in writing format. People write the news, but now the way people consume the news is so different. It's consumed on video. And there's this gap leaving many journalists and newsrooms creating content that goes unseen. And beyond that, it leaves people on social media who are clipping content and things like that, stealing that same news content. From the journalists in the newsrooms. And it is because of that we developed Editorial. Editorial allows journalists and newsrooms to turn their writing into journalist-led social media videos. So every newsroom can get an in-house production suite without spending thousands or increasing headcount. Now, how does it work? Basically, this is a live product, by the way. A journalist would go in and they can upload the article they just wrote. We take that article and we analyze it and we look for a way to basically optimize it for social media. We wanna make it engaging, but short enough to send people back to the article. Then they can edit that script as much as, as much as they would like. And then when they're ready, they open up the teleprompter window. And just like how I'm talking to you guys on this Zoom call, they just read into the camera with our teleprompter, and then we can take that footage and automatically add B-roll, branding, captioning, animations. We can pull face— like say you're talking about a senator, we can pull the picture of that senator automatically and put it into the video all on its own, all in under 10 minutes. So that's kind of how the product works high level. If anyone would like a demo afterwards, feel free to hit me up. Traction-wise, we are live. We are very recently post-revenue with some of our Substack clients converting. Beyond that, we are working on converting some newsrooms as well as some companies in the pharma space. I'm actually pretty interested to hear more about the pharmaceutical stuff that was mentioned earlier. Here are our main 3 customer segments: freelancers, publishers, and holding groups. I'd say right now with our kind of recent post-revenue stage, we are kind of ICPing under the freelancer and small publisher area, but holding groups are definitely on our radar and something we're kind of getting ready to go through. Now, looking at expansion markets, we want to expand this product across several industries such as pharma, but also in PR, comms, and overall social media marketing. I'd be happy to answer any questions about that. Our market size as TAM is $1.23 billion, SAM $324 million, and SOM of $7 million. Looking at the small-term, short-term news industry. Here's our competitor matrix where we are the only company out there currently offering journalist-on-camera content that's built for newsrooms, a teleprompter recording, and quick to publish with B-roll connections. Here is my lovely co-founder Zubin Hydri, and that is myself. We are both juniors at the University of Southern California. And here are our lovely advisors. And this is our assets, the fund we were raising, our pre-seed round of $135,000 to help recruit additional developers, developers, scale some early go-to-market efforts, hire a BDR or two so I'm not swamped all the time, and get some work going on our social medias. I'd like to thank you all so much for your time, and I'd like to invite you to help us build the production layer for modern journalism. Thank you.
Good presentation, Dave. I love how crisp it was, how fast it was. I have questions on the GTM side of things. So yeah, what's the revenue like right now, and what do you hope it to be once you've done the raise?
We are very early on in our post-revenue. We probably are getting like $30 a month. I, like I said before, we are working with a lot of beta with newsrooms, like beta customers. We're still waiting to convert them. And then as for go-to-market plan, our big thing that we're trying to hurdle over is this trust gap. I mean, there's so many people trying to sell tech to these journalists, tech to these newsrooms, so it's hard to kind of stand out. So our leverage and our method of doing that is going through pre-built networks. So two that we're working with is INN, the Institute for Nonprofit News, which represents about 500 newsrooms. We just sent out our first webinar with them on Monday, or probably gonna— not webinar, newsletter piece. And then we're probably gonna host a webinar with them. And then next one is Outvoice, which is more on that freelancer side. They have a network of about 30,000 journalists, which we are gonna be setting up a program with to try and convert some of them to our page.
What's been the feedback so far from journalists? I'm bullish on Substack. I run my own Substack, making money with it. I know a bunch of people are making good money with their Substacks, but I'm not bullish on journalists, generally speaking. So can you give us a bit of the stats and conversations you've been having with both journalists and Substacks and which one you're most bullish about?
So I think there's two separate parts to that question. First off, I think we've been doing this for a little while now. And our product, like, if you looked at what our product looked like 3 months ago, It's night and day. We have done countless user research calls and I've had people tell me to my face, like, this sucks, we need to fix this. So we know what we need to do or what we fixed it. Like a lot of the stuff is like there's a certain level of journalistic standards that you need for news content that can sometimes fly for just standard social media stuff. So it was like a really big thing, like getting our script writing process correct or getting our animations right every single time. So that was like some of our biggest feedback points for consistency. And then in addition to that, you're talking about these Substackers versus journalists. I think I could ramble about this for a while, so don't get me going, but there's this big fragmentation that we've talked about a little bit before where now it's like every journalist is expected to be a creator, even when they're represented by a newsroom. You see newsrooms that have individual journalists with their faces on camera, and it's like you're expected to create your own brand. So even when you're within the newsroom, it's almost like this creator economy is evolving out of it. And I think because of that, our software does do well in this space.
Sunny, any questions for David and what he's building?
Yeah, no, great presentation. Yeah, I was gonna ask you about what part of this gets better with scale and data.
I think so the big thing I'm most excited about when we hit scale is, okay, right now we've been, we're, we're all just like making the content. We wanna make it good, we wanna make it fast. The next step that we, like, I'd love to say we are, but we're not there, is optimizing that content. Now it's like, okay, we'll make your, help you make all your content super easy, super quick, but I will wanna be able to say, we'll make your content do well. You're gonna get engagement, you're gonna get viewers. And I think that's something that we're gonna have to learn through doing. And then we're gonna learn that at scale once we have people using our content, using our software and seeing what content that's made through our systems do well and what doesn't perform. Does that answer your question?
Yeah.
And then where do you see your guys' kind of long-term moat then?
I think our long-term moat really is— I mean, I think this is a very scary time for software in general. I think moats have become smaller and smaller, and I think our moat comes from— now, obviously I won't say our connections to people we know, but I think it's through the infrastructure. It's not necessarily a technical-heavy moat where we have this super patent-pending AI tech. It's more that we understand our market and we understand our workflow. It's a workflow solution, which is what we've developed. No one's done this exact thing where we really hold the user's hand through the entire process. Like, there's these video editing tools for newsrooms, but it's like you either have to start with your own video or you have to do all the editing afterwards. And I've yet to see someone really put all the pieces together. And I think that's what our moat is as of right now.
Can you give us a case study of, uh, someone, uh, that's a Substacker that's reporting news, that has potentially a podcast as well, that's extremely successful in your space? Would that be like Ben Shapiro or someone like that?
Ah, I mean, this is just looking at like Substackers in general. I'm not sure about podcasters. I mean, one Substack group that I follow a lot is DropSite News. It's, uh Ryan Grimm. I've seen them, I've been following them since like, I don't wanna say when they launched, but like when they were at like 75,000 subscribers, which now they're at significantly more than that. And I think they are like an excellent success story and like, you know, you really can do it on Substack, but you can also do it way smaller. They are a small newsroom at this point. You could do it on your own. And I think that's an even bigger spot for us to hit. Did I answer your question, Elso?
I, yeah. Yeah. I think it's promising. I love how crisp the deck was. Let's go to Brandon, our second founder. Thanks for presenting today, David. So Brandon, the stage is yours. I can see your screen as well. We hear you loud and clear. You can present.
Perfect. Sounds good. Well, it's nice to meet all of you. I'm Brandon, co-founder and CEO of Scalys. Prior to this, I started a Shopify professional services firm, actually, when I was going to USC as well. We scaled that to about 65 full-time team members, exited it in 2022. And it was out of the pain points that we had hiring, recruiting for that organization, as well as my younger brother's experience on the job seeker side of the market, receiving recommendations from LinkedIn and then ZipRecruiter to go be, you know, the CTO of the Walt Disney Company or a forklift operator or the air traffic controller at the Dallas airport, that we decided to go solve the root of the problem, which is the actual recommendations themselves. Why are all these platforms so bad at matching the right candidates to the right open roles and vice versa? And this is also during an era of AI where every other business function outside of recruiting has experienced increased efficiency. If you look at sales, development, finance, they've all experienced leaps forward. But if you look at recruiting, 2025 versus 2022, applicant volume has exploded by almost 600%. The average time to hire is up by about 2 weeks, and the average cost per hire is up by about $2,000. There's never been more noise in the space. And so this is what really excited us about digging into this. And we uncovered a lot of gaps, but the biggest one is the one-way flow of information and data that exists in the legacy recruitment stack. So to take a candidate from sourcing to offer letter sign, it requires anywhere between 3 to 8+ tools for an organization to use. And outside of it being expensive and cumbersome, which is a problem in and of itself, there's data loss throughout each of these steps and there's no feedback loop into the sourcing platforms. So when we were taking candidates from LinkedIn, our sourcing tool into Lever, our applicant tracking system. As soon as they leave LinkedIn, LinkedIn has no idea if they even submit their application, let alone if they're a real human being or not. Are they qualified for the role? Which round did they make it to? What are we ranking them on our scorecards? What is the interview note-taker picking up during the call? What are they scoring on skills assessment tests? If we reject them, why did we reject them? Just a tremendous amount of training data has gotten trapped in these downstream tools. And so that's what led us to— we need to create a closed data loop. The only way to do so is own the full recruitment lifecycle. And unlike the legacy solutions, we own the candidate data. So as soon as they apply into the Skilless platform, we, as soon as they're rejected from that first role, funnel them back around into our own marketplace. Because long-term, what we're competing against is LinkedIn and Indeed. We built the B2B software as the wedge to aggregate the candidate data, which will be our long-term moat and differentiator. I had the SMB owner experience. I didn't have the TA experience. And so we've brought in experts like the chief people officer of Greenhouse, the chairman at Employ. As significant investors in the company, not to mention our product advisory board of about 14 super users. We launched in, uh, September of last year. We just surpassed a million of ARR this month. We were at $250,000 in February, $686,000 last month, close to $1.1 million this month. And this is our classic use case consolidation. So businesses that are using some of the legacy applicant tracking solutions out there, a bunch of other third-party tools stacked on top. We consolidate it into Scalelist, save them a bunch of time and money, but use all that data to not only train the recommendations model but power our agent Bella, who helps them with sourcing, pre-screens candidates for them, inbound prioritization, etc. Raising a $5 million round, we already have $1.8 million committed, and, um, up until this point, the business has been primarily self-funded through my prior exit and then a lot of angel investment from exited founders and, uh, other strategics in the space.
Dude, I love this. Um, how did you scale that fast to $1 million ARR? What are your secrets?
So we have a longer sales cycle than most of the AI point solutions that are out there because we're the full infrastructure. So we've been building pipeline for the last year and a half, well in advance of our launch, and we're displacing annual and multi-year agreements with large, you know, mid-market and above organizations. So really, honestly, it's just been keeping our head down, doing more of what we've already been doing. The 3 areas that have been most advantageous for us have been our cold outreach infrastructure. We have 225 different email accounts set up and instantly through Apollo and Clay list building that generates us anywhere between like 40 to 100 demos a month. And that's also how we drive attendance for our dinners. I host a dinner every night of the week throughout the US. We've done 41 already this year from LA to New York, San Francisco, Chicago, even Oklahoma City, St. Louis. We bring together 8 to 30 of our ICP into the room, and over 36% of our business has come through that. Not to mention recruiters live on LinkedIn, so I post about it the next morning, everybody's resharing it to their networks. And so the follower count and the engagement that we've been seeing has been increasing dramatically. Uh, and then the third is our partner program. So there's a bunch of fractional talent consultants and agencies that will support early-stage companies with getting set up with their talent infrastructure. We've signed 51 of them over the last 3 months, and then they onboard their customers onto Skagulus on our behalf. So we have no sales cost there, we have no service or implementation. They are handling it on our behalf for a 20% commission. Um, so part of this raise is the offload. I'm our sales team of one right now. Got a team of 20, but they're all engineering and customer success. So one of the four areas of what the raise is going to is to offload those responsibilities onto dedicated team members to free up me for more strategic things.
Um, question for you on the HR side of things. What are the best practices to hire top talents nowadays at startups? For example, I hire my own clients as employees for my company. That's one of my favorite funnels. I also start with cold email, right? Everything starts with cold email, and I think this is so underleveraged to recruit candidates. What have you seen that is more original than LinkedIn outreach and posting jobs. You know, I think posting job is dead.
Yeah, and especially with all the bots that are out there. I mean, somewhere between 70 to 90% of the pipelines we're seeing in the legacy solutions are bots, and they're either, you know, fake, like bad actors, like from North Korea or something, or like Indian, like, dev shops, or they're just, um, you know, basically fibbing on what they actually can do. But Cold outreach is one of them. I mean, our system handles everything from the sourcing of the candidates to the email verification to the outreach to them to get them to come in and apply to the role. And when you're doing that outreach, you're mitigating a lot of the candidate fraud that exists because we're layering in a variety of different third-party datasets to, you know, bring in GitHub or LinkedIn and things like that and provide a refreshed view. The second would be referrals. I mean, referrals is a huge source and we have a whole referral engine built out within the platform. And then the third is candidate reactivation. You know, anecdotally, a lot of the companies we've spoken to, they pay external agencies a bunch of money, and a third of the time the candidate already existed within their systems. They just don't have a good way of finding them within their existing systems. So one of the things that we do, our profiles are dynamic because these are not static profiles when they're applying in. And so if the candidate themselves is not updating it by applying to other roles in the ecosystem, we layer the third-party data on top of it. So that when somebody posts a new role, we can go tell them, hey, these 25 people that are already sitting in your database, you have had communication with, who have gone through an application process, they're a great fit for this position. Do you want to enroll them in this outreach sequence we made to get them to come back and apply? So those are the 3 main ways that we've seen.
What's stopping you from reaching out to investors via cold email and raising this round yourself?
I mean, so that What we've been doing primarily is networking pathways. So we did not begin a formal process here until like 2 weeks ago, because we got a lot of inbound from the growth. And I've been chatting with firms since, you know, 2 years ago about what we were doing. And then we decided to go the, uh, forgo the pre-seed route and just bootstrap. Um, and so that has led to us going the networking path. So we have all of our LinkedIn connections, our existing investors, my other exited founder friends, and those get us into partners and managing director conversations. Frankly, I don't want to speak to the associate. If I speak to the associate, it's going to go to their Monday morning review. They're going to talk about it for 2 minutes. It's going to be completely lost on them and then it never goes anywhere. So that's the path that we've chosen to take.
Yeah, I'd be happy to help you and David on that front. Yeah, I love both of you guys' pitch. And yeah, if you win the contest, we give you the goodies, me and Sunny. So Sunny, any questions for Brendan?
Yeah, I love the feedback loop, right? So my mind always kind of goes in that direction in terms of, so like how fast does that loop improve match quality in practice?
Yeah, so we're already seeing an immense amount of data. We have close to 400,000 job seekers on the platform and those are all organic. We haven't spent a single dollar on acquiring them. And for each of those job seekers, we have anywhere between scorecard data all the way through rejection reasons. And just last month alone, we had over 40,000 rejections submitted within the system on candidate profiles with the metadata associated with why they were rejected. And so that's just scaling rapidly because every open role that's posted in the system, we get about 736 applicants to it. So when I onboard a mid-market company who's hiring 100 roles a year, that's 76,000 net new job seekers that are contributed to the platform. And then all of the associated data with it. And where it gets really exciting, like right now a lot of our companies are AI and SaaS cuz we're just taking business away from Greenhouse and Lever cuz they got bought by PE in '22 and '21. So there's just the lowest hanging fruit. But as we start to get into some of these industries where LinkedIn isn't as predominant, now I don't just have profiles that we have more data on, but they still have a LinkedIn profile with like a lot of the AI and SaaS type of candidates. But like the senior living facilities, the educational school districts that we're talking to, the blue collar transportation and logistics. All of their candidate flow creates a candidate talent pool that doesn't exist anywhere else. And so that becomes like, as both sides continue to grow, they lift up one another. And now when, you know, I'm selling to the, you know, the fifth company in that industry, I'm not just selling the software, I'm selling all of this candidate data and our ability to match them and remove the noise. And we have identity verified, we have skills badging associated with those profiles, so it cuts out all of the BS that they're experiencing right now, that only continues to get worse because you've given— it was already a flawed, like, underlying infrastructure. You've given AI to both sides and now it's just AI duking it out and no one's even speaking to one another anymore. And it's causing so much noise, which is why it's one of the few verticals that's gone backwards or business functions that's gone backwards versus others.
Interesting. Thanks.
Brandon, why not, uh, scrape job posts and come up with an email which, uh, structure would be like, I saw on your website that you had this, uh, senior machine learning position, and then insight about that, right? Like, usually these guys are really hard to find on LinkedIn. They don't have all the time in the world to go to apply to job posts. And then the paragraph after, here's a solution here's our niche solution to reach out to these guys and have them apply and actually accept your job post. Are you doing some of that with Clay?
Uh, not at this moment. We've tested some stuff around like job postings, but there is so much competition and noise in the sourcing category. There's Juicebox, Pin, Findem, Hireezy, SeekOut, GoPerfect. I mean, there's literally like 30 different solutions that have spun up over the last 3 years. Not to mention all of the agencies that exist. So the way we've been positioning ourselves is, is as that AI-native infrastructure. And you have like two paths. You can continue on the legacy path where you're bolting on these point solutions, like all the ones I just mentioned. It'll cost you 3 to 4 times more and it'll, um, be less effective than if you just rip the bandaid off and consolidate into Scaleless. And because of all that noise, referrals and the in-person and the one-to-many relationships we've built with like Even in, uh, like Charlotte as an example, they have the Charlotte Recruiting Leaders Community, which we're very close with. Same with Minnesota. Like these exist everywhere and not to mention the Slack communities, et cetera. So that's why we wanna bring in that dedicated partner resource because there's so much noise. And yes, cold email can help drive some of the demo volume, which it has, but like you're not buying $100,000 software and ripping out your whole recruitment infrastructure because I can fill a senior ML position for you, in my opinion.
Right. Hey David, you had that question for Brandon.
Um, yeah, yeah, first off, excellent pitch and really cool product. It's also nice to see another USC person here. I'm actually at Fortuna right now, which I'm sure you probably know. Um, I really loved your— one of your sales strategies you mentioned about inviting all your ICP out to dinner. How did you start doing that? How do you think about that, and how did you execute it?
So I've always been about, uh, you know, schmoozing to get into the, uh, the right, uh, areas. Um, with the professional services firm, like in, back in December of 2020, I sent a case of wine to every single Klaviyo sales rep that existed, which was about 130 of them. And then for the entirety of 2021, the first person that came to their mind when they were selling a Soylent or a Fashion Nova on migrating from Salesforce CRM to Klaviyo was me. And so that's how we went from like 12 to 45 people in 7 months. Not that in, you know, Entirely, there had to be everything else in place, but like, it definitely plays a role in building trust and building relationship, especially when you're buying software at the mid-market and above level. And I originally started by doing like coffee chats and like, you know, get taking people for drinks and whatnot, but that's just one-to-one and there's not a lot of value being provided for the attendee other than they get to meet me, which I would argue is a lot of value, but outside of that, not a ton of value for them. And so that led to how do we make this more scalable and then also more valuable for them? And so that's what led to the dinner events and they just continue to snowball because a lot of recruiters are also remote and so they don't get that in-person interaction. And so we've done 41 of them already this year. Um, and it's been extremely impactful. It's allowed us to bring in other technology partners as well, who we then do account mapping and co-selling with. And our cost per lead is like $41 a lead because we bring in those co-hosts. So I hosted 36 people in Austin for, a $6,000 dinner, but I had 3 other co-hosts and on a per lead basis, it's $41. 80% of them booked demos. 36% of those are already customers. So if I could do it 5 times a night, I would. I'm just a person, a single person at this point though. So. Wow.
That is awesome. That is really cool.
Thanks, Brandon. Thanks, David. We're gonna go to next contender. Paulo, welcome to the stage. We can see your screen, hear you loud and clear. You can start presenting.
Hi everyone, so I'm Paul Martinsch, founder and CEO of Vitruvian Shield. We have headquarters in Switzerland, Lausanne. We are present in Portugal, Armenia, and UAE. We developed the stripe of clinical trials at home. So we have developed the only platform that allowed to, in a few hours or in a few days, to start doing clinical trials at home. We also allow to connect with more than 500 wearables, and you can also make studies, e-surveys, without any wearable, using only the camera to measure the vital signs of the, the patient or patients. About clinical trials, very, very quickly, 80% of the trials is missing the enrollment, so they fail. The, the cost per wearable is more than $100K per, per rebel in terms of integration. And to deploy one trial is several months, sometimes more than one year. And also the cost per patient is very high, average $1.2K per patient. What we offer is two solutions, very, very, very quite simple to, to understand. One is the full platform, the, the Triventional Research, who offers a TMS and RPM with white label, so you can use it, you can start in a few hours. And the other one is the research, the VTube Machine Connect, sorry, which is like an API infrastructure that allows you to choose the models, and we complement your system with all the models that you need in order that you can start decentralized clinical trials few days. So the— we start our sales in January. Um, since January we have 70% of traction. We have pharmaceuticals who, like everyone here may know, AstraZeneca, who just request us an offer for, um, a study with more than, uh, 8,000 patients with 3.5,000, uh, remote patient— we need remote patient monitoring. We also have partnership with the Swiss hospitals, research foundations in Portugal, and we are now starting the integration on the national health system in Portugal. Also, we asking to between 2 to 4 million of our Series A. I raised 1.8 million since we start in 2023, the platform. So we also opening our bridge loan open right now before the Series A to allow us to conclude all the previous contracts that I show you. And yeah, this is, this is what we are looking for, and we are offering the infrastructure layer for every clinical trial on HEART, not just in Europe. But my objective right now is to be the benchmark in Europe as a start, and we have an estimation of more than €2 million revenue until end of the year.
Thanks, thanks, Paulo, for the pitch itself. One quick feedback piece, like when asking for a round that I'd prefer to say as an investor, like a fixed ask instead of like, oh, we're raising 2 to 4. Needs to be like one fixed number, right? It needs to be like certainty. So maybe go with like 3. I think that would be a great start. Um, the presentations, I like some slides, but some AI images sort of diminish the values. Like, some thoughts on this? And I think what you're solving is super important. I think, uh, as a guy that's first company was a nootropic, right? I think when we can crack clinical trials from a distance and drastically accelerate the time to drug deployment, I think that would be a massive service for humanity, right? Because technically right now people are dying because drugs are not being deployed fast enough, right? And maybe that would be good to start with that slide, actually. What do you think?
Yes, it's a good idea for sure. But what is important also to say is that But today, the most— the main issues is the lack of patients and lack of data. So, we also try to solve this in order that in the platform allow you to work with patients anywhere in the world. So, you can have access to more patients, and patients can have access to physicians' teams directly. And so, this change a lot in the clinical trial.
Yeah, maybe also like start with the fact that you're in Switzerland, which is like the capital of, uh, clinical trials and pharma, I would say. And yeah, you're 10 years in the space, shows like real commitment. We discussed about how to brand it last time that, uh, we spoke. Um, yeah, what would be the go-to-market plan? Like once we fund you, right? Like what would be the next step? Like where would you actually invest money to get clients and accelerate the revenue here?
Well, the market is, uh, is a small market. I mean, you— our clients are CROs, uh, research companies, biotech companies. So it's not the mass public market. So we easily— and I easily create a CRM with 90% of the European clients. So we have access directly, or we contact directly the, the clients like this. And we use also multimedia content that we're going to use, or we are using, starting using. And the investment will be mainly for certification and, and, and, and research part that we are working on AI with different health institutions. So to answer is certification, finish the sale, the first sales, or conclude the first sales, and open new markets also.
Thanks for making it, by the way. I know it's late your time, it's like 10 or something, so thanks for making it. Any questions for Paulo?
Yeah, just again, what's the kind of the fastest proof point that shows this working in the real world?
Well, there are several ones, but mainly when you are looking for biomarkers, let's say you are going to keep something very specific on epilepsy. Today, we still have not found the biomarkers because we cannot cross, like we are now doing, cross different types of data. So EEG, ECG, EDA, today we can, with our platform, we can collect this data in the same time and confront the data to find easier the biomarkers. So this is one thing that can and should go make faster the discovery of treatments or algorithmics in this specific case to detect or to prevent before a seizure happens.
Great, thanks.
More data, more quality data. This is the, I think, the answer to your question.
Question slash comment, Paolo, and you know, like, I'm a lot about neuroscience and psychology and how people perceive you and investor will perceive you, like the logo itself, right? It looks straight out of like, I don't know, The Matrix 2005, you know? Like, could we, could we rework on that and, uh, make yourself look better, I guess?
Yeah, sure. But let me explain why this logo is— I, I developed it because Vitruvian Man, everyone knows what it means, is the, the Da Vinci design that represents the perfectness of the body, of the human body, and the shield to protect it. So, and the cross with that, with 01s, because it's digital health. So, but yes, of course everything can be improved, and I'm hoping to improve every— everything with, uh, who need to be improved.
Yeah, yeah, we'll, we'll give you a hand with that. There's always a way for the logo to mean something to you, but a logo should mean something most importantly to the market. right? So that's often the problem we see with founders. They design something that means something and that looks good to them, but the market is, uh, it's much different. Regardless, I think you're working on something very important, right? I'm really excited about like everything that we're seeing in pharma and how the FDA is loosening up, right? And how the deployment from, um, trials to drug is shortening itself. So, uh, glad to stay in touch with you. Let's go with your next contender. Jeremy, Jeremy, welcome to the stage here. We see your screen, we hear you loud and sharp. The stage is yours.
All right, all right, thank you. All right, so I'm Jeremy and this is Wayfinder. So right now the enterprise learning is broken. L&D is spending more time trying to schedule collaboration meetings with your SMEs instead of building, polishing, and publishing, and that doesn't keep up with the pace of AI. So, and it's not a content problem, right? Because you can develop content using AI tools within, you know, 2 to 10 minutes, but it really is a structure or governance issue. The Wayfinder was created to solve this problem, and it's different because we have 3 different pillars. First is the structured curriculum generation, and this What we do is we take a prompt and we develop your learning path, and from that learning path, we develop courses, and we also develop an AI knowledge context module that tells your AI agent how to work with this particular role as, as you move forward. So you're ready day one. And then we also have the LMS delivery and MCP agent support. So as you're developing content and you want to get your SMEs engaged, what they can do is connect their AI agent, which has their context, and then instead of spending an hour trying to hash this out and taking notes and going back and developing, they can say, for instance, your, your engineer can connect their AI agent to the course, have it evaluate the content, determine whether or not there needs to be an update provide the update, they approve it, send it to the curriculum owner, and within 2 minutes you've got new refreshed content that's ready and is technically accurate. Same thing with marketing, right? You can do— take care of the, the positioning language. Same thing with customer success, make sure that the customer understands everything they need to know in order to be successful. All this can be done through AI agents and MCP, making this a central location to edit your content and collaborate without having to spend hours upon hours upon hours, uh, in meetings. Next, we have our enterprise context portfolio. Here you provide corporate-level context for every AI. You also provide your role-specific context that was generated when you did your role, uh, generation. And you can also create your personal context. So the corporate context is, this is our our ethics, this is how we work as a company. Role is this is how you're gonna work as a customer success manager. For personal context, this is gonna be, this is how you as an AI agent are gonna be able to help me as a human. Let's say you're autistic, you have problems with your executive functioning, you need to have reminders set so you keep on track. Your AI agent needs to know how to do that. That's gonna be your personal context. You keep this in a portfolio, and so then if you move from say, OpenClaw to Hermès Agent, you keep that context with you. You don't have to rebuild it with every new agent. Okay, this is a massively underserved market. The LMS market is a hun— is $11 billion plus, and most of that is LMS with content creation kind of bolted on, right? We can easily reach $100 million in this market, and we expect 1 to 10 million in the first 12 months. So we're building an infrastructure layer for AI-native enterprise learning. This isn't just course generation. This is making sure that we're able to manage knowledge at scale, and we are looking for partners and investors who can see this opportunity to modernize how organizations manage knowledge. At scale.
Thanks, Rob. Thanks, Jeremy. Um, question for you: do you have a landing page for this? Like, uh, because I've checked your LinkedIn, for example, seems like you're on to some other things with Rob Clan. Uh, where can I get more info about this one?
Uh, robclan.com has, uh, the intro to it, and then there's a link to, uh, wayfinder.robclan.com, and that's the app itself.
Okay, okay. Go-to-market, have you started getting a couple of clients? What's the price points? What's the feedback from the market so far?
We are in pre-revenue. We've got a pilot program running, really good feedback. Price points are going to be $39 per month for 10 learning paths and about 70 courses. And then next level up is $100, $119, and that is for 50 learning paths. And then the next level up is your enterprise, and that's $399.
Tell me a bit more about your ICP. Where do they live exactly?
Our, our primary target is going to be for tech companies because they're going to be the most, the most likely organizations to move to AI and utilize AI agents, which is where you're going to really see most of the benefit. But this can easily scale to any organization as AI agents become a normal part of the business. And once you have, you know, I'm sure after June when WWDC comes out, Apple is revamping Siri, they're gonna be turning it into an AI agent. Copilot's probably gonna do the same thing. Finally. Yeah, like a real AI agent, right? Copilot's probably gonna do the same thing for Microsoft. Google already has it. This is going to explode and it's gonna go into the enterprise, whether the enterprise wants it or not. They need to be prepared, and we prepare them.
I love the branding generally, but I think, uh, the wording could be a bit more precise on that landing page. Me, as a learner, as a guy that's obsessed with learning, I just want you— I want you— I want to know how you— how your agent can help me learn as fast as possible, because I'm already using multiple AIs on a daily basis, follow the Socratic approach, uh, to an extreme degree, right? Like one question leads to another, and I think that's the best way ever to learn. I'm not sure if I need videos. I do listen to podcasts, but how would your agent be able to take my learning to the next level is my question.
Yeah, absolutely. I'll update that. The way it works is it creates the structured learning that takes you to your, your business out. If you need to learn how to do XYZ in your job, like these are the tasks that you need to perform in order to be successful. These are the subtasks in order to complete those tasks. This is the skill, knowledge, and attitudes. All that gets developed into the training and then that gets, it gets published. And then part of the next iteration, which I just developed and I'm just ready to get to release, when you're in your environment and you have a question, you can pop, you can pop up, you select the little question mark and it'll populate that part of the training, that section of the training that's relevant to where you're at to help you learn as you go. So it's not a one-and-done type training. It's a constant reminder of, you know, I don't quite remember how to do it in this particular area. This is— that's what I needed to know. So you get it as you, as you, as you, um, as you need it.
Okay, I love your background. Just one last question before I give the stage to Sunny. I'm just interested, uh, if you're like full-time into this, if you're looking for a job, because your LinkedIn says open to work. I know that you've worked at these big enterprises, uh, so are you like full full-time, all in into this, or is it just a hobby?
That's a good question. Right now I'm full-time, all into this. If I get another job, I would— right now, so the reality is I need a job of some sort. Right now I'm full into this because this is what's getting me where I need to go. So that's what I want to have happen.
Yeah, yeah, I guess. I mean, for an investor, we need like full convictions, right? Or just like return the money if you accept the job or something. But you know, you're competing against savages that are full fucking time all in into this, and that's like what we're sort of looking for as, um, as VCs. But I love your background. I think should be included early in the slides, you know, like ServiceNow, all your training roles. This is very powerful. Sunny, any feedback for Jeremy?
Yeah, I guess, Jeremy, my big question was how do you make sure that these agents don't hallucinate, right? Especially in this like work context.
Really good question. The idea about the agents creating content is primarily— it's there for creating the scaffolding. That's why approvals are built into every single layer. Like, you've got to go in, you've got to look at it, you've got to approve it as a human. The AI agent, when you have it write something or have it update something or have it look at something, it's going to be evaluating the content, it's going to look at it based off of your context, the way you work, and then you're going to review it. You're going to make sure that it's what it needs to be, and that should be the workflow of every AI experience. Is that going to be the reality? No, but you know, the thing is your SME is going to provide their update. You as the content owner, you're going to want to make sure that that's correct, so you're going to review it. And so there's always going to be a human involved.
And what's the hardest part to build?
Oh, the hardest part to build is the the in-situation, in situ training, making sure that, you know, when you're getting that to pull content out and put it into your in-context training, that's probably the most difficult bit.
And then just my last question, Jeremy, is why can't an LMS just bolt something like this on?
Good question. Because there's so much that has to be rooted in the MCP background and in the way AI works, most LMSs are general platforms that are built for human consumption and human utilization. This has reimagined that and it's really taken the parallel network, the parallel internet in mind. You can see everything, you can still do everything as a human, but it's optimized really for AI agents to work with it. It solves in an AI world, and this is getting into a little bit of diatribe. The AI world, you're going to have AI assistants that are going to be personal, that you're going to be working and are going to have do work for you, and they're going to know your context the most. You're going to have workplace AI agents that are reactive and do whatever you need them to do, and they follow workflow, and that is our course generation. And then you're going to have MCP or CLI-connected platforms that will work with AI agents to allow for editing and record keeping and all that fun stuff, and that's, that's where our MCP connected piece works. And then, and then the last bit is you're gonna have humans that maintain all of that. So we cover 2 of the 4, and most just cover just the one.
Thank you, Jeremy. We're gonna go to our next contender. Uh, then I'm glad to have you on. The stage is yours. We can hear you. Uh, so move ahead with the wonderful presentation.
Okay, okay, let this sink in. AI does not rank, it selects, and most businesses don't understand why they're not being selected. Axosuite helps B2B companies understand and improve how AI models recommend them. Your best customer just asked ChatGPT who to select. You weren't mentioned, you weren't selected, and you had no idea it happened. And this is happening before someone ever visits your website. Discovery has fundamentally changed. It used to be search, click, decide. Now it's prompt, select, engage. AI is making the decision before you're even considered, and this creates a new gap. Be— being known is not the same as being chosen. Most companies have visibility, but very few are actually recommended, and that gap is where inbound business is is quietly disappearing. Access Suite is built to solve that gap. We call it AI Discovery Intelligence. It analyzes how AI systems interpret and evaluate your brand, shows you why you're not being selected, and gives you actions to fix it automatically. Our AI agent executes those fixes autonomously. We do not just report the problem, we start solving it. And this is what it looks like. The system continues scans AI platforms like ChatGPT and complexity, identifies where competitors are being recommended instead of you, and generates corrective content to improve how often you're recommended. It does not just show the problem, it starts fixing it. This is a new category for me right now. We are starting with AIware SMB and mid-market B2B companies, specifically SaaS service providers and agencies that depend on inbound demand. That represents a $750 million initial target market based on current pricing and adoption that sits within a much larger $42 billion shift from search to AI-driven discovery. We are early and intentionally staged. We have active pilot users with direct feedback already shaping the roadmap, growing inbound interest, and early signals that users immediately see value once they're— they understand the gap. We are pre-scale but post-validation. This is a SaaS model, price per brand starting at $175 per month. We expand into teams, agencies, and enterprise accounts, increasing ACV as customers grow. Revenue scales with both customer growth and adoption of AI-driven discovery. Existing tools were built for search. We are built for how AI systems select. That means different data, different models, and a focus on execution, not just monitoring. No competitor currently offers an autonomous agent that executes fixes. This is a meaningful product category difference. With a background in finance and operation, I identify this shift by analyzing hundreds of real AI outputs and built Access Suite to solve it. I spent the last 6 months building the product and the past 8 weeks validating it in market. I'm focused on executing quickly with support from Enterprise AI Advisory, Mark Smith, and we're turning AI selection from a black box into something businesses can actually control. We are raising $200,000 at a $2 million valuation. That gets us 50 paying customers and approximately $100,000 annual recurring revenue within 12 months, along with the repeatable acquisition channel. Capitalist focus on product stability, distribution, and building a lean foundation for scale. Every brand, every AI, every answer. Access Suite becomes a system businesses rely on to be consistently understood and recommended by AI, and the companies that are recommended will win. Thank you.
Thanks, Dana. Yeah, generally bullish on your niche. Uh, the deck, uh, maybe a bit more practice so that it feels natural because it was, uh, felt like you were reading from teleprompter or something. Um, I had a bunch of clients in that niche, some of the biggest guys that already raised like $5-10 mil, uh, right, including TryMeridian.com. Why, uh, are you the right person to build this?
Oh, um, I identified the shift by analyzing hundreds of real AI outputs and built the product around that insight. I'm also trying to be a category creator, which you really can't hire someone to have been first.
And tell us about your, your background in itself, right? Because, um, I feel, uh, yeah, you were VP at some point, you were owner of a business for like 16 years, right? Like, you've seen a lot, right? Uh, maybe tell us a bit more about your background and why you've got that fire in you.
Yeah, I did have my own bookkeeping business for about 16 years, um, and then I sold it and, and just went on to, uh, continue with finance and operations. And, um, I just am hard worker.
And, um, your website, Phil, would need like a bit of rebranding, right? Like if you're selling like AI SEO, I mean, use lovable would be a quick piece of advice. I love how meta this strategy is. And my partner Sunny on this call, we always talk about meta framework, right? In your case, it's improving your own SEO, right, and getting clients top of funnel. That have landed on your website from AI, uh, slash, uh, geo. Uh, do you have examples of results that you've got for yourself or some clients so that we can trust that you're really onto something and that you've cracked the codes, which is so hard to crack at that level?
It is hard. Um, we're just at the early stage. Um, the, the product has been built and we're just trying to actually get to that point. I have the two users that are working working within it, and I got that through organic content.
And it would be nice to see proof, right, in the slides that you have something and you have a repeatable, auditable process. You know, like, how I feel about that is, is the same in the 2000s with SEO, right? Like, there were all kinds of hacks to get to the top, but most of them lasted like, what, 1 or 2 years, or like a cycle of Google algorithms, and there's been so many. In your niche, like I said, there's a lot of players, a lot of them claiming onto something. I would go in a different direction. I would zig while they zag, and I would probably develop an algorithm to prove that I really crack the system in an objective way, right? Rather than just like, uh, marketing that, yeah, you can do better at that level. But yeah, I'd be here to try you guys out. And just one last question before I let Sonya on stage: what percentage of your company is, uh, SaaS, right, automated, vs. agency?
So right now it's probably about, uh, 75%. SaaS. We're just building into the agency part right now.
Got it.
And into any, any questions for Dana?
Well, I mean, great. I think this is very topical. It's something that I definitely think a lot about for my companies. I guess, can you help me articulate why this is a billion-dollar problem and not like a feature?
Uh, because AI is becoming the primary discovery layer. It's not just an add-on to service. Businesses depend on being recommended by AI to get customers. This definitely becomes a core system across every company, not just a nice-to-have tool.
And Dena, how fast is this shift happening? Are you able to quantify it somehow?
I'm really not, but it is increasing faster. This is what keeps me up at night. That it's forming so quickly on the focus and it's moving fast and capturing early users is becoming very competitive.
And how do you actually measure if a brand is being selected by AI? I'm sorry, how do you, how do you know? How do you actually measure that a brand is being selected by AI?
So, pardon?
Objectively, right? Like, can you audit your method to distinguish if a brand is, is being, yeah, selected by AI, not just because they've— it's been pre-prompted, right? Like, do you have a bunch of accounts of GPTs out there? Um, yeah, how do you audit if a brand is popping up?
Yeah, so it does scan on 3 different, uh, it does ChatGPT, Perplexity, and, um, Claude right now, it scans all those 3 and tries to find, you know, the brand and where it comes, and then it brings out all that information into one. So it's not anything that's like a placeholder or any particular prompt that's set to this is what it needs to be. It automatically brings in what it finds. It does the scan on, you know, on the web.
And then the last question I had was, like, I'm curious whether the bigger insight here is brand visibility or model behavior, uh, because that seems like the key thing that decides, right, if this becomes a real platform.
So it would be, um, visibility to be recommended.
Thank you.
All right, let's go to the next contender. Uh, thank you, Dana. If you can unmute yourself, uh, you have the stage, you can see your screen. Presentation is yours.
Hi everyone, my name is Effy Schwentowski. I'm the founder of Toolba Media. So in the bigger picture, we are operating in the space of media buying. We streamline media buying and fixing one of the biggest problems in the industry now, which is basically us spending our good budget on showing our ads to the wrong eyeballs. So Tuva Media serve ads to the right audience at the right time. We're using award-winning GenAI, we're using Watsonx, we partnered with IBM on that project. Um, the way we do it is we build audiences, we're using hybrid of data aggregation, data mining, tapping into large other APIs as well as social listening. We then, um, putting, um, GenAI in action where we, um, run analysis, and I will expand a little bit more about that. It's all to do with improving ROAS, return on ad spend. Um, if I would, uh, be, um, bold, I will say that we operating in a shift that's happening in digital marketing mainly in research and media buying, where we're replacing manual placement with automated AI-powered strategies, all to do with basically improving campaigns' performance, getting more leads, getting more sales for our money. If I would scream, our slogan will be: understanding your audience Understanding how your audience think, react, and engage should be at the heart of every decision you make. That business belongs to a very, I would say, old-school methodologies where brands using surveys, cold calling, and all the rest of it are engaging such and such an Ogilvy for large budgets, you know, that to market research and competitive analysis. We automate all that cycle. I will jump quickly showing you guys how it actually works. That is a live campaign. So in any given time, you can see with how many people you're communicating in, in, in that space. We are SaaS product. So a campaign looks like that. You can hear me, guys?
Yeah, see you, hear you.
Yeah, so a campaign looks like that. You choose a geolocation, you give a red distance so you can zoom in or zoom out. You choose some keywords. Here is— here we're acting on behalf of a large real estate in Sydney. Trying to sell apartments in Sydney CBD. So basically, once you choose a geolocation and a keyword, the first prompt called purpose, we ask to identify with potential buyers for ultra-luxury apartments. So every individual, every user in that geolocation that is actually engaged with one of our keywords, whether he watched a video or comment on an image or looked at an image or shared an image or passed a comment or any sorts of engagement with those keywords would be aggregated into our dashboard. So that is our dashboard, and basically all our audience sits here. So that individual 14 hours ago just posted, he listed the property, and this one is looking for to to buy a property with a water view. This one just posting that he sold his property and all the rest of it. So basically each and every, um, each and every player in a user basically that engage with our keywords will be sitting here. This is where it's getting a little bit—
you got one minute on that.
This is where it's going to get a little bit more interesting. So we're using sentiment analysis to filter the noise and basically created— creating, um, an inventory of users that are relevant to us. Um, we then profile the audience. We use classification and extractions to build the ads. Um, so we first breaking our audience into sub-audiences, and then we build the headline and primary text, which is how we're going to communicate. With the audience. If we want to quickly look at how the ads looks like, the ads are living—
30 seconds left.
The ads are living right here. So we basically build audiences, build audiences, studying the audiences, and based on the AI report, crafting ads and copy, and then serve it to various publishers, mainly Meta and Google. So we designed this application to improve ROAS, basically.
Nice. Yeah, I love it. The first time I think— I mean, my first company was an ad agency about, what, 7 years ago. I used to use HiROS, right? Like, during these days, they've gone pretty Savory Exit, I think 5 years ago, that was Alex Becker. There's a lot of these platforms right now. Why do you think now's the right time to launch this?
The last research of the, the last major research of Howard shows that we are, we are living in a, in a digital space now that are almost 70% of the budget is actually wasted, goes on the wrong eyeballs. For many, many years, the problem is well known. The problem is the context, the context of the users, you know. So if you're raving about AI tools for HR, it can be that you're raving about AI tools in HR in positive context or in negative context. Yeah, you can say that this industry is premature and all the rest of it. It's a waste of time to show you that. And, and for many years, it was very hard to fix that problem. I think Watson X out of the— out of IBM nailed that niche with a very interesting mechanism to build the tone, the tone distinguished, and it works.
It works.
It's proved itself and it works. We serve almost 300,000 brands worldwide these days, and we minimum 4x your ROAS at the moment. That's our main goal, which is the bottom line results.
Roger that. When you said 300,000 brands, can you double-click on that? Is that like mostly free trial? How much paid customers you have? Can you cover the revenue and also how much you're asking?
All paid. There is no— we don't have free trial. There is no free trial jazz in Mediavine. You have to jump in. You can jump in and spend less and scale on the fly once you see some results. So all the 300,000 are paid. We happy to expose numbers and whatnot to specific relevant potential partners. So our CFO always happy to discuss with potential partners. But yeah, it works. It's rock and roll. It's really good days for us and I come across that podcast 2Bee in Australia and I was happy to talk to people, mainly to get, to get, to get your opinion.
Right. You have a free trial on your website, so why, why do you say the—
We don't have free trial.
Turbomedia.io, that's you, right?
Yes.
Okay. I see a free trial here, um, and you're charging $150.
You're talking about, you're talking about the register button?
Uh, no, in the pricing, the pricing section. Um, but yeah, you, your prices, they start at $150 AUD per month, right?
So $100 USD, approximately $100 USD buys you one license which is restricted to one geolocation and 5 keywords. And that's the pricing model. Yeah.
Okay. Because you said 300K users, that is gigantic. That's the size of Canva, right? So according to that, you'd be making $30 million USD per month. Is that the stats on the platform?
I'm happy to expose some numbers to any potential candidate and any potential partners. As I, as I mentioned, it works really, really well at the moment. The, the thing is that when we launched, we launched together with the partnership with IBM, so we had access to IBM's clients to start with in APAC and USA. So our go-to-market was fairly smooth. Having access to IBM clients was a big shortcut for us.
Okay, uh, Sunny, any questions for Effy here?
Yeah, really impressive, by the way. So why can you build better audiences than Meta and Google?
Um, so it's not that I can build better audiences, is that I can deploy various mechanisms using GenAI Watson X to fix problems. So the first problem is the context, as I said. I would like to call it the tone, the tone of the profiles. I want to build inventory of users, potential users to buy my product and to engage with my services that are positive context. I will give you an example. If you want to sell your SaaS software to help people raising money and it's built on whatever, you know, AI, LLM model or whatnot, and you're engaging with audiences that always constantly talking about new platforms for raising using AI, if that user talk with the is overall his sentiment is negative, which means he's often mentioning that the market is premature and it's a waste of time and energy and money and all the rest of it. The concept is not to show the ad. So the concept is to dig into large-scale databases of potential buyers and users and start basically filter the noise. I heard a few people using that term today. I like it, filtering the noise. And the noise is the noise are users that are less likely to engage with you. So we do all those beautiful things and based on the know-how, of the learning curves of our audience by slicing and dicing our potential audience into sub-audiences and then understanding how we should talk. And we basically, in Touba, designing ads at the end of the funnels to each profile individually, talking his language, which is, you know, if you want to do it manually, you just need a lot of people to do manually work. Manual work, and it's also not possible.
If you want one last question, so you know the 300,000 brands, uh, what does retention look like?
Um, very good. As I said, I'm not here to, uh, uh, um, um, basically, um, um, pre- launch or anything in that sort. It's looking very good. This market is starving. It's a starving market of brands, whether it is a mom and dad that actually runs the show or a large brand like JLL or Deloitte or IBM or Netflix. The waste in media buying today is gigantic. And not just that, it's also creating a very negative brand exposure when you actually engage with the wrong audience. You're just opening a window for negative comments and negative feedback.
All right, thank you, Efrim. Let's bring everyone on stage. All our contenders today to see, uh, yeah, who's the winner or who are— who is your favorite pitch? I'm gonna start, uh, maybe Sunny can go second, but, um, I would go Brandon for sure. Not sure Brandon's back, he had a call. Uh, David as well, close second. So that would be my two winners for today, for the present. The pitch was good, the vibe was good. Um, even if, um, yeah, like David, I know you're early. Brandon definitely has some revenues, are very investable, but David, you're young. Um, me, I fund founders, right? And the, the younger the better, generally. So you're gonna have a long, promising career ahead of you. So I would bet on you.
Thank you.
Any thoughts on your end of, uh, who you like most today?
Ah, I thought everyone did a great job. Uh, Brandon, though, stood out for me big time.
All right. Um, Dana, I'm not sure if you had the chance to listen into some decks. Uh, who was, uh, who was your favorite of the day? You're muted.
Um, I did like David's.
Thank you, I really appreciate it.
Jeremy, any favorites for today?
Uh, definitely David and Brandon. They did a really good job. Paulo, I really like what, what you guys are doing there. Um, I have a lot of— I've worked a lot with, uh, the healthcare space both the University of Utah and University of California San Diego. And so that's pretty awesome. And Effie, you've got some really, really interesting stuff going on there. And Dana, you too. I mean, I liked everything. It was great.
Paulo, who was your favorite founder today?
Well, I think we almost everyone agrees with Brandon and myself. I also like David and Effy.
Thank you.
But yes, we, we all should progress in some way. So that's my, my wishes for everyone.
Effy, any favorites, uh, for today?
Yeah, I like, I like everyone equally. It was very impressive. I can feel the motivations of each one trying to drive as hard as you can. Everyone, such as good luck, just continue, push hard, go for it. Even on those days that it doesn't look so shiny, just go, just go, go, go, don't stop.
There you go. Thank you. Thanks to all. That was the 6th edition
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