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AI-powered impact: Vertex AI for startups
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[Music] Well, thank you everyone. Welcome. How's everybody doing? Are we in the in that moment before or actually after lunch now and we're like, okay, we launched now. We see it and we're going to relax. We're we're going to try to keep the energy up, guys. So, this is a great session. So, first of all, welcome to Next. Hope you're having a great time here. My name is Ilana Kines and I lead the customer engineering team for North America startups at Google Cloud. So we have a startup hub downstairs in the expo floor. If you have any questions about the startups, please come talk to us and meet us there. We be thrilled to have you there. Uh but today this session and these amazing founders are going to help us understand some very specific applications, use cases and innovation that they are building uh using Vert.Ex AI. So for those of you in the audience that know the product or are interested in learning more about the product, this is a great session for you. We're going to have a strategies, lessons learned and some specific uh capabilities that these founders have used. So with that said, let me set the stage and welcome our founders. So I'm very happy and very grateful that you are all here. Um I'm going to start with Tete Tetia. He is the CEO and founder of a company called Prompt AI and they provide a visual intelligence platform. So Tete has been in this space for many many years and what they do is take all the information that they capture through visual devices and then act upon them and the information that they can gather. So thank you Teta for being here with us today. Thanks for having me here. Of course. Uh and then Akil Gupta and I should say Akil welcome and thank you so much for such a big flight. He comes from India so great founder from India market leading uh application there. His company is called nobroker.com and what they do is they disrupt the real estate industry. Basically providing a way to think about removing the middleman from the real estate transactions and do automated property matching and detect fraud as well. So we'll hear more about that in a second. Thank you so much for making the trip and being here today. Wonderful being here. Thank you. Thank you. And and then last but not least, so hi Ahmed, welcome here. CEO and founder as well of a company called Resemble AI. So many of you may be familiar with them. They do fantastic innovation in generating voices and automating voices with different models. So think about all the fantastic spaces about cloning your voice. Anyone has tried that? So please do come to this company. Uh congratulations also on your launch of rapid voice cloning 2.0 just in February I think of this year. Yeah. Thank you. Appreciate it. Happy to be here. Great. Thank you so much. Well with that said let let's deep dive on this thing. So why don't we start with you? So hey on that one. So I think a lot of people in the audience may be thinking okay you're in a different industries all of you. So what dropped your decision to use for instance Vert.ex AI to build your applications to build your solutions because I'm sure there were things that were out there that you were looking at. So what dropped your your decision to use Vertex? Um so we started resemble uh five and a half years ago at this point. So five and a half years ago there weren't many things actually out there. So actually Vert.Ex text didn't exist at all at that point. But um uh we were developing our own models um for generating voices and um one of uh we tried a lot of things to really get scale in terms of the number of amount of compute we need uh because we had this interesting problem where every customer every user would come in and they would be building their own voice model, right? Um so we had this weird like one user has many model relationship and uh what we really needed to do needed to do is find a way to like scale up compute really quickly. Um you know we tried other services as well for this uh solution. Uh but we landed on at that time was ML engine uh what was called ML engine at that time which is now called Vert.Ex AI. Um but effectively we found it to be super scalable in terms of us having the ability to train models very quickly and um uh there were layers that we actually ended up building on top of at that time ML engine which is now vertex which are now just incorporated right into vertex. So uh you know we've kind of seen the evolution and the growth of uh uh the product and obviously um compute has become a huge concern uh scalability and uh availability of the compute has become a concern as well. So uh for us just having that compute available and having horizontal scalability for training purposes is what really got us into the product in the first place. That is great to hear and I know scalability is a big thing just across the board for for all the founders here. So uh let me let me ask ail or or tete do you find that been a similar uh point of decision point for you to use vertex AI? Yes, that's that's one of the consideration which has really helped uh even no broker and the convo and what uh the product what we have uh because it makes it easier earlier if you go back 5 10 years back and we we are a 11 year old company so he says that he didn't know about vortex 5 and a half years back 11 year back there was nothing so if you had to do something uh in terms of whether you want to leverage AI ML to build models or create something it used to take a lot of time and that time has drastically come down. It's like you can now do it in few minutes to few hours and there are a lot of foundation models lot of models which are available with vortex which can easily seamlessly do the basic task for you. So that has definitely improved. So and that's a great point right because we have scalability and we have different models as well that you coming into the picture and then you can decide among them. So that's great and and t do you have a similar experience too on the models or the scalability side? Absolutely. uh I mean we are in the phys visual physical AI space. So in there like there's no like a uniform model like you know Gemini for voice or or for text for example. So we have to train and deploy a lot of different models for different purposes and I think that's why Vert.Ex X AI is is really really helpful because you know it's every couple of weeks we might have to address uh you know a new application a new use case that might mean like we have to tweak the existing models or you know train a new one just specifically for that. So we find vert V vertex AI uh to be really really useful for scalability. Nice. So I love this fact and and I love the fact that all these are good points for us to know about Vert.ex AI. Let let me try to see also on the other side for instance when you were going through your journeys on Vert.ex and learning what you could do going from like nothing before to this to ML ML and then Vert.ex AI any specific lessons learned that your yourselves or your teams actually went through that may have been a little bit hard at the beginning that were much easier or just things that were pivotal moments in that journey. Sure. Yeah. So basically if you if you see uh no broker we are a uh world's largest broker free real estate platform. So we save close to a billion dollar of brokerage in India every year and it's been 11 years. So we close more than 70 18,000 80,000 properties every month. So I'm talking about 2,000 properties being closed on the platform uh or 200 300 properties being closed uh as we speak. So now that needs lot of uh uh smartness onto the platform and we don't have any field on uh field uh force on the ground. So identifying brokers so that happens basis and brokers brokers are called agents here or the property consultant. So there's a very very different phenomena with which these people behave and they uh work identifying their uh insights their signals to when people are uploading pictures. So what we do we tell owners okay you please upload your pictures uh via WhatsApp and when somebody sends you the photograph for silly reason they'll just select 10 15 uh photographs together and one of the photograph can be a good morning or a happy birthday uh image and you really don't want those images to be available on your platform correct so there has to be a scrutiny which happens so I'm talking about uh 1617 we had built-in model uh with Google and that time obviously vortex object was not there with the object identification where we started identifying what all objects are there in the images with that we used to classify whether this image is for a house dining hall kitchen bedroom and then we used to accept or uh reject the images. So multiple use cases and to do this particular use case it took us few months at that point of time and rebuilding the model training it tuning it and there was no AI it was machine learning at that point of time the beauty about our industry is that with every 2 years or 3 years you'll have a new buzzword and people start using that particular thing but we were building machine learning models but now with Gemini and with Vortex uh the models which are available that thing has become so seamless for us we had built our own model. We had deployed it. We were incurring a cost on that particular thing. Now it's a SAS model. We just pass on the image and we tell uh we ask the model whether this is a image of a property or not. And not only it tells me whether the image of the property, it can also beautify the image and give it back to me. So those are the seamless things which have uh definitely help helped us in uh with Vort.ex AI. Thank you. I think uh the the lessons that we've learned over time for Vert.Ex have been uh how well it fits into the other ecosystem of uh uh solutions that exist on Google cloud. Um so I think Vert.Ex itself has broadened quite significantly over time. Um obviously we were using to train models initially. So being able to train a model, evaluate the model on how successful it is. If it's like for example like Gil mentioned for object detection, uh if you were training models right now, having the ability to evaluate and figure out how accurate your model is. um and you know combining with other tools to kind of make that work whether they're Google related or whether they're not like you know we we use a product called weights and biases to kind of make sure that the product that we're or the model that we're creating um is you know evaluating correctly is actually performing correctly there's no regressions um but also in terms of like you know we we have this continuous stream of foundation models that we're creating and then being able to scale on vertex AI from there uh has been phenomenal and in terms of data storage you have you know a bunch array of options, anything from like hyperdisk to just plain Google cloud storage and how that all kind of integrates together um into this one product where I think a lot of the development team is very happy to kind of work with a bunch of Google tools but everything kind of exists in one product or another which is actually a a great thing I I guess for the audience to know right so part of the power of vertx AI is that integration integrated vertical stack and solutioning for you to make it easier for your teams and your developers to actually go from prototyping experimentation on new things and then actually deploying into production for your customers. So Tet how have has these particular function functionalities from Vert.Ex text help your in your case right using the models that are provided using the APIs using the all the integration capabilities absolutely so uh prompti is uh uh still in this early days so we've been operating for about 18 months and that's kind of an interesting experience so I kind I want to share that from that perspective so when you're early stage company many times you have to do a lot of trials and errors and iterating a lot through these use cases and processes so um and I was uh I had my PhD at UC Berkeley before and that was very different from doing academic research where you want to get everything right and many times in startup you really need to get a direction right first do these iterations and try to be as efficient and fast as possible so sometimes we make engineering compromise and sometimes we look for off-the-shelf solutions first before we decide to delve into that and I think vertex AI has been tremendously helpful in that sense because you know we can put together a solution very quickly and and this was not un unimaginable a couple years back then and now we can just put them together and very be very fast in like actually go to market and deploy it uh to a either it's a group of uh test users or the our entire user base and sort of iterate from there and if we realize okay we got to do model training we'll do that afterwards but then we can still deploy them on vertx for example and Gemini for example has has been really really uh transformative because now it's sort of you have this uh general almost like a computer where like it understands instructions it understands natural languages um so we are able to uh build very high level uh applications very quickly using these these APIs which is great and I I think you all pointed out to to one thing which is your teams like vertex they like developer uh developing with the platform but let's get a little more specific on that. So how long if you can give us a sense right for the audience especially um does it take your teams to go from let's say an experimentation phase or like a trial phase for new parts of your product your solutions to then having uh the model train and everything ready to go and then deploying. So can you give us a sense of like what that journey looks like? How long does it take? Oh I can share a actually an interesting story. So we were uh building up this pet feature. So it's you know visual AI helps you with anything kids pets of course. So we were I want to see my dog in Absolutely. Yeah. We got to recognize them. I understand like this your dog is a neighbor's dog and if your dog is doing thing any any sort of uh anything it's not supposed to do right. Um or anything just that's interesting and fun. So we were sort of like you know uh implementing this feature and our uh uh design product officer uh he moved pretty fast but he was taking his time. It was like, oh, the engineering team was going to, you know, take a while to implement this thing, especially, you know, we're implementing this as a a full scale feature that's going to be pushed out to everybody. And, uh, he estimated it was going to take us six weeks to get it done and we got it done within three. Three weeks. Three weeks. Oh, so that's 50% cut of the original time frame that you guys expected. Absolutely. and he usually, you know, uh says like I tend to bend space time in the company uh because whenever they say it's going to take eight weeks, I like how about four? Let's let's work that out. How about four? Um and this time it was genuinely surprising like we we got it done within such a short period of time. That is pretty amazing. So it's and great productivity uh gains for you as as your development team. Absolutely. And meet this deadline. So that's fine. You just need to be careful, right? Next time he will tell you two weeks expecting it to be ready one week. I'm always greedy. There you go. All right. So, well, actually he and so any any similar experiences hopefully. Yeah, I can go I can go first. Um, so we have a we have a model that we've uh deployed into production that can detect deep fakes. So can detect images, audio, and video if they're if they're AI generated or not. Um whether that's from you know a Google openai doesn't matter who's producing this. except it's open source models etc. Uh a key part of that is actually like uh a curation of synthetic data. Um so uh we actually use uh we're continuously like upgrading these models and this is almost an automated functionality now. So the idea is that um we have this crawler that goes out uh and that is observing different GitHub repositories and a hugging face etc. and it's trying to figure out if there are new commits or new models that are being that are being published. It scrapes data, puts them into a cloud storage bucket, um, uh, puts them into a Excel sheet or Google sheets. At this point, this is probably most untechical part of this entire process. We'll remove that Excel we're recording. And effectively what ends up happening is uh, as soon as some QA person says like, oh, this is actually valid. Here's a here's a data set or here's a model that our model has not seen before. And the regression test shows that the current model has low coverage of this. um it'll immediately trigger a model training on Vert.Ex through the to a training platform a custom job that's what they call it um and effectively train a model immediately to get that coverage. So for example in the last week or so there have been week and a half there have been um three models that have come out like Gemini 2.5 now supports image generation OpenAI supports image generation through chatbt and Midjourney came out with V7 and all of those models even though Midjourney came out on Monday or Tuesday uh if you go upload a picture from that product today it'll tell you that's fake. Um, and the reason for that is because it quickly gathers data, does a regression test, and it immediately kind of fires off a custom training job to kind of train that model and get that coverage. So, it's uh it's kind of built in a way that kind of puts all the pieces together. Well, it's saving you but also saving all your users a lot of time, right? Yeah. The users expect like, you know, if there's a new model that comes out um uh an image generator, video, etc. Uh our users expect coverage almost immediately, right? Otherwise, if you have like a firewall or a spam filter that can only catch spam like a month ago, then it's not very useful because your attacks are are enhancing almost every day or every week. That's true. And and what an amazing use of the product too, right? So detecting deep fakes and all that. So that's great. And using that for productivity gains, but also time to service, time to market, right? That's pretty pretty important, pretty good for you too. And with those all those properties I killed right and all the services that you provide how does that work for you in terms of the time I think uh there are uh hello yeah so if you see at no broker uh we don't only help people find houses or buy houses. We work in the all facads of the property which is like you may want to get moving services. You may want to get your uh rental agreement, sale deed, property deeds, uh you want your house cleaned, you want your house painting done and if you see all these uh services they have a touch point and they need uh somebody to go visit your house maybe to see how much of the area has to be painted so that I can give you the quotation for that particular thing. when you are moving how much is the uh quote how big is your house because typically and I'm sure this happens across the globe whenever somebody asks you how much stuff you have to move you will always say I have little but when the truck comes which is supposed to take the luggage and with the people who are married you'll suddenly find so many lofts which have sudden uh stuff coming out and typically it overflows so for that uh we used uh Gemini and uh some beautiful applications what we have done. Now what we tell our customers is that take the new broker app and if you are moving just roam around the house with the video on and when you are roaming around just open your wardrobes. If you have the beds which has the storage just show us how uh of the how much of the stuff is there. Let us know if that fridge has to be moved, this sofa has to be moved, TV has to be moved and then we calculate what is the cubic capacity, what is needed to move this particular house and uh what will be the cost of moving that particular house. Imagine earlier we were doing a guesstimate which 60% of the time was not working well because of the hidden stuff which is there uh in form of toys of your kids or maybe the old clothes what you have all those things we are able to do now so that's one and this this phenomenal then second one is when you do your lease agreements again uh year uh typically in India it happens after 11 12 months you had to ask all the details on the form Now what we do we just tell them whatever lease agreement you have in whatever format whatever language it has been written just upload it we just scrape it we use uh OCR we get all the details and we ask three four information like what's the new rent what's a new deposit just fill in those details click confirm boom your rental agreement is ready so all those things which were taking like days and which was uh earlier needed human intervention to do this stuff all of it we are able to do with AI now which is Great. So, not just eliminating the middleman, but all those uh potential services of someone going to check on what's the space require and the service and how much we're going to cost and all that. So, that's pretty impressive. Thank you, Ailio, for for sharing that. And actually, I could use some of those services too. Not in US yet. Not yet. Not yet. Not yet. All right. So, um so now let's think about you've been working obviously with Vert.ex already for a while and uh the product has evolved, right? it was non-existent then it was ML then it's vertex AI today and there have been a lot of announcements um at next this week right about vertex and some of them are related to agents and agent building and some of them are related to new models so what I would like to take the conversation now is how are you looking at the future for your companies and how some of these announcements some of these new developments advancements actually can help you power those that new next layer of innovation that you you are thinking about for your companies. So if you can let us know a little bit about that and and that will give us a glimpse also of where your industries are going too. Ted. Yeah, I can go first. Um so uh imagine the future where like these spaces are are watched by AI so that we don't have to spend hours watching these videos and also this information coming back to a centralized place and uh we're just able to ask questions about what had happened and uh the insights of of what had happened and that means for example the well that means the first step is to understand environment right visual understanding And after that it has to be agentic because it needs to connect uh the things that had happened to intentions to uh what we as uh operators or users, homeowners, business owners, what they'd like to see. And these things are all different. Sometimes they are personal. What I want for my home might be very different from what you want uh for your home. And uh a uh retail shop owner what they are trying to uh uh get might be very different from a hotel owner for example. And these models will have to and systems I would say have to be able to understand intentions and work in a way that that different people uh might want very differently. And uh I think for example uh vertex AI can be a a very um important role in that. uh for example reasoning capacity for for these models and now they have to think and step step by step laying out what they have to do and now they have to go to the uh prospective uh parts of the system uh whether it's a storage they might have to check some data in a storage or they might to go have to go into the database and and uh come up with a a SQL query uh some keyword to search for some information and on top of it they need to synthesize this information and then decide what to do next and or stop and present that information to people and sometimes it's even a voice interface. So we're really getting into the stage where computers are getting really sophisticated and uh and also like we just have AI to automate a bunch of uh the either the boring task or sometimes it's just very heavy for human beings to do. Yeah. And I love the fact that you mentioned I think all these things that you mentioned you started with saying it's agentic right it's a lot of these process flows that are going to be built on top of that and vertex can give you capabilities right agent SDK and the agent builder and all those uh those parts of the product so how is that um helping you or potentially helping you and let me go with a or so um are you planning on using those are you already in that journey of the agent u building how agentic those solutions will be for you uh any specific things that that you can share with us? So uh so at the scale of no broker where we have like close to 5,000 employees working for us and most of them uh a big chunk of them or a majority of them work in our customer service department where they have to touch base with the customer answer their queries understand what they need like I was talking about packers and movers I was talking about cleaning painting and all those things so then but for a customerf facing company the SOP is that you should have a consist consistent great quality service which is unbiased by the mood of your agent. Correct? It should it should it should not happen that I had a fight with my wife tonight and or early in the morning and I'm disgrunted on my customer and I'm not happy to help that particular customer and that had always been in my mind that as we grow big how are we going to solve that particular thing. So we started building models very early. So now what we do we have built a platform called convoen.ai which is like zen out of customer conversations. C uh customers can be conversing with you on a chat chatbot emails SMS WhatsApp and on your call center. We take all those conversations and India the beauty is uh we talk in multiple languages. So we have like 14 15 languages which are actively used otherwise we have hundreds of languages and people switch languages. Uh so they'll be speaking in English and suddenly Hindiad that's what I did and it it comes very very naturally to us. So none of the models were able to solve that particular problem. So we created our own ST models and now once we had that particular thing we were able to create agents like agent assist where there is a virtual agent who is sitting on top of our platform and one of my call center executive and he's he or she is talking to the customers it can tell you what exactly is the history of that customer that okay she came to no broker platform 3 months back this is what she had or maybe she has active service going on she had sent you an email she's not happy about something which is going on this is what you need to tell so basically uh the things like okay sir can I put you on hold and then I'm going back I'm going to search with my manager all those things immediately goes off now when you talk to the customer you say okay hi this is what is happening I see that you have a packer remover movement and our partner has not reached I have already put uh uh put a touch with my partner and he or she may be reaching uh in another 30 minutes so that that levels up your experience uh to a different level. Then after we did that we realized that there are a lot of task which don't even need human because I feel as a human we should do something which is non-mundane we should we should be thinking we should be uh creating new stuff we should be doing something smart so then we created our own virtual agents uh you can say humanoids which can talk in Indian languages uh and that's what I was talking with Zah also like uh it can make a call to you it will feel as If a human is speaking to you and if let's say you have a property visit scheduled it will just call you and say hi I see or you have property visit scheduled and are you coming or not and then somebody says oh no I see there is a traffic oh I also see that there's a traffic okay so that means that you'll be delayed by 45 minutes that that is what Google map is showing let me just reschedule the appointment for you and I'll also inform the person on the field who was supposed to be with you on that particular visit so things like that we have started automating and that's where the agentic theme has started coming into uh our platform and because it was so beautiful uh we have started selling it out as a product uh to other companies also there you go so another business revenue stream there so that's good congratulations on that hill and thank you for sharing it so what I'm hearing also is that it's not just the internal experience that gets better with all these new advancements but it's also the experience for your customers of course right so not just for the internal developers that are using the platform but also the end result So no, no company can be successful until your customer is happy. Absolutely. And I love hearing that it's actually good for you to use our technology on both sides. So thank you for that. Um but so hi so let me ask you because in your space specifically right there's a lot of innovation going on with models out there from you from other companies there's a lot of competition there's a lot of innovation that we're bringing to the table. So how are you navigating through that and how do you see really the future for resemble AI is going to look like with your technology with the help of of Google but also with things that are going on out there that are coming out. Yeah. So I'll answer this in two ways. So um we're kind of lucky that we develop models and our customers go use those models and applications. So we have a lot of insight and oversight as to what applications are are um are very useful uh and where where they're creating an impact. Right? So we see everything from like call automation and I think uh these two gentlemen have talked a lot about different automations and different agents that are really applicable and everyone here is probably tired of hearing voice AI for the last two days. Um so, uh one of the things that's probably the most impactful in resemble and you know we we've uh I've actually like worked with a circle of other founders to kind of implement this in inside of companies and we're we're really bullish on this actually is um I I'm a firm believer that every company should have one dedicated person ideally a team but if you're if you're a startup one dedicated person in just exploring different agents and how they could applicable as employees in your company. Um, and that has like tremendous benefits to the company and it's now way easier than ever, right? So, you can actually get employees that could do programming, you know, there there are literally software out there if you if you wanted to get something off the shelf. There's Devon, you know, uh there's plenty of others. Uh there's customer success uh products out there. Um with a with a the real power here is um every company like every human is also slightly different from one another. uh but the building blocks of you know using Gemini using OpenAI using different models to achieve different tasks is a matter of plumbing work together and then the core really becomes how it works in your workflow right so a lot of us um and I'm really bullish on this is the most valuable AI company the most valuable agent AI company is probably Slack right now and the reason is because every AI like agent that your company will interact with it's like an employee within Slack so why would that be any different um So having these agents being deployable, it's like having uh staff that has 10-second SLAs's. No human staff member can give you a 10-second SLA. Uh but an AI agent can. And there's a lot of uh uh a lot of uh great stuff happening within these companies, including Resemble. You know, we're deploying uh bots that are effectively helping customer success, internal, external. There's different ones. Uh we're hooking them up to different products. We we have a bot that typically sits on our um on our document page which helps people make integrations because at a certain point we're not going to write and maintain SDKs for every single language. Uh it's too much work for us. Uh but what we can do is we can effectively have a have a you know a chatbot that's geared solely geared to understand our SDK and our documentation and then the user can go in and say oh I need to plug this into Genesis or I need to plug this into Unity. How do I do that? Right? And of course, we're not going to write a guide for every single integration, but this thing can this thing can do it on the spot on the fly. Um, so creating these like agents, um, particularly internally, which is kind of where I have the focus right now, is it can pay a lot of dividends and it helps your company learn extremely quickly. So, uh, I'm not sure what the audience makeup is. If you own companies, you should probably be doing this. If you don't, then you should probably go to your boss or manager and say like there should be a team or a group of people dedicated to just experimenting with agents just internally making those workflows better. that I love that idea and let's actually quiz the audience. So just by show of hands uh how many of you are maybe already doing that creating agents internally going to your managers and saying hey we need to to do this for some of those employee tasks and functions that are very repetitive or that are intelligent but could be better done with AI right now. Showing of hands. There you go. We're like 30% of the room. 40. Yeah. Yeah. Just about. There's a while to go. It's a lot to go. Yeah. Exactly. But we're just starting in that journey. So I think it's coming. It's coming. So yeah. All right. No, that that sounds great and thank you for sharing that. Um All right. So since we have also of course founders in the room, one of the things that uh that I'm sure they are probably thinking about also is with your companies. You are in different stages, right? So TE's companies earlier on, you guys have been uh for a few years already. So what's next for your company? What are you excited about for your company for your next milestone? Uh let me go first. So so if you talk about no broker we are 11 year old company the only prop tech unicorn in India but given uh at this stage also we are just present in six cities in India. So we have a lot and lot of uh ground to cover and uh with AI and with kind of automations uh what we are able to do at the uh and the rate at which technology is changing uh I think companies will become global. So it will be the solutions uh which you'll be able to create from one country and it will work across the globe and that is something which people keep asking me that uh when exactly are you going to come uh to different countries uh because in US also you see that uh the amount of interpretation cost is extremely high and with lot of things happening uh on the uh on the law side. So there's opportunity for us uh there also but right now we are focusing on India uh very big opportunity uh with no broker no broker services what we have and the convoen which is uh our customer uh intelligence uh AI product what we have built so we'll focus on that great so hopefully we'll see you soon too in the US and then we'll be happy to move you there you go all right thank you Tede what's next for you absolutely so promi was founded by a group of PhD students and professor from from Berkeley. So all of us uh have been working on computer vision for for myself personally it's been a decade and for uh one of my colleagues he's been working on it for more than three decades since early days like um when he was at MIT. So we just had this frustration back in the days of like well we've been developing so many different algorithms and research works and but how come these cameras are still dumb cameras like how come they are just recording and I have to go back to it and I have used a slide tiny little slider and to look for what I'm trying to get and they can't really talk to each other they can't not uh really they don't really understand any sort of information and how come that we've been doing so many years uh so so many years of work in computer vision and they still can't tell you whether your cat has jumped onto the couch or not. It's not supposed to be that hard. Okay. Um so that's why we started and now we're getting closer and closer. I think we're we're at the down of of visual physical AI. I mean like you heard the word physical AI all the time, right? Robots and drones and autonomous agents um everywhere. But then you think about it like who's going to watch them, right? And you you got to deploy these cameras everywhere and so that you make sure that they're not they're not functioning or or doing bad things. Um and I think our our goal is to uh sort of have computer to be able to do anything that only requires a pair of eyes. If we just need a human being to sit there and watch, please do that with a computer because um you know we humans can do much more interesting things and we can spend our time more efficiently. We can spend our the time with with family uh with friends and and focus on the work that actually require our attention uh rather than just like these tiny little things. So that's why we're I'm really excited about the future. I think these uh technologies can transform how people interact with the home with their with their pets and their environment also like how businesses function. It's going to make us more secure uh feel more safe and more connected. Great. Thank you. And I I guess what I'm hearing from you also is that there's of course software solutions that you provide today and maybe the hardware pieces are coming up at some point too. Yeah, absolutely. And I think a lot of these uh hardwares have been really commoditized like 20 years ago. I remember like a camera, nice camera would cost uh at least hundreds of dollars if not like thousands of dollars and now they cost 20 bucks. You can buy them from like anywhere almost. Um and it's not hard to to manufacture them either. So the reason that um many people are still not buying them uh is that they really don't find a use case for that. It's like I buy a camera, I put it there, so I forget about it and I pay like cloud storage for that. uh and now finally people are able to get some usage out of it and I think it's just going to drive this very positive cycle where people keep buying more cameras and as a result we discover more use cases we try to automate them and they become happier they buy more cameras true and that that point they will need your visual intelligence platform too so absolutely which is great all right thank you for that so hey so let me close that this section with you in terms of uh what's next for the company and what are you excited about. Yeah, there's a lot to be excited about. Um, just to give you context, uh, 4 years ago now, 2021, we're in 2025. Yeah, that's four years ago. Yeah, time is a blur. Uh, four years ago, uh, one of the things that we, one of our customers actually published a show on Netflix called The Andy Warhol Diaries. Um, it the entire narration in the Andy Warhol Diaries, Andy Warhol of course passed away uh, in the 70s or early 80s. Um and every narration from him in that documentary was completely AI generated. Uh I called this the pre-hat era. Um and uh that gave us an idea of mainstream use of generative AI that was nominated for four Emmys uh that show or that documentary series. And um that got us thinking or got me thinking a lot about okay this piece of technology that you know in 2021 four years ago is able to reproduce something that a normal consumer that's watching TV cannot tell if it's AI or not anymore. And um if you fast forward today you have this in pretty much all the modalities. You can go and obviously create gorgeous videos with open uh with open AI or Google with V2 now etc. Um but you can also go in open source and do them. And I don't think open source is slowing down. I think open source is keeping ahead with the pace of where uh the frontier models are. So the thought really comes in when we're creating these platforms especially as resembles creating these models and allowing you know millions of users to use and create models themselves is how do we do it in a safe manner? How do we get people to not be able to scrape a video off of YouTube of Akil and effectively just, you know, clone his voice, take his face, create a version of him, you know, and that could be extremely dangerous. Nobody will do that. Somebody might do that. You know, we've had people on YouTube that have said like, "I found my voice being used by a different channel." Uh we've had people, you know, complain about I never was on this ad. I never promoted this, etc. You have politicians obviously. Um and uh the the thing that we are really bullish on now and that we really want to have impact on to be honest. We hope that the company plays some part in this is the deployment of responsible and safe AI. And those are not just meant by guard rails, but you know, as I'm in the Bay Area, Tete's in the Bay Area, the the way we think in the Bay Area to be honest is technology is the answer to problems as well, right? Technology can be solved by technology and not necessarily policies, right? Um, and so we've been, you know, building models around watermarking. We've been building models around detecting defakes. We open source models around like, uh, speaker identification and person identification. Um but all of those are kind of coming together and we're trying to really wrangle around this this foreseeable problem where you know early in January this year 55% of the internet according to a lot of research researchers um was being created with generative AI right uh and the projection was by the end of 2026 that 90% of it would be created by generative AI. I think by the end of 2025 with the uh image and video models that are coming out that are widely accessible on your phones at this point, 90% is a pretty pretty conservative efer AI being used in content being produced. Um so that really opens the door for malicious users on the other end that can also use that content. And what we want to do is actually give tools and give models to people and companies that are deploying these models to also offer ways to kind of prevent um kind of the responsible or encourage the responsible use and prevent malicious use of those models. So I think that's where a lot of my attention and focus is going because I think generative AI is out of the it's out of the box. This these models are going to improve. I have no doubt by the end of the year it'll get faster, better, higher fidelity. Um that that's a given at this point. So the the response is well what's the what's the what's the uh what what's the counter to what's uh what we're about to see here happen in the world. True. And I'm so glad that you mentioned that because the general concept really when we think about guard rails is like regulations and policies and what can we do but you mentioned something very specific which is well technology can also regulate technology. So that's an interesting concept and I think a lot of companies are actually looking into that because policies regulations will not be able to advance as fast as we need them to catch up with what's happening in technology. So that's an interesting concept of what you brought brought today. So thank you for that. And actually we're getting close to closing the session. So I'm going to say kind of like a rapid fire oneliner. What would be your advice for founders in the room who would like to use Vert.ex AI in their solutions today? And uh what advice can you give them? Just oneliner very quick. Let's start with Ted tip please. Yeah. Um so speed is really important for listed startups. You got to try your best for that. Thank you. Yeah, I think uh the same. So basically the rate at which you can innovate uh with vortex and anything else uh is uh extremely fast. So things as I was mentioning 10 years back things which were taking months few years back which was taking days now is taking hours. So if you think about a problem which you want to solve uh you should be able to do a P of that particular thing extremely fast to know whether it's going to work or not and that can define uh how fast you want to work on a problem. Thank you for for that. I hear just go to aistudio.google.com and click all the buttons and you'll learn everything really quickly. That's a good one too. Well, thank you so much for that. I hear that um hopefully this session has been useful for you. Um I have to say thank you to all the founders obviously everybody else in the room and if you are not this is my commercial if you are not familiar with Google for startup cloud program please come talk to us startups hub in the expo hall and uh thank you for being here today. Thank you for investing your time with that with us today and thank you for evaluating or using our technology already. Thank you and have a great rest of your day at the PL. Thank you. [Music]
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