Behind the Product: How Keybotic’s AI-Trained Robot Dog is Changing an Industry
Irene Gomez Alemany, CEO and Co-Founder at Keybotic, explains how they identified the market gap for their autonomous hazardous inspection robot—and their Robot as a Service model.
You know how it goes: One day you’re studying international tax law; the next, you’re creating a company that builds autonomous robot dogs capable of entering hazardous environments and preventing industrial accidents.
Not a common story perhaps, but it’s the extraordinary career path of Irene Gomez Alemany, CEO and Co-Founder at Barcelona-based Keybotic, recently chosen as one of just five startups from across Europe invited by the European Commission to present in Brussels.
Irene sat down with us to explain how she, along with partner Hilario Tome, co-founded a robotics company that’s filling a neglected market gap, serving a genuine customer need—making industrial plants, and their employees, much safer in the process. She told us why they chose to bet on themselves, competing opposite the best and brightest MIT has to offer to win 1st prize in the DARPA Robotics Challenge—and how a $1 million cash prize helped them found their company and bring to life their first robotic dog, Keyper.
- How Irene’s tax and compliance background set the stage for her robotic start-up.
- The importance of hiring the right people
- That market gap that drew Irene to robotics
- The high-risk, high-reward gamble of DARPA competition
- Building the MVP during COVID
- How AI’s teaching robot dogs to walk—and jump!
- Taking Keyper to market: the business model, sales, and implementation
- The importance of sustainability in robotics
- Lessons for tech founders and IoT developers
We start our conversation looking back on Irene's interesting background in law, economics, and taxation to find out how she transitioned into robotics—and how she still applies the lessons learned in her past lives as a CFO and COO.
Tell us about your background and how that led up to you founding Keybotic.
The truth is I’ve always loved companies and finding ways to help them.
I actually studied law. That was my first degree and then I studied economics. After that, I started studying taxation and earned a master's degree. From there, I moved to international taxes because that was the most complex part and I loved it.
It meant understanding different countries and how they worked. So not coming at it from just a national, domestic point of view but with international law so you know how things are taxed internationally.
That's actually when I realized I liked business more generally, not just tax law. I liked the big picture of how companies worked. By that time, working as a strategic consultant, I had a lot of international customers—most of them startups and tech companies.
One of them, ALLOut Security, pushed me to go work with them, so I joined as CFO. However, two weeks later I became the CCO. Nobody was doing it so I took the role. And then, in six months, I was the COO because nobody was managing the business so I said “Okay, I’ll do it!”
I’d been on the board of directors for a few years before taking these roles, so I knew how everything worked—it's not that I just flew in! I was at ALLOut Security for four years and I loved it very much.
At Keybotic, I’m doing the same type of work, but this time I’ve built it from the ground up and entirely in my own way. Instead of adapting something that already existed, I created the kind of company I always wished I could work at—that’s what Keybotic is.
You make a difficult, complicated thing—founding an award-winning robotics company— sound extremely simple! What did you learn in your previous roles that influenced how you built your own company?
The most important lesson I’ve learned is how essential employees are to a company’s success. In the past, I’ve often faced challenges that could have been avoided with the right team in place. So, at Keybotic, we have a very thorough interview process. For some positions, this has meant taking up to six months to find the right candidate.
We might have interviewed 200 candidates. We get a lot of applicants, and it's not just me choosing the candidates, we probably take 3-5 people through the process. And we’re not just looking at their CV or the knowledge they have but their attitude.
We do a questionnaire and go through exams before we bring people in for an interview, but even if they score 10 out of 10 on the test, they don't get in if we’re not convinced by their attitude.
I always want to go to work on Mondays. That's my dream. And I think that's a very important part—possibly the most important part—of a company because, at the end of the day, a company is just a bunch of people doing something together, right? What differs is what that thing is, but it's ultimately just a bunch of people doing something.
So who this bunch of people is is really important.
So why did this bunch of people get together to focus on robotics?
That's a fun thing to talk about because my partner, Hilario [Tome, Keybotic’s CTO and Co-Founder], comes from creating humanoid robots, the first ones that were commercialized worldwide back in 2006—20 years before Tesla!
Basically, we noticed a gap: while most of us use automatic vacuum cleaners at home, similar automation hasn’t reached industrial settings. For instance, in chemical plants, automation is minimal, because robots have traditionally struggled to navigate complex environments, such as those with staircases and slopes.
We realized that a four-legged robot could overcome these challenges. However, to provide real value in this context, the solution must also be fully autonomous. There’s no return on investment if you have to babysit the robot.
So you identified a gap in the market. How did you start addressing it?
We started with the automation part and realized that, in the U.S., there’s the DARPA Robotics Challenge. It's a big robotics challenge with enormous impact worldwide—autonomous cars, for example, were conceived in the first competition.
We took part in the third one, which was about robots finding people and objects in a 12-kilometer-long cave tunnel underground in place of police officers, soldiers, and firefighters who usually have to go in. We said, “Wow, this is exactly what we want to do.” We want robots that can enter a hazardous environment by themselves, find out whatever has to be found out, and come back, right? That's what we have to build for the industry.
So we participated in 2020 and we won an intermediate prize. That’s when we registered the business. We kept working on the development of our first robot and, in 2021, we won the final prize of the challenge, which was worth around $1 million.
We invested this money fully into Keybotic, and that’s how we were able to further build the company into what it is today.
You’ve mentioned in other interviews that entering the DARPA competition was a high-risk, high-reward strategy versus looking to get funding a traditional way. What were the risks involved?
It was extremely high risk, high reward. Hilario worked super hard on the project for several years on his own. It's not common to win in a world like that working alone!
We were competing, for instance, against MIT, which had a 40-person super team. I think it was ultimately his belief he could win that made him win, because if I’d known MIT was there I might’ve thought “It's impossible, so let's find another way to finance the company.” But he was sure that he’d at least win the third prize.
At that stage, nobody believed in us. Nobody would’ve thought we could compete against the big corporations we’re competing against nowadays. But we won!
So, besides financing, that’s what we achieved by winning this DARPA challenge: people started believing in us.
What did those two years working on that project alone look like, without a team and support? Was Hilario just holed up in his room working on it?
In a way, we were lucky that it was COVID time, because there was no social life. So Hilario worked every day from 10 am to 4 am. Every day.
We’d just bought a little Nissan Micra and, one day, he was driving back from the office at 4 am when the police stopped him. He must’ve seemed crazy, with Flamenco music blasting out the car really loudly so he didn’t fall asleep.
So they stopped him at a petrol station thinking he must have been out partying and he just told them “I'm working on a robotics competition for the US government!!”
That was kind of the atmosphere at the time. He was obsessed with the project.
What did it look like at the MVP stage, with DARPA, versus what you have now with Keyper, your flagship robot?
Well, we didn’t have any real robots at the competition—it was about the software, the navigation software that makes the robot navigate autonomously. What we had at DARPA was the first stage of what we have right now—it can do much more now, like working in larger spaces rather than just the confined space we had to work with for the challenge.
Back when you were taking part in competition in—2020, 2021—the current AI boom wasn't happening but it’s now part of your product. How has it helped you develop Keyper?
For us, AI is just a technique to make things faster and better. We use it in many, many ways that you won't even realize.
One of the main ways we use it is to help the robot understand the environment, for instance so the robot can read a gauge and understand if it's outside a particular, expected range.
We were already using AI even back when it wasn’t fashionable to say we used AI. For example, we’d already been working on the robot being able to teach itself how to walk using AI for a year and a half before the AI conversation really accelerated.
Now, the new advancements are allowing us to work in a completely different way, with huge simulations running with thousands of robots walking.
While before Hilario was typing out various algorithms—math, it's pure math—we can do things that math is not able to with AI, like teaching robots to walk when working in a plant with oil on the floor, with water, with slippery things, with ice.
We can use AI to train the robot to balance itself even on a moving floor. These are great things for us, because they enable our robot to work in very harsh environments.
So we use AI mainly for locomotion and perception, so our robots actually understand the environment. For example, now they can tell if an employee has a helmet on or not, as well as reading temperatures and things like that.
When they're learning how to walk in particular scenarios, is each robot learning on its own? Or does one robot learn how to walk on ice and then you kind of share that across all of the robots?
Once one of them learns how to walk on the ice, it means that they’ve all learned it. So it's always improving. They’re learning to walk with just two legs or even one leg. Or no legs when they’re jumping.
When you have a simulator, you can put certain constraints on them and get them to solve the problem. For instance, put them in the middle of a staircase and ask them to turn around. And then you increase the complexity of the problem. And once one of them does it right, it means that you have the right algorithm.
A lot of people will think of Boston Dynamics when they see a four-legged robot walking around like Keyper does. What differentiates what you’re doing from the sort of viral robotics videos they’ve shown off in the past?
Put simply: without them, we wouldn't exist. They were the pioneers in robotics back in 1986 and they’ve created a superlab where they’ve developed so many impressive things, which ultimately help us incredibly in terms of marketing.
We wouldn't exist without them because we didn't have the resources to let people know that this type of product exists. So when people see a four-legged robot from Boston Dynamics that can dance, it’s great for the whole robotics industry.
What we add to that is that our robot has a final application for industry customers.
Our value proposition is that we provide a service to customers, and that's actually why we also rent Keyper, we don’t just sell it. Our business proposition is saying that we’ll improve the process you have in your chemical plant, for example.
We’ll detect gas leakages in a way you couldn't before. We’ll automate and digitize all your plants, and you’ll be able to monitor what’s happening from the control center. Until now, a person had to go around and check these things manually.
It’s so big that you can do that. If you wanted to do something like this before you’d have to have sensors everywhere, which isn’t possible across a big industrial plant. So we’re a mobile sensor platform. And, with artificial intelligence, we can actually detect anomalies in those plants.
We haven't created the best robot in the world: We've created the robot that works in that environment and for that purpose. It's cost-effective, and it's modular so it's very easy to transport. Everything's integrated, even though you can add extra sensors on top, which aren’t ours.
What made you decide to go down this road of the Robot as a Service (RaaS) model rather than only selling units?
It's a way to have zero barriers for customers to try it. So we want customers to try it and that model makes it very easy for us to establish a collaboration: they know we’re with them for the success of the project.
It's not that we sell it and exit: We’re with them throughout the whole process.
How do you think being a European company—a Spanish company—makes you different from if you were based in Silicon Valley with this concept?
The truth is that it allows us to have European customers that otherwise we wouldn't. Because of the data protection laws, we have to comply with the laws and certifications in Europe, which the other companies cannot always do.
So that's why you can’t really find many robots from Boston Dynamics working in Europe, for example.
And then there’s the simple fact that we created the company in Barcelona because we wanted to! Hilario and I wanted to be home and we knew a bunch of good people that could start with us.
As I mentioned earlier, the first employees are the most important ones and actually we got the best of the best. If we’d gone to the US, even if we would be better financed, we wouldn't have the people we want because we’d be competing against companies with 10x the funding.
And we’re very globalized so next year we could go to the US and start there too. That's not a problem. I think it's more complex to come to Europe because you need to understand how different cultures work across different countries.
The markets are smaller than in the US and that also made us create a robot that’s very cost-effective because otherwise, we wouldn't have been able to start in Spain, where the budgets are lower, for example.
What are the main problems you face getting quite conservative industries to embrace new technology?
Already from the beginning it actually wasn’t very difficult when I cold called because we’d already gone viral within the industry and customers wanted to see what we had. They wanted to understand what was happening in the environment.
It’s not like selling software, for example, which is hard because there are lots of alternatives. In our case, they wanted to know what’s happening in the world of robots. They want to talk to you even if they don’t want to buy, which is a huge advantage.
They want to know what their neighbors are doing. So they answer 86% of the cold calls we make.
What sort of impact have you seen in those hazardous environments where Keyper has been deployed so far?
Right away we were doing a proof of concept and we detected a gas leak in a big plant that they didn't know about—in an explosive environment.
This industry is very interesting because, when you go to a customer, they never want to tell you how many accidents they have. It’s not until you talk to them the third or fourth time.
We have a customer that has 366 leaks every year. Not all of them are dangerous, but when we got there, the previous week, two people were in the hospital because they thought a leak was water and it wasn't.
So accidents happen in those environments. And what you want is to decrease the ratio. We’re detecting them earlier and in a way where people don’t have to go into hazardous situations to do it.
We also help them decrease costs.
For example, at one of the companies we work with, because the robot could take thermal images, we could prove that the plant had no leakages and it made a big audit pass in a single day.
So there's savings in compliance too?
Yes! In that particular case, because of the thermal images they had from all the plants, they could show that there were no problems and the customer was super impressed. Previously there wasn’t any way you could have a thermal map of an entire plant.
We actually increase safety standards in cases where the regulations aren’t there.
What approach do you take around change management in the organizations you work with, because it could present a big shift in ways of working?
Right now it’s not about reducing the number of people, even if that’s a long-term factor for our customers. Right now people who would have been monitoring the plants manually are redeployed and can do something else.
There’s a lot of work to do across these huge plants, so we take over the low-value activities they’re performing right now. Because making rounds 2-3 times a day to check gauges manually and then write a number on a piece of paper isn’t a great use of a person’s time. And the robot can actually do these jobs better.
What limitations does Keyper still face in the scenarios you've described?
So what we have right now is a passive robot. It goes around, but it does nothing other than report issues—it can’t take direct action to solve an issue. We're trying to change that.
And we're creating our second robot too, which is IP67—which means it's waterproof and dust proof. This means that it can go into very harsh environments.
Right now, where there’s a lot of dust and pollution in the air, or salt by the sea, it presents challenges to the robot. The new one will be able to function in those environments much better. And we’ll get our explosion proof certificates soon, so we can go into explosive environments.
You've mentioned the importance of sustainability in robotics. What's been your approach to environmentally friendly design?
So we always need to balance sustainability and also cost effectiveness. The aim is always to make both things work at the same time.
Because the robot is modular, we can reuse all of it. So imagine that after three years, the computers don't work anymore, because, like your computer, we've used it so much that it's old.
Then, rather than build an entirely new one, we can reuse all the old robots for a new robot. So the legs are detachable, but you also have modules inside the robot. So you can plug and play them.
It’s not just about creating something in a way that’s environmentally friendly but it’s also about creating something reusable.
Everything we’ve designed for the robot has been designed to be as simple as possible.
And then the robot’s purpose is to detect gas and steam leaks in chemical environments very quickly, which is crucial for the environment.
What fresh approaches did you bring to this industry coming from a non-traditional background that somebody wouldn't have had if they’d spent their whole career in robotics?
I think that there's one important thing that I experienced before, which is the importance of easy implementation.
In my previous role, our software didn’t have any sort of complex implementation. From day one, from minute one, you could start using it, while competitors had a huge amount of implementation, consultancy, months and months of training—it was very complex.
In our robotics solution, it was important to me that there’s zero implementation.
The robot starts working from minute one. That's very complex to reach and that's why most companies don't have that.
Right now we have videos to help our clients with everything from how to get the robot outside of the box to making it work, but in reality, if you click the button it starts. There's no implementation required. The robot is very robust. Right now we go with them and show them how it works, but that’s about a client-customer relationship more than anything else.
It works right out of the box because you simply remote control operate it the first time so it can create a 3D map of the entire environment. You digitize the whole plant with that. And then you show the robot what has to be checked. For example, “now we go through these gauges. Stop here.” And then you stop the robot. The robot detects the gauge. And then you select the objective for that mission that the robot will follow in the future.
It's super simple. That's all the customer should do the first time. Right now, we help them do it. But it's super simple to do and, in the future, we envisage a post-sales process where the customer can simply open the box and press start.
Finally, what are the broad lessons you’ve learned with Keyper that might apply to other IoT products outside of robotics?
I’d say, don't try to create the best product from a technical perspective, but the product the customer actually wants. It's difficult because all engineers want to create the best product. You have to stop them!
In our case, you always have to stop and say “how does this functionality help us reach our objective?” If our team wants to make the robot jump, for example, but the customer doesn't want the robot to jump then we shouldn’t develop something because it’s cool or technically impressive.
If the answer is “we need to make the robot jump so it’s much more robust” then that’s ok, because you have a final application for customers. I think this is relevant everywhere because product owners want to create the best product and you have to be very close to the customer to understand what they actually need—and that's the hard work.