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How AI Handles 30 Lakh Calls Daily in 10+ Languages | Manish & Rashi Gupta (Rezo.ai)
317 Plays1 year ago
"In our current capacity, we are 100 people strong working equivalent to 20,000 Warm Bodies."
This staggering metric from Manish Gupta highlights the sheer efficiency and disruptive potential of AI in traditional industries like call centers. Rezo.ai isn't just automating tasks; it's achieving workforce multiplication through technology, fundamentally changing how businesses scale customer interactions.
Meet the Guests: Manish Gupta & Rashi Gupta (Ph.D.) are the Co-Founders of Rezo.ai, the husband-and-wife duo revolutionizing customer experience with AI. Manish (CEO) holds an M.Tech from IIT Delhi and previously served as CTO at RateGain. Rashi, a data science expert, earned her Ph.D. from the University of Helsinki and two Master's degrees from IIT Delhi. Together, they've built Rezo.ai into a profitable venture (for 8 consecutive quarters!) handling 30 lakh calls daily and targeting a $10 Million ARR.
Key Insights from the Conversation:
- Value-Based Pricing: Shifting from simple rate cards to demonstrating clear ROI based on the enterprise's cost savings and value gained was key to unlocking larger B2B deals.
- Human + AI Collaboration: AI excels at handling high-volume, short (<3 min) interactions, freeing up human agents for complex, empathetic, or emergency situations requiring a human touch.
- Vernacular is Crucial: Supporting multiple Indian languages (10+) is essential for the Indian market, requiring sophisticated NLP and speech engines beyond simple translation.
- AI Augmentation, Not Replacement: Implementing AI hasn't led to agent layoffs; instead, employees are upskilled and redeployed for higher-value tasks.
- Enterprise AI Needs Guardrails: While powerful, generative AI like ChatGPT needs careful implementation with boundaries in B2B settings to ensure compliance and prevent brand damage.
- Funding Purpose: Raise capital strategically to fuel growth (like US expansion), not just because VC money is available.
Chapters:
- 0:00 - Intro: Meet Manish & Rashi Gupta
- 1:18 - What is Rezo.ai? The AI-Powered Call Center Explained
- 2:23 - The Human Touch: Where AI Stops & Agents Step In
- 5:46 - Founder Journey: IIT Days, First Startup Failure & Rezo's Genesis
- 10:18 - Finding PMF: Pivoting from Text Chatbots to Voice AI
- 16:02 - Landing Maruti: Building Trust & Vernacular Voice Capability
- 19:04 - Tech Deep Dive: Speech Engines, NLP & Handling Indian Languages
- 26:58 - Integrating Generative AI (ChatGPT) Responsibly
- 31:57 - Pricing B2B AI: The Power of Value-Based Selling
- 38:39 - Rapid Deployment: Onboarding Enterprise Clients in Days
- 49:48 - Scaling & Profitability: Reaching $10M ARR Target
- 51:43 - Funding Strategy & US Expansion Plans
- 55:35 - Competition & Advice for Aspiring Founders
Hashtags: #RezoAI #ManishGupta #RashiGupta #AICallCenter #ConversationalAI #VoiceAI #CustomerExperience #CX #B2BStartup #SaaS #EnterpriseAI #StartupIndia #IndianStartups #IITDelhi #FounderThesis #Podcast #AIinIndia #VernacularAI #NLP #MachineLearning #Profitability #Bootstrapping #ValueBasedPricing #FutureOfWork #Automation #CustomerSupport #TechPodcast #Entrepreneurship
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Transcript
Introduction: Rezo.ai and AI-Powered CX Cloud
00:00:00
Speaker
Hi everyone, this is Manish. This is Rashi and we are the co-founders at Rezo.ai. So it's primarily AI powered CX cloud for enterprises.
00:00:21
Speaker
Did you know that the first employment of your host Akshay Dutt was in a call centre? Yes, that's right. Akshay was hired by EXL service from campus as a management trainee. EXL service at that time used to employ 5000 plus people and it was a massive operation spanning multiple buildings in Noida.
00:00:39
Speaker
Today, that same level of output can be achieved with just a room full of people with the help of AI and chatbots. Don't believe me? Listen to Akshay's conversation with Manish and Rashi Gupta, the husband wife duo behind Reso.ai.
Disrupting Traditional Call Centers with AI
00:00:53
Speaker
Reso is a technology company that runs call centers for brands using voice bots. If you want a first-time understanding of how AI is disrupting traditional service businesses, then this is the episode you must listen to. And don't forget to subscribe to the Found A Thesis podcast and any audio streaming app.
00:01:17
Speaker
To simplify this a bit, you can treat us like as a virtual contact center. You reach out to a contact center for all the sales-related calls to generate all the leads. In our current capacity, we are 100 people strong, working equivalent to 20,000 warm bodies.
00:01:36
Speaker
And we are roughly doing around 30 lakh calls in one single day. In today's world, if you look at it, right, it's the humans. Sometimes we don't realize that how efficiently we could be. But the idea is if you're not efficient, the end product is basically reaping is either the enterprise or the customers. Right. Which just doesn't make sense. Right.
00:01:55
Speaker
We are enforcing efficiencies in the call centers, taking away something which is a recurrent job that Reso can do. If not, then bringing in efficiencies to the human agent and end goal is basically helping enterprises do more at less other cost.
Automation and Human Intervention: Finding the Balance
00:02:09
Speaker
And the construct here is that we will be able to do a lot of outbound inbound calls, just like what a human would have been doing. We do collections, we do scheduling of appointment, anything and everything that a contact center would have done. We are now doing it at the power of the bots.
00:02:24
Speaker
Is it there must be a spectrum of, let's say, simple calls to complex calls? Where in the spectrum, till what stage in the spectrum is Rezo able to handle? Because I'm sure there would still be some cases where you still need human callers. So where's the line for you right now?
00:02:40
Speaker
The construct is very clear. Calls which meet human intervention. We don't try to attempt that. Imagine there is a death in the family, you are trying to get an insurance, get some money. You don't want to be talking to the bot. You are in the middle of the road and you are stuck with your family at 2am.
00:02:57
Speaker
It's a mix that we bring on the table, right? So where you think of time, where Rezo can pitch in, can do the needful, we bring that. Processes from a simple to a mid-level processes is what Rezo automates. Something which needs a human intervention, we let the human do the job.
00:03:14
Speaker
And then for the rest, we also have a combination that we pass on the ball to the human agents. If Reso is not able to understand in maybe like two probing, if Reso still doesn't get the context, we pass it to the human agents. And then there is a whole feedback mechanism, machine learning at the backend, which is constantly learning.
00:03:30
Speaker
How do you define, like you said, simple to mid-level complexity? Give me some example of what's like a mid-level complexity. Let's say if I'm calling to
The Human Touch: Emotional Sensitivity in Customer Service
00:03:39
Speaker
change my plan to Airtel, for example, would that be something that Reza would do? Just define it a little. What is the current capability? What kind of complexity can it handle? And what it can't handle?
00:03:53
Speaker
I think what Rashi was mentioning about is, one, if there is some kind of emergency, it requires a human touch. And that human touch is something that we are saying right now the AI should not be kind of interjecting on those emergency situations.
00:04:09
Speaker
Everything else, it should be handled by the bot. Typically, any situation, any call where the average handling time is, let's say, less than three minutes is handled by the bot. Anything which requires much larger engagement per se, that's where the probability of the agents coming in will be helpful. So that's the kind of way it is.
00:04:32
Speaker
Yeah, that's a useful definition of three minutes as the okay. And why is it three minutes? Is it that the more longer the conversation, the more harder it is to contextualize something said at minute five with something said at minute one, like you need to contextualize
00:04:49
Speaker
Three-minute is more like a ballpark kind of a scenario. What also happens, Akshay, is at times the agents will have to look through the systems, look up the different systems, and give the answers. And that looking up in different systems might take long. What happens when a solution like Reso gets deployed? We do API integrations, backend integration with all the systems, and the response rates are very quick. We are seeing AHT getting reduced dramatically when a solution gets deployed.
00:05:19
Speaker
AHT refers to average handling time. So an average handling time comes reduced dramatically because now instead of switching between the systems looking up into the CRMs, the system is able to get that exact information through the APIs, the backend integrations, and is able to serve the customers rather quickly. So that's
00:05:41
Speaker
Right. You have an agent with infinite knowledge in a way. Absolutely. An agent with infinite knowledge, agent with infinite cloning capability, and 24-7. So can you take me through the journey of starting Rezo and just for the listeners? So you and Rashi are not just co-founders, but you are also husband and wife. So how did you guys meet? How did you decide to take the plunge, a little bit of that journey? Sure.
00:06:08
Speaker
So yeah, so I mean, we met during our college days, we both were studying at IT Delhi. So we started our first startup, I mean, the earliest startup, we started in 2012. And that was into data analytics, that was into some sort of machine learning, per se. And we had it for close to a year and a half. We had a product. That was a product or a service? Like, what kind of startup? It was a services.
00:06:36
Speaker
But we were also working on seeding a product at that point of time, which was more on the media mix modeling and media measurability, marketing measurability across all channels, including the outdoor media. But yes, so we did services as well. And we kind of realized with time that it was way ahead of the curve.
00:06:59
Speaker
We ended up talking to different companies, telling them as to what machine learning is. And we were getting compared against the average formula of Excel. How are you different from that? I think it was like writing on the wall, probably we missed it. So we ran it for a year and a half, didn't see much.
00:07:18
Speaker
headway there and that was when we had to pull the plug. We took up the respective jobs. And then again, as part of our jobs and different responsibilities, we saw this opportunity of unstructured data coming into the industry, which is like a road blocker for the enterprises.
00:07:38
Speaker
Anything which is structured can still get consumed very easily. But the moment you have unstructured data, it's kind of a roadblock and you have to employ humans to handle it. Give me an example. What's an example of unstructured data coming in? Unstructured data would be voice, would be free text, would be documents. Anything which is not numbers is unstructured data.
00:08:09
Speaker
which is not consumable, you cannot inference from it directly. And that's what we saw. I mean, as part of the business operations, almost all the enterprises generate this data. And in order to consume it, they have to have humans, they have to have people solve that thing for them. So it's like a very simple example.
00:08:36
Speaker
you want to take a feedback, you want to take an NPS score, you want to take a CSAT. If you can do it over the forms, that's okay, but a larger volume, you'll have to call them up, understand what is the feedback, what is the voice of the customer, and then someone will have to punch it in into their CRM in a one to five or one to 10 band, give a score. And once the score is received is when,
00:09:05
Speaker
the workflow will continue. So that's why we realized that, why can't we look at given the evolution of technology, of AI, cloud, why can't, you know, this be solved? So we started deep diving into it and that's where we realized it's, it's might not be 100% solvable problem, but it's a problem which can be solved to a great extent. So rather than solving a hundred percent, probably we could solve 80% of the cases.
00:09:35
Speaker
That was the leading hypothesis with which we started. And I think we started in 2017-18 kind of a window, spent a couple of years in terms of getting the product definitions, the business requirements, because you cannot just take the hypothesis, you cannot take the technology and just sell it like that. It has to have the business meaning to it because ultimately someone will have to sponsor, someone will have to give the money to it for it.
00:10:05
Speaker
So we spent good two years making the business viability out of it, the product features out of it, a lot of small pivots, a lot of features here and there, kind of putting the things together, stitching it together. And that's where we found the product market fit where the value is high. And then that's what we've been kind of selling in the market for the last three, four years.
00:10:29
Speaker
So when you both quit, you were CTO of rate gain when you quit and Rashi was at WNS, you quit without any business on the horizon. There was no like prospective client or a lead where you thought, yes, okay, I can start generating revenue in a couple of months. Like it was purely the idea that let's build this product even if it takes us two years. Typically, one would expect something to kind of fall in place maybe in
00:10:59
Speaker
eight to twelve months. I mean I kind of started doing some odd consulting jobs just to make sure that I mean the pressures should not mount beyond a certain limit. So it's like you have an IPL, you are limited over match, your required or net should not go beyond twelve. So for first year, one year we were like kind of putting the things together trying to see what can be done.
00:11:22
Speaker
But then just to make sure the pressures don't mount up, I kind of took some consulting jobs on the side to kind of keep the ball rolling. And one thing we were very clear Akshay from day one that we do not want to get into the services side. I mean, there were ample opportunities for us to pick up the services projects. And services is a very different ball game and we were very clear on that. We don't want to go that route. And we want to go to the product side.
00:11:51
Speaker
Between the two of you, who was trying to get business, like who was selling and who was building? So I think, given the kind of background and expertise that we bring on the table, I was born on the tech side, Rashid was on the data science side, and the solution side. And collectively, we both were selling. I mean, because I would say we were both equally novice and equally specialist in sales. So it was like, I mean, we have to go out.
00:12:20
Speaker
divide and conquer let's both sell and with time now I think Rashi has picked up the sales pretty well so at the moment it's Rashi who's selling and so it's like anything in the front of the office Rashi takes care anything back of the office I take care.
00:12:37
Speaker
Amazing. Did you start by selling what you're selling today? Or there must have been a journey where you would be selling something and over time, based on what you heard from customers, the product evolved. Just tell me about that. Like what were you selling initially? What response did you get? And how did you finally discover product market fit? What we started essentially was more in terms of automating or having the chart as automating the chart responses.
00:13:08
Speaker
then we kind of move towards the email automation, social media automation.
00:13:14
Speaker
And all these nuances were something which was short text, long text, their understanding, conceptualization, semantic analysis. So all these things is where we kind of spend good amount of time. Incidentally, voice was the last major segment that got added. Voice would need more time to build. It would be like more time and investment to build voice capability.
00:13:38
Speaker
I mean, text sounds relatively easier to implement. Actually, I would say voice in the end didn't take us that long. I mean, it was much faster than we anticipated, but somehow we undermined the potential of voice.
00:13:55
Speaker
So when you were selling text, I'm assuming it would have been a crowded market. There are a lot of other players also offering text, chat automation and social media listening. So is that what led to discovering that voice is the niche which you can dominate? I think competition has never been a concern for us. I don't want to sound ignorant or arrogant here, but competition has never been a concern.
00:14:24
Speaker
Reason being, I mean, two major reasons. One, the market is super huge and you can have 10 unicorns operating in this space. So if there is so huge a market, competition is not a concern. It will be a concern maybe three years down the line, but not today. There is enough land to grab. What really we were looking at is
00:14:51
Speaker
What is the value that we are getting for those enterprises? If I, for example, do a chat automation, what is the ROI the enterprise is getting? Let's say I go to a 2005,000 crore company and I talk about a chat bot.
00:15:08
Speaker
and they are giving me 50,000 rupees a month, 1 lakh rupee a month. I mean the value ROI they're getting is 1.5 lakh, 2 lakh, 3 lakh rupees a month and of which they are giving me a portion of it. The question was why am I not able to give a bigger value to the enterprises?
00:15:26
Speaker
That was the question I was solving. If I start giving a bigger value to the enterprise, I can go and ask for a bigger part. That's the problem you are solving. Because it's like whenever you are in a B2B space, enterprise space, you have to give a bigger value.
00:15:44
Speaker
If you are just a superficial or a periphery service provider, you're like a fly on the windscreen. You might stick on to it for some time, but you might get replaced anytime.
00:15:57
Speaker
And did this come from customers asking you for voice or you only started checking with customers, hey, would you be interested in voice? In the first leg, it was our customers said, you know what, we like the team, we like the concept, we like what you're bringing on the table. But we would want to have a single vendor which can handle everything. And in a way, our clients kind of pushed us into it.
00:16:23
Speaker
Okay. Okay. Got it. And I believe Maruti was like the first big client for voice for you. That's correct. Okay. Tell me about that. Like what was there ask and how did you deliver it? So essentially we applied, Maruti runs a incubation program with the name MAIL, Maruti Automobile Innovation Labs.
00:16:45
Speaker
And it's a program which kind of enables startups to apply and they would then want to imbibe the innovative startups to kind of come work with Maruti's different verticals, different companies and teams.
00:17:03
Speaker
do something big, something different. And at the same time, it probably also allows a balancing act for the enterprise in terms of the size they operate at and maybe a combination of little agility. So honestly, we applied in that. I think we were in cohort two, if I'm not wrong. So we were in cohort two and we applied for it. And luckily, or I would say destiny as it would say, or whatever it was, we kind of got selected.
00:17:32
Speaker
And we were one of the winners of that program. And that's where we kind of got onboarded. We got a paid pilot. And the success, the results of the paid pilot were satisfactory for Maruti. And Maruti wanted to do this NPS survey through voice. Yes. One is doing an NPS survey, but it was also about getting the VOC, voice of customers. So the calls that were happening, what were the customers talking about?
00:17:59
Speaker
And this was like after servicing, like when someone gives their car to a Maruti dealer for servicing, after that a call goes asking about the experience. What is that? I mean, that was one. Second was also in the normal conversation, in a customer support conversation. What were the customers talking about? Were they really happy? Was there any hidden dissatisfaction aspect?
00:18:24
Speaker
So that was like monitoring the human calls that are happening and creating a dashboard based on those calls, like how effectively the call was handled. Was the customers, what was the customer's emotion? Was he positive or negative about the brand and things like that? Absolutely. I think your sales pitch is better than mine, I believe.
00:18:45
Speaker
Okay. And Maruti wanted this to be multilingual, right? Yes. So essentially any business, if you see in India, not just Maruti, but even otherwise, what we have seen is almost 60 to 70% unless and under any enterprise or a company is regional in nature.
00:19:03
Speaker
Typically, 60-70% of volume lies in Hindi and English. So for everyone, the first version, first iteration of deployment is do this in Hindi and English.
00:19:16
Speaker
once this milestone gets achieved is when the regional languages are very critical. So if I was to say till date, we do around 10 languages across India, all the major languages of India. From the technology side, what is the product doing? Is it essentially a text-to-speech engine and vice versa, like a speech-to-text engine, and then that text is analyzed for intention? Is that what it is?
00:19:45
Speaker
The reality is that it's like there are nuances in the speech engines. So where you have to, whatever engines are available in the marketing, we have a couple of our own versions of speech engines. So we have to kind of create an ensemble layer on top of that and see which engine works the best in what scenario. So we kind of do that. What is a speech engine?
00:20:13
Speaker
So it's ASR, you have speech detection, text to speech. Those are the speech index. Then obviously you have an NLP layer which kind of works and which is our own proprietary in-house. And what does NLP do for people who don't know like what's the full form of NLP? What is it?
00:20:31
Speaker
Yeah, so what we do under the NLP engine is we hear the voice and this is at the backend of the product. There are a lot of these elements, a lot of features which are embedded in the product. We understand, it could be understanding the free text because what we enable the whole system is to understand the voice of the customer and just get it to thrash that data and bring it to the enterprise key. This is not what you wanted to hear, like five things that you wanted to check.
00:20:59
Speaker
but your customer is also talking about this 50th thing and the 60th thing that you never even imagined. So that was what we wanted to bring to the enterprise. With that as a thought, now you imagine you are anything that you built as a function on the voice or on the chat. You are basically, this was the backend enablement that we wanted. And we wanted this to be enabled in all our Macular languages. Now the complexity, when you look at the landscape, right? There is anybody can talk whatever.
00:21:28
Speaker
It could be any verbatim, right? It could be a linguistic comes into play, slang comes into play, short forms comes into play, anything and everything, because it's like what we, you and me are talking to figure that context. So.
00:21:44
Speaker
And then basis that there is this whole generative AI where you basically have to revert in a manner. For example, I may just say, my remote is not working, right? On behalf of a brand, like the brand, the customer is saying this, now the brand needs to handle it in a way, right? And we are the one which are handling on behalf of the brand. So all interpretations, right? So the backend engine has to be so powerful.
00:22:08
Speaker
to be able to get the gist of the customer, do an intent identification, figure out from the backend of the enterprise what kind of a response, what kind of an API do I have to fetch to be able to give out a response to it. Now you do an amalgamation on this simple construct with all vernacular, with all sentiment, with all emotion embedded into it. So it's a
00:22:31
Speaker
It's way in simplistic like Mani said the base was that but then you built in the complexity of the sentiment of the emotion of the open verbatim of the slang of the background noise because we work with a lot of people who are so you're driving and you're talking to the bot right there is a lot of this honking road noise vendor
00:22:51
Speaker
I mean, all sorts of noises are there, you have to cut that. So that is where, and the bot should still be able to interpret everything in a unified manner and should be able to roll appropriately. It can't do any hang-ups in between. We did chat, we did build engine on the chat. We started interpreting chat either on social or on the website.
00:23:13
Speaker
Then with the ask of our brands that we were working with, they said, yeah, this is good. This is what you have built is good. But can you take it to the next level? Can you bring in speech? Can you bring in? We said, OK, speech. We got into voice. Then they said, can you do vernacular? And we did vernacular. Now, can you do vernacular? You make it open-ended, right? We don't want a one, two, three sort of a bot. You make it open-ended. Let's hear. So that is how the complexity for us kept happening. And then now the ask is,
00:23:40
Speaker
bring in variations, bring in generative AIs, bring in, incorporate the chat GTPs. Then the other ask is bring in, add emotion, add sentiments, add tonality, interpretation. How the product gets carved is bases are deployment and bases. When we see the ask coming from our brands that we are doing for a rollout and the competition is where, and you look through that. If you do this, then you are there, right? So that is here.
00:24:09
Speaker
where a lot of debating internally we do the basis of the ask and then we put forward the ask to the product team and the tech team. This is what we are looking at and then we build this whole science together. Okay, got it. There is a handover between the speech engine and NLP engine or they work together. Like the speech engine would convert it into text and then the NLP engine looks at the text or
00:24:30
Speaker
It goes seamlessly, right? I mean, because the reward has to happen within a couple of seconds, right? So the whole embedding is like the speech to text will happen. Interpretation will happen. API calls will happen at the back end. Generatory responses will happen. And then a reward would go to the customer. So imagine all of this, like the way you and me are talking, it has to happen like that. So it goes seamless. And if it is vernacular, then it's translated to English for the NLP engine or the NLP engine is processing in the vernacular language.
00:25:00
Speaker
It's processing in the vernacular because then once you do the interpretation, the context changes. So it's good for, if you have to do one or two pieces, I think it may still work. But if you have to look at a broader picture, what we are trying to solve, like open-ended conversation, it will be a showstopper. Initially we tried that, but after a point of time, we realized that this is not working out. So we rolled back.
00:25:23
Speaker
Right, it sounds very unnatural, like translation removes the flavor of the language from it, right? Okay, okay. And so the speech engine for our Nacular language, did you have to build this or was it available? English, of course, there would have been a lot of work done and would be available, but how did you
00:25:43
Speaker
build capability for let's say something like Gujarati for which you may not have had an off-the-shelf speech engine available or something like that or like just help me understand that. So I think there are two things here. One is the corpus is available. I think the Indian government is putting
00:25:59
Speaker
a lot of effort towards building this corpus. There are universities working in silos, I mean all this time they're working in silos, now coming together and set up a collaboration amongst themselves along with the Government of India. So the corpus is getting
00:26:16
Speaker
By corpus, you mean the dataset, like labeled data? That's it. So that's getting kind of built up. Then there are this dataset corpus is also available in different universities online. It's there. In fact, so much so that I know you'll be a little surprised. Why not the movies?
00:26:36
Speaker
the closed captioning already done, what better dataset would you want? I mean, so all the movies that are there in the market and someone has put in the effort or has been paid to create the closed captioning manually, that's the corpus you need. Okay, correct. So you were able to develop the speech engine for vernacular using the corpus which exists? Yes, I mean, so we were able to build
00:27:05
Speaker
just to some extent with this corpus available. We also leverage some third party engines which are there and a mix and match is what kind of is working for us.
00:27:17
Speaker
See, the mantra is not to say no to business. When the business is coming, you don't have to say no. And you have to figure out a solution. If you have something built in, you roll. If you don't have built in, you don't shy away from figuring out how can you leverage third parties, competition, whatever it is. But the idea is crux is never say no to business. Just keep rolling. So that is what we have adapted and moving on.
00:27:40
Speaker
And you were talking of some things on the roadmap in terms of incorporating chat GPT. How would that become a feature in the product? What would that imply?
00:27:50
Speaker
See, we're testing it out, right? Because the idea is we're also working with a lot of NPFCs, a lot of financial organization, insurance companies, banks. So we just did a few weeks back, we've rolled out a version, incorporated something which is already existing. We're testing it out because it can't be open-ended. Tomorrow, it's going to be talking something, right? And then the price will be paid by the enterprise, right? We have done the solution. So the construct is that a huge
00:28:17
Speaker
in fact, the government themselves, right? I mean, there is so much investment going into seeding this. But then on the other hand, there is a larger investment, which is now getting the grants have been issued to basically also put the boundaries because if you allow it to talk, it can do anything, right? So
00:28:34
Speaker
What does it look like, this chat GPT-powered version? Does it allow the bot to give answer in longer sentences and show more empathy and emotions while talking? Or why incorporate? That's what I'm trying to understand. What is the value it will add? How will chat GPT add value?
00:28:52
Speaker
For example, if a user, someone, let's say Manish, goes to a bank's number and trying to play a prank and start ordering a pizza. I mean, our resource system and obviously what
00:29:09
Speaker
the permissions we have from the bank, because as a part of the roadmap, there are certain guided rails within which you have to operate. So our system will say, sorry, I will not be able to help you out with this. Whereas charge equity will say, that kind of thing.
00:29:32
Speaker
Slightly more humanistic, quirky responses, like that's what chat GPT will allow. Yes. And what we also again, this is again a hypothesis and probably will get tested with time. But remember in the beginning we said a three minute AHT is a limit. This is something which might help
00:29:53
Speaker
kind of go beyond three minute boundary. Because you're able to engage people more. That's correct. So it's same as if you see a parliament session or if you see a keynote by anyone in which is let's say half an hour or one hour, they need to
00:30:11
Speaker
put some jokes in between to kind of get everyone's attention. So similarly, chargeability, one use case that we see at the moment is getting that engagement factor there.
00:30:24
Speaker
Okay, interesting. And currently you are using generative AI already like that you have been using rather. So generative AI here refers to the fact that you're generating the responses of the bot. Is that what it refers to or what is generative AI? So generative AI essentially overall, if I was to say, is to generate content which doesn't exist. So it's not in response to a prompt. That's correct.
00:30:53
Speaker
So typically, chargeability has two components. One is semantics or understanding the content, intent, context, whatever you might want to call it. And then basis that, come up with some solution, some answer, which chances are very high doesn't exist in totality. Because if it just gets you or fetches you a solution or an answer which was there in one of the websites, then it's a search engine. Then it's not a generative content.
00:31:22
Speaker
But if it can, with certain thesis hypothesis in the system which it has been trained on, it can generate the content and give it to you, assuming progress isn't there to that level. And it was not copy pasted from any website directly. So that's what generative content is all about. OK. And this you have been using right from when you started generative AI, because your answers of the bot are essentially generative AI.
00:31:52
Speaker
Yes, so that's correct. So we were having this versions there and it was kind of fine tuned or perfected for the B2B for the enterprise space because there are certain boundaries in which you will have to operate when you work with enterprises. You cannot go beyond, you cannot go all over the place.
00:32:16
Speaker
In fact, I just want to kind of cite an example here and without naming the enterprise, what happened was they rolled out a chatbot on their website and within two days they had to take it down because the answers that gave were not in lines with the legal compliance of that company.
00:32:39
Speaker
This was a chatbot for answering customer queries. So take me through your pricing journey. When you started, how did you price it at? What did you learn about pricing a B2B product? What do you price it at today? So I think, as I was saying in the beginning, when we started, the pricing was, I mean, the best we could charge was 50,000 rupees a month, one lakh rupees a month. And what we saw was, I mean, obviously for any company, again, as a startup,
00:33:09
Speaker
also but for any company the ticket size, the average selling price or ACV which is annual contract value, you have to try to maximize it.
00:33:23
Speaker
we tried kind of doing it and what we realized was the value that is coming on the table for the enterprise is not that exorbitant or is not what is in lines with what we are looking at. So we had to kind of flip it and we started doing a value-based selling and which really helped actually get a much better ROI to the enterprises and basis that even our ticket sizes started to increase. What is value-based selling? Value-based selling is all about
00:33:52
Speaker
What is helping the enterprises that I even understand the overall value that this offerings that we have brings on the table. So one is it's same as if I say that I give you, I sell you a pen. I sell you a pen for 10 bucks, right? Probably you'll say, you know what, I have more pens, but this 10 rupees pen, I might buy it, but
00:34:20
Speaker
What if I typically lose pegs? I have that habit of losing pens every one week, every two weeks. The ink starts leaking and this and that. So one approach is I start selling you 10 rupee pen and I say, you know what? I'll sell you now. I'll sell you again after a week. I'll sell you again after a month. That's one approach. Second approach is I try and study
00:34:48
Speaker
Typically, what is the average shelf life of a pen with you? Right? And I see on an average, you need a replacement every sixth of the day. So what I'll say is, you know what? Don't worry. You use as many pens as you want. This is the package. You want to change the pen on a daily basis, please be my guest. Right?
00:35:14
Speaker
And obviously, instead of charging 10 bucks, I'll be charging you a different structure. And you will say, you know what, this is too expensive. I'll help you understand as to typically last year's what you spent was this amount. I'm giving you a discount over that because since I'm selling you a bigger ticket size,
00:35:37
Speaker
So I'll not, my unit price will not be 10 bucks. My unit price might be eight bucks or seven bucks. So actually I'm helping you save 30%. And your pain point goes away. That every time you have to think again about buying a pen, which pen to buy, it's a win win for you. It's a win for me because I have a larger, a bigger horizon.
00:36:03
Speaker
Got it. So in your case, you would probably look at the salaries that they would be paying to agents and how many of those agents you would be able to replace through Reso. That would be the basis of pricing. Let me try and explain as to how we take it. We say what is the total cost of running a call center. Of running a certain function. Let's say, of capturing the NPS code.
00:36:34
Speaker
What is your total cost associated with getting the NPS score? What is the delay that you get? What is the opportunity loss you have? What are the pain points you have? We start with that and then see what all we can solve for them. The solution solves what other things that we can help solve. And basis that we say, you know what, today you are spending, let's say you're spending
00:37:05
Speaker
50 lakh rupees on getting the NPS score structured with a delay of one week. What we bring on the table is something which will cost you five lakh rupees real time. And it's a plug and play and you deployed and you forget about everything else. You'll get the results within a matter of, let's say, two hours, whereas you are getting something in seven days and things like positively transparent because it could have been managed.
00:37:37
Speaker
whereas this is like the automated system, everything there, all the, no human intervention. So it's more about comparing as to what it is as a status quo and what you bring on the table and the value that you bring on the table. And in this just, there have been ample of cases.
00:38:02
Speaker
Where, for example, if, for example, they're spending 50 lakh rupees today, annually, and we, when we did our math and we saw that, best case scenario, I'll be able to sell, give this service for 45 lakh rupees. I tell them, do not change.
00:38:23
Speaker
I mean, there are ample of cases even today. In fact, I had a call with one such customer a week back where they wanted automation rolled out and he said, sir, do not change it. What you are doing is the right way of doing it. Do not deploy a solution of Reso. In fact, I would suggest do not deploy from anyone because the size that you operate at, the use cases you have,
00:38:53
Speaker
Automation solution will not be able to help you out and you will not get the ROI and nor will the implementing partners. And it will be burning the bridges three, one, six months down the line. So it's better to be upfront. Okay. You sell directly to organizations or you work with the implementation partners and like, is it like a channel sales model or is it direct sales?
00:39:17
Speaker
At the moment we are selling directly to the enterprises but we do have some channel partners, SIs, kind of there who are started selling now. Little early in the construct but yes, it's picking up.
00:39:31
Speaker
And once you sell, let's say, this NPS survey, how much time does it take to go live? How long is that onboarding journey? Because you would need to collect data from their systems. You would need to read data from the systems. You would need some access to the phone numbers. You would need some sort of a line, calling line setup or something. How does it happen? What's the onboarding journey? So our typical onboarding journey, I mean, if the enterprises are ready,
00:40:01
Speaker
I mean, the decisioning is made, and they have the information, API, documents, data handy. So I'm kind of discounting the delay because of the enterprises or our clients. We can get started anywhere between two days to maybe one week's heads up.
00:40:22
Speaker
Okay, okay. This kind of selling approach of value-based pricing, doesn't it reduce the speed of sales? Because every time you go to a client, it's not like you can give them a rate card, but you have to first ask them and get. No, we do, again, we do give them the rate card also. I'm not saying that we don't, our rate card is fixed. I mean, we have a certain rate. What's the rate card based on? What is that like?
00:40:48
Speaker
on the complication of the use case, the volume that is coming in, what kind of cover print we have. So read cards are fixed. So again, it's fixed in a sense, it's in a certain range. And it's very well defined.
00:41:01
Speaker
So I'm not saying that we inflate our prices, basis the value. We don't do that. But you're able to demonstrate value to get the conversion. Correct. Correct. Correct. Because so our rate cards are in the similar range. It's just that this approach helps the enterprise come to the conclusion faster. So actually this approach helps selling faster.
00:41:26
Speaker
It's the, in addition to what Manish said, what we bring on the table is they may have a current process and they are sitting on large data. They are doing things in a certain way. When we come in and as Manish was saying that our rate cards broadly, it's a volume based pricing. It's a fixed sort of a structure, which is there. What we try to, we know. Which is like a per minute or something like that.
00:41:50
Speaker
Yes, it's correct. It's a permanent depending how the larger the volume and all of that is. But broadly when you go to the enterprise and you talk to them, it is very important to understand they want to automate a process or they are looking at something was running in with in a certain way.
00:42:10
Speaker
What we bring on the table is the success. The selling is majorly on a success base. Today you are getting X as output. I will be able to take it to 2X or 3X.
00:42:22
Speaker
in such and such time and that time duration as Mani said our deployment times once the client is ready on the basic construct. We like a lot of my competition where the deployment time could be three months we do it in flat 10 days right along with the UAD built-in and success is what we drive. We say that if you today are able to get x as a as an output we will be able to do it 2x 3x depending upon the complexity of the problem and
00:42:51
Speaker
that is one the other thing that when we talk to the enterprise when they are just talking there is a lot of base right these are your customer base and you are making revenue from your customer base in a way right you're serving them you're making custody or making revenue from that they would be always an untapped
00:43:08
Speaker
bet that you don't even know you've not attempted. So when Reso comes into the play, we are more data, right? We, our decisionings are majorly powered by data. Where are you sitting today? What is your current input? What is your current output? What's broadly your cost? There's not too much of conversation like what you're thinking that this is going to be taking a lot of time for me. No, it's very straightforward because
00:43:32
Speaker
You just say, Arsen, where are you today? And where are you wanting to be? Can you do this with the current bandwidth or the current infra that you have? The answer is no. OK, so we will come in. We will look through your data. And we will get you from here to here. And you can measure the success.
00:43:49
Speaker
So that's the construct. And when you put that construct, irrespective of who's by competition, right, it goes, these discussions goes much faster. And once they generate faith in ASCII, yeah, I mean, they know, they value my data, they're able to hear me out, they're able to understand that is where they say, this is my untapped customer base, I have not even looked at this base, can you do something about them? So then you do a lot of co-inventing with them, and you talk to them, you figure out this is what it is, and it doesn't take a long, right, because it's the idea is,
00:44:19
Speaker
If you start looking at the data, you start looking at the problems that they are facing, right? And you propose simple solutions, not very complicated, simpler ones. You're able to roll in a much, much quicker manner. Does the product get customized for each customer? Like, let's say, male voice, female voice, or stuff like that, or like, what all customizations happen?
00:44:39
Speaker
So you can define that we have seen that we were working with a lot of driver base at certain point of time. Now, when we were working with the driver base, they were not liking the female voice. It was not just working out, right? So they wanted to hear a male voice telling them instructions. Another time we were working with a brand where collections were happening. So we got a mandate that only the women voice is going to work. Bring in a women voice of from age 30 to 35, right? So that's the kind of, and they also, because they have to run
00:45:09
Speaker
these units for a very long time, they know this much clearer than what you can do. So we amelicate and put things together in this agility that we have to be not hard coded everywhere and listening to the client and bringing things. This basically is playing in our favor. And so these are like available off the shelf voices like 30 year old Indian woman voice is available off the shelf. Does it sound human, close to human or it's obvious that it's a machine like
00:45:38
Speaker
No, no, it's pretty human-like. In fact, we have rolled it both in the urban and the rural areas. The rural latches on to these voices beautifully because it's like, it's easy. We are rolling it out at the farmers, right?
00:45:55
Speaker
people hearing about various policies, about what is happening. You can ask anything and everything. As far as the urban is concerned, what I can sense is like, for example, unlike you being hogged by the multiple calls during the day, if you make like one single call for an insurance policy and then you get like 10 calls in a day till the time you pick up the call, you answer 15-20 question, this
00:46:21
Speaker
These kinds of calls are happening, like we leverage a beautiful concept which is a smart contact centre strategy. Once I've understood the DNA of Akshay, I know what are your plus and minus, I know your transactional history, right? And this is what we bring on the table. It's not just conversation. I'm messaging your brand.
00:46:42
Speaker
Okay, interesting. And you start the call with a particular language. When the call starts, maybe the bot would speak in a language and the customer responds in, let's say bot speaks in English, the customer responds in Hindi, so that changes what happens on the spot. The bot will also start. Okay.
00:47:01
Speaker
Yes, immediately, yes. So the introduction is basically in one or two languages because you can't do it in all languages. But for example, if the call is in Hindi and the other guys started to talking in Telugu, for example. So the search will happen in Telugu and the rest of the conversation will be in Telugu.
00:47:17
Speaker
Okay. And then you have data that next time you should use Telugu with that customer. How does the cost compare to a human agent? Let's say maybe a human agent would give you maybe 200-300 minutes of calling time a day into 20 days, so maybe say 5,000 minutes in a month. What would 5,000 minutes of Rezo cost?
00:47:39
Speaker
So basically the cost reduced dramatically. Let me answer it this way. And also the fact that though I would say ballpark, yes. Like less than half? Depends again, depends on the complexity. It depends on the, what kind of a volume is there. A lot of factors. It could actually be lesser as well. I mean, it kind of ranges anywhere between
00:48:04
Speaker
I would say a 40% to maybe a 60-70% depends on a lot of factors.
00:48:11
Speaker
I mean, a cost of a human would be, at the very least, maybe 25,000, 30,000 a month, right? Because you also have the cost of providing a laptop and the space, et cetera, et cetera. So it's a sitting cost. It's the training cost. It's the hiring cost. It's the onboarding. It's the offboarding. It's the PF, ESI, everything. I mean, then the TL cost and the QC cost, everything put together. So there are like multiple factors.
00:48:40
Speaker
In fact, I'll give you a very interesting perspective as well. I was talking to someone in terms of helping them derive, as I said, value-based, ROI-based positioning. And this person was telling me what we pay $20,000 to an agent.
00:49:00
Speaker
I said 20,000 you pay, but that is not your cost of operation. Yeah, it would be double of that probably. Yes, but then obviously you can't just say double. You have to justify. You have to, one thing that really came out was as per the government law, you have to give them certain deals. The leaves could be easy around 25 to 28 offs.
00:49:29
Speaker
In a year, this is besides the Sundays because typically any employee gets 18 leaves in a year. Plus there are festivals. Plus you have 52 Sundays. There are medical leaves. There are maternity meals.
00:49:48
Speaker
And then health insurance and the office infrastructure. Your typical sitting cost can range anywhere between a thousand rupees to maybe a 10,000 rupees per seat. I mean, again, depends on a lot of factors. Then you have the bosses above TL to maintain this entire hierarchy.
00:50:10
Speaker
account managers there are who kind of manage the accounts so I mean electricity cost internet cost as you said laptop right and in your case you are at half of what it would cost for a human to be running this yes and I would say again one thing which again is I want to really kind of call it out is
00:50:35
Speaker
The first impression or the first thing that comes to the mind is job security for these agents. And interestingly, when we work with these enterprises, not a single agent has been let go. They are redeployed for higher value work. They are redeployed for higher value work, in fact, which essentially requires their upskilling.
00:51:06
Speaker
Right. And upscaling, I mean training more.
00:51:11
Speaker
I want to understand as a business how you're doing. What is your current ARR? What kind of customers do you currently work with? Which sectors, like customers from which sectors? What's like your biggest sector from where you have customers? Is it like VFCs or is it like automotives or just give me an idea of what your revenue and the breakup of revenue looks like? So we currently focus on automobile and BFSI and telecom. So three verticals that we're focusing on at the moment.
00:51:39
Speaker
Currently, we are purely based out of India and operating in India. All the clients that we have around 15 paying clients today, another five in the pilot stage. Who are some of the clients that you could name? Are you at Liberty to name some? Like Maruti, of course, we've already discussed. So Maruti is there. We work with L&D Finance. We work with Rata IH.
00:52:03
Speaker
We work with delivery, Usha. Delivery would be like when there's a customer who's expecting a parcel, so a call would go and say that your delivery will come at 11 o'clock or like feedback for the delivery or something like that. So delivery, it's been a non-voice function, non-voice automation for them. But yes, the use cases are those straight line use cases.
00:52:27
Speaker
And in terms of where we are, in terms of our revenue and others, so we are, let me put it this way, we are targeting to hit 10 million ARR USD by end of this financial year. Okay. Amazing. How much is that in crores? It will be around 80, 85 crores as a run rate. And in terms of our otherwise health, we are a bit of a positive company. We are a profitable company.
00:52:58
Speaker
We've been profitable for the last eight quarters now. Wow, amazing. And this includes your salaries also? Yeah, absolutely everything. Do you need to raise funds? Are you planning to raise funds?
00:53:12
Speaker
Yes, so we are in discussions to raise capital supporting stage conversation. But why? I mean, you are profitably growing. Right. So essentially what happens is actually that in fact, we've just set up a US subsidy. And what happens is now is at the growth stage.
00:53:32
Speaker
you need to kind of experiment, you need to kind of invest in the sales and marketing engine. And also, as part of the growth, the experimentation needs to be quicker, which requires capital infusion. And that's the reason why we cannot raise in capital. But your product is already mature, right? Like, it's like, yes, I mean, I mean, OK, product is already matured. It's already there.
00:54:00
Speaker
in terms of the scale is there and everything is there. But still the feature sets will keep evolving, keep improving.
00:54:08
Speaker
bases the customer feedback. Okay, like going beyond that three minute handing time. Absolutely. So what happens essentially, I mean, just for example, recently the feature, not recently, but sometime back the feature that got added was about switching the language on the fly. So for example, you and me are talking in English, but now we have designed the system in such a way that we bought a switch.
00:54:38
Speaker
And the voice sounds like the same person only talking? Yeah, absolutely. Are you getting used for sales calls also, or it's mostly just like data gathering? We are doing a lot of sales calls as well. We help setting up the appointments.
00:54:57
Speaker
what kind of sales. Like, say, ad tech companies generally do a demo of the product, so setting up those kind of appointments and things like that. One is setting up those appointments, then lead verification. In fact, we've started selling high ticket size items as well, like an automobile.
00:55:18
Speaker
And this would be like a cold call or a call to someone who filled a form on a website or something. It's kind of both. That is again, you're adding a lot more value than because then you're doing revenue generation. So you can capture more of the value if you start doing like sales is obviously every business is lifeblood. So. Exactly.
00:55:40
Speaker
Amazing. So, I mean, it seems like the opportunity is huge in India. Why go to the US right now? I mean, for example, like Telcos is an unscratched market for you right now, right? And Telcos would be a massive market. In India itself? Absolutely. So you're right, India is a huge market. No doubt about that. And in fact, as a market, it's just scratching the surface.
00:56:05
Speaker
Yeah, I mean, 15 companies just, I mean, there is so much potential for you here. Absolutely. So, see, it's like there is a certain consumption or a capacity for an industry, for a geography. And we are trying, we are already maximizing and we are kind of, we are expanding as well. As Rashi said, we are hiring for 50 positions right now. So while we are expanding, while we are kind of consuming this market, we would need
00:56:33
Speaker
more geographies to be added to be to kind of fill up our capacity or our hunger, I would say, for growth. And if you seed a new market, that new market itself has a gestation period of 12 to 18 months. So if you start now that you will see the results in next 18 months, not today. And that's the reason why adding new market is important. Plus what it also gives us a much better stability.
00:57:01
Speaker
much better learning of the feature sets or the use cases and the product evolution.
00:57:07
Speaker
Right. Your product becomes, I mean, obviously going to US would help the product also grow because that customer is a lot more demanding. Exactly. Okay. Okay. Who are the other companies in this space? So there are companies in different kind of sections. So I would say, so there are like the likes of Unifor and Observe, which are sitting more on the data analytics part for the speech analytics part. Then there are the likes. So they would be like monitoring human conversations and
00:57:37
Speaker
sharing like a dashboard with feedback. Feedback, also nudging them, prompting them as to what next to speak. Those kind of things are there. Then there are players like... How big a part of your revenue is this? You also do this, right? Like monitoring. Yes. So we do this. It doesn't have that much of contribution to our top line today.
00:57:58
Speaker
But in terms of our pipeline, I would say almost one third of the sales pipeline and the pilot that we have is this. Then there are obviously players like Cognici, there are Asap and Verent, to some extent. So these are the players. I've not heard of these companies. What do they do? So these are the companies in the US I was talking about, which kind of focus on the voice automation, voice watch, or getting those data points complete in the workflow.
00:58:27
Speaker
Okay, so they are doing the conversations and transactions, etc., like talking to customers. Absolutely. There's none in India. These are all US-based? Yes, these are all US-based. There are some in India. Again, but as India, when things are at a very little early stage, I would say there are players like Nani, there are players like Skittas now.
00:58:50
Speaker
kind of move to relocate it to US, then there is as well. So there are players there, but again, I mean, everyone is a little early stage, trying to kind of working on different kind of use cases. And what we have seen is, for example, Saarthi is big time into collection process only.
00:59:11
Speaker
And although we have a massive rollout and massive deployments in collection as well, so we do see some cooperation from that perspective. Nani on the other hand is major lead with the government side. I mean, those things are there. So again, government side like political campaigning and all or like promotion of schemes, government schemes, et cetera.
00:59:34
Speaker
I think that as well, we're working with different government entities. So again, I mean, there is, as I said, there is ample of opportunity, ample of stuff to be done. And the market is opening up at the same time, market really wants to have evolved products because no one wants to have unfinished product and get a backlash from the customers.
01:00:02
Speaker
Okay. Okay. Got it. Okay. What's your plan to open more accounts in India? I mean, because any company which has more than 10,000 headcount would have at least 10-20% of their workforce doing this kind of calling role. So there is a lot of opportunity here. What's the way in which you think you can capture it? What's the way to grow your sales pipeline?
01:00:28
Speaker
We want to kind of grow fast. In fact, we've been growing almost 3X three times here only for the last two years. And we expect the same growth going forward as well. I think probably one of the big thing that we bring on the table is we do not do sales that much. We are focusing big time on the product, on the technology. We keep sharing of sharpening it. There are certain outreach programs that we have, which we run.
01:00:56
Speaker
and we kind of creates have some awareness programs letting the enterprises the businesses know that this is what can be done this is what we do and we prefer it that way because our clear mantra is whatever we do we need to do it good it's I mean we do hear a lot of enterprise coming to us and saying we haven't even heard about you we didn't even know that
01:01:23
Speaker
a company like you existed with this kind of a numbers that's kind of a scale you were not even aware and we are like that's okay because it's okay to kind of talk to hundred companies and not thousand but whatever hundred companies we talk to we need to do a good job we want them to be satisfied with the deliveries that we do so it's like a balancing act that that we kind of follow
01:01:50
Speaker
I'm wondering why none of the telcos like Airtel, Jio, Vodafone, none of them are using you. Are they already using some products or is there a resistance? Because I'm guessing they would have the maximum number of such calls happening. So we work with Airtel. Which could eventually become your biggest account, right? Because Airtel would have so many such less than three minute calls. Well, I would say, I mean, there are hidden gems in the industry.
01:02:19
Speaker
We know Airtel for the scale. I mean, there are other enterprises which actually operate at a much larger scale. The need is much larger. Okay. Like NBFCs would also be doing a lot of collection calls. Oh, yes. See, I'll also tell you a very interesting use case. Let's say in the collection process,
01:02:43
Speaker
Only every enterprise, let's say, given an enterprise X, they would have, let's say, a thousand agents doing a certain function, a certain collection process. But their books are so huge, so much of work to be done. A thousand people cannot reach out to each and every customer.
01:03:04
Speaker
whoever is there in the queue for the collection, they'll just probably pick the top 10% of buying. Yeah, they prioritize it. Right. Exactly. Exactly. So that prioritization probably will, they'll only be able to touch 10% of the customers. But the 90% goes unserved. But a solution like Reso, there is no scale problem.
01:03:29
Speaker
probably even with our kind of offerings, the bottom 10% would not make sense in terms of the ROI, but it's the middle 80%, which is there up for grabs, right? So when you look at only at the call center, we're probably looking at the thousand, which is like handling the top 10%, but the actual opportunity is for 10,000, right? So these are the hidden gems in the industry.
01:03:54
Speaker
Got it. What's your advice to founders who are planning to start? And because I mean, you've scaled up to soon to be 10 million AR with no external fundraise. So what kind of advice would you like? Sorry, just we had one funding. It was a seed round like three years back and at the beginning of the COVID. So what advice would you like to give to aspiring founders? So my suggestion would be that
01:04:21
Speaker
Do not. I mean, you have to be very clear in the beginning itself. VC money is good, but you need to be very clear that are you going or doing your business to raise money or you are raising money to grow the business. That visibility, that clarity has to be there. Both have their own pros and cons. That is important. That's point number one.
01:04:44
Speaker
Point number two is do kind of have your family, your friends, your social circle, kind of a good circle around you which can morally support you because this is going to be a hell lot of a journey.
01:05:00
Speaker
And that brings us to the end of this conversation. I want to ask you for a favor now. Did you like listening to this show? I'd love to hear your feedback about it. Do you have your own startup ideas? I'd love to hear them. Do you have questions for any of the guests that you heard about in this show? I'd love to get your questions and pass them on to the guests. Write to me at adatthepodium.in. That's adatthepodium.in.