Founders Adda Raw Conversations with India’s Top AI Pioneers

Thank you. Thank you. you you Thank you. Thank you. Thank you. so how do founders learn about these changes the only way you can learn or maybe the best way to learn at a conference like this at a summit like this is by listening to each other so it’s not a pitch there’s not going to be any talk about business there’s going to be no funding conversation it’s only about product so I’m going to request all the founders who are presenting to come up and then we’ll go sequentially and the other request on the presentations to all the founders who are presenting is please come I mean feel free to come up is that please use jargon because the intent is that the audience will understand it however also be mindful that if you want more people to learn who may not be AI natives may not be AI people may not be technologists may not be AI people but it’s still important for them to learn and understand.

So if you can simplify it, it’s fine. If you don’t want to simplify it, it’s also okay. So the format we’ll follow is that each one of you takes a little bit of time to talk about your product. But like I said again, only product. No business, no pitching, no money, nothing. So I’m going to request to start with, if I could request Ravindra Kumar to talk about what is it that you’re building. So quick introduction and then the product that you’ve built.

Hi everyone, this is Ravindra from Technodate AI. And we are aiming to automate automation itself. Everybody says AI won’t take away any jobs. We’re like, let us do something about it.

Do you want to stand there at the podium or you want me to start? Whatever, whatever. No, no. Yeah, you can start your presentation. Shall we do that? Yeah, yeah, we should. Because I want people to really get into the product.

Can I use the clicker? What generally happens is that there are already very sophisticated automation equipment available out there in the market. Before starting Technodate, I have been working with this company, which happens to be world’s largest manufacturer of industrial robots. Way back in the year 2010 or so, they achieved 100 % automation, which means no human on the shop floor. Still, the manufacturing is happening at 100 % capacity. On the other side, if you see globally, including India, manufacturing is not even successful. People are not able to. use automation to the fullest extent. That is something which Technodate is aiming to solve. We want to make automation as easy as DIY using agentic AI. So what it does is basically help you in three ways.

First, to conceptualize an engineering robotics and automation engineering solution on your own. Then to deploy and commission that including robot programming etc. etc. And then eventually it also helps you to troubleshoot when something doesn’t work. For this, we started as okay, we have to do something like this because the idea of this discussion is how do we go from experimentation to real world deployment. So when I came up with this idea, the first thought was that you need to build a foundational model. But we are in India. It’s not that easy to raise money to build a foundational model. But then how do you approach this? The idea is okay, let us go talk to customer.

Let us experiment with what all options are available out there and then figure out in the process, do we need a foundational model? So we started working, started talking to customers, started doing some initial deployments. Today we stand again back where we started from that we need a foundational model for this. But in the process, we have already started deploying application, including with people like Fortune 500 companies. This is how the team comes from. We are working with some people that I’m sorry, Archana, but being a founder, some pitching comes in by default. But then, yeah, this is how the team looks like. We are collaborating with people like Dr. Sumit Chopra, a Ph .D. under the godfather of AI and Lincoln.

He worked at a fair earlier. We are exploring or we are rather going to deploy a use case very soon with Indian Air Force itself. Of course, the team comes from IITs. I have a small demo to show to everyone. There’s some music to this. But it is. It’s just music. There’s no audio in any case. So what it does is, as I said, three modules. it helps you to conceptualize robotics and automation solution it helps you to build that the agentic AI what it does is it acts mimics automation expert it really finds what it takes to deploy that solution in a real world scenario it gives you the complete architectures it gives you the programs it gives you the step by step procedures how do you put these systems together and then eventually in the final I mean of course you can also ask it to make changes what happens is in industrial scenario you change one equipment it has to talk to all other equipment so everything changes so it does all that on its own autonomously by using agents in the background you can also see how that solution will look like on your factory which has been conceptualized by the agents and then when it comes to robot programming many people ask me that Chad Jibadi can’t do this why do you want to build a foundational model for this when it comes to robotics necessarily we are interacting with the real world you have to understand what is the object what needs to be done how the robot needs to move all that data needs to be injected into the systems only then robotic programming can be done anyways uh then there is something called cnc programming cncs are the mother machine so every aerospace component every automotive engine be it two -wheeler four -wheeler they’re all machined on cnc machines for that matter to build other machines you need a cnc machine so all those programs can also be generated by using uh agentic or generative ai in this case in defense use case it’s like for example this is a case of aero engine where you just say the error code the 3d model explodes and the generative ai tells you where and what steps to take to solve that particular problem for example you will be able to see you said the error code it shows you where in the whole machine that error belongs and these are the steps you need to take to solve these problems so yes this is it from me we are exhibiting at hall 14 see you all there who want to discuss more

so anybody has questions on the product you including founders sitting on this panel can ask questions on the product any question yes please

we’ll use their model see I have no I am not fond of building foundational model my aim is to solve the problem of my customer absolutely so one thing that this is this will never be in a human history these kind of tasks will never be simple chat response kind of scenario you need common play workflows, right? So even if, let us say, OpenAI wants to do it, he will have to build a custom application for this, right? So this is an application layer. Model can become ASI, the super intelligence level. You still will have to build the application. So that is our first approach. Second is for industrial domain. Even if OpenAI wants to do today, he will have to build a foundational model for this, separately.

Because it is related to industrial world. The 3D actual world, the data is proprietary, customer doesn’t share it with you. So your application has to run on his premises or on the virtual clouds.

Okay, thank you. Weber, you are next.

Thank you so much. Thank you. Thank you. Thank you. first of all I would like to thank Karan and Archana from Rukam Capital for giving me this opportunity India doesn’t need more wrappers we need infrastructure and that’s what we are building at Quonsys AI my name is Vaibhavath Shukla I’m the founder and CEO of Quonsys AI we are building the voice infrastructure for India and so India is the customer support capital of the world it is 55 billion dollar industry for us it’s roughly 2 % of India’s GDP and the problem is that this entire model is outdated in the agentic era so that’s what we are solving we ask this question to ourselves if we can automate the call centers itself and the call centers can automate and completely run by themselves so for that we started solving this problem and we started building from scratch for what exactly is required to automate the entire call center pieces and that’s what we initiated with Quonsys AI.

So Quonsys is the default layer wherein you don’t need humans in the loop which can automate the entire call center and BPO infrastructure and we can completely run end -to -end for the processes. So these systems can listen, understand, act, respond and solve the entire purpose for any particular use case. So it’s not a concept anymore. We have been working with some of the top enterprises like Paytm, CRED, PropBotX. We are also partnered with OpenAI for the infrastructure. We are working with them on voice and Indic languages infrastructure which we have developed by our own digital data engine and we can generate data at scale. So we are different in a way because we have solved the entire layer for be it your application layer, for orchestration layer, then organization, on the model layer and the data layer itself.

So for example, anything and everything that is required we are basically making the entire suite of the… automation layer for call centers. So it’s completely, you can say call centers are completely running on itself. We have built companies before. We have a really good research team which is helping us in developing the entire foundational layer of it. And we have deployed some of the use cases when we have already worked with some of the large enterprises already. Yeah, and I’m happy to answer

Any questions on the product?

Yes, yeah. So the call lands on the somebody’s phone.

Correct.

So it’s like again a kind of thing.

Correct. those kind of scenarios yes it can be can you be more specific on the use case

yeah for example uh i got generated a lead on google ads or say a training uh on digital marketing right

yeah

so that customer is calling to a particular number

correct

this lands on say in this phone

yeah

so can i put this agent into this phone which can attend that call and answer according to my requirements

yeah it can definitely do so what it will do in the back end and then you can you know have a handshake handshake of web sockets when your number and the other number that we have uh we can basically merge together and the conversation can float from there and it can answer the questions because these are dynamic questions it’s not a fixed kind of question

right

right it can so all the knowledge that you can you’re going to give it so for example i’ll give you a use case of real estate right so uh if somebody’s making an inquiry about the real estate project you basically fill the form you get the number there the AI agent will make the call it is already trained on the entire data set of your real estate project where is it what is the per square feet size the cost of it what are the amenities and all those things locality all those things which is already trained on that it will talk to you on the basis of all that information it will record the interest level from you whether you want to visit the site or not and then it will automatically book the site visit as well and you can trigger SMS WhatsApp email whatever you require so everything that was previously done by a call center agent is completely automated using AI agents and it’s end to end process so basically the purpose that you have given it it can completely solve for that

and can like institutes can take this or companies can take this on stand alone basis or you have put in a subscription mode kind of thing

so it’s more like with charging per minute kind of subscription at this point so you set it up one time then or whatever the number of minutes that you consume with us you pay for that

Okay. Any other question?

Yeah. I mean, you talked about building foundational models before the ending language, right?

Yeah.

So if you could tell me how you’re scaling on the same because foundational models are very good for demos, but when we scale, we have even seen Servam breaking.

Right.

So how are you…

That is right. We basically gave a demo with Servam and… Guys.

Well, that was too loud, but then, yeah, how are you thinking of combating that scenario?

The main thing is basically the data engine, right? So, I mean, data that you have basically trained it on, that’s the most important piece. Initially, when we tried it with Bhashini and Google data sets, all the public libraries that are available, we basically tried to fine -tune and generate, basically train the model on that data set. But unfortunately, like you mentioned, there are so many problems with that. So that’s why we built our own data engine. As you can see, we… We won an award from Prime Minister Modi as well. It was right here in the Bhatman room last year. so we basically generated data generate data from our own data data engine and that is what we are basically putting it in the model so for use case by use case for example Paytm that is working with us at scale we are making tens of thousands of all with Paytm for those kind of use cases we basically take it what exactly is the use case on right for example merchant is a very complex use case yeah right right right so we are working I mean concurrency currently is around 50 now we are going to increase that as I mean as the model grows we will increase the concurrency like right right right right right right right right right correct so there are two kinds of uh problems right so there are smaller companies which are employing five guys ten guys so if you talk about that that’s not something where we are currently focusing on and it’s not the industry can’t focus on that i mean the pricing will come down drastically in the next couple of years but there are companies like sbi insurance they are employing tens of thousands of people in particular building right so from real estate from managing the security the parking spaces uh the hr management team managements all those things subscription headsets machinery all that those things if you take it down to the last minute so that costs roughly 25 to 30 minute rupees per minute for this thing this particularly maybe cost three rupees per minute so that’s more like 90 percent of the cost saving for those kind of companies so that’s where the current market is and that’s what we are basically focusing on

thank you thank you very much I request guys a round of applause for all the founders I request Pradyum Gupta to now come and present be generous with the applause at the questions both please yeah I mean founders are taking time out to talk about their product

thank you ma ‘am for providing me this opportunity okay hi Hi everyone, my name is Pradyum. I am representing Papri Labs here. So, just giving a simple example what Papri Labs actually do is that, for example, you are today all coming to Bharat Mandapam. Now you must be using, maybe if you are here from Delhi, you might not be using a map, but I am from outside Delhi, so I was using a map from IIT Delhi to Bharat Mandapam. Now what happened was that it said to me that these gates are open, but these gates were all of them were closed. I was just looking around all over the parking areas. And normally this is a common problem in the map system today.

What map system have done is that they have brought a great navigation system. So, you want to go at a particular place that could be anywhere all over the city, you can go down there. But that navigation will never be so much aware that the kind of awareness that you require. So, for example, that there could be a ploy. There could be a place that the gates are closed. There could be something which is happening. Maybe a very heavy fog is there. Now that is not updated. What our company does is, it is not updated. is that we update the map, any kind of existing mapping system in a very instant way. How we do it?

We work on a visual system. So for example, any kind of a vehicle which are having on the ground, they have our cameras placed in. So these are simple dashcams, CCTVs, anything which is visual, we basically place all over the cities and we work only in the metro cities as on date or only on the major highways. We take out all the data and we plot it over the map and then we not only place the videos or the images, we categorize them. So like for example, right now in Delhi, we work with a local transport here, DTC is a Delhi Transport Company. So we plotted about 8 ,000 units and then we were getting like 100 petabytes of data from all this thing.

And then we will categorize them so fast that you basically see the entire Delhi life. And the best part was that you can search from them, what’s going on there. So now, what are the use cases that we… we brought probably three use cases in the market so for example there’s a company called JC de Cox they own about 4 ,000 billboards all over New Delhi the problem in all these billboards is these billboards come at a standard price so for example you own like 10 ,000 billboards but you don’t you usually sell it only on a like a basis of if it’s a posh area then I will charge maybe more if it’s a less posh I will charge them less what we brought as a new kind of pricing mechanism that you charge on the base of impression count what digital arts brought for them and that’s how we were able to increase the revenue for about 40 -45 percent because now they were charging more on the revenue basis we work with the company called autonomous cars of this mg motors they had a hectare hectare vehicle which when they were entering in India back then they were bringing that thing in the autonomous set that one they were bringing internet edge inside one of the problems that they had was that they had luxury passengers but they wanted to know that okay what is happening on the road Like instantly, even if it has a fog, they need to know that, okay, divider is broken or not.

Am I safe in there or not? So we started to update. There’s a company called MapMyIndia. We started to update their systems very fast. Third, we worked with a company like BCG. BCG is a consulting firm which basically consults government to take decisions on the ground. What we told them that this is where the demand is there. This is where the capacity is high. That’s how we brought a root rationalization algorithm. That’s helped DTC on the ground to basically manage all their 8 ,000 buses to where they need to actually deploy more buses so that they can increase the revenue. But the second perspective was that more passengers can actually board the bus. So we update the map on the cases that they want.

Now, we have been penetrating in news. So normal daily newspaper that you usually read on a regular basis, there’s an image tree that is attached to it. In that image, there’s an about. 8 ,000 to 10 ,000 people usually just on the ground. do a basic job is to collect these images. What we do is because we have a huge volume of videos that we have, we are just updating them and they are creating a news out of it. So like you want to search anything, any news you want to create, you can create from there. How we do it? So because this is a more of a product business, so one of the problems that we faced in India when we were trying to scale this product is that even everyone is talking about AI, but today if I am just going to be asking any single passenger just to put a phone in their car and provide us data, none of you will do it.

And this was a very basic problem. We realized that people want it. In India there is this perception is that they want to absorb, they absorb the technology really fast. But to give that information is very hard. So we created a mechanism in which the customer started to supply the data by itself. So for example, when we started to deal with passenger bus service, we started to deal with passenger bus service. So we started to give them passenger bus service. So we started to give them passenger bus service. So we started to give them passenger bus service. So we started to give them passenger bus service. So we started to give them passenger bus service.

So we started to give them passenger bus service. So we started to give them passenger bus service. So we started to give them passenger bus service. counting because the problem was that they didn’t know how many people come inside the bus on a particular bus stand where we need to run more buses. When we started to deal with packages like logistic companies, logistic companies use digital locks. So today any truck which goes all over the country, the problem is that they put a digital lock and then they expect that the truck is safe. But that digital lock even gets opened, any kind of thing is not evidence in a court. What we added was a small camera in the particular container.

That thing counted how many goods were getting inside and how many goods were not, like what was the exact tally value. That’s how we deployed in our highway sector. So here if there are three sides of the data part, one is the passenger, that means we are getting city data. If we are deploying in the logistic sector, we are getting city data. We are getting highways data. And if you are deploying in the normal commercial cars, we are getting the lane information. And the perspective was. to just to get the front imagery. The back imagery they use it for themselves and we are certainly not interested in that part. That’s how we formed this entire information.

One of the problem that we faced to one of our customers was so we reached to one of so Delhi police and we started to sell this entire platform. They started to Google that like basically they want to search everything that where people are not wearing helmets because they want to cut the chalance very instantly. Now these things was that we created layers but we didn’t have a system that we can create very dynamic layers for that particular person. So what we did was we added that’s where the LLM thing came in that we started to describe every image and then internally we were searching everything for them. So like you have anything any idea in your mind you want to for example a person comes to me he says to me that find me all the CCTVs.

In New Delhi find me street lights which are not working just prompt it up. internally we are a video analytics company like we are so we keep we are running on a bare metal like hundred petabytes and then we’re just processing them really fast and you can then the best part that we brought was let’s start to compare like what changed now and what was previously before like six days back year back what was the development going on and this is how the basically the end customer gets so for example if it’s a local bus passenger company they wants to know that how many passengers actually board so we we provide a system to them but internally we use a front camera system so they use a fleet management system then we brought popular over the top so this is an example that we brought with DTC this this was funded by JICA JICA is an investment corporation which funds the government of New Delhi and that’s how we scaled in entire New Delhi second thing was that if you want to know any count like where the people whether how many cars been gone through or how many buses pass through that particular portion or where the two wheelers are or where the ambulances actually cross, we started to take out every information all over New Delhi and this is all real time.

So if you want to do it today, you want to compare it for like last six months and then you will track and target them, you can do all of it. So we brought these pay systems because JCD Cox was the organization for. So we are

Yes. Hello. So thank you so much for presenting. I am curious about knowing that you mentioned a certain petabyte of data that you are using and data you know is a very debatable topic right now after DPDP. So how are you handling that? How are you DPDP compliant? Because you are going to give this to certain other businesses also. So because you are getting a lot of personal data too, like getting images of people, getting images of the car numbers and all of these things. So how are you dbdp compliant and ensure that?

So there are two things. One is that inside videos we never take out for the public information. Even though the clients are ready to pay even 10 times over that value. Second thing is like this is the rule of property labs internally. Second thing is only front data is used. Front camera data faces are blurred. Number plates are also blurred. Second, third thing is that right now we don’t run on any AWS. So we don’t use hyperscalers right now. We only use bare metal servers. So bare metal is stacked in which we keep everything in Europe right now. So Europe, Hetzner, we have taken a portion of their data centers. And the second thing, so in India there’s a big problem.

how are you handling that? How are you DPDP compliant? Because you are going to give this to certain other businesses also. So because you are getting a lot of personal data too, like getting images of people, getting images of the car numbers and all of these things. So how are you DPDP compliant and ensure that?

So there are two things. One is that inside videos, we never take out for the public information, even though the clients are ready to pay even 10 times over that value. Second thing is, like this is the rule of popular. It loves internally. Second thing is only front data is used. Front camera data faces are blurred. Number plates are also blurred. Second, third thing is that right now, we don’t run on any AWS. So we don’t use hyperscalers right now. We only use bare metal servers. So bare metal means stacks in which we keep everything in Europe right now. So Europe, Hetzner, we have taken a portion of their data centers. And the second thing, so in India, there’s a big problem.

problem. One of the things that people say that, okay, GPUs are a lot, but the reality is that GPUs are, the companies which are actually selling these GPUs never purchase from them, rather purchase from CDAC. So CDAC is an organization called Aravat. Aravat is providing us supercomputers and like a bare, dirty price. So if you’re going to be searching on Aravat, just purchase their GPUs and then keep data on bare metal on your security premises, then it’s very safe. And it’s very cheap.

Okay, one more question. And in the end, we’ll take more questions because once everyone’s done their presentations, please go for it.

I want to ask, because I’m a performative on the product, and I just want to ask, like, what are the incentives you are giving to the dashcam holders? Like, I heard you are giving incentives to the local DTDC buses or like…

So we don’t pay incentives, they pay us.

So like, what is the leverage you are holding for them to…

For example… So this company… DTC, Dairy Transport Corporation, they burn about 80 crores every year on not providing the timely bus service. And they had a very low revenue. Like they had a revenue loss of about 800 crores as I had a talk with A .S. Sachin Shinde back then he was there. Now A .S. Jitendraji came in. Now when we came into the system, we actually reduced on the revenue loss for them. So for example, if you see this number, 27 is the demand which is there and 25 is the capacity. So in India what happened was that when Amadvi Party came in, they provided female passengers as a free bus service. Now every party started to criticize all these parties that you are providing free for the bus service to the females.

We were the first company which actually gave them a mandate that 1 % is the actual female passengers are operating. So that’s how they were able to save their lives. So that’s when, you know, when we came in, we saw there are a lot of… operational issues.

We can access it through our apps or like…

Nothing is possible. We are a pure B2B company. We never intend to be B2C.

Okay. We’ll do questions at the end. Let’s finish the presentations and… Okay, quickly. But short answer on your part. Yeah, but short answer, please.

So, we sell on tile basis. So, for example, this particular area, this comes at 25 by 25 square kilometers. This starts at 1 .5 lakh rupees. Per tile. This is for only valid for one day. And this usually multiplies at a volume at the company comes in. Thank you so much.

So, now I’ll request Meenal to come and present, please.

Hello everyone, I am Meenal Gupta from EasyOPI Solutions and so nice to see you over here. Who all are founders over here? Oh wow, so many. So we… Founders are here. So founders should be here. Who all are founders over here? So I love to be with founders. I know they share the journey and the struggle, they know it very well. So we all three women, mostly known as GreenDeviya because this name was given to us by Mr. Narendra. Okay. So I am Narendra Modi. So Meenal Gupta, I am the founder. I am the founder. Noor for… Noor for… and Sheetal Tarkas. We all started this journey. Our platform, we have named it as Imagix AI.

It’s an AI driven precision imaging to treatment planning for cancer. So we are HIPAA compliant. We have four patents in hand. We are ISO 13485 certified company. We also have SEDESCO license. Talking about SEDESCO, people who are from medical field, they might be knowing that there is a license which is required when we want to take our solution to hospitals. So there is a ICMR agency that certifies your product that is software as a medical device. Once it is certified, then you can take it to any hospital. You can actually commercialize post that. So we are a company. We are SEDESCO certified. So talking about the problem, we have a lot of people who are from medical field.

We know there are around 20 million new cancer cases every year. And there is not like that doctors don’t have the intent of solving the problem or treating cancer. But the main problem is the shortage of clinical experts. We can increase the devices like diagnosis devices can be increased, imaging can be increased. But the problem that is facing is the shortage of expertise for oncology. So and finally the treatment planning. So talking about the problem, once a cancer is being detected, the patient is sent for CT scan. Once the CT scan MRI. Once it is being done, then. Tumor board decides whether the patient has to go for radiation therapy or they have to go for surgery.

or the combination of both. I can understand everyone can relate it because almost every family in India or world are having someone very near and dear who are facing through cancer and they have gone through just such challenges. So what happens is because of this shortage, it cause life. It cause life or the stage of the cancer changes. Either it changes from first level to second stage. It changes because of this unavailability. So this is where our solution comes in. So this was our own personal experience where all three founders have personally experienced cancer to our near ones and we have gone through this radiation therapy where we had to wait in queue because of unavailability of specialist, because of unavailability of treatment planning.

So this was a very big bottleneck. You can see over here. So this was a very big bottleneck. You can see over here. So this was a very big bottleneck. So this was a very big bottleneck. So this was a very big bottleneck. So this was a very big bottleneck. So this was a very big bottleneck. So this was a very big bottleneck. Once a patient is recommended to go for radiation therapy, there is a planning to do for that radiation therapy. For this planning, there is a manual process where wherever there is a tumor, all the surrounding organs of tumor, they are to be segmented. And this is a manual process. I can proudly say that in India, there is no one who is solving this problem and we are the only one who have this solution.

Here we contour all the, contouring is the masking. We mask all the organs which are on risk and are surrounding tumor. So here the main purpose is that all the organs that are surrounding tumor, they should be saved because they are healthy organs. And radiation therapy should be as less as possible on those healthy organs. the manual process it used to take somewhere around 960 to 90 minutes we have reduced it to at the max of 15 minutes the reason is uh for complex uh radiation therapy when it is head and neck cancer it takes lots of time so maximum 15 minutes and minimum 5 minutes so we have reduced it here uh what we do is once the patient is diagnosed with cancer he goes for city scan tumor board this city scan is being done it is uploaded on our cloud tumor board have the access of this city scan through our own uh dicom viewer uh dicom is the format uh through which this images can be seen they decide whether they have to go for city scan radiation therapy planning or surgery and we have various suits we do ai analysis where do we do first level of analysis where we mention the load of the tumor and second level of analysis is we have various suits we have XraySuite, NeuroSuite and OncoSuite which works on this scans and finally we give the final report.

So this is our product. We have trained our AI on about 5 million of data set in which around 30 % is Indian data set and this 30 % we gathered it from northeast region. So northeast region is very tough terian and we got support from Niti Aayog where we went and collected data. It is very tough terian. Means taking off AI over there is very challenging because 4G has not reached there yet. So we had to implement it on -premise solution so that we can get the data and we can help them solve that. So 30 % of data we have almost deployed it in 14 states in India. We got our data 30 % from that. Accuracy is around 92%. So it is around 92 % to 99 % depending upon the data.

complexity you can see this data we are working in Gujarat in seven district where we are helping to scan to do CXR chest and lung analysis where we have helped we have helped we have made somewhere around 1 million of scans we have detected around 4 ,000 TB positive cases till yet in which there were around six lung cancer cases where early intervention is still possible we have done around thousand of radiation radiotherapy plans till yet and talking about in last three months we had done around five fifty thousand of chest x -rays where twenty seven hundred of TB were flagged so we could save TB these are live photos where handle x -rays and all are being done so I this was our solution was recognized first by Mr.

Naren Indra Modi and day before yesterday we were invited in Microsoft by Bill Gates to show our solution to him.

In health tech, I’ve observed that trust is a very big factor in terms of AI adoption and you seem to be implementing it across India. So how do you make sure that the technology and the science behind it is trusted by the people who are being benefited by it?

Yeah, I understand. So here, our solution, we are not replacing doctors. We are just assisting doctors. We have made their manual process easy process. But final approval has to be done by radiologists. So it is human in the loop. We are not claiming that directly our AI will solve it.

Thank you. I’ll request Vivek now to come

Hi everyone. So first of all, thank you so much team Rukam Capital for organizing such a vibrant event and the energy is full of high in this room, I can see. It can be higher though. Yeah. I think Yes. So my name is Vivek Gupta. I’m the founder and CEO of company called Indus Labs AI. So we are building the voice architecture of India. So we are basically building the whole layer of . operating system of voice, where all the layers like speech to text, text to speech, the LLM, speech to speech, all of this infrastructure we are building, right? So it is a common platform where anyone, I said anyone can come on this platform and build their own voice agent.

As sir was asking the question about whether you are running a campaign on Google, you have put a number, you can build your own agent by yourself, right? So it’s a DIY platform where we are training, we are primarily focusing on Indian languages because the problem, linguistic problem in our country is if you see after each 20 kilometer dialect changes, right? So we are working with couple of banks, NBFCs, right? And whenever they run a campaign, they run a basically cold calling in let’s say Mojaffarnagar region, in UP, right? And whenever they call in Gorakhpur region, the Hindi is totally different. But global players like 11 Labs or maybe, you know, the Indian language, they are all different.

You know, some different global players, players like Azure and Google, they are providing a generic Hindi, but we need. a company in India who can build the infrastructure of voice in our country based on our directs. And again, so while we are building the infrastructure on our own GPUs and servers, hyperscalers we have inbuilt in our system, so that’s how we are able to reduce the latency. So we have some sub 400, 500 millisecond around latency into the system, which is like more human conversation, you can feel it. And you know, the complete analysis of the call is there, right? As soon as the call gets disconnected, you will find the sentiment analysis of the call, the outcome of the call, and it will log into the system.

So what is the expected outcome of the call? It will go into your CRM. So the journey starts from your CRM and ends with CRM. You trigger the calls from CRM and it ends with the CRM. So again, as I said, like native dialect mastery we have, and we are ultra low latency. And again, you know, call. somebody was also asking question regarding how effective it is in terms of cost. So if I talk about existing system cost we are reducing the cost up to 70 % right. So up to 70 % cost can be reduced and operationally you are enabled like 24 7 availability of the system is there. System is multilingual right. You don’t need to have multiple people for different languages.

Single system can handle 24 7 and that’s how you are able to reduce cost and operationally efficient. And you know the important part is emotional handling right. So like one year back I started this company 2 .5 years back right. I used to be director of an engineering company in software company in Bangalore and 2 .5 years back I quit and started Indus Labs and my background is from IT Delhi right. So the core problem was emotions right when I started this company. I always thought like if somebody is laughing over the call how would AI system would recognize the person is happy or angry. That’s how the agent would say sorry or congratulate you. Right because you need to understand the emotions right.

So we were working on this. is speech -to -text model since last 1 .5 years and on the 16th of this month in the department only we launched this model called it’s basically no emotion of your STD so we launched this model here in part of the monthly and we are basically you know distributing it to our customers existing customers now so that they can start using it’s a PUC phase right now and the good part is since we are an Indian company the whole the data is going to reside here on our sovereign feel is there right so we are pure Indian origin company so as I said like if I if I compare with now global pairs like Google and 11 labs so we are cost so like let’s say I hope many people knows what 11 labs is right so their cost is somewhere around eight rupees per minute right but we are seventy percent lower we sell at two rupees per minute right and we are superior in terms of Indian dialect accuracy and we are superior and streaming latency is somewhere around three hundred to four hundred millisecond and emotional expressiveness is already there in our system as we recently launched it and Indian data residency clause is obviously there because we are an Indian company.

So, I mean, so we are a huge case agnostic platform. We don’t say like we are having mastery in this huge case. You can come on our platform. So, like as of today, we are working with multiple use cases, right? We are working with banks. We are working with enterprises in FMCG. We are working with, you know, customer support people. They are building their own voice agents, but they use our STT and TTS through API, right? So, not anyone can build STT and TTS. So, instead of using 11 labs, they use us because we are cost effective and obviously good in terms of Indian dialect mastery. So, it’s DIY platform. Anyone can come and build their own agent.

We have different flows. Workflow is already there. You can create nodes. Each node can be connected with webhooks or API and that can be used to build your own voice agent. So, we are working with customers. We are working with customers. We are working with customers. We are working with customers. We are working with customers. We are working with customers. We are working with customers. So it’s completely guided journey and you can create and you can also integrate your voice agent with telephony. So we have already partnership with Airtel and Geo. So that’s how you can give the SIP channels through there and connect with your voice agent. So it’s complete end to end journey.

And again, so the core market is B2B enterprises. Right. And we are also platform for developers. So developers can use our APIs into their existing systems wherever they want to use. Right. So you just based API and it is again per second based costing is there. So as how many seconds you will use, you will be the credits would be detected. So it’s a recharge based system. You recharge and you can use it. And also we are into we are building channel partners as well. So like we have a couple of partners. One is in Dubai. One is in Germany. So we are white white labeling our platform for them. Right. So they can onboard their clients on their platform internally.

They would become our client shared. and we can share the revenue so we have right now we have developed four to five partners globally so we are building from Bharat for the globe so we have foreign languages as well we have Arabic we have German we have French and Mandarin Mandarin is in building stage right now so we have core and English all the accents we have or still in accent British accent American accent all the male female voices and again you can clone your voice as

thank you any questions quick questions any questions

yep absolutely it’s a no -code platform it’s a journey based platform you need to trigger what you want to do what you want to basically build out of it so let’s say you are you want to build an agent for inbound agent for your leads right so anybody who is calling from Google Ads would land on this voice agent. So you will define the journey and how you want to integrate. Let’s say you want to, some meeting has been fixed for your product, right? So it is connected with Google Calendar, your Google Calendar. So as soon as AI agent books a meeting, you will get an email on your Google Calendar. This meeting has been fixed and your Google Calendar will be blocked.

So my question is like, I have to make this like, there are some nodes, I have to connect these and make a flow or you have all things are made up, we just have to click and the agent will start walking.

Yeah, it’s like DIY platform and we have tutorials as well, right? If you are stuck, you can see the tutorials. So and still you are feeling you are not able to build it, you can support the customer center. So our team will help you in that case.

Okay, quick question.

Yeah. How did you start? When you start, left your job?

We don’t have so much time. You can talk to them offline, but the data is a question.

I’ll make it short. So, journey started 2 .5 years back. So, I initially started building voice agent and started using TTS of somebody else. Then we figured out that this TTS is having this issue. How can I solve this? Because my customer will ask the issues complaint to me. So, we were able to solve this problem of pronunciation of some words because these issues were there already in the third -party system. So, we thought of initially building our own infrastructure. And we pivoted in a model that people will use our APIs. That’s what we want to build it, right? So, then, firstly, we used public data, publicly available data. Then we started creating our own data, right?

So, we create the data and we have multiple hyperscalers available. And scalability -wise, our system is so much scalable that you can put a thousand requests at a time, it will handle. So, it will scale 0 to 1000 within 10 minutes. So, that’s how we built it. Thank you very much.

thank you so much for all the engaging questions that everybody did make the effort to ask we are time constrained over here so I want to thank all the founders for sharing your product and any questions you have for the founders please do connect with them and do continue the conversation it’s just that we need to leave the room and I request all of us to do a quick picture together thank you Thank you. Thank you. Thank you.