Building a Data & AI Company a Founders Journey

Curtis Hays 2:41
Hello, and welcome to an episode a bonus episode actually of the bullhorns in bullseyes podcast. I’m your co host, Curtis Hays. We’re missing my sidekick and partner Tom Nixon. And today he couldn’t make it with us. But we’ve got a special episode for you guys. With an old friend and colleague really Jigar Navadiya. And Jigar has been working with me for gosh, man, I think it’s been. We’re close to working on 10 years. And he’s a he’s a data engineer by background has now started and co founded a company with his partner Amar, which is called Dataslush. And data sloshes is a partner of ours, you’ll find them on the collideascope website. And so wanted to bring in Jigar today because he’s actually flown from so you guys are headquartered in Ahmedabad India, and flew across the country came into Vegas for Google Next Conference on AI. And then he was over to another conference after that, and then and then down into North Carolina, and now he’s up here in Michigan with us. And so we want to take this opportunity to sit down in the axiom brand new Auxiom studios we’re in today where the be BLTNT podcast is being recorded hosted by Matt Loria, and their team was gracious enough to let us use their space today. And so without further ado, I’d like to welcome my good friend cigar cigar. Great to see you today, man. Thanks for you coming on the podcast Yeah,

Jigar Navadiya 4:24
thank you Curtis for inviting me and thank you axiom too, for the invitation extending the invitation and this amazing podcast set up I really like it.

Curtis Hays 4:34
Yeah, yeah, this is great. So So you got in yesterday, let’s just start at a sort of, you know, personal level. You had a long trip. I think he told me 40 hours Yeah. From from India to get to eventually LA and then over to Vegas. There was some plane issues, but thankfully, thankfully you got there safe. Yeah, all went well. And so you started out well actually before that. We met let’s talk about how we met and then we’re gonna get over to your travels. So going back about 10 years ago, I was trying to solve some attribution problems. And I was on a website called coders clan, you and I think you were you were on coders clan, and I put out a problem to a solution that I needed. And you reply to that. That post, which is kind of like coders claim was like an up work. Yeah. And brilliantly solved my problem. And you and I have been working together ever since. So from that journey, kind of, you were sort of what we would call here and Moonlighting, you know, you’re doing some side work with your full time job, but tell everybody your background, like what type of work you were doing. And then lead us into now, data slash, and how you got started with EMR and all that. Yeah. Give me that journey first.

Jigar Navadiya 5:53
Yeah, sure. So I think it’s before I started my, you know, carrier, right. So. So I started with the coders clan, right, where I was very passionate about solving the problems, right. And I wanted to see what part of the other world right has certain problems which I can solve using my skills. Right. And that’s where I got introduced, you know, by one of my friend, the quarters claim. So I remember still that day, right. When you posted on I think it’s 2am India time. Yeah, right. And it was still up. Yeah, it was still up and I was checking, you know, who is posted because coder coders clan is the platform where it’s first. First, our first first also ever is solving. Right, that that will when the when the opportunity to continue work with the customer. Right? That was kind of setup. Yeah. And you posted and yeah, it was, you know, like, pretty good with the JavaScript. Right. So I wrote that. And I yeah, here we are, we have this 10 years of amazing relationship. So So I started with the technical analyst, you know, carrier, right. I was very good with the JavaScript. As I mentioned, also, data collection was one of the thing that I was doing at that time, right, using the Google Arctic’s. Right. So at that time, it was Universal Analytics. Right, right. And I spent six and a half year in that consulting service company, which was based in Ahmedabad only we were close to 10 people, then it grew up at in 170, when I left, so I was taking care of all the technology side of things, right, I was managing the team technology team, then I service is something that I already experienced right now, I wanted to have the experience of the product side, right. That’s where I got to know about another company where I got opportunity of the data engineering side of the carrier. So I was leading the data engineering team there, where I, so actually, before that, I met Amar. So Omar and I are from the same company, right? The previous one, but we never met in the same organelle you were

Curtis Hays 7:59
you were employee number 10. And he was employee number four or five? I don’t remember. So he was a little bit before

Jigar Navadiya 8:05
before he left. And then we met one of the common marriage function, common friend marriage function. Yeah. And that’s where our journey started. We were having a lot of ideas where we wanted to build a, a generic recommendation engine for either content product or anything, right. So we wanted to build that recommendation engine. And I was having experience in JavaScript and PHP and WordPress and all those things, right. So we wanted to build a plugin, which basically solves all this problem, right? And that’s where we wanted to, you know, work together. And we started, there was a venture capitalist from London, right? He also interested in that idea, and we started working towards that. We build that, right. But then unfortunately, it didn’t work out in with respect to the raising more funds and all those things. Right. And then we dropped the idea. At the time we got married as well. Yeah. And we thought, let’s circle back, right. We don’t want to be settled in the comfort zone. Right. So

Curtis Hays 9:08
about five years ago, you you got married to Daraa Yep. And Mr. He’s got married, what’s his wife? wife’s name? She does Sheetal Yeah. And so, you know, family starts to become a priority and those types of things but then you guys you guys reconnect and come up with the idea for data slash right.

Jigar Navadiya 9:26
Yeah. And before that, we actually started with the training company. So we wanted to train Training Center you can see that way right. So we wanted we have experience the data and AI industry right since long time, right. So we wanted to see how we can contribute back to society right? And we come up with the idea of the training center, data engineering and data science summer is good with the data science and AI right so we design the course for the data science design. And of course, for the data engineering and we wanted to give it a try. We did a pilot training session at one of the co working space. And that got a good response from the people, right? I mean, the students and the professional folks, we got close to 20 folks signed up as well. But then COVID got hit. Yeah, right. And, you know, like, you cannot do anything, right, physically. So we dropped that idea again. But we will keep discussing and exploring what we can do next. Right, what we can do next. And that’s where we thought, okay, let’s work together, at least in the product company, right. So we join a product company called scipio.ai is a US based company, right? And we worked together, close to two and a half year there. Right? And then it was like, you know, we call it as, you know, if you don’t do it right now, there’s no time, right? And that’s where it got stuck in our mind. And I said, I’m gonna let’s do it together. Right. And we started data slash one and a half year back with the, we wanted to have this, you know, consulting with the research angle, right? So research and innovation, right? And that’s where we started data, slush,

Curtis Hays 11:13
right? So you guys help help me on the analytic side primarily. So we do some PHP development, some JavaScript and things that you guys help help us with. And then, but the analytics team is really where I get excited and working with your team or our they’re all brilliant individuals who, who help us anywhere from well, a lot of work lately was for moving from Universal Analytics, which we worked in together for many years, moving our clients over from Universal Analytics to GA for Tag Manager implementations, which we’ve been doing a lot of our attribution tracking, which we do, which is I mean, you’re the you’re the brains behind that idea that I had a putting that all together, which we’re now on a whole new version of our attribution tracking, which we call attribution. 2.0, which is a server side tagging of the attribution script. So basically, what that does is it pulls the source data that you would see like let’s say you had a CRM like HubSpot, HubSpot is going to tell you where that visitor came from, when they say fill out a form, sign up for your newsletter or something like that. So they’re going to show source medium, we would, we would say, like Google CPC, if they came from a paid campaign or pull your UTM comes from maybe a Facebook campaign or something like that. So we have a script that we’ve developed together that basically grabs all that information, including the client ID from GA four, including the G Clid, which I talk a lot about on this podcast. And we use that for Google ads to optimize campaigns and do offline conversion. So we capture all this data that’s there for us to use as marketers. Now we’re working on a lot together for cookie consent, which is really important, I think it’s going to be really important for all the US businesses here in the future, get our cookie consent banners up, which are not just a banner on the website as a notice, yeah, the cookie consent actually controls the cookies and whether or not if a user based off of preferences wants cookies to fire or not, then they can control those preferences. So our teams are working on that together. And we do a lot of reporting, data visualization and data analysis together conversion rate optimization. So you’ve got a great team over there working on the analytics stuff. So you mentioned a Mars big on the machine learning and AI side. And so you guys have been doing a lot of work there, which we’re going to get into. But this isn’t your first visit to the United States. You’ve been here quite a few times before. In fact, you were here five years ago and visited me in Michigan, because you had some Google conferences you were at? Yeah. So there’s another Google conference that just came up the Google next conference that you went to first. So tell us tell us about the Google Next Conference and what you learned there and what all you got to see.

Jigar Navadiya 14:03
Yeah, sure. First of all, it was like a three days conference. It was on episode nine to 11. It was really huge. There are more than 30,000 people were attending that it was in Las Vegas, and Mandalay Bay. Yep. Alright. So my biggest takeaway from that conference is the agents right? So

Curtis Hays 14:27
yeah, what’s an agent? So describe an agent for Yeah,

Jigar Navadiya 14:30
sure. So So agent is basically a you can say it’s a boat itself right. But it it it eliminate the human walk right and it helps us to collaborate directly with the technology to solve any specific task that you want to do okay, in Nutshell. So, they have the six types of agent that they have, you know, or talk about there. So one is the customer agent. Second is the creative agent. Third is the coding Jun 14 is the data agent. And 50 is the security agent. Six, I’m missing five out of six is pretty good. Yeah. So, yeah, so this type of agents that they have launch, Google’s launching specifically, they talk about the concept, right? You can use Google’s technology to build these kinds of agents for your specific need, right? So for example, you are in retail, right? Okay. Or you, you are in E commerce, right? You want a customer agent, the customer agent sits between you know, your customers and your business line, right. And it, it, it helps customers to solve their queries, right? It gives the human touch, right? It’s a, it’s using the generative AI technology. So it’s backed by the vortex AI platform, right, the underlying models, you can use any open source model Gemma or any model garden families there, you can choose that. Otherwise, you can use the latest Gemini one dot 1.5 Pro, right, which is their latest multimodal, right. And you can get resolved the customer square directly, right, you can use as so one of the demonstration that they have given right, it was very powerful. Were the person is actually trying to figure out the specific tailored, you know, clothes she wants to purchase, right, and they just upload the picture, it got figured out, and it search that particular, you know, item, and it figured out the nearby location as well. Okay, where you can get that right physically, right. And it books through the voice call throughout, right. So it’s searching that item, figuring out where you can get from the nearby location, as well as booking that for you right now. So you can directly call it directly connects with the store as well. And it book that particular item for right. So that is powerful. Right? So, and that’s more on a purchase journey. Right? But if you talk about the customer support, right? You will also get your customers queries solved real time, right? Yeah. So

Curtis Hays 17:12
what? What are some of your biggest takeaways? I guess from from this conference, like, what? Where do you see the future at least? What was Google saying the future looks like as far as I mean, there’s more tools that they’re putting out that are allowing developers like you guys to be building things like where do you kind of see this is headed from an AI perspective? Oh,

Jigar Navadiya 17:33
yeah. So from the from the developer? Economy, right, or developer community, right? It’s, it’s more for the core agents that I’m talking about. That will be the next big thing, right? Because they have integrated this code agents literally in all the cloud services they have, right? So as a developer, you want to debug your code, right? The code agent is there, you can analyze the logs, it can help you to improve upon your code, right? That increase the productivity, right? And accuracy of the code and all those things, right. If you want to, you know, test certain use cases, you can use the code agent, right? If you want to automate certain tasks, right? You can use the code agent, you want to have the, you know, write the code along with you, right? It’s a co pilot, right? Yeah, you can use the code agent as well. Right, right. So some of the things where you need certain initiation of the projects, or deploying the particular services on the cloud, those things got automated using this code agent. So literally, you know, I would say, it definitely increase the NX productivity for the developers. Right? Yeah.

Curtis Hays 18:44
I mean, I use it just just chat GPT itself to help me solve, you know, HTML problems I’m having, or I’ve got an error in code and give it the chat GBT. And it says, Oh, this is why you have an error, you know, you missed a quotation mark, or, you know, a colon or, you know, whatever. So, I can’t imagine, you know, what you guys are doing on that end, using these agents to, you know, help you develop faster, you know, again, have cleaner code, you know, improve efficiencies, you know, and all those types of things. So, from the next conference, then you went over to San Diego, and there was a ASU GSU Summit, ASU GSB Summit, ASU GSB. Summit. And now that was specifically what’s happening in the education space. As as far as AI is going and you you guys have a relationship with with ASU and developing some things. So let’s first you know, kind of in general, let’s talk about that conference, and kind of where things are going and headed with AI in the education space. And then we can talk about what you guys are working on there. Sure.

Jigar Navadiya 19:48
So So before the ASU GSB Summit, it was the actual by CSV. Yeah. So so it’s add to show basically, AI revolution. Indian education, that’s what they call right? Where it was opened for, you know, everybody, the educators were there, the tech companies were there, the companies like Google, AWS, right, Microsoft, right folks were there and there the there are people who are showcasing their you know, ad tech products, right. And I was there for you know, experiencing the education world right, like how what are the what are the the people are using the AI? Right? In the education right. To my mind, we are talking about kids, right. And the technology, which is very powerful, right. So, ethics and appropriate message, it should be there, right? When you talk about Oh, sure. Yeah. And when we give this kind of powerful technology to students, right, efficacy matters, right. And we glad like we are working with very innovation driven organization, right, which is ASU prime academic. Right? We are working since last one year with them. Right. And we started working with the POC, right, let’s build a POC, where we wanted to build the AI tutor. It’s not AI tutor, literally, but let’s call it as you know, for audience understanding, right math AI tutor, where the goal is we wanted to improve the math literacy. Right? Because in the United States, I don’t remember the states. But but the when it comes to math, students are really struggling a lot. Yeah,

Curtis Hays 21:43
we’re we are falling behind in the United States in some of these areas, including math, right? Yeah. So

Jigar Navadiya 21:47
as you prep goal is we they want to improve the math literacy, and we literally join that mission with them, right. And we started with the POC, suggest, figured out if EA is able to generate the right hence for the students, right? So it can fill the gap of the tutors, right? I mean, the teachers, right? And students are, let’s see, if two students are able to solve by themselves, right, those practice problems. So right,

Curtis Hays 22:18
and this is the danger, let me pause you there for a sec, because this is sort of the danger of of AI. And that, you know, this even sort of existed in some subjects of just students using say Wikipedia, right? Who was at the invention of the internet, internet. When we go on study, you could just go to Google and ask it a question, and it’s gonna give you an answer. And we can just use those answers and our homework and you know, those types of things. And then, you know, really sort of ask the question of like, well, what is learning? Right? Is it? Is it providing the right answer? Or is it the journey? That gets you to the right answer? Right? So does that student have the ability to problem solve? Do they have the ability to think critically? And so you know, yeah, in the area of math, understanding the fundamentals of math and problem solving that get you to an answer, what we don’t want is a system where I can put in the equation, and it just gives me the answer, because what did I really learn there? Right, so. So this, this AI math tutor that you guys have worked on is essentially acting like a teacher, in a sense that what you told me last night, you know, a teacher would come over and wouldn’t just if you had a question, and you couldn’t solve the problem, they wouldn’t just give you the answer. The teacher would walk you through the steps, give you some hints, right? To take you on that journey. So you’re, you’re you’re learning, right? And they’re showing you how to problem solve, right? So you’re using AI, to essentially, you know, replicate that in a way, right? So providing hints to the student that gets them to the answer. And we actually got a chance to test that with my 15 year old daughter last night, and, and pull that up. And she went through and built out some problems, which was, which was pretty cool. Yep.

Jigar Navadiya 24:07
Yep. And it’s all thanks to David Silverman, who is the CTO of ASU Prep Academy. Right? So he said that we shouldn’t, right, we don’t want to kill the cognitive ability of the students. Right? And that’s why we started brainstorming, right? How do we want to, you know, God relate right. It should not give the answers right directly to the students, right, it should give the hints, right. So what is your primary focus when it comes to you know, I mean, as a teacher, let’s say if I ask, right, like, you want to ensure that students are able to learn math, right. So right now it’s for algebra one with few specific topics that we have, right? That we have added as a capability and that student will focus on solving the equation not there is no free forum where they can you know, chat and interact Right, right like other bulls available in the

Curtis Hays 25:02
they’re not asking questions to the bot. Yeah. Right. They’re specifically trying to problem solve, right? Yeah,

Jigar Navadiya 25:09
it is same as you know, you are like you are writing. So there is the equation on the paper, right? And you’re solving it and teacher try to, you know, give find you there, the the incorrect, right and highlights there and it gives you the hint or something. Like now, it’s not highlighting a specific thing. But what we have is the where we have the hint, right? It shows the hint. Okay, this is where you have made a mistake, right? And it anchorage students, right? Okay, this is what you can do next step, right, it’s gives the little name, then you can try to solve it, if you’re able to get it, it appreciate. Right, and it gives the next step right, it gives the if you want, right, what will be the next step? Right? It will give the another hint. That way it is, you know, helping students to solve the equation. Right, right. Yeah,

Curtis Hays 26:00
that’s amazing. And so, you know, conceptually, this idea gets brought to you guys, you guys have been in development with it, working with them. And we got to see a demo of that last night. It was really, really cool. So what, you know what else at this conference? I mean, other concerns, you know, other than that in the education space, or I know, you got some, you know, other thoughts and ideas about different technologies and things that people are working on. But what else? What else? Can you share that you learned there?

Jigar Navadiya 26:26
Yeah. So you know, right, I recently become a parent. Right? Two boys? Two boys. Yeah. Yes. I’m same for Omar as well, he has one boy. Right. So we were talking about the education because in India, it’s completely different, right. But when we were exploring the school, the pedagogy and all those things, right, we saw that there was a huge gap in the education, right, and we are worried literally worried about the next generation, right, then alpha, right? Like how they will, they are pretty smart, right? But the the education or these edtech tools, or any pedagogy should not restrict their, you know, the creative thinking and all those things. Right. So if you are using it right way, AI, so for sure, degenerative AI or AI will not, will live with us. Right? So you have to learn that right? But how do you educate students or kids right with that? Right. And that’s where one of the things that I’ve seen, right, in this kind of Summit Summit, right? Where you brainstorm with educators, right, with, you know, the people who believes in the innovation, but at the same time, as I see, as I said, right, they, they, they, they care about the values, right, the ethics and appropriateness, right, when it comes to the, the AI in the education, right, and make sure the efficacy is there. Right. Right. And we are glad that ASU prep is, you know, working with us. Right, right and

Curtis Hays 28:01
straight, yeah, there’s some real concerns, I think they’re, you know, we have, we’ve had a teacher shortage in the United States for quite a while there just simply aren’t enough teachers, for all the students, especially in rural areas, you know, across the country where like, algebra, you know, you might be in a rural area where there is no certified algebra teacher that lives in that area. And so, you know, what, what can these schools are going to have to adapt to technology in some way, but then there’s the concern again, about, you know, the technology itself and does it do as good of a job which nothing replaces an actual teacher and a human being that’s teaching somebody else that’s irreplaceable, but you know, there’s things that can supplement that with, with AI, but again, using the AI in in a constructive way that allows that student to grow and so it’s really amazing what you guys are working on and really at the forefront of what’s what’s going to happen with with AI and technology and education and all those types of things.

Jigar Navadiya 29:01
Yeah, right. So, we started with this r&d Innovation Research and Development Innovation Lab at data slush, right, where we are almost spending 20% of our revenue directly into the research and development right. So, we started exploring how AI can help with the stakeholders like educators, teachers, parents and students right to ultimately achieve the learning outcome right and we started building small small prototypes along with that like how teachers can generate the lesson planner using some techniques right. So, there are strategies the add up strategy or I do you do we do right. So, we have one you know solution there as well which generate the lesson plan for teachers right. You give the you know, like you you give the certain information, right, what is your class What do you want to achieve? What is the demographic? Right? What subject topics, right? Once you set up that you start, you choose the strategy, right? And it generates the lesson plan for you. Right? Where teacher can focus on executing that lesson planner right now, if they want to modify, they feel free to modify as well. Right? So that is one of the solution. I mean, it’s, it’s still we are testing it, right? And that is something that we recently developed, right? Another thing that we are experimenting is generative, gendering the video out of it, right. So, for example, you want to explain certain concepts to students, right? Or you figured out okay, these are the concepts or techniques, that student required more improvements, right. So how do you use the storytelling, and the creatives, right, combined together and generate the video, which explains that Right. Right. Right. Right. So that is something that we are now experimenting with that right. So we have a you know, ambition project called teachable machine we call we have a codename for them that project, in that we are doing this multiple pilots, right, we are bringing so many demos, the lesson planner, this content generation, the storytelling way of the content generation, which students really resonate, right, and they understand, okay, this is the concept. Right, right. Now imagine you have the personalized way of personalized data and you blend it with that right, you know, little bit more about students, right? Yeah, their interests and all those things. Right? Then it will be more personalized video for them. Right. And that can help them to achieve the learning outcome. Right.

Curtis Hays 31:43
Yeah. And I mean, these types of things, it sounds like free up the teachers time to I mean, it’s building a lesson plan and are helping build an individual plan for a specific student that has certain types of needs. Yeah, that frees up the teacher to spend more time with that student because they’re not behind a desk and a computer trying to do those things. So it’s really cool what you’re doing there. So I, let’s switch back over the data slosh. There’s you guys have been growing how many employees are you at now?

Unknown Speaker 32:11
Right now we have 2020. Yeah,

Curtis Hays 32:13
so it’s pretty cool. You know, just these are, you know, fun journeys to watch and kind of go along the ride with with you guys happen to be just the two of us that work first, and then you introduce me to Amar. And now, you know, there’s a whole team of people there and Omni bas that are working on these, these fun and exciting projects. Now, as you’re growing, we’ve had some conversations, you guys have a specific process or like fundamental process driven business plan that you guys are working through? What what does that explain that to us?

Jigar Navadiya 32:44
Oh, yeah. You know, right. I will. I love processes. Yeah. So

Curtis Hays 32:49
it’s one of the things I do I do like about UGR, you know, documentation is always good and follow process, which a lot of developers you know, I gotta say, there might be great developers, but sometimes aren’t great communicators, and documentaries, and those. So you know, your team is always great with that. So that’s good. So

Jigar Navadiya 33:06
day one, we ensured that, you know, we wanted to make, we wanted to build a team, which is not heavily dependent on the people, right, there are more processes in Word, right? And that’s where a more came up with this OCR process, right? The objective key result? Right, so we read one book, right? And was it at a let’s start having the OCR right. And earlier, we have we were having these processes, we have defined certain processes. Let’s say we wanted to have this delivery setup, right. So in the delivery team, we wanted to have certain processes and all those things, right. But then we come up with the Okay, more structured way of executing things, right, because we are startup right, we have a customer delivery as well as we are talking about the r&d and product development, as well as we wanted to ensure that you know, the organization structure get get all these processes as well, right. Yeah. All right. So we started with the OPR. Right, where every quarter, we defined the Okay, this is our third quarter. The first quarter was literally miserable. Like, we we had a you know, we wanted to achieve 100% You know, yeah, it shouldn’t be right, right. But we ended up with 30% Okay, 30% Yeah, yeah, we openly you know, talk about that as well with team as well as we above Amara road blog as well like, right, what went wrong as well, right. And next, but we never settle with that. Right? We thought okay, let’s do it again. Now we know right? Yeah. What is the the ambition versus reality and all those things and what went wrong? Right, we had lesson learned. So we did another quarter, we reach to 70% Right. Now, these are the numbers, but actually, what is the impact that we have made right with this process? Is that is the most important thing, right? Because OKR is something that you set on a company level than team level and then right, you know, at the individual level, right, that’s what we are following, right? There’s a really good tool, right, which is free and available for everybody, which is sugar. Okay, thanks to you know, the sugar Oka team for this amazing tool. We are using that for setting up the the this OCR process, right. So in that, what we are doing is we said I and a more besides on a company level, okay. Then our, the leadership team, right, where we have Aarthi the right zeal from our HR team, right? We get together and define the department or the team level. Okay, so right now we have delivery team, we have HR, and we have sales sales, I’m alone right now. We don’t call it as a sale, we more of a customer engagement and all Yeah. But yeah, but still, we have a virtual team. So I take care of sales marketing, and the finance, I’m gonna take care of overall delivery delivery has the analytics team engineering team, and r&d and the team are the tech leads the analytics team leads the r&d and the engineering emulates the soon we will have, you know, Under Armour will lead the side of the things as well. So this is the pretty structure, right? We have. So we define that all the team level objectives we discuss on that right? What is more important, and everything should be, you know, look up to the company level, objective, right or the team, right, whatever it is, right. And once this freeze, we put it in the Super OCA tool, we share it with team then individual team will work together, right with the individual sites, team leader, and then they further drill down into the individual level of care, right. So this, this is the process that we are following, right, and just the new quarter get started. So our financial year is April to March, right. And this is our third, okay, our quarter, but it’s the first quarter of the new financial year, right. And that’s where we have defined and this time, we are having a really, really amazing, you know, the objective was to pick out 10 prototypes, we are building, right. Some of them will be in education and other industry as well, we are tapping. So those are the you know, OKRs that this is the process that we follow, right to define the OKRs right, and everybody will now work independently, right, with the same goal, which is the company level, if they are able to saw you know, contribute to their individual OKRs, which translate into the team level achievement and team level will be the company level. So this is what

Curtis Hays 38:06
we follow. Yeah. And 20 employees, you said, Yeah, which is awesome. And you need these types of systems to grow, I was with an agency that I helped grow to 22 employees was our max, we use a platform at the time called success factors. And that was very similar. So we set our organization goals of the top leadership did that was passed down to the managers who then worked with the employees, and they all set their goals and their goals had to align to, you know, the organizational objectives. So that’s amazing, you guys are doing that. And it really, you know, gets everybody aligned, gets the culture aligned to where you guys are headed. Nobody’s guessing about what you guys are doing and those types of things. And so So, you know, bravo to you guys. And everything that you’re doing there. Anything you want to share, you know, on the forefront you guys are working on or anything, you know, coming up next for data slash any goals you might want to share. I mean, where are you guys looking to take this for we close up?

Jigar Navadiya 39:04
Yeah. So yeah, I mean, I no more are very keen to solving the problem. And having the products right, and the solution, right. So so we started with the consulting because there was no solid idea on the product side, right. But since we are experiencing now the service side, the next journey will be on the product side, definitely come up with some products that we have a bunch of ideas that we are validating. Right now, it’s not concrete. I’ll be very honest here. So that’s where the Okay, I’ll depend prototypes that we have defined, right? We’ll use the r&d innovation, right, to build this such solutions and the products right. And we will validate it. Yeah. So that that will be the next journey for Right,

Curtis Hays 39:55
right. Why don’t you guys have a blog so everybody could follow you at data slash.com And then you and Mr. You’re both on on LinkedIn as well. So people can find you on there we can, we can put those links in the show notes. So it’s been a pleasure, you know, just just being your friend and working with you all these years and looking forward to the many years to come as we continue to collaborate work together. So, yeah, great. Great to have you too. Gar. Any final thoughts? Any last words?

Jigar Navadiya 40:22
Nothing specific. Yeah. Thank you. Thank you for having me. And I really look forward to this partnership. Yeah,

Curtis Hays 40:28
yeah. Safe travels back to India. Yeah. All right. Yeah. Great to see everybody. Yeah, same here.