AI for Good Innovation Factory Startup Pitches 2020-06-05
4:42PM Jun 5, 2020
Morning Good afternoon. Good evening and welcome to the second startup life pitching on AI for Good innovation factory. My name is Apogee from the ITU International Telecommunication Union. And I hope you all have been saying staying safe and have a crush on telecoms Telecommunication Union is the United Nations specialized agency for ICT. information communication technologies. And we are the organizers of the AI for Good Summit alongside the XPrize foundation in partnership with 36 United Nations agencies, ACM, and Switzerland The goal of the summit is to identify practically applications to achieve the Sustainable Development Goals and to scale them for global impact. The AI for Good innovation factory is part of the Summit and has the goal of building community a diverse community of scallops, startups, entrepreneurs, and try to identify the most innovative solutions using efficient engines to achieve sustainable development goals. Now like most of the world, they are AI for Good Summit has done this and we are moving forward with weekly online program. And you can always find our program our website, the AI for Good and it will tie into that to let you all know that the cost for startups and innovators is and will be open at the end of this to our website. So today's live started pitching the second one of the AI for Good innovation factory. The first startup pitching is also can can also be found on our website. In the innovation factory pitching session startups are being evaluated by our junior experts we're going to see in a minute based on their AI solutions, like how strong they are solutions, social impact and change in the development goals, and the business and sustainability model of their startups. Selected startups will be involved in various AI for Good Summit and will be elements and will be presented as outcomes of the 2020 AI for Good Summit. Return the year. Before I introduced the startup mentors, I will be acting as judges and evaluating the startups using additional scorecards that you can send us identifying all the potential scalability possibilities of the startups and innovating there. PII Solutions within their social impact. Let me start with a quick question to our attendees, because we'd like to know who's in the room, right. So I would ask from the attendees to let us know, on the chat using the chat functionality. Where are they connected from which city? Which country? And we have the UK here. Dubai, Ireland, us. Ice then van de us, Germany, Canada. While some reason when Osiris Mexico did Viking, which was Ecuador, is everywhere. Great. So I think with this diversity, you have a lot of brainpower in the room and a lot of these watching all these startups pitching. And let me start with some housekeeping rules. Okay, so we're going to have five startups. This is going to have four minutes short, we're going to be very strict on the time, followed by q&a from our judges and mentor. And again, as mentioned the judges We're going to even start off using auditions contracts. So now we're going to use our mentors and I will start with Nicole would not. So Nicole is the CEO of Niantic Labs, and leading AI solution provider that sits on the board of 40 to the CX. She also has been building technology solutions for clients and implementing machine learning solution for Fortune 500 companies and governments all over the globe. For many news article, thank you very much for being with us today.
It's media this year is from Berlin, where From where?
I'm actually coming from Switzerland today, but
I know next week, right, the board is not going to gently open.
Yeah, check your executive restrictions are getting less and less. So maybe a quick question since you're the CEO and co founder of man fix lab, which is basically a company that builds AI startups in our companies and also you build AI capabilities to other companies. A very interesting question would be How can we make sure that we use the AI machine learning is the right direction for good, as we say.
So I think very much. For us at least, I think it's a dual a dual responsibility. So there are some things that we can do as a company, structurally to build up a team that is very much focused on using their capabilities on the right areas, right, we select Miranda select startups in which they invest in as a fund. And obviously, there are certain areas that are that we like supporting more than others. But then also, you know, it's about we know that in AI bias is a big topic, we want to avoid that. So diversity, hiring, all these sort of topics are, I think, important for companies and investors also to have in mind from a structural perspective. And then also, you know, each and every one in our team has a responsibility, I guess, as well, right. Each and everybody of our team members, we encourage discussions. And we we encourage a culture of really, you know, the sense as well. So if you're not on board with something, please voice it, please bring it up. And I think that's extremely important to make sure and it's also something by the way that I think you also have to bring as you're looking for good talent, because there's also something people want a voice, right? And if you're looking for good talent, then you have to supply this culture. So I think that's, those are two components, I think to make it
work. Yeah, good work for good. So yeah, working I find AI innovations for good is one thing we'd love the Summit, but also helping to scale them. So maybe if you've got experience, what do I mean? What do you say in terms of what would be the most challenges or obstacles against killing a good sushi?
Ah, data, data data? I mean, I always have a like, my team is making fun of you have this buzzer right data has arrived. I think, I think very often depending on which sector you're working in, making sure that you build an operating model. A business model that aligns well with others and enables you to really build scalable data sets across across multiple players in an ethical way. It can be a challenge, right? For example, on one of our one of the portfolio companies of more antics varas is in the breast cancer space. And, obviously, to build a meaningful data set across different clinics across different health practitioners in this with mammograms is a is a challenge and you need to make sure that you respect everybody's interests and bring them bring them on board. So I would say that's a that's one.
This is the main driver semitones would say against killing. For sure. Thanks so much, Nicole for this and I think we're gonna move on to our next judge who is actually a good friend as well. So next career, so Max is the head of mobile development at GSM. A, and mobile development is exceptionally well positioned at the intersection in mobile ecosystems. And the development sector as well. So you drive a lot of innovation with this technology, using quality as well as yourself. So hi, Max, and thanks for the investment.
Thank you very much for having me, Ahmed. And Hi, everyone.
So next, I mean, you contribute a lot in different innovation accessing across different countries. And a very obvious question would be how do you see the effects of the current COVID-19? And then on technology, innovation and automation systems?
Thank you. That's, that's a very good question. And if anything, the current crisis is really showing how critical digital technology can be in responding to shocks generally, and obviously to this one in particular, in virtually every area we're involved in and GSM MPD whether you're talking about water and sanitation, or agricultural value chains, for instance, we're seeing government, private sector, and citizens really work together to respond to the crisis and leverage technologies. There are areas like digital health and occation big data, financial services as well, in particular, they're getting a lot of attention, and understandably so. But we're also seeing the limits of current models to a certain extent and and when the time comes to focus on recovery and preparedness for, unfortunately, the next crisis, we really have to work together to make sure that we keep the momentum and leverage what we've learned from this crisis to accelerate the development of digital services in an inclusive way. Just to finish, I think that digital technology has the power to bring people together, and to help us build more resilient economies and societies but only if we do work together and pay close attention not to translate the inequalities that we're seeing in our physical world into the digital world, basically.
Yeah, we talked about strategy and inclusion and how to get everyone to using this kind of service in a quick very interesting question. We, as I mentioned data before, I mean, if we talk about technology or efficient as per se, innovation is the difference between using mystical And kind of leveraging your technology for for good for student development in developing and developed countries for development.
I mean, you can't deny that that different challenges depending on the country, but unfortunately, what we're seeing is that our societies and our economies wherever you are not equal today, and the challenges and the opportunities will be different, of course, and so will the solutions. But the potential for existing and new technology and AI is one of them will will exist regardless of the market. In any case, I think the right way of doing things is really to start with the people start with the problem rather than start with the technology. And what I like about the pitches that we're going to be hearing today is that the intrapreneurs are not using AI for the sake of using AI for them identify a pain point for citizens and then or an inefficiency in the value chain and they're really using technology to tackle it. In my opinion. That's how the best solutions are born.
Yeah, great. It's a great confusion how to see the above digital technology having control over the world for this kind of pitching sessions. I just you know, from the theme that we have more than 30 countries presented and attendees I think you call us from London, right?
Yes, I'm here in London.
So thanks again, Max, and I think is one with our next mentor is also a good friend, Mr. sassy, and Melissa of IBM is the chief penguin of IBM hyper protect accelerated what a titan asked about this title. But this also works on early stage entrepreneurs and digital and business transformation. She leads IBM must've colorful code. And before I began this spent several years at Microsoft. And she built a lot of things from internet energy access metrics to using universal skills for what you call the newly connected and founder of mentor nations Chair of the Arctic intelligence Working Group. I mean, Lisa, thanks very much for being with us today. So many hats, and but they're all around technology startups and entrepreneurship. So maybe a very obvious question would be, what are you working on right now? And what if there any new initiatives and projects you're working on at the moment?
Wow, that's such a that for me because I wear so many hats. I think that that's a whole lot of stuff for me to say. But I think a couple things that that I can I can highlight, I think the first one would be at IBM, I'm currently recruiting 30 startups in the FinTech, health tech and insure tech space. So all companies that are dealing with data, we're very focused on data protection, privacy and security. So I would say if you're, you know, if you're interested in applying for an accelerator, I've got one for you. So I'll put the link to the application page in the chat window in case anyone is interested. That would be the first thing. Second thing I'm working on is taking a lot of the concepts material that I've had in my in my head and in my team's head. And this sits outside of my role at IBM because I believe that we all should be an A and not a nor. And what I mean by this is, I'm both an entrepreneur and an intrapreneur. So I have my own companies, but I also innovate inside of a big tech company. And I think there was a time when, you know, you had to choose between being an entrepreneur and an entrepreneur, you had to be an employee or, you know, do something else. And I think the time has come where we all have side shimmies, and some of us those side, shimmies are not such side shimmies anymore. I'm a big fan of making sure that you know, young people are digitally skilled. So I do a lot of work when it comes to empowering youth through digital skills, entrepreneurship, and, and professional development. Most of my work is in the Middle East and Africa. When I'm thinking about my my side shimmy, which isn't such a side shimmy anymore, but you know, in my role at IBM, it's very global. And I'll echo you know, some of the things that I've heard that data is such an important part of everything that we do using AI in a, in a responsible and transparent and ethical way. But you know, making sure we're doing that in a safe and secure fashion.
Yeah, great insights and motivation Xu working on and essentially your initiative you also work a lot as a mentor as a judge in many events and given it for sure, but also many others. And we're going to hear from from great,
I'm so excited about this is one of the favorite, my favorite parts of my job is having an opportunity to see all the innovative solutions that are out there.
Listen to these startups. What are the main factors you're trying to look forward for acquired trying to look for this type of facility? Because this kind of of recipe effectors probably have a good chance of succeeding and scaling. What are you looking for in the startup?
Yeah, I think that I think the biggest thing is you know, first off, are you you know, what is your What is the problem? you're solving? Are you fulfilling either, you know, an unmet need? Or, you know, maybe a need that, you know, consumers don't even know they have? Is their market opportunity? Is their product market fit? Do you have the right team? You know, is that team reflective of your audience? You know, what is your business model something that's sustainable and strategic? You know, have you really thought about? How are you you pitching, that solution isn't scalable. And I think that another thing that is really important is there was a time when you had to choose between doing well and doing good. And what I mean by that is now we can create solutions that make the world a better place, and still turn a profit. And so I see a lot of ideas come my way, and a lot of them don't have business models. So I think the biggest piece is how are you going to sustain yourself without relying on philanthropy without relying on charity and how can you be a self sustaining entity
Yeah, thanks for listening. I'm sure we're gonna have a lot of questions coming from you startups. We started pitching. And I think we're gonna move on also with our next judgmental. So Reiki. So Reiki is actually a partner metric development book after parties. And he's super passionate about impact controversial innovation used to be for impact of Geneva and Roseanne and worked very hard on accelerate 2030 which is mainly into identify ventures with tested scale ready solutions in line with sustainable development, which is very much relevant Virtual Training here focusing on AI applications and scaling that we have to work on advancing the state of development. So I recommend everyone should invest in it.
Thank you for the invitation.
region, bamboo Capital Partners is one of the leading impact investing platform. And I mean when we talk about impact investing a very obvious question that mean all the startups be asking? What kind of stuff you investing in and book after party? Are you looking for in startups to say, Okay, this is a good investment opportunity? And what exactly we mean by impact focused startups?
Yeah, thanks for the question is always a very important question. But like very interesting
point. First, how do you measure impact. So impact is a very interesting word. And it's a very difficult concept to understand for many people. But impact is basically all you do in the way you are making your business profitable. It's a guy. Now how we see it on our perspective is more related to the social environmental impact that you were building and always towards this Sustainable Development Goals. So that's the way we measure our impact right by we use our old mainframe to our own framework for That, we focus on the achievements of the SDGs. And we we work closely with the ventures in order to really first understand their impact. So, how the company is performing internally. So, how the employees are actually connected to the company, what is the vision of the company and further with the clients or the services that they are providing. So, these in the long term means how they are going to achieve or help achieve those goals together with the industry, the sector, the country, and of course, the other players in the India ecosystem. And so, you're answering to your first question, I started first, second one, but now answering your first question where we are investing so we invest in emerging markets, in companies that basically show that sense of impact of social and environmental impact already in the market, we already have minimal viable product, that they are consolidated and they are able to scale. So we focus on those companies that can become global. And in five main sectors, so access to energy, access to health care, and what we call now or before with microcredits. Now, it's financial inclusion agrotech and adaptation technologies. So in these five sectors, we look at the countries where we can make actually a support and that we can partnering it's for us is more partnership than just like investing and then create a long term strategy to achieve our main goal. And I think, well, we do these through the correct strategies that you build inside on each of the regions. So we are focused in Latin America in Colombia, we have an office, there we are in Singapore in Asia, and we are in Nairobi in Africa. So this is how we actually connect with all the different regions.
So just so we have quick follow up question that because you mentioned investing many, many regions and verticals, while many technology centric startup, like all sorts of technologies in sectors, how do you see the AI innovation and the impact investment ecosystem during an after COVID-19? I mean, would you think it's going to be like, the new normal as well in this domain? I was gonna just go back as it was before, maybe less funding more funding was more innovation. How do you see this during and after?
Yeah, I'm gonna take that more from the person. So I'm gonna think about myself and breeki about the thoughts so how do I see it?
I believe that there is
there is definitely a different way of working because of the acceleration of of digitalization of projects right like these, these crisis is opening all the door to really go faster on on the digitalization of many different things. And one that is very interesting in the market that I see is the digitization of assets. So, these means assets meaning not just things that you own like physically but also assets that you own in the paper like assets that you own in a company and stuff like this. So, I do believe that Yeah, in the future by having these digitization of different concepts and basically now in assets as I mentioning, artificial intelligence will be a key aspect of how we are going to define the next rules for investments. So after analyzing the different assets that we have in a very digital way, we can see where we can focus our attention in the needs that are existing in the markets and how we can improve those. And this also comes with all the type of technologies right like blockchain, big data and stuff like these that are also in blind. And now I will complement it a little bit with what we are doing at bamboo. So through our phones on the SMP 500, our daily basis in or focused completely on technology for good as we call it, I like technologies that can improve the life of the of the people in emerging markets. So and leapfrog technologies, right like this is where we see future so you have a
vision, we're going to see five amazing innovative solution using AI for achieve. Thank you very much, Ricky. And I think now we've gone for our lesson. Definitely not least, mental judge for today. So it's Renzo Niala. So hey, Lorenzo, thank you for being with us. And it's great to have Renzo because he's the head of ecosystem development attach for now. And he's one of read people like very few people who like have a really hands on experience. And also he likes to get his hands dirty with impact tech ventures, and impactful product and solve, I mean, humanity grand challenges. And that's also the aim of our school as an accelerator. And I think, for now in supporting more than 200 entrepreneurs and ventures and startups through his work. So, Reza, thank you so much for joining us today. Thank you.
Thank you, Martha. It's a pleasure to be here and to contribute.
Pleasure to have you and I think also maybe, looks like similar question but I'm really interested in this idea of impact tech startups and and what would be the differentiation between Some of you would cause impact tech startup and maybe like a business focused on a non impact x or how would you suggest that we define this?
Sure. So, we we consider impact tech like the use of front depth technologies for mean like AI blockchain virtual reality Big Data Center images, renewable energy IoT, like deep deck to solve some of the humanity's grand challenges of our time. I mean, the main difference is a strong purpose beyond profit. So, these ventures are focusing on compare other organization and at that we truly believe that this organization will be considered the individual essential to foster better quality humanity and drive sustainable change. And at the same time, looking at the ecosystem where we are in investors, VCs impact investors well, like in Riga from gravity as an example, they are always looking for a scalable solution with a strong impact. And that's why scalable, interactive ventures will become more relevant in this in this field. One of the main differences is that they have to put on place not only a kind of financial framework, but also an impact matrix, to be able to show how they are all working on global challenges shape the society and the environment we are living in. It's like having kind of two companies in one, and to be honest, is not not easy or sexy at all. It requires out of work and commitment.
And maybe, as a follow up to what you're saying, and I would go from a bi perspective now. So I mean, you've worked a lot with AI centric startups. And trying to use machine learning and AI to what you would call them as an impact text or use AI technology to achieve the Sustainable Development Goals. And how do you think how much of a promise would with AI as a project actually hold for achieving sustainable development goals? Wondering what we think it's really going to be something in the domain, you think it's an overhyped? I think it's, it's an illusion illusion, how much promise I think I can bring to the stage of development was achieved.
Yes. So we're really And personally, I really think that the AI is one of the greatest enabler to work on impactful projects that can change the world and that they accelerate and find new solutions towards the SDGs.
But at the same time, one of the main
reasons the most relevant aspect is the experience on field This is central to shape the use of AI and try nap adapted to the different scenarios from energy has agricultural or different fields, financial inclusion, so the this technology and the array is at the core of the driving the fourth industrial revolution that we are part of. And so on the other end we have a huge, huge amount of data available today. That is at the core of the technology. And I mean sometime is not so obvious, these linkages between the SDGs or sometimes it's still rare, but it totally it's a thing. It's a reality and are growing trying to explore.
Great, thanks. Thanks so much Renzo. And I think I think natural mentors and judges with this insights and answering all these questions. I'm sure all of us are thinking about and I think it's time now without further ado, let's move forward with our with our pitches. So as many We're gonna have five startups today. So we're gonna have lucidity from Iceland, we're gonna have pink line. And then we're gonna have justice bot from Uganda. We'll have ag shift or actually from that states of America, we're gonna have chat with the chat and practice concierge from Germany. So it's very interesting and diverse as well. Startups from from different geographical places and countries. And I think we'll start with the city. So please open your mic.
Good afternoon. Nothing Justin, thanks for being with us today.
I'll start sharing. You can start sharing your screen and as you know, you have the format speech and then followed by q&a from our mentors and judges. We also have Mike Leake and the team here to let you know chat. If you have one minute left, then at the time and I opened my view and I was starting to feel a new sense So please join us now.
Sure. Good afternoon guys. My name is Justin Bostick and I'm the city's head of data science. At Atlas city. We're about bridging the gap between humans and AI to empower us to tackle money laundering and financial crime and enhance trust in the global financial system. Human AI runs for the core of our DNA and vicinity. It's about synergistically bringing together the unique brilliance of human cognition and the technological prowess of artificial intelligence. Money Laundering is becoming more dangerous. It's becoming more innovative. It's becoming more profitable, fueled by heinous crimes, such as human trafficking, corruption, bribery, and arms stealing. We, as a global society need to come together and tackle this scourge on society. We need to be cognizant to the rule human cost of money laundering. In fact, $2.7 trillion alone for the global financial system every year with less than 1% of these criminal proceedings caught by the authorities. In fact, banks spend around $40 billion on AML generally, a bit outdated transactions monitoring systems, whereas criminals spend about 130 $5 billion on r&d. The difference between the two is very clear. This is where we fit in. lucidity provides human AI SAS solutions to our clients to help them continuously improve that offense against money laundering and fraud. We connect our clients from API interface and start to use behavioral AI algorithms to detect complex money laundering behavior. We send these cases fruit to our human AI powered case management system, which basically shows the investigators the case in general, and they can use explainable AI and reinforcement learning to determine whether the case should be for the fruits or regulators. At lucidity, we utilize the concepts and various manifestations of behavioral AI and deep learning. And we use this to find illicit criminal activity within our clients data, specifically deep, deep learning deep neural nets. recurrent neural nets and graph neural nets are used to find temporal dependencies in the data hierarchical feature obstructions that allow us to find more complex networks of money laundering especially To just looking at single actors by themselves, we also utilize reinforcement learning in our front end in our UI, which I'll demo now briefly. The landing page provides an orderly overview and insight into the cases. Let's look at the case of David Butler. The AI generating case summary sets the scene for the case and displays all relevant high level information. The actor card provides a statistical and transactional overview of the act of being investigated. The observation card lays out the potential illicit behavior provides behavior specific visualizations and transactions and allows feedback to be injected back into the AI detection algorithms. Lucy sets things in context, utilizing reinforcement learning explainable AI to optimize investigators workflow, the automatic case duration and summarize the case in a regulated relevant manner. We see Lucena D as a win win for our clients and society, we're able to surface more complex money laundering or productive cases, reduce the number of false positives and find the criminals higher up the pecking order in the hierarchy. We're also able to empower investigators With tools such as explainable AI are to make them faster, more efficient and accurate in the processing of cases. It lucidity, we encode all our intelligence within our knowledge graph, which basically helps us explain things clearly to investigators and our clients. We track the connection between regulations, behaviors, observations, features and models, and allows our investigators to conduct the case with as much transparency as possible. We've developed and Payton's and patent pending a federated learning approach that allows us to holistically improve our customers defense against money laundering, without the sharing of any individual client data. We see ourselves as the enterprise ready startup born out of tier one banks in innovative fintechs and global regulators. We have a culture of action, raising our first seed funding early last year of $2 million sign in several clients, including a large to one US Bank, and we're currently raising four series AI right now. We're also developing our self service onboarding which allows us to scalar solutions across customers and geographies, allowing us to basically get the customer to sign up, click one button, start the system and start detecting money laundering immediately. And listen to it. We're about meeting the goals of the SDGs, specifically 516. We want to see elimination of exploitation of women, the reduction in illicit financial flows, corruption and bribery and the ending of the scourge of human trafficking. At lucidity, we're about using human AI to make money. Good. Thank you.
Justin. That was revisions. Sometimes when we go the US know our judges and mentors, if you have any questions can just even believe us and Mike and Ryan, it just isn't working.
I just and thanks for the pitch. Very interesting. Thank you. How do you make sure that the objectives that you have are During the match in the last slide, the ones you're mentioning,
sorry, can you just repeat the question?
So in the last slide, you mentioned the the objectives that you have to achieve the SDGs. Right. Like, my question is in regards to how do you measure those? How are you planning to to actually hit on those goals?
Great question. So whenever our objective is to stop money laundering within our clients, as we grow our network, obviously, we can stop money laundering in a larger, larger network than just one client, especially with the use of federated learning, we can start to learn across the ecosystem. And this actually helps each bank or financial institution stop money laundering within their organization. So we see a world with this ecosystem growing and being a critical part of meeting these SDG goals, especially of stopping corruption and bribery. So it's not something that we're able to measure immediately. It's something we what we will measure over time, but in our case, it's about how many how many criminals can we weed out of our client And I suppose that's the measurement that we use. Of course, criminals can move to other banks, but however many we can get out of our clients. That's the key measurement when it comes to the SDG goals. And
then I might just suggest to be more precise in the future on how you will like to do that. And what you can do right now, in the sense of your money, laundry, and then in the future, we will be we will like to focus in x, y, z, and this is how, maybe just as an observation that was on my mind.
Thanks, Enrique. We'll do
that. Thank you for the pitch, by the way. Very cool.
Okay, go with No, go ahead. And they're laser. Good.
That's okay. All right. Cool. Um, I just had a question about your, your tech stack. So talk to me a bit about your architecture and how you were making your technical magic.
Okay, got it. And and what what role does data protection privacy and security play within within your company and how are you protecting sensitive data?
Thank you for the question. It was similar to when we started we had this issue that banks don't want to share data. So we actually developed a another patent we have to Payton's currently pending. Our first patent that we developed was called was around pseudo and on a pseudo anonymization of data by using deep learning specifically deep autoencoders that were that was able to To sudo and on anonymized data into a non human readable format, essentially numbers, but wallets still usable for data science algorithms. So essentially, we take in the bank's data, and we basically anonymize any PII information about those people, names, addresses, etc. and we can still pseudo anonymized to send that data, fruit to our front end, or all basically our detection engine and use it in machine learning algorithms. So that's the first thing that we do whenever data enters our cloud.
Thank you. Thank you.
Good one. One quick question. on my end, thank you very much for the pitch. That was really clear. My understanding is your current clients are mainly bank and their issues linked to money laundering, I mean tax evasion, but also corruption. Are you thinking of addressing different clients like other very large multinationals or international organizations or even governments in some countries?
Just absolutely. So currently, our focus is on financial institutions, also payment acquires as they're called, like payment processes. We will eventually scale to more, I suppose deeper financial institutions, including regulators and central banks on but currently the focus is on banks. There are also, you know, a large, significant proportion of money laundering occurs in certain organizations like the gambling industry. So we see the potential to scale as well to these industries. And essentially, it's all about, it's all about building the behavior that we're trying to detect. So currently, we have about 80 behaviors that we tackle. And that might not just be in banks that might be across multiple organizations. But as we build this behavioral suite, we'll be able to tackle money laundering in other organizations as well. That's definitely the objective.
Okay, thank you. That's pretty cool. Thanks, man.
Hi, Justin. I also had a very short question, thanks for the very clear presentation. And it was also pertaining to sort of the data in which you operate on right so you already talked about Federation so I my guess would be that The second pending patent is related to that. And I just had a question regarding sort of private bank clients and commercial bank clients, because I could imagine the complexity sort of becomes higher when you're dealing with corporate structures and all the embeddings around that, how do you deal with that?
Great, great question. So, it does become more difficult. I suppose, as the banks grow in size, how we deal with that, I suppose, one thing to remember is that our CEO a head of engineering myself, have a background with these banks and regulators. So we we have a good understanding of how the process of of the sale pitch and and essentially the large, I suppose, value proposition pitch goes, it does take time. So how we treat, you know, larger commercial clients is about starting the conversation. And, you know, given our move towards self service, you know, an API interface that allows them to connect the database as possible. We actually see this as significant easier for our clients to connect to, as opposed to competition? So I think the answer is that it's difficult, but we're trying to make it as easy as we can, and definitely easier than the competition.
Okay, it's a follow on question very short, maybe to the data sources. Right. So my understanding is you're working off transactional data that you obtained from the banks and on bank accounts. Do you combine this with other data sources?
So? Absolutely. So we have our external enrichment sources. I don't I won't get too deep into what it is. But it does include public sources and some private sources. You know, essentially we need to if we see someone transacting with a certain company, we need to determine who the ultimate beneficial owner of that company is, we need to determine the network and linkages between it. So we need company data and you know, from companies such as Dun and Bradstreet or the like, they can actually connect us all the way for the chain of the transaction, you know, and understand exactly what's happening. So external data sources are critical for us. Yes. Thanks.
I have a short question. Thanks for the pitch amazing. My question is about the team like that, which kind of experience to us and which cannot break down? Do you ever and how big is a team because they So, so many pictures, okay. And so many logos, okay, it's fine. But it's kind of confusing. What does it mean this logo? how people are full time how, how many people are part time like, is your team distributing in several countries areas times on how we are you structured in this way?
Thanks. Thanks for so we're distributed in Reykjavik in New York at the moment we plans to grow. We currently have 15 team members and actually a new one to join this morning part time so we have 16 but that's 15 full time and one part time we're broken into it. Look, we already Engineering focus firms. So I would say a significant portion of our firm is engineers and developers, data science as well. And then of course, we have sales and marketing, generally run by the CEO in the marketing department. So we're 15. We're growing very quickly, we're finishing a series a round, which will allow us to scale very quickly. So I'd say that's the current status of the team. It's generally one of the most brilliant teams I've ever worked with. And they're all committed to the cause. They're committed to, to come into work, helping find these these patterns of money laundering and stopping these criminals and doing it in a way that we can scale across clients globally. That's the current team setup.
Okay, thank you. Thanks.
Thank you very much, Justin. I think we need to move on with our next startup so inclined, you ready?
Yes, we're ready.
That's great. So please You know, there was four minutes, show your screen was taking this time to make the mentors of the judges take some time to fill in the scorecard, if they want to do it now, not at the end. And
four minutes. Do
you see my screen?
wonderful. We are pink lion. We are about bringing humanized AI to the world app teams to make a difference in how they build, develop and deploy technologies. I am the CEO Jennifer Bunning. our CTO is Andrew burkhalter and Rick Felice is our chief operating officer. Our team started the last 10 years together as a group solving large scale problems and challenges for companies across the globe, including in the gaming industry, with big tech at Dell at Target Corporation with Oracle, Microsoft, Google and Disney. What we saw as a common theme was that we wanted to build an organization that Had impact at its core, but also saw technological challenges for organizations. We started with social responsibility from the beginning, we wanted the ability to apply the solutions we were developing not only to technology organizations across the globe for profit, but also technology solutions that aided in creating a more positive future. We're working right now to apply cutting edge AI technology to match first gen college students with mentors and opportunities to help close the equity gap in our post pandemic society. We are working with lead the way to provide technology solutions to children who are in hospitals for extended stays due to illness and disease that they're facing. We are working with green tank to bring in children and leverage our technology solutions that are used in enterprises to allow youth to use those solutions as they're building, developing and deploying their technologies in their spaces that they want to impact in the world going forward. So we started this organization saying, how do we create a more diverse, inclusive set of technology solutions? How do we impact the world as we build this fourth industrial revolution, where we're engaging, multi generational diversity of thought, and including folks from all different backgrounds in building these technology solutions. So we started by getting involved in organizations to help assist and deploy the solutions we were building into places where we could have a social impact. While we did that, we were able to quickly gain momentum and revenue as well, are solutions that we deploy inside organizations for profit, we were able to in under six months, gained almost $4.7 million in contracted revenue. So very quickly With the ability to scale inside enterprises, corporations and organizations, while in parallel, using solutions to operate in a socially impactful and responsible way, at the same time, we believe that there's a need for humanized AI. We believe that human centered design is key. As we're building the solutions to assist people who build, develop and deploy websites, mobile apps and technology in the world. We've seen that the adoption hasn't been as high as what we would expect in these technologies. So what we are looking to do at pink lion is create the App Store for AI, a trusted market space with social impact at its core, we look and evaluate every technology and tool out there that can be leveraged for the software development lifecycle, and understand what it is good at what types of technologies it's using, and then build gap solutions to fill those gaps for customers, how we do this, we create custom bundles of AI solutions for enterprise and corporate clients. We understand what at their core, their challenges are in their software development lifecycle. That could be in testing, development, Product Management, business analysis, UX or design. Once we understand the challenge, we're able to drill deeper and say specifically, what's causing your challenge. That could be their UI automation, it could be responsive design, it could be defining a PDFs. These are things that are taking immense amounts of time for their humans, where we want to leverage AI solutions to uplift and augment the humans. So we're providing them with the insights to drive decision making processes. Once we have this information, we easily can bundle AI technologies to solve the problems in those corporations and help them get the uplift they need from AI and leveraging the data and insights to do decision making. we summarize all of this in a portal and integrated console that shows them no matter what AI technologies or solutions they're using to aid their software development lifecycle, how that can impact the decisions they're making every day in their work. We are creating datasets that they haven't had access to see previously, and allowing them the ability to make decisions and change the future of work for all of the folks that are doing their jobs every day to build, develop and deploy technologies in organizations. We've used this across various industries, including the gaming industry to help an aid as new games and products are released. We've used it in the cruise line and entertainment industry. We've used it in the retail and e commerce industry to assist them across the board. Having access to this data and information is providing valuable insights while at the same time giving These options to have this information not only to them for their bundled subscription services that they're able to use, but through our bundled services, allowing customization to help aid and help people understand how to do their jobs in the future, leveraging both AI and humans together to create a better future.
Jennifer, those cool those great any questions from the mentors from the judges? Yes, I've won this.
Hi, great pitcher. Congrats. And very clear. I still have one question related to your business model. Could you please provide more information both about the business model as well as the inbound model, how you can measure the impact of Your venture?
Yes. So, um, two things we do we build solutions that are in enterprises, our business models, those bundled subscriptions to help them achieve their goals. So we provide our own solutions, our pink line technologies in our app store that we're building that are gap technologies based on understanding the needs of the market space. So we will create and develop pivotable brains that solve multi multi problem faceted problems in industries, but we also leverage folks who've built technology out there already, and put them in our app store, very similar to Apple or any app store. We build and put apps in our app store that are trusted that we vetted for data security and privacy. We've checked that, that ml and AI has a high level of accuracy. we've ensured that they do what they say they do in the market space, and then put them in the App Store. Once we understand that we're able to do that. So We have exclusive contracts and partnerships with AI providers that provide different applications. And then we build our own tech as well. When we're building the tech, some of it is not all for enterprise and for sale, our impact that we're doing is using and developing our pivotable brains that may be partially used in enterprise to assist in things like creating the eHarmony for first gender college students to match them to mentors jobs and opportunities. So we'll take that same tech pivoted and understand how many lives we're impacting by using these technologies to serve another need that has a social impact in the community.
Okay. Hi. It's Melissa. I have I have a question about, you know, when you think about ethics and AI, and how, you know, I think we'd all love to have a global definition of what ethics and AI means, and I know that there are, you know, lots of publications out there that, you know, talk about, you know, specific ethics on how do you translate those ethics over markets where ethics might differ. So for us, it's one of the things we decided, as we launched, the organization was living on the principle of the organizations that we choose to work with and partner with. We want to have a certain level of cohesiveness around transparency. So we measure are they transparent in what the data is being used for? Do we know what the usage is? Are they transparent in the type of technology that's underlying it? And is it truly machine learning based? Or is it what we're calling fo AI where people put a label on it, almost like when organic came out, we label it as AI, but we don't know what that means. So having a common definition and understanding of are the underpinnings actually a lot Looking at the models and the algorithms that are being used to ensure we understand what those are, and then making sure that the companies we're working with are using the data in the way that is transparent to the consumers who then are engaging and interacting is highly important to us to make sure that people trust the solutions we're providing to them as the face on it. So does that mean that you're carefully selecting which markets you go into?
All right. I know that was a tough one right there.
Please go ahead. Thank you, Enrique. Um, thank you, Jennifer. I had two questions, actually. Thank you for the presentation. I had one question as to the innovative use of AI. So I I was a little bit left wanting for more on what is actually how to use AI in this and what is actually your core tech. clinical expertise because I saw a lot of different fields that you brought up from design and bi and UX, and so on. So I would be curious to hear that. And then also, I didn't quite understand to be quite honest, how that related to the college to the young people looking for opportunities or kids that have just gone through illness. I couldn't match that together be great.
Yes, absolutely. So it's a lot to cover in four minutes. Because the two pieces we do is create pivotable bot frames we call them where we can pivot the brain to solve different challenges or solutions. Those solutions could be an enterprise solution, which is one of our clients. For example, who's an looking for I'll give you an example of COVID-19, price gouging, trying to understand what the prices are various products out there on the internet that are available for and then you find mass in hand sanitizer. You may need to understand when those prices adjust or change we can leverage ITU go out and look at the particular sites we're targeting. Look at those products, pull that data and information back, identify when it changes, visualize it on the console, so that the human then just instead of having to check all the time, what they do is really look at the data drive their decision making process, that pivotable brain that searching and grabbing all of that data and information we can pivot to now say we're going to do criteria matching on students who are looking for opportunities with certain levels of skill sets, to employers who are looking for placement of certain individuals into their organizations for either internships, mentorships, or opportunities for jobs. So leveraging these pivotable brains to solve multiple challenges in the world by looking at the trends of what consumers need is where we're really targeting that innovative solution have the pivot capability around the software development life cycles. pacifically
One quick one on it. You have done. Okay. Okay. Very good point for you. Thank you very much for the presentation. There are a lot of discussion and research being done on how AI might possibly possibly be perpetuating gender inequality, racism, and so on the way it is designed. As you test the different tools you put on your app store, do you look into this specifically?
Yes, we actually work with several organizations as part of our partnership, one being I vow, and others who look at culturally inclusive data sets. So one of our important tenants was culturally inclusive and diverse data sets and evaluating those at our core. So one of the reasons we engage as we test our own technology we're building who tests our technology isn't just us and our customers. We engage youth to test our technology and they actually give us Some of the best feedback as well. And then we're also engaging experts in security and other fields to ensure that we are inclusive of thought of how we're building and what we're building.
Great. Thank you very much.
Thank you very much. I think we're just out of time. I know Andy had the last question that I hope you have the judges asked he asked him the same question if possible, and we have to move forward with our next startup. Thanks. Thanks. Very good. And actually from Uganda, so we have a justice bot right. So please enable your mic Let me press everything show your screen. Hey,
was here. And as you know the rules for minutes and followed by q&a and please to go
Hello, my name is Mike easy says yes co founder and CEO of justice both will provide legal information and advice in simplify Man, we all can agree that the knowledge of law and legal procedures are the first step to get something inexpensive and timely manner. 65% of Ugandan seek legal information whenever faced with a legal issue. We also did a survey both in Uganda and Kong. And we found that 70% of people surveyed said access to legal information and legal advice is expensive, and is difficult. This causes access to justice can be time consuming, expensive, and causes stress, but also lunches that caught us as African by backlog, which also if it's cold, but also they see their case cases get jammed or dismissed them. Just it's much easier to change this with our online automated platform and SMS service that provides free legal information. So all the user has to do is for instance, go on Facebook or messenger type his question and get answered instantly. And this is good because both rural India And urban area people get answered 24 seven, and they have access to information whenever they want. The platform also provides lawyers, lawyers to answer like to give me one advice, and this via a mobile app. And they can just guarantee their customer via the mobile app without having to meet and if they want to meet, they can just request to meet. And this is going for this pandemic pandemic period where distance is acquired. The questions are answered by an AI system, which makes the thousands of people get answered instantly. But also a lawyer allows lawyers to answer effectively because they are supported by the AI system. Facebook has 2.2 million Ugandan users. And whenever we are thinking of Uganda with which I get these people end up Uganda is more than 8000 lawyers. So far, we have 33,000 users registered and we've been able to help 76,000 people by preventing their age violation Or solving the language. Our prediction is 77 3000 USD annual revenue. We've also been able to make lawyers get $815 and we've been able to make $144. And now to sustain ourselves out lawyers give us 10% from whatever payment they received from the plant. We have many key differentiators. One of it being for lawyers that having the accessibility and comfortability of working from wherever they want access to buy into 24 seven, but also for us as people who have legal issues, they have access 24 seven to freely information and sometimes they also proposed a worker or a really good advisor. We are seeking 150 USD and with this want to be able to help 300,000 people by preventing their rights violation or helping them and with this one sort of provider Access to job opportunities 750. Yes, we are seeking this money as grant or equity. Our team is composed with the various skill set that is needed for this project. We have lawyers with seven plus years working in law firm, we have software engineers with a great experience being for a long time in the industry. People are destroyed because of lack of knowledge. Our vision is to provide that access to justice to 6 million people and provide job opportunity to 6000 lawyers across Africa by 2025. Thank you.
Much so yes, and I think Time now for q&a. Any first questions or anything I
can do the first one if you want to have one ready. Thank you very much for the presentation. That was very clear. Actually on your on your last slide you your vision is pan African is not limited to your home country. How replicable is your solution? We know the law is different for every single country, is the algorithm still applicable to a new question? Yeah,
yes, it is goes. So far we've tested like when we started in Uganda, and we tried to test something with combine it and it's worked perfectly. Okay. Yeah. Good.
Oh, yes, I would have another question
was when he called he's got
great videos. So yeah, I was wondering about how do you how do you train the system and how do you so you
base it on? What will be the input and output of your of your of your training algorithm? I guess? Because it's for me, right in the legal field, right. Often one of the challenges is to scale data sets, across different clients, different law firms and so on. And it would be really interesting to hear how you tackle that.
Yeah, so on Basically we provide we provide information to our system AI engine. And we we train it with training to, to know the patterns and to know how to answer and, and from the other side, we have questions that we receive from, from our, from our people who have questions. And that's a passport for like, for having data to train with our engine. And so far we've received around more than 120,000 requests, those are messages coming from people. So whenever we see some new cases, the AI system is able to detect it in order to train itself or we take one of these fields we have to come in. So that's it. I mean, we can bring in more money. But most of our work is unsupervised learning.
Okay, and so basically then you also match it to like, I guess there's also some reinforcement learning in there you match it like how happy are people with their with? Okay, interesting. Yeah. Cool. Cool. initiative. Thank you.
You're muted. Ahmed. did
this. Thank you. Thanks very much for this position was great pitch and I think we'll move forward with eight. Oh, we have a question from him. He is a very quick very quick so
you were starting by saying that you are already taking some investment. Yeah, let me Can you elaborate a little bit of where your, your numbers of these points or your revenue your targets? How much you see your company closing by the end of this year and yeah, what is coming in the future after coming down all this?
Yeah. So once once these days, we like so far, we've been in the market and we've been mainly focusing on getting your product market fit. Production and it has gone through the phases in successful and from what I said like our projection is unreal we can make around the VM in 770 3000 annually. And we have we have developed different business model that can help us even achieve more was the solution that you've built with realize internally that it can even use it can be even used by other organization whenever they need and we can we can turn our solution into something like assess, which can help us generate more revenue. I don't know if I answered all the questions correctly, but if you need more clarity or opinion
Do we still have time? Yes. Okay.
Well, let me be very straightforward. How much is your revenue right now? How much is gonna be the next year and how much you are planning to people for investment?
Okay, so this year this year, I mean, so far, we've been able to make 140 Key 4044. And in total ICA, we've been able to help lawyers make our 815. And this I mean, the investment that we are seeking, we are seeking it for eight to 10% of our company.
Fantastic. Thank you very much for the clarity.
Thank you, Ricky. Thank you. So you see, and there was a good pitch again, I think you have any more questions. So let's move forward for the next next startup. It's a shift or shift from states. So you shift ready?
Yes, please just make sure that you're unmuted and speak so that we can know that mic is working.
We talk to you You
How about now?
Yes, that works. So please just put on a slideshow mode and yeah, right.
Hi, everyone. So this is a nickel from like shift. So just wanted to give all of you a little bit of background of actual vision of reducing food waste and how we're applying AI to do that prior to actually start have done multiple startups and have led many initiatives for companies like IBM and VMware. But growing up I grew up in India, smallholder farming community in northern India, where, who a lot of mango farms, but the idea is that as a fourth generation Farming economy, I am very well aware of what it means to be in this ecosystem and how food waste impacts on one end the zero hunger on the other end. So with x shift, humbly it gives me the opportunity to combine everything that I have learned from technology perspective and apply that to something very, very meaningful in terms of in terms of food. So essentially, what we are doing with x shift our vision is to bring all efficiencies which are possible from technology perspective to food supply chain, and there is a lot of room for that. As an entrepreneur, my last startup was acquired by IBM and I was leading IBM Internet of Things initiative, right. I was working with a lot of Companies dealing with sensor irrigation, farm data, robotics, drones and all of that. And it became very, very evident to me that there is a visible gap in how many eyes how many entrepreneurs are actually trying to bring technology to food supply chain. So with that Vision X shift was started. And our vision is very simple. We want to bring efficiencies to how food industry goes about doing quality inspection, because quality is the heart of the entire supply chain from a business perspective, but it is also at the heart of each one of us as consumers. So with that notion, we have built industry's first AI based food quality automation platform and Very simply put what you see here, this is our patented analyzer, which is called Hydra. And this is what is running across many different processing facilities in us. And also in selected facilities in Vietnam. The from the technology perspective to simplify it at the highest level. When you talk about AI, mostly, the first one of the first applications that comes to our mind is self driving cars. For us, we took that technology stack, mostly the same technology stack, took all those building blocks, learning fundamentals from there and built what is celebrating strawberries, or celebrating elements or celebrating cashews, right. So the idea is that we optimized it for food quality inspection as an application. And what you see here is that typically if you look at how inspection is done today, in any organization and we are not talking about small or medium sized organization, we are working with some of the largest organizations in the world. But the quality inspection process is hundred percent manual where you have to train people, and people have to go through counting blueberries or counting 500 grams of elements. And the process is so tedious when you do one inspection every 10 minutes that it is error prone, it is bound to making mistakes and it's a very subjective interpretation. So, what we are doing with AI is we are augmenting this process so that people can do better smarter jobs and AI can do most of these visual defect assessments and thereby bring operational efficiencies to the supply chain.
So, where do our solutions So go they are applicable if you look at the supply chain, and this is very similar to wherever we are, it's not just specific to us. This is the food supply chain. And we are applicable to every player in the supply chain where you have to do quality assessment. And as I said, since this is quality, everyone has to do the inspection, either to determine the quality to determine the grade to determine how do you pay your supplier, upstream or downstream. So even a single shipment of food actually minimally gets inspected at least four times between the farm and the consumer. So the applicability we have is massive at a global scale. And these are some of the organization you're working with. So our systems are running at discos, which is world's largest staff very organization at fame. So where are we is one of the larger elements processors in California with Olam which is we are running the automation on cashews. So overall, our vision is that we are able to bring operational efficiencies improve the number of samples which can be inspected because a machine can inspect four x five x eight x of the samples which can be done manually. And if you increase more data points for sampling based inspections, then statistically you are able to improve the quality of the inspection and assessment of the inspection. And we are able to do it at a scale where you can have efficiencies and a digitized audit trail for the entire supply chain. So, what's unique about what we are doing? This is the first and the only application where AI is used to automate batch based or sampling based Start infections for food and it remind the food quality. And
you just have to wrap up in the comment 20 seconds, please. Is overtime.
Yeah. Okay, so I'll go very quickly in terms of commodities, we are working on fresh berries edible not, but we can scale to hundreds of commodities, which are out there. We have the patented Hydra technology which we are doing. In terms of our second uniqueness. Essentially, we are the only company where we have clean curated, labeled and annotated data set for these kind of quality assessments. And this doesn't really exist anywhere in the world, because these organizations have never taken images to do quality assessments. These are some of the commodities on which we are currently working on. As I said, our focus is on fresh produce and edible not for now. Where we are working on commercializing our solution And this is our team, we all have very different backgrounds in terms of building large scale enterprise software, and in terms of having access to the food ecosystem. In terms of our social impact as we are touching food, we're touching many, many aspects of food. So we are directly reducing food waste by improving the assessment of food further up in the supply chain as opposed to the last mile. We are bringing automation because the process are completely manual and we are disrupting it with automated solutions. food quality is improved because we are able to increase the samples and we are able to digitize it. And food waste is directly contributing to the greenhouse gas emissions. So 15% of the 6% which is contributed to to food.
I'm putting it I'm done. I'm done.
Yeah. And you have more time to maybe clarify this in the questions because you will actually extra few minutes. So that's Yeah, so maybe if we have some questions from The judges are mentors and you can clarify more about your solution. Any questions?
Yes, I have one. Yes, please.
So thank you for your presentation. quite clear.
So the first question
is about the roadmap.
And could you please share where the main next steps for for your organization for egg shift? And what you want to achieve? And second is the revenue model. So, if you have any recurring revenues, how they can integrate within your business model, how you can say
how you're making money and basically this
picture on the first question, our current focus is to commercialize our AI automation onto commodities, strawberries and elements. We are already running across as a shared some of the largest organizations with schools and From our hydro systems are in active commercial deployment for more than 12 months now. So as the season picks up in California, which is our first focus, we are working on commercializing, commercializing our solution for strawberries and almonds. So strawberry season has started and elements will start in August. So that's our first focus because our a solution is ready on these two commodities. In terms of the pricing model, our pricing model is very, very straightforward. It's a SaaS based model. So we charge X dollars per hydro per month, because we are not in the business to sell the hardware. Our goal is to essentially have that as a means to an end and end being clean curated, one of the most powerful data repositories in the world as it applies to the quality assessment and the visual defect assessment of the commodities. We are working on.
Much I think you have a question.
Um, yes, I do. Can you hear me? I did change that. Okay, cool. I have two questions. Sorry, both of them are a bit
much you are muted for a second. You know,
okay, so you should be able to hear me now. Okay, very sorry. And the first one is your algorithm will tend to select products that are deemed sellable, which might be like looking good for the end customers. But would that potentially have an impact on the amount of food waste all those strawberries or almonds that do not look good enough to be sold? And how do you deal with that? And my second question is more on the employment side. You're going here with distribution from manual to automated, my understanding is the manual process might be Mostly by people who are quite low skilled, how do you mitigate this impact on on employment and basically on jobs potentially being lost by automated this process?
Sure. So on the on the first question though, we these inspections are not typically done at the fonts these are done in the supply chain. By For example, this could be the shipper or you have a wholesaler or a retailer or aggregator and they do the inspections based on random samplings and that's a well defined industry process for any supply chain. So, they will do their inspections because they have to determine the quality and the sampling size and the price which they pay the grower but what happens is that at least today for example, you are only inspecting say hundred strawberries to determine the fate of a $5 million shipment come upon. with actual Hydra, you can inspect 1000 berries and that way for the benefit of the grower. Since you are able to increase the sample size, the organization is in a much better position to determine the actual true quality of that shipment so that the grower is not penalized with a reduced sample and with a subjective or unbiased interpretation from the manual inspections, if there is an issue with that fruit since you're able to consistently inspected much earlier in the in the transit as opposed to much later, at least you have the pathway. If it is a grade D versus a grade A, then you can find secondary markets tier two and tier three markets as opposed to incurring the the loss of that entire shipment being rejected at the last mile. So it's a very valuable and and kind of valuable position which growers are supporting along with organization because you are at the end of the day doing it in an unbiased way over tenfold increased samples. On the second question. It is not that no workforces going to lose their jobs, essentially the organizers nations are able to give them a more higher value added training, for example, they can get trained on Hydra, they can be doing other aspects of the inspection as opposed to counting 400 elements or 1000 berries, they're able to expand their skill sets and capacity and capabilities by having them work on many other aspects of the business and the and the quality and and food safety. And in the light of the current code crisis, as you know us has been impacted very strongly. So what is happening is that a lot of this food is coming to the facility. But since people are not able to go to the facilities organizations are operating at 25% of their capacity and all that food is going to waste because you don't have enough hands to do quality assessment, which is something which we are able to solve because one operator, even with a lower skill set for an organization can now operate multiple Hydras and thereby they're able to retain the job and Same time organization is able to move that volume of food even in absolute crisis, which we are going through in in California right now.
Okay, thank you
very much Rico and we're going to proceed with our last but not least start up from Germany. Chat practice. So so I'm ready.
Yes. Hello everyone and just sharing the screen and
let's we can see within the slideshow. Okay.
Okay, thank you very much. Yeah. We are really happy and honored to be here with you today. And thank you for this occasion. And the onset of the COVID-19 pandemic has exposed severe shortcomings in health care systems across the globe. In Europe, we have witnessed overcrowded medical health plans, patients being sent home from hospitals and local health ministries, hiring volunteers to follow up with cases given the shortage of medical personnel. The continued monitoring of outpatient patients with Corona or other chronic diseases is practically not possible. Patients at home are left to their own devices, where often the course of disease can turn critical abruptly with the team from breakfast concierge, and we have developed homeowners a system to automatically monitor individual records of symptoms over the phone. Hello.
How are you doing today?
I'm feeling better but still quite tired.
All right. Did you already measure your temperature today?
Yes, I took the temperature is around 39
That's actually pretty high and your temperature has increased since yesterday. let me mark this here on the dashboard. Do you want to connect to a doctor right now?
No, I think I'm fine.
These calls are triggered automatically in regular intervals and create a diary of symptoms. The system can be operated from a hospital or a health ministry, where medical personnel checks incoming results on an online dashboard and can take immediate action. The stakeholders of our solutions are the patients, the care providers, and the insurances due to cost savings by timely treatments. What you see here in these bubbles is the number of people per nurse in various regions of the wh O. These bubbles would even be larger for the people per doctor. shortage of medical personnel along with aging populations, and chronic diseases is the biggest drivers for solutions in remote patient monitoring. Changing legislations around the world are creating more and more scope for digital solutions here. Many solutions in the market pet on smartphones On wearables or various kinds of IoT sensors, we believe that with a human like dialogue over the phone, we can capture the well being of a patient better than with near Vital Statistics. Our solution is built on a scalable infrastructure, and can easily be adapted for all common languages. We are convinced that our solution can actually further the progress towards the goals of sustainable development. The low technical barrier can help increase outreach of primary care as well in areas with low access. For example, no internet remote regions, while the efficiency gain can lead to more resilient systems, healthcare systems. The collected data could serve population statistics for early detection of national and global health risks and accelerate the timely response. Our team combines PhD level expertise in the Learning and especially natural language processing with a strong track record of successful business initiatives. Over the last eight months, we have successfully developed solutions for automation of phone services in clinics, like appointment scheduling, follow up prescriptions and analysis before the actual appointment. We have set up companies in UK and in Germany. We're currently implementing our solutions with partners in Europe. We hope the AI for Good community will connect us to a wider range of healthcare players in healthcare institutions. Join us on our mission to make great health care accessible for all. Let's make this world a healthier place. Thank you.
Thanks, Simon. It's a great pitch and time as well. Questions from the mentors judges? Yeah, right.
Yes. Hey, summer.
So, um, this is a very, very interesting topic and I think is something that is going to be a trend if it's not already there, right. Like even more in emerging markets, telemedicine and the evaluation of patients without being really in, in the in front of the musician or the doctor. One question in regards to the trends that you were speaking so how are you planning to actually cover the different trends that are existing in the emerging market or any other markets to grow? The capacity of the system? What What is the logic that you're putting in the sense of where is the analysis that will go in the future with this technology? Is it maybe I mean, what I'm asking is maybe just to understand, where do you see these going, not just But in the future, and how you are tackling that future in the better assessing the needs of the patients. Yeah. And you mean in terms of the use case, or in terms of our scalable infrastructure? Yeah. Let me tell you why I'm asking these so precisely. So I've been thinking and also analyzing some some projects in this same sense. And the one big trend of one big issue that everyone is having is they they don't really know yet what kind of disease what kind of pragmatics they can tackle. I mean, it's very general. Do you already have that interesting trends? As a company and as a solution?
Yeah. And yes, so I mean, what we propose here is really a model system set up for an monetary and symptoms. So triggering calls, leading people through our conversation and checking With their symptoms, which can basically use for chronic diseases like diabetes or heart problems, or I mean, we have come up with this system for in the in the COVID-19 context, where we use a specific questionnaire for for accommodate specific symptoms. But in general, I mean the system can be adapted to, to any kinds of chronic conditions as well.
And is there any way to measure what is happening in the population in a certain area region? What, what will happen in the future by the diagnostic that you're making? I think yes. How do you see these in the future? What would we see? I'm going a bit more in the two years from now three years from now, what do you see going forward in this in
your directory here? It is very interesting. I mean, currently, we really consider this more as a as a tool for for timely treatment. as well for remote diagnosis and so on. But and it's it's I mean, we perfectly see this as well that as a tool to collect data across populations, right. And then and that would allow us statistics based on several regions and give early indications of of outbreaks are region specific
diseases as well.
So I understand you haven't yet explored the option of having these as a solution for countries or for healthcare systems in regions and stuff like this. That's where I'm going. So it's not yet on the plan that we have. Okay.
Yeah, it's it's definitely part of our mission and vision but no roadmap, and we are not there yet. We are in the market since and then the focus has been more on Europe as well. And where people is, is very sensible. How How do you use their data as well? So we didn't really make this a stronger case so far.
Yeah, the good thing, you have a great solution thing big.
Now, honestly, is a really great thing. So if it works, you should start from the where, where you can really, really go under
your personal protective.
Thank you very much. Great, great idea. Great, thank you.
Okay, we're gonna make this connection actually afterwards, if any of the starts from the connection to any of the mentors or the way, we'll do that. Thanks. And I think we have reached the end of our pitching session. And thanks very much to the startups and for for everyone. And also, we've been a bit over time, I'm sure you notice, but maybe it's good to hear some very final comments from our mentors, and close. So we gotta start with Nicole because you have short on time. So we just defined the fun of common finger inside can be something that you saw in the pitching, you'd like to just go All right, what do you think?
Um, first of all, thank you, to everybody who pitched really inspirational pitches and great topics you're tackling, I think it was really good to see that. For most pitches, ai was really at the core of it. And I, I love for you for some of you to get in touch with me again, because I even have ideas of how you can maybe deepen the tech component, even more, and I think, also have some ideas on how to really make the operating model and the data collection model, right, because that was kind of critical for some of them justice, but also for you, with the concierge. Also, actually, right, how to get the data, right how to maybe combine different data sources and find other stakeholders that might be willing to pay for what you're what you're doing. So thank you very much. It's been a great pleasure and wish all of you a lot of success.
We can receive all this and we like to just find a comment, any insight, anything you like very much anything you think.
Yeah. So you As someone who's very focused on, you know, recruiting health tech startups, you know, to join my program, I'd love to talk to you and see if it might be interesting for you to join my accelerator program. I would be interested in understanding how do you currently protect your sensitive data since you're getting access to healthcare records?
Read things, listen, and next funny comments.
No, thank you very much, everyone for pitching and then to you all men in the in the fellow judges, I think I think it's great. As I was saying at the beginning, there are so many ways that technology can can provide solutions to real life issues. I mean, we've seen agriculture justice, money laundering, health, and then the list is so long, so very refreshing and a great way for me to end a very long week. Thank you
very much next, Ricky.
any final comments about the voting session or a specific
really enjoy it is really, really interesting. And I think beside you know? Yeah, it's awesome to see you face to face, but it's exciting, you know, like all the logistics of making these things happen and having more than 100 people connected and from seeing the thing. I think the only thing I can say is that like keep doing this is really, really interesting and the pitches were really, really good
to think and talk to Tom. Thanks for yoshika Renzo funny comments about the startups anything you think you might have.
Yes, thank you. Thank you, everyone. Great, find your pitches of teams and solutions. So
I mean, I don't want to repeat but like keep pushing guys. You're doing great, and you are part of this big change in the world. So I really wish you the best of luck. would like to catch up with all of you have basically like you're doing great stuff for our for our next budget we start in September. So happy to follow up later on. And congrats to
everyone to the comments, great comments, great organization as well. So welcome. Thanks very much and just to remind everyone that the pitches has gone on and on all year long so I mean, it's it's a kind of a whole online program for the whole year. You can always find the program on our website we're having next Tuesday at 4pm Geneva time CST a solution track title, AI and indigenous knowledge will be great. You can find all the program online AI for Good with it with it. I'd like to really thank all the judges, mentors, startups. Of course, the FMS is a great dynamic coaching session. And again to remind everyone you have an open quote from the startups and innovators on our website is going to be open in June sir There's any sort of bad idea which is efficient intelligence to achieve a single development goals, feel free to apply to be on one of our live coaching sessions. So thanks very much and have a great weekend.