ConverSight.AI with Ganesh Gandhieswaran

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In this episode, I talk with Ganesh Gandhieswaran, co-founder and CEO of ConverSight.AI, a platform uniquely positioned to deliver insights and action on your data, on any system, from anywhere. Ganesh got the idea for ConverSight.AI through his many years in data analytics. He oversaw a team that created very data-rich and expensive reports. However, he found that within the months after a client received these reports, only a handful of people were using them. He saw an opportunity to create more accessible data in the form of a personal assistant.

Their platform is similar to Siri, but for business data. Instead of waiting on the IT team to create and provide reports on data, the personal assistant, named Athena, will deliver any business data you may need. The system takes more of a conversational approach, instead of question and answer process. Once the data is provided, the system will then ask if you need any related data as well.

Topics In This Episode:

  • How to streamline the data report process

  • Finding a gap in the market and creating a product to fit that need

  • Categorizing data based on the different roles in the company

  • Future goals of ConverSight.AI

  • Measuring efficacy

  • Owning the outcome

What we are focusing on is the next level where, it’s not just you ask one question and get a response. Can you have a conversation? We’re envisioning our platform as more of a person.
— Ganesh Gandhieswaran

To get in touch with Ganesh:

https://conversight.ai
info@thickstat.com
LinkedIn: https://www.linkedin.com/in/ganeshgandhieswaran/


Transcript

Mike:                   
Welcome to the podcast. Today we have Ganesh Gandhieswaran who is the co founder and CEO of ConverSight. Ganesh, welcome to the program.

Ganesh:              
Thank you, Mike.

Mike:                   
All right, Ganesh. Why don't we start with a quick elevator pitch for ConverSight?

Ganesh:              
Yeah. Conversite.AI, we are an Indianapolis based technology startup. We are focusing on natural language and voice based conversational solutions. Think of us like a Siri for your business data. That's what we're doing and we are working on connecting the enterprise data with your CRM systems, ERP systems, and data warehouses. That data, connected to the people like the sales leaders and finance leaders and plant managers, warehouse managers. We told them, you know, creating reports without them going through tons of spreadsheets, or waiting for somebody from the IT team to deliver that data. We can provide a personal assistant for them. That assistant will understand your natural language, you colloquial discussion and go get the data from the database and give you, in a nice graphical way, and then you ask a question, how much sales we made last year in west region? Then you just ask, oh, how about east? So, our personal assistant will be named Athena. Athena will understand what you're asking, what you've asked and give you a contextual answer and the next step she will also tell you are you interested in margin?

Ganesh:              
Are you interested in related metrics, for example. Maybe you are analyzing by region. She'll probably ask you, do you want to analyze this by state? Right? So that's kind of how we are enlightening our users. Our idea is all the enterprises companies are spending millions of dollars in capturing the data, but when it comes to using the data, 80% of the decisions are made without data, so why are we doing that? Why not make use of the data? One of the barrier is the technology but Google last two decades is empowering us with worlds of information. So why not we provide a Google, a Siri like natural interaction for the leaders to connect the data? So that's what ConverSight, conversational insights, through artificial intelligence is all about.

Mike:                   
I love it. Can I ask a couple of questions on that because I'm kind of curious what that actually looks like in implementation. All right, so I'm going to say some things back to you and you tell me if I've got this wrong. So if I'm the end user, right? I'm talking to Athena on my phone and either giving data or asking questions of the data and I'm assuming behind the scenes Athena has access to tons of different systems, right? Whether it's my CRM system or accounting system or whatever the case may be that there's that middle piece between the raw data and the end delivery of that data, right? There's that knowledge piece like how does Athena know like, what are the types of questions I can ask it and what are the types of pieces of information I might want or might want to give it? Is that custom configuration? When you guys show up at a corporate site, you're helping them define what those things are and implementing that? Or is that something that they self serve and they come up with those different metrics and pieces of information that they're then gonna identify as things that would be fed up overtime? Tell me how that works.

Ganesh:              
I think you perfectly used the terms. It's a knowledge base. So we capture that knowledge, we maintain four levels of knowledge, base knowledge we call which is more generic, what does customer mean? Order mean? Year, quarter, month. That's kind of a general knowledge, that's base knowledge. Then we go to domain knowledge, which is like sales, finance, order management, warranty claims, kind of a domain. Then you go maybe specific to a customer, you know, customer will have product, specific products named. so training, understanding that, that's the third level. The fourth level is a specific role. For example, take an example of a CFO of a car dealership. The CFO would be asking, you know, how much sales we made last week? Now here she will be expecting dollars, right? Okay. 5.2 million dollars. But if somebody from the dealership who is taking care of the inventory, they are asking about, well how much sales we made last week? They answer they're expecting is like 24 cars. So the same question may have a different meaning. That's why we maintain that role level of that knowledge.

Ganesh:              
So the one piece of the puzzle is the knowledge that has four levels. The second piece is obviously the machine learning, the deep learning algorithms and you are asking something how we are understanding. That is very similar how as humans, we understand what we call the neural network. If you ask [inaudible 00:06:10] to go get the car, that's something she's not understanding. She lost you. Have you want this one, or this one? One had is red, the other is green. She can't say red and green. This one or this one? And you show this one and then in her memory it is stored now, okay that is a red, and there is a car. The next day you say go get the red shirt, she'll go perfectly get it, because she already learned red. Now shirt is already learned. The same way if probabilistic approach, how the human brain is making on every decision. That's how our engine is also understanding and delegates.

Mike:                   
Perfect. Okay, so thank you for that help. For somebody who's listening help with any vanity metrics or anything you can share, whether that's employees, revenue, funding today, number of questions that Athena has been asked, today. It would be kinda cool. Anything you got that would help somebody who's listening to understand where you guys are on this journey. Are you just starting out or are you guys at take over the world mode?

Ganesh:              
Sure. We started the company last year with obviously with the two of us, myself and my co-founder [inaudible 00:07:25]. While I'm focusing on the use cases and customers and all. And he's masterminding the whole technology. He has built a lot of software. So we would started and then we added a couple of bright people, who has done PhD research in natural language and some [inaudible 00:07:46] stuff. So three of us. Then we added our CFO, today in 18 months from the day one when we started, we have 24 employees. We have a global team in India and the US. And in US we have a couple of people in Florida from a sales side, but primarily in Indianapolis. We have right now close to nine customers. I'm already doing pilot for another seven customers and we launched our platform this June. We were kind of working on the platform for the last one year that we launched in June and from June, we got into this nine customers. So that's where we are. Right? I think it's answered a number of questions.

Ganesh:              
If you have one screen or one system you may be bored to go back to that, but then you're talking to somebody, you tend to ask more. So we are seeing an increased amount of usage. I can say like we have, one of the customers they're accessing their supply chain information. Like order, delivery, inventory related stuff. Like a 10 member team, I would say maybe 50 questions. So you can think about all the almost infinite questions being asked every day. About how much inventory they have, there's a part, there are these, and the number of open orders you have a lot of questions. I don't know. I think maybe I need to check with my technology leader, I'm sure that'll be like 10,000 questions maybe that I'm setting today, every day.

Mike:                   
That's amazing. 10,000 a day. And that's only over nine customers, that's awesome.

Ganesh:              
Yeah.

Mike:                   
50 questions a day per person. Like man, that's a real number. That is great. So it's interesting because that to me, like if I was thinking of like how would I justify efficacy to one of the customers, right? Like are they really getting the value out of this software and out of making this purchase? It's one of the things that occurs to me is like, well how, how often is my sales team using it, right? Or how often is this team or that team using it? How do you measure efficacy today and/or have you had any of those conversations with customers? What do they look for as a win when they implement the software?

Ganesh:              
So, if you really look at our current customers or any customers and most of the customers are spending, you know, at least a thousand dollars, 500 to a thousand dollars, to create one report. They either have a team in house, they learned it doing it or it is outsourced or somebody on the product team is doing this. So I have one supply chain customer and they don't have any IT team. Every time when they need report, they send it to the third party and they have to write a $500 check. Then two weeks later when they have some changes, they have to ask modifications and that's another $500. Now for them when they implement our solution, they can ask questions like who are my top 10 customers by revenue or you know, kind of complicated questions you can ask, and in real time, instantly, we go get that data and generate, not create, right? Generate that insight automatically. So for them, each time when they're asking a different question, they're saving $500. Otherwise, they will wait for two weeks and spend $500. So, for them it's just like instant data, how quickly I can get this without waiting for somebody to delve up and the cost, huge cost. And another thing if you're really noting is, we are working with another manufacturing customer and they have large sales team.

Ganesh:              
They have 600 different products. The sales leader was mentioning to me that she was managing growth of a million dollar portfolio and she was mentioning when she was in front of the customer, when customer was asking about details on the product, about how much inventory you have for this or how much warranty you give? She couldn't answer right off her head. She can't remember everything. So now, with our platform, she can just take the mobile and quietly chat how much inventory we have for two, three, four. So system immediately give you that. So that instant access will make the conversation between the customer and the salesperson go to the next level, immediately. Without her telling, oh, let me go back and check and let you know. Right? That you are doing it there. So, that's kind of a good one, I would say.

Mike:                  
I'm so glad I asked that question. That like put a whole new level of granularity, like those examples. Right there were fantastic. That helps me a lot in terms of like real use cases. That was great. Thank you. So when you think of competitors for CoverSight.AI, who do you think of? Is there somebody else out in the market doing exactly the same thing you guys are doing? Is there anybody close? Like what does that look like when you break down the segment of the market that you're in?

Ganesh:              
Yeah, 90% of the current reporting is done by the traditional players, the reporting and dash boarding players like Tableau, Click View, Microsoft Power Bi, and then you know, Cognos, Oracle BI, those kind of players. But in all these you have an IT developer. Somebody in the IT side working on creating those reports or somebody from the business side learned how to do it, but every time you need, you're creating a physical object. So that is, I would say like generation one kind of side. Then the next one we have a company called Thought Sport and they're like seven year old, I guess. They have provided a Google like guided analytics. So that one is very interesting. They have definitely gained a tremendous traction in terms of sales and adoption and so they are, if you go to Google, you ask a question and you'll get the answer. What we're focusing on this next level where it's not just you ask one question and get a response. Can you have a conversation? We are envisioning our platform as a more of like a person, so you are onboarding your data person, now. You have a discussion with her, what we call Athena.

Ganesh:              
You ask her one question and she's not going to stop giving that one question. Google is going to give you one question, and wait for it, right? Our solution is conversational. You ask one question, I'll give you the answer, but I'll ask you a related questions giving you the answer to that later. The second one, what we're also focusing on is we believe the conversation systems are required, not just on the postmortem, like last week data, yesterday data. It should be now, we can connect our system to any transaction system to understand how much open orders you have, how much inventory you have, those kinds of items, so we have the ability, one, to do it conversationally, then to connect with the transactional systems and third, you can also not just read only access to the data you can see what open opportunities you have and if you have something to approve, approve it right away through the conversation. We are really looking at the design as more than the conversational reporting and BI. That's how we are differentiating.

Mike:                   
And is that, I'm trying to then imagine when you're going into a corporation to kind of pitch a pilot and get a conversation started. I love, by the way, I love the like first generation, second generation. It's a great way to distinguish against the kinda especially established players in the space. What does that conversation look like when you just pick an ideal customer for you? I don't know if that's a manufacturing client, financial services client, whatever, like walk me through that conversation when you show up there because you know, obviously they have some solutions in place already. How do you like to shape that conversation when you're saying, okay, that's all well and good, but this is what we do. This is how we're different.

Ganesh:              
Yeah. So in small organizations where either they don't have tools, or they don't have people to do reporting, provide insights, the leadership is really struggling and a lot of spreadsheets being used. So in that kind of a situation or ideal situation is like, you know, working with the business leaders, director of operations, CFO's, to see how we can connect their transaction and data basis, and provide them instant data, and we kind of typically show a quick demo with our sample data and then we connect to their data and show, in a day we can show a quick demo with their own data. That is kind of an approach. But then the large organization, they are already spending like maybe 20 or 30 million dollars in building, what do you call, data warehouses, or data lakes. How many reports you can create? You can't create reports for every data you have. Retailers are putting data in your data lakes.

Ganesh:              
You can't create the report for everything. So our idea is, you create that, have the data and ask our questions on top of it and it's also not just to, you know, very simple questions. You can ask about margin erosion, you can talk about, there are a lot of algorithms involved in this machine learning we are using to identify the data anomalies to bring the data back. Then when you ask what's my margin look like, we're not just going to say it's like 43%, we're going to say it's year to date 23%, but your two day margin. So that's kind of a little bit of an implicit response that we're giving them. So that's where the real value for the large customers.

Mike:                   
Raises another interesting question for me. How often are you educating the customer around what questions they should even be asking? Right? And what I mean by that is, I can imagine there's so much data in some of these companies that you're seeing that where maybe, you know, it may have been impractical to send somebody in there to create a BI report through traditional methods were for you guys, maybe it's a whole different way of thinking about how to get at that data. Do you guys ever find yourself almost reeducating and saying, hey, look with our technology, here are the types of things you could ask that maybe you wouldn't even have asked traditionally because it would've been too hard to get at the data.

Ganesh:              
Oh, awesome. I think you brought a completely different language, and that is exactly like ... So step one was automating what you are asking quickly giving it, but the step two is like, you know, giving answers to what you never ask. Let me give you an example, right? One of our transportation customer they have a large warehouse as well, when we are connecting their data and providing the insights, one of the question they asked, give me my audit volume by week or nice and healthy, it was kind of growing week over week for this year. They are in a growth path now, but then Athena offered, do you want to see this by day of week? Yeah, yes. Why not, right? They never analyzed that by day of week, Monday, Tuesday, that type. So, the system showed Monday was like 9,000 orders and then Friday was like 3,000 orders. Can you imagine kind of the workload, what that host people will be going through? They never had that kind of that plan. So then you showed, when the system shows that some related system, that's where you really see the value. Now another one is what we call, in our organization, if you have 10 people, two people are really data savvy and they ask a lot of brilliant questions and analyze that data very deeply and we have, we automatically create a dashboard called frequently asked questions.

Ganesh:              
So for everybody that has a dashboard which will say these are all questions frequently being asked, so that's another avenue where, oh, I should analyze margin. Oh, you have data by dynamic margin. You have data by this region. So people will be enlightened on data. Especially think about onboarding new people. They don't know what you have, what system you use, whatever you capture. It's so easy. You just ask one question and then you just need to click, click, click, or ask the ... just acknowledge to get more insights. So, it's so easy. Yeah, that's a great perspective. We can empower them on what to ask.

Mike:                   
That's great. Where do you see the platform going? So you guys launched roughly six months ago, give or take and obviously I'm sure your head's down, how do we bring on customers? How do we grow the customer base and revenue and support those customers? Right? So I'm sure you're doing platform updates as you get customers actually using it and learn more stuff, but zoom out for me for a little bit, let's say maybe 24 months from now till, which hopefully is unclear enough for you that it removes you from the day to day. So 24 months from now if you think you're going to look back at ConverSight.AI and where it is at that point, what do you think you're doing then that's different? Is it just bigger and more customers or do you think there's new features or a whole new way that you think about the problem that you're trying to solve that you've taken to market at that point?

Ganesh:              
Yes, so two areas, right? So, in a customer journey, what I'm really looking at now is step one, you know,, we are connecting to their data and generating some insights. You know, internally we have just telling your, somebody from IT team creating reports, we can generate it for you. That is step one. We're doing it. The second one is, you know, have a conversation with the system and then we understand what are you interested in, right? The margins, revenues, kind of metrics that you're interested in. Then you kind of asking the same four or five questions every day, morning, I want to bundle that and give you as news. Today, all of us have, when we get up in the morning, we just open up the newspaper, radio, TV, to understand everything in the world. Politics to sports. But the moment we turn into our own enterprise, if I am a retail owner, I want to know what happened in store yesterday and I have no clue. I may need to look at my email or my spreadsheets or I'll call somebody. Right? So can Athena give you a business briefing? So that's kind of one part, right?

Mike:                   
Yeah.

Ganesh:              
So the second part is we want to keep it more transactional. If you're, I know, on the road and you are looking at, okay, how much inventory we have. Okay. You have eight open orders for this, you need 400 items, but your inventory has only 300, then it should ask, do you want to reorder? Then it should say yes and the reorder is created. So we want, the data should be actionable, and the platform should help you to perform an action, not just giving you the insight. So in 24 months from now, you should be able to perform an insightful action focusing on insight today. We want to take it to actions in 24 months from now.

Mike:                   
That's fantastic. I love it. How in that roadmap, especially when you think of transforming it into action, right? So my inner geek in me immediately goes into like, how do you do that? That feels like a ton of potential integrations that you might need to build out to all of these platforms. Right? So whether it's accounting, CRM, ERP systems, whatever the case may be, how do you think you'll start approaching that? Or is it, look, we've already had to integrate with all those systems just to get the data to begin with. So we'll, you know, we'll already know how to talk to those systems when we get there. Or do you, are you viewing that in a different way?

Ganesh:              
Yeah. So right now we are integrating to what our customers, current customers or potential customers are using. There is no way we can integrate this with everything in the world is broad. So one approach is to go by, for example, we have integrated a couple of modules in Oracle ERP and Fish Bowl Inventory, QuickBooks, Sales Force, some of these. Now we are going and acquiring customers, there we have already indications. That is step one. And then all we have a new customer coming in who has software and potentially we can penetrate into a larger market than we are integrating that new one, adding into that, but the other whole new approach, what we're also looking at this a partnership, right?

Ganesh:              
So we want to see if we can have a [inaudible 00:25:53] partnership with that software partner and where our platform can bring in natural language and voice interaction on to of their data on top of their APIs so they can now start giving natural language interaction to their customers, their users. So, that is another way that it's not like we need to do everything. We'll partner with them to do it. Then we also third approach is we have a channel partners. We're partnering with, right now and they're so specialized in ERP and they are building the data layer for us and we're building, so the third party will build and then we can do a joint marketing. So we are adding more interaction integration with the software as we go, but in three different possibilities.

Mike:                   
So I'm curious a little bit in the origin story for this product. So tell me a little bit, you and your co founder, how did a year ago you guys started down this path? Where did that come from? Did one of you have experience in this space? Is just born out of first person frustration? Like how did you wake up one day and say Athena, that needs to exist?

Ganesh:              
Great question. I was in the data and analytics business for last 18 years. I have delivered, I don't know, 200,000 reports, myself and my team. I used to manage a large data analytics practice, part of a very large company. I sold at least 100 plus fortune 500 companies, in data analytics. One thing, my one thing I'll say, I wanted the outcome. I wanted to see what we are delivering, it should satisfy what their outcome is. Is it sales traction? Is it growth? Is it margin performance? Whatever, it should. One thing I was having a very tough time in many of these large organization was, we go deliver a data rat holes and analytic system, we spend the day and night for six months and you deliver it, maybe they spend a million dollars, but then after three months when I go look at how many people are using it, just three, four. That was like, why? Why are you not using it?

Ganesh:              
Because we spent a lot of money and afford, to me, my own team, I must have asked my team to work in the weekends and nights and a lot of [inaudible 00:28:26] and you're not using it. But then the answer is, it's complex. There are too many dashboards I have. Or I'm on the road. I'm in front of the customer, so I'm not using it. Right? So now we made something that, how do we make a Google? Everybody using Google, nobody's complaining Google is complex and nobody is teaching us how to use Google and now on Siri and Google Home, Alexa, that's bringing the entire world onto voice command. So that's when we thought, okay, there's a problem. Companies are spending money, not utilizing it. That's not good. But on the other side, such an easy system where everybody's using it. Why not migrate over there? Why not deliver something like Google like, Siri like Siri expedience on the enterprise data?

Ganesh:              
My primary purpose on the whole thing how to make the leaders, the business leaders, connect to their data without depending on anyone and make better decisions that everybody benefits. That's kind of why we founded this company. And then I'm so glad that I helped, this was my goal and aim, but you need to make it happen. The complex part, the technology piece. I came up with some dream ane tell my partner. He makes it happen. Right? So the larger team. So, I'm so far very lucky to have a great team and they're making it happen. And now customers are so far loving it. And every time they ask when ConverSight person comes, they're like, high five. I never expected that to become so, those are all the high fives that we're waiting for to see more and they should enjoy the platform by getting the information instantly when they ask. When they think it's so complex question, we should just give it right then.

Mike:                   
So much wisdom by the way in that answer. You had a couple of gems in there. One of the things that you said in passing, however, that I just can't let slide by. You said a phrase own the outcome, right? That when you were doing the consulting work back in the day, you wanted to own the outcome. I'm super interested, when you're building a team, whether that's back then or now, how do you instill that mentality of like owning the outcome, like it's not enough just to do what you are technically asked to do, but like we're actually going for results. How do you communicate that to the team? Reinforce it. Like I'd be super interested in any tips around that philosophy.

Ganesh:              
Yeah. One thing I tell my team, right? Anytime anybody coming and asking any work, even before you need to understand what is that work? But before even worrying about how, you need to ask why? Why are we doing this? Even if I just ask, Hey, I need this button? Why? Ask that why? Understand the purpose. Understand the business purpose of it. Understand the functional need of this. Understand who's going to use this. Is it for the CFO, is it for [inaudible 00:31:44]? Understand the person who's going to use it, and how critical this functionality, when I say critical, business criticality and is there a billion dollars behind this button or it's a nice review button. Right? So I understand that. So, why i important. Many times in a technology world be design something without understanding the why part. And then that means we don't understand the whole outcome. That means we can't own it. Right? So, what I mean is if you understand the requirement really clear, then that's how we onboard everybody in our team.

Ganesh:              
We have very experienced, 10, 15 years to a year old. Everybody, we want to treat them, we don't take questions on why we are doing easily. We explained them. So, more and more you give that broader picture. They will start thinking they will not just put you that pitch that one button. They will really give you some innovative suggestions and ideas. Then why not we do this? Only if you explain, those ideas will come back from them. You know, surprisingly, they will have an amazing idea which will transform the user experience than what I may have initially was thinking or anticipating. So, that's the magic, I will say, of what you should do.

Mike:                   
Thank you for that. Have you discovered any interesting nuances to shaping a user experience for voice versus shaping user experience for a screen? Like I'd love some thoughts on ... I mean, you're building a product, taking it to market, but the UI that you're working with is very different than probably what you've done in your past. What has that learning curve been like and what have been some of the takeaways there?

Ganesh:              
Oh, very interestingly, we wrote a blog on that new role. We need dialogue writers. We had all the UI and UX engineers, like designers, they were worrying about which color button to put, which form, left side, right side, like that. If we are designing an application, a voice interaction for a senior leadership that says somebody on the field, technicians or truck drivers or school students, so you need to have a different way of communicating, interacting with them, their [inaudible 00:34:21], jovial. You know, there are many things you have to bring in. It cannot be just a blend, right? You're asking me a little bit of humor you need to add. So, that is what I would say like the dialog engineers should bring. Interestingly, in one of our applications, where we wanted to bring the entire dialog, we probably reviewed that dialogue with non IT people like people who are nothing to do with our industry, technology. They are very good in English and they're writing scripts for us, and grammar and they are so fond of writing stories, those people. When they write dialog, our UI dialog, and they bring some of those changes, it's some minor tweaks that makes a big difference. That is the same way how you have UX engineers to design screens, you need very good dialog writers for this.

Mike:                   
Do you match ... So some of the nuance of the, let's just say the personas that you laid out there, right? So which could be regional, education level, type of job, whatever the case may be. Right? So the way that you would talk to maybe a bus driver, versus an accountant might be different. Do you bake that context into the question that you might get asked? So like, well we know bus drivers might ask us these types of questions, therefore we'll design the response really to be more favorable to them or do you really ... Is there some way that you can interpret on the fly some of that context around who the user might be and then real time try to tailor your answer to who that person might be.

Ganesh:              
So right now, it's one platform conversation. And we have multiple different solutions for different domain, right? So it's not, I wouldn't say it's hard coded questions, it is more contextualized to a particular domain. So, for some, if it is less tech savvy people, we need to, what I call, progressive elaboration. So you ask one question at a time and queue one answer at a time, but if they are somebody who understands the data really well, and you expect them to ask, how many orders I have received? I can just say 20 or you received 20, which is 25% higher than yesterday. So, that is option whether you want me to give you add on insights or just give me just the answer so we have options to turn it on or off and depends on the person or role you can turn it on and off as well. So that's where the design comes. Sometimes the words we use, or the words they may use, it will also be different. Right?

Ganesh:              
So that's where I was telling about humor, right? So, wow, interesting enough, hey, you beat your sales target. That will be so nice, then start telling your sales target is 25% and you have got this much. Right? And so maybe you can bring that little bit of humor. That's what we're working on. I don't think I would say we are done. The voice and dialog, those are becoming super cool now and to your earlier questions in 24 months from now, we should have a real human light with all this humor and all this in our responses. Right? So that's what-

Mike:                   
I love that. That's an audacious goal. That's great. Ganesh, man, I feel like we're just getting warmed up here. I have so many more questions, but I want to be respectful of your time as well as the listeners. If people would like to be able to connect with you to ask questions themselves or if they want to know more about ConverSight.AI, where can they learn more?

Ganesh:              
Yeah. So they can come to our website, www.ConverSight.AI, or simply can write to info@thickstat.com or they can look me in Linkedin, so you just Google it, Ganesh Gandhieswaran, you'll find my Linkedin or my email ID. Just come on over. We can discuss.

Mike:                   
I love it. Ganesh, thank you so much. Great conversation. I love what you guys are doing. I would maybe even love to whether we do it on the podcast or not, I'd love to check in with you in a few months in and hear how things are going. I love the space that you're in.

Ganesh:              
Oh, absolutely. Thank you. Thank you for giving this opportunity. Awesome.

Mike:                   
Awesome. Thanks, Ganesh.