 Welcome, everyone. My name is Rodrigo Arcia, and I will be joining you on today's webinar. I'm the Chief Strategy Officer here at UIT, and I'm here to tell you how to turn an idea, and an idea that specifically came from a hackathon into a real-life product called Cogniflow. So in general, when we talk about building digital products, the term agility pops up into our mind. They need to carry out a rapid process of definition, construction, and validation because mandatory. But the truth is that in general, we're talking about processes that consist more of sculling existing products or launching new features. And when we usually talk about time, we are referring to a really long-term or really long-term pro-roll maps. But today's story takes us to a previous step of ideation, prioritization, and validation of an idea. It's just an idea that 18 months later becomes a product in a state of capital raising. For an initiative of this magnitude to be generated, this is necessary to combine the right context and individual initiative. And when we speak of content, we refer to the existence of a company like UIT, which supports innovation in the creation of digital products. Creating digital products means supporting an idea, validating it, being a partner of both our clients and our more than 100 product enthusiasts. We have been doing it for more than 13 years of experience, and we have a background of launching more than 150 products. That approach, that initiative, to give people the space to propose and to build something new and innovative will give space for a promising idea to appear. It's more specifically, we're talking about Waldemar's idea. So meet Waldemar. He's one of our machine learning engineers and who once had an idea that seemed interesting and promising, the idea of building a solution that allows the integration of machine learning functionalities to web and mobile application. All Waldemar, as we call it, Waldemar was the possibility to share his idea and take it to the next level, beyond theory to make it real. So context plus energy from the sum of the innovative entrepreneur support from UIT comes the possibility of holding an innovation contest in July of 2020, with the format of a hackathon where the winner, that is whoever has the most innovative idea, will get 100K in funding. So Waldemar did not hesitate and took his idea to the contest presenting, as we said, a no code machine learning platform which facilitates the process of adding machine learning solutions to web and mobile apps. And of course, as you can imagine, Waldemar won the award. And we need the price not only means earning the money, but it means that Waldemar had an idea and has UIT support. So how to move forward? How to know that the idea could be something great, could be something with a future, or could be a real product? The answer is through a process of defining products from early stages with different steps that allows him to validate very quickly and cheap that he was facing something great. So let's see a little bit how the process is like, but we can see just like a glimpse of that process in this slide, right? We are talking about creating or building the perfect team. We are talking about conduct, solid research, term words into product, and specifically rapid prototyping, and to validate it until it's ready. So as we said before, first of all, what we needed, what he needed was a team. And when we're talking about the team, we are not just talking of a group of people. What he needed was the right roles. And everyone, all those different team members pushing to the same place, convinced that what he was building was the real solution. So considering that it was going to be a machine learning solution, what was needed was a multidisciplinary team composed of both backends, frontends, U.S. designers, and of course, Walde as a proud owner, ensuring the fulfillment of his vision. So now what is needed is the role, the role to create a successful digital product has to be cleared out. And for that, they started an intense process of understanding how our existing players in the market were doing it, identifying the strengths and their weaknesses. That process has allowed them to see that other actors, such as gravity AI or Akira, were quite complex and difficult to use. In addition, all those actors had something in common. And it was the specialization in text or in audio. None of them allowed different types of data simultaneously. So there was an opportunity there, right? And as we talked before a little bit, ideas are powerful, right? But if we want to build something great, we need to create elements. We need to see it. We need to see the real product. So therefore, a rapid prototype kind of approach or stage was carried out in which the functionalities and central flows were defined. And luckily, thanks to that, they were able to see how this solution was going to satisfy the user needs. And so with a correct team already working and analysis that defines the path to follow Ray Donn and a functional prototype Ray Bill, the product validation instance could be carried out. And Norman Nielsen Group, the company that based his work in analyzing user experience, new user testing among other things, said that they were doing user, that after when doing user testing, after the set user test, usually you reach the information, what they call of information overload. In other words, after the set test, you start repeating the findings and only confirming the hypothesis that you had before. So we could make a parallel with what was our circle of iterations. So as the product was tested, new findings were found, such as incorporation of option to create models or the need to facilitate the process of creating projects. But when we reached the third iteration, we were already in the presence of a solid product whose findings were mainly limited to small improvements. So we realized that after that, after the third iteration, we were ready for the next step. And the next step will be the definition of a first solid product concept. So we are talking about a platform to help product teams to add smart features forwarded by AI using any type of data. As we talked before, this was one of our main differentiations. So we're talking about data such as text, images, video, or even audio. So now we came to the moment of truth, the time to test the product with a larger audience. For that, what they did, what we did also in your IT was the hackathon with the idea of validating the audience, inviting both technical and non-technical people to propose ideas and use these cases, and use these cases as using our product and using specifically Cogniflow, the product defined or made by Walde and his team. So the task was clear. What product will you create with a platform that allows you to use machine learning? And different ideas appear, both like offensive language identifier or an app review analyzer or a game to identify sounds, like for example, animal sounds or things like that, and a quick car damage estimator, all different ideas, different use cases that were based on different type of informations and data. But the hackathon has had an extra particularity, you could say. The people who participate were not only, as we say before, people with a technical background, but also non-technical people. And they were able to build the proposal without problems. So this is how the hackathon brought up them a great conclusion. The conclusion that the product that they had built was not only a great solution for the technical audience, but also was a great solution. But for non-technical people, and now they found that they could use it, we were really in front of a no-call solution. So at this time, we already had a clear and validated audience with a solid product that was also validated. We need to go to the next level. They need to validate the market. For that, what they follow are three great steps. In the first place, a series of interviews were carried out with what is considered the customer archetype. This is C levels of companies that needed a solution like what was proposed, which not only confirmed the solution, but also gave them visibility that the focus that they were putting on it, being a solution based on no code and seeking the approval of productivity was correct. Then on the second stage, a partnership was set with a local company which allowed them to conduct real use cases with potential clients, offering free trial to local businesses. So as I said, their cognitive flow is currently being tasted by researchers within the healthcare industry working on a model that seems to recognize where or not a brain scan present a brain tumor. The first client as I said there, the first client is one of a Mexico's biggest supermarkets chains. So they are working with cognitive flow to process hundreds of thousands of client feedback tests and sort them into actions in different areas to offer a better shopping experience. And further, finally, and this is today, the solution is more than a product. It needs to be a company that has raised capital. And while it's talking with accelerators such as Y Combinator and Tech Start, that allows cognitive flow to not only be just an idea, but to be a reality, to be a real organization or a real company. So this is how we get to today, to an existing solution that started from an idea from one of our developers, push it by an entrepreneurial soul that stand out in your IT and that's taking an advantage of the support and the spaces of foreign innovation and construction of digital product, such as a hackathon, allow the creation of a product, a real product, which in a very short time, we are talking about 18 months, became a company raising capital. So if you want to learn more about cognitive flow, here you can find the link and feel free to get in touch with us and with the rest of cognitive flow team. And thank you for your time. We will meet again. And now, let's see the real product. Let's see the result of all this idea and activities. Let's see cognitive flow.