>> Announcer: From Milpitas, California at the edge of Silicon Valley it's theCUBE. Covering autonomous vehicles. Brought to you by Western Digital. >> Hey welcome back everybody, Jeff Frick here with theCUBE. We're in Milpitas, California at Western Digital offices for the Autotech Council Autonomous Vehicle Meetup. About 300 people, a lot of conversations about the not thousands but millions of problems that have to be solved before we get autonomous vehicles on the road. But there's so many angles to this whole story besides just kind of what you think of as just an Uber, a self driving taxi, or even a self driving car for your personal use and it's really a cool start up here that's actually celebrating their 100th round trip transaction. We're excited to have Daniel Laury. He's a CEO and Chief Product Officer of Udelv. Great to see ya. >> Nice to meet you Jeff. >> So you just came off your keynote presentation and you were showing a great highlight movie of your product, so tell the folks what are you guys all about. >> We're the first public road enabled autonomous driving delivery company. And this is, our aim is to cut the cost of last minute deliveries in half. And to make deliveries easier, more convenient for consumers, more ubiquitous, faster, and cheaper of course. >> So it's pretty interesting. So the use case that you're doing now is you're in San Mateo and you're delivering groceries from Draeger's to the neighborhood. >> Yes, we actually now have four customers. >> Jeff: Oh, you have four, okay. >> Yes, in the matter of a month. We gained three more after Draeger's. Draeger's was our first customer. We've been working with them for the last six months to find the, you know, the best cargo space, the way to organize the compartments and everything. And it's been a fantastic partnership. And so they were our first customer and we're doing deliveries for them almost on a daily basis. And then we added three customers. As people were seeing this orange vehicle in the streets, they started calling us and they say "Hey, can I do it?" So, now we have a florist out of Burlingame and a couple of restaurants as well. >> And how many of these vehicles do you have on the road? >> So for now we have one of them. We are getting our second one next week. There is a third one that is going to be ready in about four weeks from today and then we have a production ramp up from there. >> So what are some of the unique challenges in creating this vehicle and delivering the service that people probably never thought of. >> Right, and it's, in our case, first of all we're not a science project, we're a real business case. Probably one of the first ones in the autonomous driving world. And for us to solve this business case, it's not just about autonomous driving, it's also to have a best customer experience. And so we're not just doing autonomous driving. We're doing a bunch of things. We're building a cargo space, that's mechanical engineering that is adapted which is basically a system of compartments or luggage on wheels if you want. The second thing is we are building apps on the merchant side and the customer side. Third thing is on the autonomy side of things we are doing something that very few other companies are doing which is mastering the first and last hundred feet. Slow motion, high precision. One to two centimeter accuracies. To be able to maneuver in parking lots, be able to back up in driveways and things like that nobody else is doing really that kind of thing. And the last thing is which we're doing and we're probably one of the world's most advanced companies doing this is teleoperations. We have to be able to take control of the vehicle. First of all, monitor the fleet. And second take control of the vehicle in case of a special situation. And we're doing this with an ultra-low latency less than 200 milliseconds between the image we receive from the truck and what the command we're giving back which allows us to actually drive the vehicle in the streets as if it was a video game but it's the reality. >> Right, no we did a piece with Fan Amato. I don't know if you know Fan but we were doing kind of a general purpose. A version of that same capability. It's really, really amazing. >> Frankly I think that autonomous driving is going to need that capability for at least the next decade. >> So the last hundred feet is interesting. You know, I went to a Ford Smart Cities event a little while ago and they talked about kind of curb management because when you have all these kind of fleet vehicles getting people in and out, making deliveries in and out. Kind of the curb in that interchange of the curb is really a tricky thing. It take a lot of nuance, you know. Know when to double park. Can you double park, should you double park. Can you, as you said, get into a driveway. So when you, what your ideal scenario when you do do a grocery drop off, you try to get into the driveway? Get off the double parking situation? >> Yes, absolutely. This is a critical part of what we're doing. And parking lots are actually lawless places. You see cops everywhere but you don't see them in parking lots so you have people backing up from a spot, children pushing carts, pets, you name it. So those are very, very complex situations. Mastering those situations is super important for us because of course our vehicle is going to park in those parking lots to pick up the goods and potentially to deliver. So we developed an AI stack, Artificial Intelligence Stack that starts with a scene estimator. We estimate the scene to see where, what spots are available or if it's a driveway if you have cars parked on the curb and then be able to actually maneuver in that spot. >> Right, but you're writing off a lot. So, you're doing the apps, you're doing all the infrastructure with your partners, you're doing the complexity of the vehicle. And then you've got, you've got to worry about perishable goods, you're taking milk as well as warm stuff. So a lot to chew. How big is your team? Where are you in your development as a company? >> Yes, we're about 30 people right now. And we are going to grow this team quite significantly by probably double the size of the team this year. It's a very ambitious project. It's a very ambitious company and yes, as Elon Musk puts it, success is one of the possibilities. One of the possible outcomes. But not necessarily the likeliest. but we're doing that race, we're in that race. >> So, just before we wrap I want to talk a little bit about the human factors. Cuz a lot of conversation earlier in some of the keynotes about trust and no trust. On one hand people don't trust these things. They said that, you know, they show the survey. I don't trust them. On the other hand we see people in autonomous vehicles as if they were a level five, right? They're sleeping and doing all sorts of crazy stuff. When you engage with customers what are some of their reactions on kind of the trust or not trust? How do they respond to this truck driving up and they walk out and pull their groceries out? >> That's a great question. In our case we're in a very different situation than all the ride sharing and passenger vehicles because we don't, by definition we don't carry passengers. So they only interact with the truck in the sense that they have to retrieve their goods. That's the only thing they do. And so they look at this a lot more favorably than, because it doesn't, they don't have that sense of danger from the vehicle. It's actually more like a wow, this is so interesting. And now I'm getting my deliveries. I know it's going to be exactly 16 minutes. And I get my push notification four minutes before. It gets there and then it's a simple, very, very simple way of doing things. It also will be very, very convenient for returning goods. You will be able to summon the vehicle to your doorstep. You'll put into a locker. It goes for you to UPS. It takes a minute to do it. So people love the service. Their reaction has been overwhelmingly positive. And it's a far less dangerous thing to do than having passengers. >> Right, yeah the first time I saw the video of it I thought was Amazon Lock or which is such a convenient way to interact and so importantly as we move to smart cities because what you don't want is the proverbial sticker on your door that you missed a delivery, like aw rats. So this is such an important part of the enablement of smart cities so really, really cool story. Alright Daniel. So last words, getting excited. Going to get out of individual company relationships and start to have more of a generic service that people can tap into? >> Yes, I, we're tremendously excited about the future of this company. Within two or three weeks from having launched a product on January 30th, we've had, we've received phone calls from every large retailer you name it, in the world wanting to do business with us. So it's a very, very exciting start. >> Alright Daniel, we'll keep an eye. >> Thank you so much, Jeff. >> Thanks for stopping by. Alright, he's Dan, I'm Jeff. You're watching theCUBE. We're at the Autotech Council Autonomous Vehicle Event in Milpitas, California. Thanks for watching. Catch you next time. (upbeat music)

>> Narrator: From Milpitas, California, at the edge of Silicon Valley, it's theCUBE, covering autonomous vehicles. Brought to you by Western Digital. >> Hey. Welcome back, everybody. Jeff Frick here with theCUBE. We're in Milpitas, California at the Autotech Council Autonomous Vehicle Event. Autotech Council is an interesting organization really trying to bring a lot of new Silicon Valley technology companies, and get them involved with what's going on in industries. They've done a Teleco Council. This is the auto one. We were here last year. It was all about mapping. This is really kind of looking at the state of autonomous vehicles. We're excited to be here. It's a small intimate event, about 300 people. A couple of cool, dem hook cars out side. And our first guest is here. He's Suneil Mishra. He is the strategic marketing for Tensyr. Nice to be here. >> Thanks, Jeff. Appreciate you having us. >> Yeah. So, give us the overview on Tensyr. >> Sure. So we're a Silicon Valley startup, venture-backed. We're actually just coming out of stealth. So you're one of the first folks to hear about-- >> Jeff: Congratulations. >> what we're up to. And we're basically doing software platforms to actually accelerate autonomous vehicles into production, doing all the things around safety and efficiency, and ROI that will be important when we actually want to make money on all of this stuff. >> Right. So what does that mean because obviously, you're in Palo Alto. I'm in Palo Alto. We see the Waymo cars driving around all the time. And it seems like every day I see a few more cars running around with LIDAR stacks on top. You know, those are all kind of R and D login miles, doing a lot of tests. What are some of the real challenges to get it from where it is today to actual production? And how are you guys helping that process? >> Sure. So yeah, I mean a lot of what people don't think about is these R and D kind of pilot cars. They actually are doing R and D. It's trial and error. That's the whole point of R and D. When you get to production, you can't have that error part anymore. And so safety suddenly becomes a critical element. And part of the things of getting safety is being much more efficient on the vehicle because you have to do a lot more software in order to be safe across multiple different kinds of examples of streets, and locations, of weather conditions, and so on. So, we basically provide essentially all of the glue, all of the grunt work, at the lower levels, to make things as efficient as possible, as safe as possible, as secure as possible. And also making things adaptable and flexible. There's lots of different hardware coming down the pipeline from all different vendors. And if you're a production vehicle, it's which ones you choose. There may be different configurations for different cost points of vehicles. And then of course when you're looking to the future as a production vehicle manufacturer, how do you know which pieces of hardware to use and whether your software will work or not? We kind of give you a lot of insight into all of those things that allow you to certify that your products are safe. And so we don't build the stacks themselves, but we actually take people self-driving models, and we accelerate them onto the vehicles. >> Jeff: With your software in the ecosystem of the self-driving car hardware. >> Exactly. So we have an actual runtime engine that will set on the end device, in this case a vehicle. And it will actually optimize the scheduling, the orchestration of all of your code. That makes it much more efficient. And we can monitor that so you can mitigate for safety. And if something does go wrong, we're essentially like a black box where you can actually see what actually happened to your software. >> So it's interesting. We talked a little bit before we turned the cameras on that a lot of the self-driving vehicles are Fords. We talked to the guys at Phantom and apparently, it's a really nice system to be able to get computer control into the control mechanisms of the car. But you said there's a whole layer of how do you define being able to interact with the control systems of the car, versus is it safe, is it ready for production, and kind of taking it beyond that R and D level. So what are some of the real challenges that people need to be aware of when we're going to make that big leap. >> Yeah, so I mean, a couple of the big things that happen is when you're seeing these pilot vehicles driving around, the amount of software that they actually have on there to control the vehicles is very tuned for the particular cases. That's why you see a lot of these vehicles out in places like Arizona where it's sunny weather. You're not having to deal with snow and all the rest of that stuff. >> Jeff: Right. >> If they actually take a car and move it to Michigan for the snow test, they'll actually deploy different software to do the snow case. But when you're actually in a production vehicle, and nobody can actually come back and change that software, you're going to have to load all of those types of solution, on at the same time. That requires more space, more compute power. And so for solutions like ours, we actually allow the production manufacturers to figure out what the optimal solutions are in those cases because you can't come back and change the software. You don't have an engineer that can go tweak that code. And you don't have a safety driver, of course, to go grab the wheel if something goes wrong. These things essentially have to be able to go out there in the wilderness for years and years, and actually work. So it's a whole different classification of problem that takes a lot more compute power. And people who are seeing those giant sets of sensor rigs don't probably realize there's also a giant trunk for clarisitive, where if there's compute power in the back, running 3,000 watts of power. When you actually get to deployment, you're going to have an embedded system with maybe 500 watts of power. So you have less compute power, and you're trying to do more with it. So it's quite a challenging problem, to actually jump to production. And we're kind of smoothing out a lot of those wrinkles. >> Right. So, I just want to get your kind of perspective on kind of the Apple approach, which everyone kind of sees Tesla as. Right? It's soup to nuts, it's the car's design, it's the software, versus kind of an industry approach where you have all these different players, obviously, 300 people here at this event. There's autonomous vehicle events going on all over the place where you got all these component manufacturers, and component parts, coming together to create the industry autonomous vehicles versus just the Tesla. So what's kind of the vibe in the industry? It feels like early days. Everybody's cooperating. How is this think kind of coalescing? >> Yeah. I think what we're seeing, we basically talk to people up and down the stack, because anyone who's doing this stuff is a potential customer for us, so automotive OEMs to tier one suppliers, to the AI startups are building these software stacks, they're all potential customers for us. What we're seeing from everyone is they're saying there's so many difficult problems to solve along this path that no company can really do it themselves. And of course, you're seeing big companies investing billions of dollars. But it's great because everybody's saying, let's find people that specialize, whether it's in sensors, or compute, all the rest of those things. And kind of get them, and partner with them, have everybody solve the right problem that they're specialized and focused on. And we essentially can kind of come in and we solve parts of those problems, but we're also kind of the glue that fills a lot of those things together. So we actually see ourselves as being quite advantageous in that anyone who's doing their specialized piece, contributes into the collective. And we kind of build that collective and make it easy for the actual end vendor that's trying to sell a car or run a service, to actually access all those mechanisms. >> And are kind of the old school primary manufacturers still the focal point of the coalescing around this organization or are they losing kind of that position? >> I wouldn't say their losing it. It's kind of an interesting play. So you've got a bunch of traditional automotive guys who actually don't really, not to diss them, but they don't really understand large-scale software because they haven't had that in their vehicles until now. And at the same time you've got kind of your startup mode software experts that don't really understand a lot about automotive. But eventually, it's got to go on a car. And so what we're finding is the automotive manufacturers are really saying to get to production, we need certain kinds of safety guarantees and ROI and so on. So they're really driving from that point of view. The software guys are kind of saying, well, we're just going to throw the software over to you and sort of, good luck. So, we're actually finding both sides care, but nobody's quite sure who should be taking the lead. So I think we're getting to the point where ultimately, automotive manufacturers will be the one shipping vehicles and that software's going to be on their car. So they're going to be the ones that care about it most. So we're actually seeing them being quite proactive about how do we solve these problems. How do we get from the R and D stage to the actual production stage? So that's where we're seeing a lot of the interest on our side. >> All right, Suneil. We could go on forever, but we have to leave it there. And congratulations on your launch and coming out of stealth. And we're excited to watch the story unfold. >> Great. Thanks, Jeff. I appreciate the time. >> All right. He's Suneil. I'm Jeff Frick. You're watching The Cube from the Autotech Council Autonomous Vehicle Event in Milpitas, California. Thanks for watching. (upbeat music)

>> Announcer: From Milpitas, California at the edge of Silicon Valley, it's theCUBE, covering autonomous vehicles. Brought to you by Western Digital. >> Hey, welcome back everybody. Jeff Frick, here with theCUBE. We're in Milpitas, California, at Western Digital, at the Autotech Council Autonomous Vehicle Event. About 300 people, really deep into this space. It's a developing ecosystem. You know, we think about Tesla, that's kind of got a complete, closed system. But there's a whole ecosystem of other companies getting into the autonomous vehicle space, and as was mentioned in the keynote, there are, literally thousands of problems. A great opportunity for startups. So we're excited to have Oded Sagee, he's a senior director of product marketing from Western Digital. Oded, great to see you. >> Thank you very much, Jeff. >> So you were just on the panel and, really that was a big topic, is there are thousands of problems to solve and this ecosystem's trying to come together, but it's complicated, right? It's not just the big car manufacturers anymore, and the tier one providers, but there's this whole ecosystem that's now growing up to try to solve these problems. So what are you seeing from your point of view? >> Yes, correct. So, definitely in the past automotive was a tough market to play in, but it was simple from the amount of players and people you needed to talk to to design your product inside. With the disruption of connectivity, smart vehicles, even before autonomous, there are so many new systems in the car now that generate data or consume data. And so, for us, to kind of figure out what's the use case, right? How is this going to look in the future? Who's going to define it? Who's going to buy it? Who's going to pay for it? It has become more and more complex. Happily, storage is in the center of all this. >> Jeff: Right. >> So we get a seat at the table and everyone wants to talk to us, but yes, it's a very big ecosystem now. And trying to resolve that problem, it's going to take some time. >> So what are some of the unique characteristics, from a storage point of view, that you have to worry about? Obviously environmental jumps out. We had the guy on before talking about bumpy roads, you know, the huge impacts on vibration. And now you spent a lot of money for a Toughbook back in the day to put a laptop in a cop car, this is a whole other level of expense, investment, and data flow. >> Right. So, for us, I think with all this disruption happening of full autonomous, people are, very much focused on making that autonomous work, right? So, for them it's all about connectivity, it's all about the sensor, whether it's Lidar, or, you know, cameras. Just making that work, right? All the algorithms and the software. And so, for them storage, currently is an afterthought, right? They were saying, once we meet mass production we'll just go and buy some storage and everything's going to be fine. So while they're prototyping, right? They can use any storage that they want. But, if you think about a full autonomous vehicle out there driving, not two hours a day like we are driving today, right? 20 hours a day, from cold to hot, going through areas without connectivity. Suddenly, the storage requirements are very, very different. And this is what we're trying to drive and explain that, if we don't design the future storage solutions today, What's going to end up, is that people are going to pay much more for storage just to make a basic use case work. >> Right. >> But if we start working now, and I'm talking about five, seven years out, we can have affordable solutions to make those business models work. >> And is that resonating in the industry, or are they just too focused on, you know, better cameras? >> It definitely does, but as companies change, right? So let's just take the car makers for a second. They didn't necessarily have a CTO in place, right? To drive engineering and semi-conductor. So you got to find those figures, and you got to start working and educating them. It definitely resonates if you have the right person. Once you find him, yes, it's on the list of priority. So we need to push. But it is happening. Yes, it is resonating. >> And it's so different because you do have this edge case. You have so much data being collected out in the field, if you will, within that vehicle. Some, to go back to the cloud, but you've got latency is always an issue, right? For safety. So, a little different storage challenge. So are there significant design thoughts that you guys are bringing into play on why this is so different and what is it going to take to really have kind of an optimal solution for autonomous vehicles? >> Yes, definitely there are a couple of vectors I would say, or knobs we need to work on. One of them is temperature. So, again vehicles do tend to go between hot and cold. Unlike many other components that just need to make sure that they operate between hot and cold, we actually have a big challenge on keeping data being accurate between hot and cold. So if you program cold and read hot and vice versa, data gets corrupted. >> Oh, even within the structures within the media? >> Yes. >> Okay. >> And people don't know that. So, for us to figure out, what's the temperature range that the car, through its lifetime, is going to go through. And make sure that we meet the use case, that's a big one. What we call the endurance on the cycling of the storage, again, if you cannot rely on connectivity, cannot rely on cloud because of latency, you need to record a lot of data in the car. So, again, a car drives for seven years, 15 years, and you want to record constantly, how much do you need to record? We don't necessarily have the technology today to meet that use case and we need to work with the ecosystem, in figuring it out. So these are just two examples. >> And I would imagine clean power, as you're saying these things, but they can need others. You're not in daddy's data center anymore. This is a pretty harsh environment, I would imagine. >> Very harsh. >> Ugly power, inconsistent power, turning off the car before everything is spun down. There's all kinds of little, kind of environmental impacts in that whole realm that you would never think of in, kind of a typical data center, for instance. >> Correct. And even, you touched power, that's very interesting because even some people think, oh, there's not power limitation in a car. You can just enjoy how much power you want. Actually, it's very, very sensitive. The battery, if you think about an EV car now has so many components to run and so even the power consumption, right? Just the energy that you need to consume is becoming critical for each, and every component >> in the vehicle. >> Right. And it's everybody's AI comparison, right? Is if Kasparov had to fight the computer with the same amount of power, it wouldn't have been much of a match. So the power to run all this AI stuff is not insignificant, so it is going to be a huge drain on these electric vehicles. Pretty exciting times. So when you get up in the morning, what's the biggest thing, when you talk to people about autonomous vehicles, that they just don't get? That people should really be thinking about. >> Yeah, so it goes back to some of the things we've discussed. Definitely, again, we're seeing the use cases change. We are working again with the broad ecosystem to explain the fundamental challenges that we have, right? What is our design cycle? What are the challenges that we have? So we start with educating the ecosystem, so they know what we have. And from that we trigger a discussion because they realize, oh, okay, because I do have a use case that, probably, you don't have a solution for, how do we go together? And we're doing it across the board. It's not only happening in automotive. It's happening in surveillance. It's happening in the home space. A lot of people don't know, but the home space, if you think about it, again, set-top boxes used to be huge, sat outside in the room. People are moving to these sticks, right? And they're behind the TV and they have no ventilation and they're small and they record all the time. And they get to temperatures that we've never seen in the past. So we even need to educate the telcos of the world, the set-top box makers. Everything is changing. Automotive is definitely ahead in a lot of innovation and disruption, but everything is changing for us. >> Right, a lot of those are fond of just the bright shiny object that everybody can see, right? We can't necessarily see a lot of IOT that GE's putting in to connect their factories. Alright, Oded, well thanks for taking a few minutes out of your busy day and I really appreciate the insight. >> Thank you very much. >> All right, he's Oded, I'm Jeff, You're watching theCUBE from Western Digital at The Autonomous Vehicle Event for the Autotech Council. Thanks for watching. Catch you next time. (electronic music)