Latest / Cyber Go-To-Market Talk: The Cybersecurity Podcast About Cyber Sales, Marketing & Revenue Growth / DataTribe Challenge Finalist: Evercoast

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DataTribe Challenge Finalist: Evercoast · Cyber Go-To-Market Talk: The Cybersecurity Podcast About Cyber Sales, Marketing & Revenue Growth

• 0:41
  SPEAKER_00: Welcome to the Cybersecurity Go to Market podcast for a special showcase episode where we are talking with the leaders of the company selected for the 2025 Data Tribe Challenge.
• 0:54
  SPEAKER_00: Data Tribe is the Cybersecurity Foundry, and they've identified five startups as being highly innovative.
• 1:01
  SPEAKER_00: I'm your host, Andrew Monahan, and today we're talking with Ben Nunez, CEO and co-founder at Evercoast.
• 1:07
  SPEAKER_00: Ben, welcome to the podcast.
• 1:09
  SPEAKER_01: Great to be here, Andrew.
• 1:10
  SPEAKER_01: Thanks for having me.
• 1:11
  SPEAKER_00: You know, Ben, I talked to a lot of people over the last few years.
• 1:14
  SPEAKER_00: I don't think I've ever talked to someone with who has been the CEO slash founder at six companies.
• 1:22
  SPEAKER_00: So I'm kind of fascinated at the gate.
• 1:24
  SPEAKER_00: What is it about the kind of being the founder, leader, innovator, entrepreneur that gets you so interested and excited to come back for more and more?
• 1:34
  SPEAKER_01: Yeah, I mean, I think maybe I am a glutton for punishment.
• 1:37
  SPEAKER_01: It's not for the faint of heart starting company and trying to grow it and and find a good return for investors and have fun along the way.
• 1:45
  SPEAKER_01: And, you know, but I've found a lot of joy in starting something from scratch from a from an idea and really building team and building product and you know going to market with something that adds a lot of value that is often at the forefront of some emerging technology.
• 2:04
  SPEAKER_01: So it's often something that, you know, I like to find what I think is going to be the next thing or you know, where technology is going and and try to craft a company around that.
• 2:16
  SPEAKER_01: And it's certainly an exciting wild ride along the way.
• 2:19
  SPEAKER_01: And, you know, I've had a couple of really good successes and exits.
• 2:23
  SPEAKER_01: And and you know, you can do the math on the on the difference between a six and a couple, but you know, so you have your ups and downs and your wins and loses losses, and you know, but it's it's always a fun ride.
• 2:34
  SPEAKER_00: And of the six, when was your first big exit?
• 2:37
  SPEAKER_00: Did you was it first time around the good you going?
• 2:39
  SPEAKER_00: Or it was.
• 2:40
  SPEAKER_01: It was first time around.
• 2:41
  SPEAKER_01: You know, company number one, fresh out of college, started a company and and exited that, you know, within about four years.
• 2:49
  SPEAKER_01: So it was uh an incredible sort of you know early win and learning experience, you know, when you're when you're that young, it's not something you really know how to do or what you're doing.
• 2:58
  SPEAKER_01: And you know, are often flying by the seat of your pants and and trying to figure it out along the way and you know, looking for mentors and people who can, you know, help guide you through that process.
• 3:08
  SPEAKER_00: Do you ever wonder if that wasn't a success, whether you would have come back for more after that or would you gone and put a W-2 somewhere?
• 3:15
  SPEAKER_01: No, I mean, I I I knew immediately I wanted to go off and do something else.
• 3:19
  SPEAKER_01: You know, it's something I've always wanted to do was, you know, start companies and and you know, be an entrepreneur.
• 3:25
  SPEAKER_01: I knew, you know, being at a at a really big company, you know, maybe, maybe at some point that'll be, you know, really exciting to me and and go, you know, find resources inside large companies that can, you know, help grow what what you know I like to do even more.
• 3:39
  SPEAKER_01: But you know, certainly early on after the first success, it motivated me to do, you know, more and do another one.
• 3:46
  SPEAKER_01: And and then those failures motivates you too.
• 3:48
  SPEAKER_01: So, you know, it keeps you, it keeps you going.
• 3:51
  SPEAKER_01: And this one, I'm you know, at Evercoast, I'm incredibly excited about what we're up to.
• 3:55
  SPEAKER_01: I think it'll be our biggest success, your biggest, biggest success in my career so far.
• 3:59
  SPEAKER_00: Well, let's fast forward to Evercoast then.
• 4:01
  SPEAKER_00: What is the story behind the story of the founding of Evercoast?
• 4:06
  SPEAKER_01: Yeah, you know, it was really an evolution.
• 4:09
  SPEAKER_01: There's no big sort of moment where we, you know, I it kind of dawned on me or anything.
• 4:13
  SPEAKER_01: It was really uh an exploration of, you know, how do we go from a world in of 2D content into something that we, you know, ultimately believe will be a fully immersive 3D world.
• 4:25
  SPEAKER_01: You know, it's how our eyes are are meant to see the world as in 3D, and yet we stare at these 2D screens for for so long.
• 4:33
  SPEAKER_01: The and so there's there's something just sort of very exciting about you know, kind of where that type of experience can can go.
• 4:40
  SPEAKER_01: And and, you know, as a sort of exploration of where are we in 3D tech, spatial technology, how do we put you know, a lot of that in motion and and make it something, you know, 3D content that moves of the of the real world.
• 4:54
  SPEAKER_01: And so, you know, it was just kind of finding, you know, assessing the market and seeing where the opportunities were and and and kind of landed on something that had been being done inside of really large kind of Hollywood caliber, you know, environments.
• 5:09
  SPEAKER_01: It was kind of used for, you know, early on in in film.
• 5:12
  SPEAKER_01: They were taking, you've probably seen motion capture where they, you know, put all the dots on people and you know make make movies out of that.
• 5:19
  SPEAKER_01: You know, what we were doing is is volumetric capture, which takes everything that you've done, you know, that they were doing in motion capture, but gives you the hair, the skin, the eyes, and everything.
• 5:29
  SPEAKER_01: And so we wanted to take that and make it something that was more cost-effective, more scalable, could reach a larger, you know, audience of you know, consumers and customers who could use it.
• 5:41
  SPEAKER_01: And that's really where we, you know, the initiative behind what we started the company with.
• 5:46
  SPEAKER_00: And for those that are paying keen attention to what Ben's saying, you're sitting there probably right now thinking, this doesn't sound like cybersecurity to me, which which it doesn't, right?
• 5:55
  SPEAKER_00: So let's let's kind of go back.
• 5:56
  SPEAKER_00: So, what exactly is Evercoast doing for us cybersecurity go-to-market folks, you know, put it in language that we'd understand?
• 6:03
  SPEAKER_01: Yeah.
• 6:03
  SPEAKER_01: So uh, you know, I think if you if you can think about where the world is going and something that we call physical AI.
• 6:09
  SPEAKER_01: Physical AI is is really AI that is actuated into the real world.
• 6:15
  SPEAKER_01: And most people associate physical AI with robots.
• 6:19
  SPEAKER_01: And there is absolutely a robot revolution coming.
• 6:23
  SPEAKER_01: You know, the, you know, we're we're building humanoid robots and robotic arms and you know, wheeled robots that deliver your food.
• 6:30
  SPEAKER_01: And there's all kinds of sort of, you know, physical, there's there's AI that is just being, you know, brought to the world in physical form.
• 6:38
  SPEAKER_01: And and what we do is, you know, create, we have a we have a software platform that allows companies who are creating data to train these robots, you know, do so uh in an easier way and a lot more spatial 3D type content.
• 6:55
  SPEAKER_01: So, you know, one of the big things that a robot companies and robotics companies are are struggling with is data.
• 7:01
  SPEAKER_01: You know, there is no internet scale sort of corpus of physical behavior data.
• 7:08
  SPEAKER_01: You know, we've had these, these, you know, chat GPT, LLM, you know, and you know, applications that have had the luxury of trillions of tokens of human language.
• 7:19
  SPEAKER_01: The generative video platforms have had hundreds of billions of hours of you know, random video on YouTube and elsewhere.
• 7:26
  SPEAKER_01: Physical AI just doesn't have that.
• 7:29
  SPEAKER_01: You know, it has you know, at best has thousands of hours of demonstration.
• 7:33
  SPEAKER_01: And sort of it's an order of magnitude, you know, way, way, way short.
• 7:37
  SPEAKER_01: And so there's this frantic effort to kind of create data and close that big data gap.
• 7:43
  SPEAKER_01: And that's really where Evercos comes in.
• 7:45
  SPEAKER_00: So, in scenario that is real for me, my my daughter just started at the University of Arizona in Tucson.
• 7:52
  SPEAKER_00: And first thing I I almost saw on the campus when she started was they had these delivery kind of robots.
• 7:58
  SPEAKER_00: I mean, there's there's four wheels, it's like a it's like a mini car almost, and it kind of just goes around, there's many of them just zipping around campus, delivering things to to students.
• 8:08
  SPEAKER_00: Not the experience I had at college in my day, I've got to tell you.
• 8:11
  SPEAKER_00: It's a it's a whole different world.
• 8:13
  SPEAKER_00: So, so how would what you're doing make a difference for the company that's developing those robots, let's say.
• 8:19
  SPEAKER_01: You know, those are about to be, we have actually had one of them reach out recently.
• 8:23
  SPEAKER_01: You know, this was kind of more of a funny one.
• 8:25
  SPEAKER_01: It's it's it's less sort of our direct market of what we're going after, but they they were actually wanted to understand the details of of how people were kicking their robots, you know.
• 8:36
  SPEAKER_01: So people would come up as fresh.
• 8:38
  SPEAKER_01: I gotta imagine on college campus, drunk kids or something coming up and just trying to kick the robot and knock it over.
• 8:44
  SPEAKER_01: They wanted to understand physical, human physicality as it impacted, you know, that though these delivery robots.
• 8:52
  SPEAKER_01: So it was it was more of an interesting, you know, we'll we'll kind of see where that goes, if anything.
• 8:57
  SPEAKER_01: But yeah, I think a lot of this is understanding how a human moves and then you know, teaching robots to move like a human or how you know tasks are performed and teaching robots to perform those tasks.
• 9:10
  SPEAKER_01: And then on the flip side of that, and that's something we call uh imitation learning, something we as humans have always, you know, done.
• 9:17
  SPEAKER_01: I've got a one-year-old little boy, and and he is imitating, you know, a lot of different things.
• 9:21
  SPEAKER_01: So it's a you know, that imitation learning is a fundamental process of learning, and that's being brought to physical AI and robots.
• 9:29
  SPEAKER_01: And then the the flip side of that is something we call reinforcement learning, where the robot can understand when it has done something you know wrong or out into the room or correct, the sort of rewards or lack of rewards to you know try to direct the robot's behavior.
• 9:46
  SPEAKER_01: So being able to capture all of that, you know, in 3D and then be able to bring that kind of data into simulation, which is really where a lot of the work is done to get a robot to do something that you want it to do.
• 10:01
  SPEAKER_01: It's it's brought into these simulation engines.
• 10:04
  SPEAKER_01: And and those simulations are all simulation engines are 3D physics-based, you know, engines that need 3D physics-based spatial data.
• 10:13
  SPEAKER_01: And that's really what we provide.
• 10:15
  SPEAKER_00: I would imagine for these companies, time to market for their new products they're developing is what they're probably trying to, you know, bring down.
• 10:22
  SPEAKER_00: But also, you know, this is a world where human acceptance is part of the equation as well.
• 10:27
  SPEAKER_00: I'm wondering, does that help with that?
• 10:29
  SPEAKER_00: The more, I don't know, lifelike you are because you've got the data to give them, the easier it will be for the world to accept whatever the companies are producing.
• 10:37
  SPEAKER_01: I think there's, I think there's a part of it.
• 10:38
  SPEAKER_01: You know, this world, you know, a lot of a lot of people debate whether the humanoid is the correct form factor for, you know, for a robot that's gonna work among us.
• 10:48
  SPEAKER_01: And and certainly there's a lot of logic behind that, and that our entire world has been built for largely for humans.
• 10:55
  SPEAKER_01: You know, the way how where a doorknob is or counter heights, and you know, everything is built around us.
• 11:01
  SPEAKER_01: And so, you know, certainly it makes a lot of logical sense to to if we're gonna have, you know, a robot doing things that the human is meant to do, or at least operate inside of our world, you know, it could, you know, be be the form factor could align with that.
• 11:15
  SPEAKER_01: Although, you know, at the same time, you have an opportunity to, you know, make the robot better, improve upon the the weaknesses of a human, you know, physical form and and make it even stronger and better and and different.
• 11:26
  SPEAKER_01: So I think what we hear a lot, there's a there's a sort of you know, phrase in in in robotics where they they uh cage-free robots basically.
• 11:34
  SPEAKER_01: You got to get the robots out of the cage.
• 11:37
  SPEAKER_01: Because a lot of the robots are sort of, you know, locked up, so to speak, in operating in cages because, you know, it's dangerous for them to potentially be out amongst humans and you know, not know how to operate and behave with humans.
• 11:51
  SPEAKER_01: And so, you know, their their ability to potentially hurt or harm, you know, or really mess things up out in the real world.
• 11:58
  SPEAKER_01: And but but in order for that sort of to scale and for people to really, you know, be able to embrace in and being around robots, they need to be trained and and proof.
• 12:09
  SPEAKER_01: You know, there needs to be proof that they have been trained on the most rigid and and you know, richest form of uh of data.
• 12:16
  SPEAKER_01: And that's really something that Evercoast excels at.
• 12:19
  SPEAKER_00: As you were talking there, one thing that struck me, there's this whole initiative or not initiative, this whole theme right now about vibe coding.
• 12:27
  SPEAKER_00: You know, anyone could just get Bolt or Cursor or one of these apps and just choose some software.
• 12:33
  SPEAKER_00: Very different in the robotics world, right?
• 12:35
  SPEAKER_00: There isn't that that same thing.
• 12:37
  SPEAKER_00: So I'm just wondering like how many companies are working on these types of things?
• 12:40
  SPEAKER_00: What's the size of the market right now?
• 12:42
  SPEAKER_00: What are you seeing as as the real growth areas?
• 12:45
  SPEAKER_01: So data collection is a is a now becoming a massive sort of industry.
• 12:50
  SPEAKER_01: And so if we look back at this past several years of physical AI and robotics and you know, trying to get robots to do things, the easiest thing that people could do is jump into a simulation tool.
• 13:02
  SPEAKER_01: Nvidia has them, Google has them, you know, Unreal Engine, a lot of these sort of game engines, these simulation tools that a robotics engineer could could jump into, create a bunch of synthetic data, and then you know, actuate that, you know, with a on a robot or with a robot.
• 13:20
  SPEAKER_01: And so it's just easier to do that.
• 13:22
  SPEAKER_01: It's easier to create, you know, simulation and synthetic data that is is somewhat you know relatively simple.
• 13:28
  SPEAKER_01: Getting someone to walk or or even just kind of maybe pick up a box or something like that can be, can be simulated.
• 13:35
  SPEAKER_01: That's something that we, you know, there's this kind of value chain of you know, physical AI, and it goes from real to sim, sort of capturing the real world and bringing it into simulation.
• 13:45
  SPEAKER_01: And there's simulation, and then there's sim to real.
• 13:47
  SPEAKER_01: And what I've just been describing is really that simulation and sim to real end of it because it was the lowest hanging fruit.
• 13:53
  SPEAKER_01: The harder thing to do is to try to go out into the real world and capture a lot of data of people doing things that's structured, that can be labeled properly, that can be brought into a physics engine, a 3D engine, like a simulation tool that can then be brought to the robot.
• 14:11
  SPEAKER_01: And so it had been neglected, I think, a lot for many years.
• 14:15
  SPEAKER_01: And so a lot of the teams have struggled to create tools and chain tool chains and pipelines that can take data from the real world and bring it into simulation.
• 14:26
  SPEAKER_01: And that's really where we chose to focus our company is on that real-to-sim side of the value chain.
• 14:33
  SPEAKER_01: And that's now having sort of you know tapped out, so to so to speak, a lot of the internet scale data, the you know, all the text, the video, the images, that's been leveraged for you know the the chat GPTs of the world or the SORAs and mid-journeys and other other sort of tools.
• 14:51
  SPEAKER_01: You know, we're now focused on that sort of what we believe to be is the next biggest market, and that is the real to sim part of the pipeline.
• 14:57
  SPEAKER_00: And what's the big innovation that you've you've figured out that makes it easier for you to do compared to everyone else who's struggled?
• 15:03
  SPEAKER_01: It's really spatial, spatial video, the ability to use, you know, and I'd say call it multimodal video, the ability to take RGB, you know, color images, depth, infrared, motion, inertial motion sensors, LIDAR, a bunch of different modal thermal, a bunch of different modalities of data, deploy that into the real world to capture data and bring it in, and then re structure that in a way that you know creates spatial context and just temporal continuity to be able to bring that better into simulation.
• 15:39
  SPEAKER_01: And that's something that's been really lacking.
• 15:40
  SPEAKER_01: There's been a lot of, you know, the people are trying to train robots in a lot of different ways.
• 15:44
  SPEAKER_01: They'll go out and just shoot regular 2D video, but there's a lot of blind spots to something like that.
• 15:49
  SPEAKER_01: Motion capture, I mentioned at the very beginning, the sort of dotted suit, you know, type type technology, that just gives you a skeleton of what the person was doing.
• 15:58
  SPEAKER_01: There's no context, there's no sort of surroundings.
• 16:01
  SPEAKER_01: There's teleoperation where they will sort of maybe wear something and try to, you know, someone performs a task and then the robot does it.
• 16:10
  SPEAKER_01: But that's often done in very controlled environments and very hard to scale that.
• 16:15
  SPEAKER_01: So I think the real innovation, you know, we've we've made is making you know really spatial capture, something that you know anybody can do inside of these, you know, these robotics labs and and enterprise enterprise robotics teams.
• 16:29
  SPEAKER_00: And are you doing the capture slightly differently, or are you taking anyone else's data and bringing in and processing it differently?
• 16:36
  SPEAKER_01: A bit of both.
• 16:37
  SPEAKER_01: You know, so there are 20 billion cameras and sensors that are being deployed into the wild every year.
• 16:43
  SPEAKER_01: And it's all to capture the real world.
• 16:45
  SPEAKER_01: It's really kind of becoming the eyes and ears of our world.
• 16:48
  SPEAKER_01: And they make those companies that make those cameras and sensors make great hardware, but they don't make great software.
• 16:54
  SPEAKER_01: That's really not what they're meant to be doing.
• 16:57
  SPEAKER_01: And so bridging that gap between, you know, the data that comes off those sensors and cameras and being able to bridge it, you know, bridge it into simulation is really where we see that sweet spot of opportunity to, you know, create software and AI that can take data that from these all of these cameras and sensors that are already out in the world and enrich it and create a better structure for it.
• 17:20
  SPEAKER_01: And then also provide tools that let the AI, robotics, and simulation teams who are who are trying to capture data using these same, you know, cameras and sensors, give them a better tool, you know, to be able to do that because it's all relatively new.
• 17:34
  SPEAKER_01: Like I said, it's been you know neglected for all these years.
• 17:37
  SPEAKER_01: And so there really aren't very refined solutions for you know how you do this kind of stuff.
• 17:42
  SPEAKER_01: And that's really what we provide.
• 17:43
  SPEAKER_00: It sounds like what you're saying to me as an outsider is very, very, very specialized.
• 17:48
  SPEAKER_00: It seems like there's probably not many people in the world that have the experience and skill set to really work on this.
• 17:53
  SPEAKER_00: Am I am I right?
• 17:54
  SPEAKER_00: Or is this an area that you're you're you're you're finding a rich fan of talent out there working on it?
• 17:59
  SPEAKER_01: Yeah, no, I mean, in terms of so from our perspective, like internally in our company, yes, it's it's something that you know, finding people who know all about this is it's a it's a very, you know, sliver of the of the population that you know understands the complexity of all of this.
• 18:15
  SPEAKER_01: Although, you know, there are incredible engineers and research scientists all over the world that we've been able to bring together, and there's certainly a lot more out there.
• 18:23
  SPEAKER_01: What we're finding is that you know, now in terms of our customer base, everybody is is looking to kind of create this sort of data.
• 18:31
  SPEAKER_01: Everyone from, I mean, we have customers like the US Air Force, Accenture to you know, Hinge Health, you know, for physical therapy.
• 18:39
  SPEAKER_01: There's a bunch of different kind of companies like that that, you know, are you looking for this kind of data, need this kind of data in order to be able to, you know, train whatever they're trying to do.
• 18:49
  SPEAKER_01: And so initially, yes, it was it was kind of very niche, but now it's it's something that many around the world are looking to do.
• 18:56
  SPEAKER_00: Okay.
• 18:57
  SPEAKER_00: Well, let's uh let's change the conversation a little bit here.
• 19:00
  SPEAKER_00: You're you're a multi-time founder, so you've had the experiences to uh have a good perspective on this question, which is what's most important?
• 19:09
  SPEAKER_00: Is it the product or is it the distribution, the sales side?
• 19:12
  SPEAKER_01: It's distribution.
• 19:13
  SPEAKER_01: If you got a great product and no distribution, it doesn't matter.
• 19:17
  SPEAKER_01: If you can, you know, create a product that meets some sort of need and may not be the perfect thing, but you know, you can get good distribution around it, then then you can at least start to understand and talk to customers and learn who you're distributing this product to and start to refine that product.
• 19:34
  SPEAKER_01: So I think there's too many companies that that focus too long and hard on trying to create this perfect product before they really start to get it out there.
• 19:42
  SPEAKER_01: And I just think it's you know critically important to start to understand how customers might use it, build some sort of minimum, you know, viable feature set that you know people can use and get it out there and do it.
• 19:53
  SPEAKER_01: But that said, I mean, you still got to come up with some sort of product that people can use and you know that's non-trivial to you know create something that you know meets some sort of threshold that can achieve distribution to begin with.
• 20:06
  SPEAKER_01: So it's always a balance.
• 20:08
  SPEAKER_01: But if I had to, you know, pick one, I'd I'd rather have distribution initially.
• 20:12
  SPEAKER_00: All right.
• 20:12
  SPEAKER_00: Well, let's look forward into this year and into 2026.
• 20:16
  SPEAKER_00: What's the biggest things on the on the plan for Evercoast?
• 20:19
  SPEAKER_01: It's really distribution.
• 20:21
  SPEAKER_01: Great, great segue into really what we're doing.
• 20:24
  SPEAKER_01: You know, we built a good product that you know is comprehensive and and is unique and has you know great value.
• 20:31
  SPEAKER_01: And now it's it's really about sort of pushing that out into the into the ecosystem and starting, you know, to get greater distribution.
• 20:37
  SPEAKER_01: We have some incredible customers early on now that we've got a product in place.
• 20:41
  SPEAKER_01: And we're just excited to, you know, and really, really looking forward to to pushing that product out to and finding customers who are just gonna get all the value in it.
• 20:50
  SPEAKER_01: And we know kind of who they are and you know where to go and how to get them.
• 20:54
  SPEAKER_01: And so it's 2026 is really all about that.
• 20:57
  SPEAKER_00: Well, we wish you every success on that plan, and I'll see you every success next week at the challenge.
• 21:02
  SPEAKER_00: Great.
• 21:02
  SPEAKER_00: Thanks for having me, Andrew.