Speaker 1: All right, Hi, welcome everyone, this is another episode. Let's started,
Speaker 1: they'll say, and we're talking today with Madnus Mueller from Greenway.
Speaker 2: All right, Mad is welcome.
Speaker 1: Why don't we kick it off?
Speaker 2: Tell us a little bit about yourself.
Speaker 3: I thank you for having me here. Yeah, so two
Speaker 3: years ago I was I was hitchhiking around the world
Speaker 3: through Central America, through Middle East from Germany, tom lest
Speaker 3: through India, Southeast Asia, and half of my time was
Speaker 3: wasted at stupid traffic lights. So I thought back then,
Speaker 3: like which century we're living in, Like this cannot be true.
Speaker 3: So doing a hacker Ton in Germany with some colleagues
Speaker 3: from my undergrad and cognitive science so neuroscientists, we tackled
Speaker 3: this problem and we built the first PROTECT pilot project
Speaker 3: for that we could actually manage in a simulation to
Speaker 3: like release skyrocket, like all the metrics like Seal two emissions,
Speaker 3: lowered downfield usage, waiting times, and we thought, wow, this
Speaker 3: this is incredible. And yeah, since then we are on
Speaker 3: this journey for almost two years now to tackle this
Speaker 3: problem with a vision to both every single traffic light
Speaker 3: on this planet. And while doing this, I moved from
Speaker 3: my undergrad to my to my master program in cyric
Speaker 3: at Etah, where I'm surrounded with many motivated people, especially
Speaker 3: around the student project house. It's like an accelerator where
Speaker 3: you're forbidden to study on your project on your university courses,
Speaker 3: so you have to work on your court, on your project,
Speaker 3: and I just love to be surrounded by people push.
Speaker 3: That's that's how I came here today.
Speaker 4: So, Magnus, I mean, why did we double click on
Speaker 4: the problem itself for a little bit, right, for people
Speaker 4: to understand better? So you said you were hitchhiking. Obviously
Speaker 4: that is a challenge by itself, right In addition to that,
Speaker 4: you are stopping at two way too many traffic lights
Speaker 4: and spending a lot of time. So uh, for people
Speaker 4: who probably don't know too much about traffic lights and
Speaker 4: how it works today, right, I mean, is what goes
Speaker 4: on today in most of the world. You think, right,
Speaker 4: why why people stop at these lights? And maybe it's
Speaker 4: a primitive way of doing it, maybe it's not the
Speaker 4: best optimized way to do it, But I mean, what
Speaker 4: is the real problem itself? Can you maybe two minutes
Speaker 4: on that?
Speaker 3: Yes? So I guess everyone who lives in a city
Speaker 3: with traffic lights knows the situation, like fix them in
Speaker 3: the middle of the night where you drive and you
Speaker 3: face a red traffic light and they think, okay, house
Speaker 3: is possible, like there's no car intersection? Why do I
Speaker 3: have read? And of course different cultures deal different with
Speaker 3: that situation. Some just go over some way. But if
Speaker 3: you think that problem, which is now really clear to everyone,
Speaker 3: fix some to the rush hour okay, where you have
Speaker 3: thousand cars from every side and you have there a
Speaker 3: stupid traffic lights which in many cities just run on
Speaker 3: static cycles, so every side just gets the same amount
Speaker 3: of brief time, no matter what's the current traffic, what's
Speaker 3: the weather, if there was a big football game and
Speaker 3: there are now thousand people coming from one side. So
Speaker 3: in many cities they have like just still stupid traffic
Speaker 3: lights and some for someone in Germany they now started
Speaker 3: to tackle this problem by investing like millions of euros
Speaker 3: and too expensive sensors, so they open the street, play
Speaker 3: some pressure sensors or install cameras to then optimize single intersections.
Speaker 3: But if you think about that like nowadays, especially why hitchhicking,
Speaker 3: I realized every day around the world uses mobile phones
Speaker 3: in their car and with that with Google Maps, tom
Speaker 3: Tom Rixia many providers, you have globally available real time
Speaker 3: data for the whole planet, and based on that data,
Speaker 3: we build models to optimize those traffic lights, not just
Speaker 3: for one intersections, but for whole cities. So those interactions,
Speaker 3: they can talk to each other. You can say, hey,
Speaker 3: I send to you now ten cars coming this way.
Speaker 3: With this communication, they can have a much more efficient
Speaker 3: optimization for the whole city.
Speaker 4: Got it? So any have you done any sort of
Speaker 4: research magnets to see how big this market is? You know,
Speaker 4: obviously you obviously seem passionate about the problem itself, right,
Speaker 4: But I mean for you to spend time and say, oh,
Speaker 4: I want to solve this. I want to solve this
Speaker 4: by starting a company. I mean, what sort of numbers
Speaker 4: market cab or market size rather any research numbers you have, yes.
Speaker 3: Yes, of course, so right now we'll do a lot
Speaker 3: of fund raising. So I'm familiar with all this numbers,
Speaker 3: Like the total available market is around ten billion dollars
Speaker 3: in that space with a forty percent growth rate. Because
Speaker 3: for example, in Germany there are now many smart city
Speaker 3: Also in India, I saw there like one hundred new
Speaker 3: smart cities so the government like really pushes those initiatives
Speaker 3: as different reason has stealed two and sustainability issues, but
Speaker 3: also air pollution. Also in Germany, Many, many cities are
Speaker 3: just don't have enough people to manage their traffic lights
Speaker 3: like manually, so they invest more and more in those
Speaker 3: automated solutions.
Speaker 4: Got it. So it obviously affects us, say every day everyone,
Speaker 4: I mean anyone who's on the road, I mean whether
Speaker 4: you're a pedestrian or whether you're driving a car or
Speaker 4: you're driving a commercial truck. So that's a very unique
Speaker 4: problem that you're trying to solve. So obviously sorry, good.
Speaker 3: Yeah, yeah, you're completely right, Like it impacts all of us.
Speaker 3: And if you think it impacts in so many different industries.
Speaker 3: If you, for example, think okay, you're sitting banganor and
Speaker 3: you're now ordering your food and your food delivery, it's
Speaker 3: like maybe for you those couple of seconds which is late,
Speaker 3: isn't a big difference because of stupid traffic lights. But
Speaker 3: if you think, like for such a global business there's
Speaker 3: like millions of trips every day, a couple of seconds
Speaker 3: per trip is like immense and you can think that
Speaker 3: from the logistic the truck through the bicycle industry like
Speaker 3: it just impacts basically everything understood.
Speaker 4: So we've spoken about the problem. I think we have
Speaker 4: a fair idea of what the problem is. So I
Speaker 4: see greenway has an AI in it. So what is
Speaker 4: your approach to solving this problem with greenway?
Speaker 3: Okay, so what was done until now was you look
Speaker 3: at the city and you calculate, Okay, this traffic light
Speaker 3: is five hundred meters away from this traffic light, so
Speaker 3: we need to switch this traffic lightlight thirty seconds later green,
Speaker 3: so that cars or bicycles, whatever you want to optimize,
Speaker 3: have kind of a seamless flat green greenway, steamless flow.
Speaker 3: But of course for us humans, that's hard to do
Speaker 3: that with twenty different lodes, twenty different intersections and their
Speaker 3: their complexity, the interactions between them, and that's where our
Speaker 3: OAI comes into. Plank we have. We can create a
Speaker 3: simulation of a city stack bangal or here, San Francisco, Berlin.
Speaker 3: Create a simulation and then in a simulation we can
Speaker 3: simulate everything. We can simulate cars, bicycle, trucks, buses, drives
Speaker 3: around like in a real world. Now we can train
Speaker 3: in AI, which in the beginning just completely stupid control
Speaker 3: the traffic light. You can imagine it's like a baby
Speaker 3: which comes to the world and there's just stupid actions
Speaker 3: like putting the hand into the fire and gets feedback
Speaker 3: and gets better and better and hair and like this.
Speaker 3: You can also imagine our AI. We can throw thousands
Speaker 3: of different like traffic situations on it. We can rush
Speaker 3: our middle of the night, big football game, and all
Speaker 3: those different data distributions can then get learned from our
Speaker 3: system to get better and better. You can just choose
Speaker 3: what it should optimize. Some cities they want to optimize
Speaker 3: for speed, some for seal tool emissions, some they want
Speaker 3: to improve buses or want to prioritize bicycles, and based
Speaker 3: on what the city wants, you can then design your
Speaker 3: optimization function to optimize statinism. Then, in the second step
Speaker 3: in the real world, you don't take the data from
Speaker 3: some Google maps or from sometimes feed it into your system,
Speaker 3: which is learned in assimilation and then outputs the best
Speaker 3: option to now in real time based on this data.
Speaker 3: Control the traffic.
Speaker 4: Got it, So I think I get it, But at
Speaker 4: the same time, I mean I'm a little I'm trying
Speaker 4: to understand a little better on the real time data
Speaker 4: of it.
Speaker 3: Right.
Speaker 4: Obviously with AI, you're training it with data that you
Speaker 4: already have based on different permutations and combinations, like you said.
Speaker 4: But at the same time, I mean, you know, traffic
Speaker 4: is always unpredictable, right, I mean, you know, a group
Speaker 4: of hundred might choose to have a party and they
Speaker 4: might be going in you know, fifty different cars, which
Speaker 4: is kind of an outlier, right, But I mean, how
Speaker 4: do you so you have a combination of historical plus
Speaker 4: real time data being fed and constantly kind of training
Speaker 4: your model or updating area. Is that the right way
Speaker 4: to think about it?
Speaker 3: Exactly? Yeah? So of course, like it's about making your
Speaker 3: models better and better and better and fitting more and
Speaker 3: more outlier cases into your model. And for example, if
Speaker 3: you take like, yeah, traffic, like you say, is such
Speaker 3: a huge diversity, Like you can just imagine, okay, here outside,
Speaker 3: is now a car from Amazon Prime? Then then maybe
Speaker 3: blocking the traffic light. Right, There's so many different cases.
Speaker 3: But by creating a model which is trained on millions
Speaker 3: of different.
Speaker 5: Data distributions and then you deploy them practice, and of
Speaker 5: course you will figure out in the beginning cases which
Speaker 5: are unexpected where which you need to handle for but
Speaker 5: over time, like self driving cars to get better and
Speaker 5: better and better from.
Speaker 3: Learning more and more and more regarded data distributions. Understand, yes,
Speaker 3: this is interesting, the cool thing here?
Speaker 2: Make you have messed this up?
Speaker 3: I think?
Speaker 4: Then I'll go ahead.
Speaker 1: Yeah, I was gonna ask, at least the solution is
Speaker 1: going to sit through with the state level agencies or
Speaker 1: the traffic agencies. How has that interaction been, right? I
Speaker 1: get like I would be curious to hear your reactions
Speaker 1: with them.
Speaker 3: You mean to go to market, so how we sell
Speaker 3: to the city.
Speaker 1: Yeah, like, because once you would have exposed the solution,
Speaker 1: I'm sure they're like, oh, why our system is not
Speaker 1: good enough? Like was that the reaction of people who
Speaker 1: were very open to oh, look that there is a
Speaker 1: better way of doing it.
Speaker 3: So for sure there are some like like you have
Speaker 3: to understand and all our customer interviews, we realize those
Speaker 3: cities especially they have nowadays a huge problem with skill shortage,
Speaker 3: so they don't have enough people to handle those problems.
Speaker 3: And for example, in normal small, medium sized cities, there
Speaker 3: are hundreds of construction sites every year which changed the
Speaker 3: road network and to handle those road blockages, those cities
Speaker 3: they pay expensive factional engineering offices who then create new
Speaker 3: traffic signals schedules by hand for those intersections. So by
Speaker 3: doing those customer interviews, we realized, okay, with those skill
Speaker 3: shortage where they really don't have enough people to look
Speaker 3: for anything, we can have one of the biggest value
Speaker 3: proposition to them. And so you really need to figure
Speaker 3: out I mean, in the end of course you benefit
Speaker 3: the whole society, but in a first step, like you say,
Speaker 3: you sell the cities. So first you need to figure
Speaker 3: out why would the city buy the solution, why would
Speaker 3: they waste time on you? And one is skill shortage,
Speaker 3: another one is still to a must reduction, and third
Speaker 3: one is for example, direct cost savings in the the
Speaker 3: public transport sector when they need less buses to keep
Speaker 3: up the bus frequency. Imagine the bus in the city
Speaker 3: wants that every thirty minutes is a bus in front
Speaker 3: of your house which can pick you up. And if
Speaker 3: now traffic is poor, they need more buses. But if
Speaker 3: you improve your the system, then they need less buses.
Speaker 3: And such a bus is super super expensive, like normally
Speaker 3: they keep one for like ten years, costs around one million,
Speaker 3: so to handle them for one year costs around one
Speaker 3: hundred to more than two hundred thousand euros per year,
Speaker 3: and that's where you can provide them like a value proposition.
Speaker 3: And if you have like those three arguments, then you
Speaker 3: can go to cities and of course, like we have
Speaker 3: heart problems as a young startups that they to get
Speaker 3: trust off those cities, and our.
Speaker 6: Strategy is to go with with pilot projects, which we
Speaker 6: offer them almost for free to say, okay, let us
Speaker 6: try this out so you can discover how much you
Speaker 6: benefit from that and.
Speaker 3: Over time win more and more trust off those cities.
Speaker 4: Got it, Thanks, thanks my migness. So one of the
Speaker 4: I don't know if it is you call it a
Speaker 4: residual effect or a side effect. This actually can be
Speaker 4: a big selling point I'm assuming is what goes hand
Speaker 4: in hand is if you regulate traffic efficiently and you
Speaker 4: regulate traffic safely, you can avoid a lot of accidents
Speaker 4: as well. Right, I mean, you know why do most
Speaker 4: of the accidents happen because you know, people are in
Speaker 4: a rushia. People are frustrated because you know, the traffic
Speaker 4: light is in red for so long and they take
Speaker 4: a you know, a risk to just cut across the
Speaker 4: street and go or you know speed or whatnot. So
Speaker 4: I mean, is your solution with Green Bay trying to
Speaker 4: pitch you know, public safety on the roads as also
Speaker 4: one of these effects. I mean, is that something you've
Speaker 4: thought about as well.
Speaker 3: So we are aware of studies which show that if
Speaker 3: you improve traffic flow so less stopping code traffic, then
Speaker 3: this increases road safety. And we can clearly improve traffic
Speaker 3: flow somewhere in simulation in rush hour we can sometimes
Speaker 3: reduce a number of waiting vehicles, but up to ninety percent.
Speaker 3: And but asn't now we have never used that fact.
Speaker 3: I think that's something which especially needs to figure it out,
Speaker 3: like what clear percentage we can say, or like how
Speaker 3: many accidents less are there. It's a thing which is
Speaker 3: really hard to measure for us right now. But long term,
Speaker 3: like this just goes hand in hand and with my vision.
Speaker 3: And the cool thing here is that if you improve
Speaker 3: one factor, then you improve so many other factors. You
Speaker 3: improves your two waiting time, fuel usage, road safety, stability.
Speaker 3: That's one thing which really amazed me about this problem.
Speaker 4: Awesome magnets. I mean, it's a it's a great idea.
Speaker 4: Obviously you started this based off your experience traveling the world,
Speaker 4: so you didn't see it in just one place. I
Speaker 4: mean this is applicable kind of in every city around
Speaker 4: the world. I mean, you know, you've you're obviously from Germany,
Speaker 4: You've seen the traffic in in Bangalore. So do you
Speaker 4: do you think, I mean greenway will work for any
Speaker 4: permutation and combination sort of thing, or in other words,
Speaker 4: it will it work for any any city you think,
Speaker 4: any modern city so to speak, or you think you
Speaker 4: have a sort of a template for an ideal city
Speaker 4: where greenway will work now and maybe work elsewhere later.
Speaker 4: What is your thought on that?
Speaker 3: So, of course we have our initial target customer group
Speaker 3: where we start with this, but our plan is to
Speaker 3: make this work everywhere, to solve of every single traffic
Speaker 3: light on this on this planet. And of course some
Speaker 3: cities are faster, somewhat slower, So we focus, especially in
Speaker 3: the beginning, just on those who love us and really
Speaker 3: want to develop it just together with us. And our
Speaker 3: vision is like long term to make the whole process
Speaker 3: around smart cities really easier for cities. Right now, imagine
Speaker 3: you're a developer and you want to develop your app
Speaker 3: for the App Store, so that everyone can benefit from it, right,
Speaker 3: so you just go to Google play Store or App
Speaker 3: Store and to upload your app. But in cities this
Speaker 3: is right now, super super hard because every city uses
Speaker 3: a different system and you have to do tenders. But
Speaker 3: like my long term vision is that long young developers
Speaker 3: and there'll be in India and Germany or in the
Speaker 3: US if the ideas can come to a city and
Speaker 3: an easy framework can bring in their ideas and easy
Speaker 3: sandbox like format to run experiments on some infrastructure from
Speaker 3: small they save parts of the city to test out
Speaker 3: new stuff. And I think with that we can have
Speaker 3: in so many different ways new innovation which benefits in
Speaker 3: the end all of us will live in those cities. Great.
Speaker 1: I'm still really more of the fact that it's hitchhiking
Speaker 1: that got.
Speaker 4: You to the solution.
Speaker 1: To be honest, I would be like, you weren't even
Speaker 1: driving at that point, you still got bothered with the
Speaker 1: traffic lights. That's just seriously amazing. But it's also it's
Speaker 1: the scale of the problem too, right, This is you
Speaker 1: said it yourself, right, You're starting with a small setups
Speaker 1: that's the one city in then expand globally.
Speaker 2: That's a massive vision, right, how is it and how
Speaker 2: is your day to day right when you go out
Speaker 2: and like, Okay, I'm gonna achieve this one at a time,
Speaker 2: how does that day look like.
Speaker 3: My day to day life. So it really depends on
Speaker 3: the face of the business. Like sometimes I remember two
Speaker 3: months ago, I was coding day and night, just thinking,
Speaker 3: brainstorming on like a flip shot, you know, and drawing
Speaker 3: architectures of our AI model. How's the whole pipeline going
Speaker 3: right now? It's a whole day like fund raising. Right
Speaker 3: now we have fund raising half a million euros and
Speaker 3: so I reach out to many investors right now, I'm
Speaker 3: here in Silicon Valley and just pitch I worked a
Speaker 3: lot along pitch Deck, learned a lot to improve there.
Speaker 3: About legal stuff, I learned a lot. So I think
Speaker 3: that's it's one of the most amazing things to be
Speaker 3: a founder, to just get insights into so many different domains,
Speaker 3: learn about legal, learn about hiring, learn about tech, to
Speaker 3: be a generalist. And I think especially for crewious people
Speaker 3: like us, they're one of the most amazing things to do.
Speaker 4: So for anyone listening Magnus, you are fundraising and you
Speaker 4: you're maybe again based on what I know I will
Speaker 4: say it and you can correct it. You're looking for
Speaker 4: angels for a start, right I mean you're in an
Speaker 4: initial seed face or you can probably talk about what
Speaker 4: sort of funding you would rather take in right now.
Speaker 3: Yes, So we got nominated for like a three million
Speaker 3: grant in Germany where we want to hack at them
Speaker 3: at the federal Ministry. And to secure this grant, we
Speaker 3: are our saying five hundred k and we have already
Speaker 3: the first angels committed. It's all around pre seed, like
Speaker 3: we're especially looking for people who bring smart money on
Speaker 3: the table, who wants to bring a network on a
Speaker 3: table in the mobility space. Can be a tech space
Speaker 3: advisor from from Google, can be also in the government
Speaker 3: sector like everything. Just someone who is also frustrated about
Speaker 3: this problem and wants to solve it. I'm happy to
Speaker 3: talk with. Can be pre seed be cs or or
Speaker 3: angels who really believe in that mission to solve that
Speaker 3: problem globally. And it's crazy if you do the calculation,
Speaker 3: like how many million tons of seal two we can
Speaker 3: save with that solution, Like it's insane and that's really
Speaker 3: what what motivates me.
Speaker 4: To somebody anyone with money in the bank and a checkbook. Right,
Speaker 4: that's that's that's that's what you're looking for.
Speaker 3: Yeah, basically someone who brings a new new ideas.
Speaker 4: I'm always so I want to do a little bit
Speaker 4: of a flashback maybe, right Obviously we've talked in about
Speaker 4: enough about your hitchhiking, so to speak. But I mean,
Speaker 4: did you always want to be an entrepreneur or it
Speaker 4: just happened by chance?
Speaker 7: I think in the world where I grew up right now,
Speaker 7: it's like a really hype it's all around ath Agent's
Speaker 7: Rakes project house that was an esoprick.
Speaker 3: Like, Okay, maybe maybe I say this also because I'm
Speaker 3: in this, baba, it could be also true because everyone
Speaker 3: around me is like an entrepreneur. But yeah, I think
Speaker 3: like also, when I was younger growing up and I
Speaker 3: had problems, I always thought, okay, like what's the chance here,
Speaker 3: Like I I remember I was sixteen, I'm selling like
Speaker 3: Tisha designs online and I need to upload those designs
Speaker 3: to like Ama Zone, and I just wasted my time
Speaker 3: with uploading. So I just started, okay, writing my own
Speaker 3: boughts to to upload that stuff. Just whenever if you
Speaker 3: face a problem, I thought, okay, okay, what chance is here,
Speaker 3: what solution could be here? Which also I don't better
Speaker 3: fits from it, and I think this is what an
Speaker 3: entrepreneur does in the beginning.
Speaker 1: Yeah, do you still think it's all hyped up being
Speaker 1: an entrepreneur?
Speaker 3: I think right now a lot here in my age,
Speaker 3: like many young people want to be really independent and
Speaker 3: want to create their own company. And I think like
Speaker 3: right now, especially online, like we have so many tools
Speaker 3: which support us with that makes it easier and easier
Speaker 3: to just go out and co found is went investors
Speaker 3: great first MVPs, like the structure to do that is
Speaker 3: become much.
Speaker 1: More Would you recommend caution or would you be more
Speaker 1: like you know, hey, more power at this point, like
Speaker 1: knowing what you know now?
Speaker 3: Yeah, if I would recommend to be an entrepreneur, yeah,
Speaker 3: so I think, yeah, of course I wouldn't. I would
Speaker 3: recommend it. Yeah, especially if you're like curious, like if
Speaker 3: you feel inside of you that you want to be
Speaker 3: it and there's just a little bit, you know, maybe
Speaker 3: a little bit of fear or just can't be anything
Speaker 3: which holds you back, then I would definitely recommend it
Speaker 3: to you. But I think we often forget also in
Speaker 3: big companies they are often like really entrepreneurs, you know,
Speaker 3: like even if you're working in a big tech or
Speaker 3: medium sized company, you can be an entrepreneur and really
Speaker 3: innovate that company and run experiments and many examples where
Speaker 3: they do everything with an entrepren basically does I think
Speaker 3: the advantage of just being like in my situation is
Speaker 3: just that you're kind of start from scratching. Really you
Speaker 3: learn like everything from legal stuff.
Speaker 8: To product to investors through hiring, and that's just really
Speaker 8: an amazing experiment experience which you don't get and working
Speaker 8: in big type.
Speaker 3: But the mindset itself, you cannot everywhere.
Speaker 4: Anything you would have done differently in your journey. Magnets
Speaker 4: looking back.
Speaker 3: A hundred things, one hundred things, like I think I
Speaker 3: have every day. I have like a list with things
Speaker 3: which I would do differently, little beaches, personal habits or
Speaker 3: especially in the in the startup, like with some of
Speaker 3: how we set up the legal stuff during the beginning,
Speaker 3: we didn't have the best thing and then like no
Speaker 3: way are really looking okay with what people you start
Speaker 3: the company so you fully fully trust them, and thats
Speaker 3: a I think a long long list with things that
Speaker 3: would I've just learned on the on the goal. And
Speaker 3: I think that's also completely fine because now I've learned
Speaker 3: that stuff.
Speaker 4: Who were the.
Speaker 2: Yeah, I was gonna say, who are some of the
Speaker 2: first people you told about, like, Hey, I'm going to
Speaker 2: start this up, this is an idea I'm going to
Speaker 2: take forward.
Speaker 3: The first people which I told this to. That's a
Speaker 3: good question. I don't remember who was like their first one.
Speaker 3: I think there's problem. This is in my brain alia
Speaker 3: for such a long time, and I thought about, okay,
Speaker 3: how to solve this, and then it just happened somehow,
Speaker 3: and then I was just on it, just I guess
Speaker 3: friends around me. But it wasn't like hey, I'm now
Speaker 3: an entrepreneur or how I have now a startup. It
Speaker 3: was never like this. It was more like, in the beginning,
Speaker 3: like a side project which we started, and then I
Speaker 3: got more and more and more. That's a good question.
Speaker 4: It's a given way. You are magnus with green Way.
Speaker 4: What are some of your current challenges? What what can
Speaker 4: people in the community who are listening to this approach
Speaker 4: you with for help and growth and whatnot? So any
Speaker 4: anything you want to ask from the community listening.
Speaker 3: Yeah, so, as a young startup, you what I think
Speaker 3: someone also learned this. Every startup has assumptions which it
Speaker 3: needs to valudate before it finds product market fit. And
Speaker 3: for someone in our case, the assumptions, Okay, our city
Speaker 3: is really willing to pay x amount of money for
Speaker 3: our solution. Is our solution really gonna work scalable in cities?
Speaker 3: And so we have a couple of assumptions, like seven
Speaker 3: assumptions which we need to validate. And I would say
Speaker 3: one of the biggest hurdles now Bottle makes, which we
Speaker 3: need to overcome as fast as possible, is just to
Speaker 3: demonstrate our solution and as many cities as possible. And
Speaker 3: we are right now starting in Ostabrook, the first Chiland
Speaker 3: in Germany. But if anywhere around the world, you know,
Speaker 3: like cities which are innovative for me, even have contacts
Speaker 3: which could be interested, or you just want our solution
Speaker 3: in your city because you're frustrated about the traffic lights,
Speaker 3: then I'm super happy to connect to and yeah, solve
Speaker 3: this problem.
Speaker 4: And in terms of where you are with the solution itself,
Speaker 4: I mean the solution is ready to go. You've written
Speaker 4: the code, it's been tested and good to go to
Speaker 4: we say uh, do a pilot in a city, Is
Speaker 4: that what you'd say? I guess.
Speaker 3: So ouri model definitely works to optimize traffic lights in
Speaker 3: the simulation, so broot type is demonstrated. But of course
Speaker 3: the integration through the real world, that's what we are
Speaker 3: right now doing in the in the first city in Ostabrook.
Speaker 3: That's a challenge where we will definitely change and a
Speaker 3: lot in the and our pipeline, our network. But yeah,
Speaker 3: especially doing the next three months, we will be robuster
Speaker 3: and robuster and you know, as a startup beginning to
Speaker 3: start to hack something together. But as soon as you're done,
Speaker 3: more growing business hundred percent sure in two years we
Speaker 3: will need to rewrite our whole code and our whole
Speaker 3: pipeline to make it more more robust and more scalable.
Speaker 3: But that's that's right now, not the not the goal.
Speaker 4: How big is a team right now? Are you a
Speaker 4: one man army Magnus or who else is helping you
Speaker 4: with this right now?
Speaker 3: Yes? So to other co founders who work on this.
Speaker 3: We have an advisor from really clever guy from from
Speaker 3: Google who gives us much advice on how to set
Speaker 3: up our pipeline, how to build like really good models
Speaker 3: which can process in the right direction. And if you
Speaker 3: get this grand it's right now. I'm supposed to start
Speaker 3: in October. Then we have like a plan with to
Speaker 3: eight other people which we will hire to tackle this problem.
Speaker 4: Awesome. So you once you get the funding in, I mean,
Speaker 4: you are going to bring in some more smart, smart
Speaker 4: people with that funding to make this better. So hiring
Speaker 4: is on the horizon. That's that's cool. And I forgot
Speaker 4: to ask, I mean, since you mentioned model intermined me.
Speaker 4: So are these more is your own custom models magnets?
Speaker 4: Are you using any of your any of the open
Speaker 4: source models out there for to run your data and
Speaker 4: all of that?
Speaker 3: Okay, this is all custom models. We tried many different
Speaker 3: architectures from graphel networks to transformers, but yeah, the whole
Speaker 3: pipeline is all custom a custom made for this problem.
Speaker 3: Is also not that they are like some some large
Speaker 3: language models which you know, we know from GVTS or
Speaker 3: Lama model which you can just throw at that problem.
Speaker 3: It's like more problem where you have to get to
Speaker 3: custom development.
Speaker 4: Do you want to wrap it up? Sorry, if you
Speaker 4: have any other questions to go for it.
Speaker 2: You can wrap it up with no, I think I
Speaker 2: think this is good Nanos.
Speaker 1: Thank you so much for taking the time.
Speaker 2: I like you're really humble, and your like ingenuity here
Speaker 2: is at display. So we wish you good luck with
Speaker 2: all this. It looks like plan.
Speaker 1: And then again, thank you for taking the time.
Speaker 3: Thank you very much for all your questions and also
Speaker 3: your interesting parts on on the safety aspect of it.
Speaker 3: Really really looking forward to solving this problem together.
Speaker 4: All the best Magnets. We'll stay in touch and then
Speaker 4: what we'll do is we'll publish all the details of
Speaker 4: your website and your email and everything when the episode
Speaker 4: goes out so people can reach out to you directly.
Speaker 4: Thanks so much, Magnus.
Speaker 3: Thank you, Bye Mae.