Beyond The Code

Maulik Sailor (00:01)
Hello and welcome to the live stream of Beyond the Code podcast by Notchup.com. I'm Alex Salah, founder and CEO of Notchup, an AI co-pilot for engineering managers to automate their developpations. Now, one of the key frustration that I keep hearing from various engineering leaders is the gap between ⁓ them and their HR or people off-stream, right? Typically, I speak with mainly technical leaders like CTOs or VP or engineering managers.

And I kind of understand the frustration they have. But today I have an incredible guest with many, many years of experience in HR ⁓ and talent off generally, right? And with him, I'm looking forward to having an incredible discussions around what's happening in this space. What are the opportunities and how AI could play a key pivotal role ⁓ in this. So without further delay, please welcome Levi.

Barbosa, who is a founder of Vezop. Levi brings almost two decades of experience in talent ops and HR across global companies like Capsico, Newbank, Microsoft, and many others. With experience ranging from recruitment, operations, know, HR systems and platforms, understanding data and analytics behind that, and overall people leadership. He got it all covered.

in his 20 years of glorious career, if I may say. ⁓ you know, Levi, first of all, thanks a lot ⁓ for joining this podcast today. Right? ⁓ I hope I did the right justice in introducing you and your background. But you know what? ⁓ Maybe I missed out something. Would be great for us to hear more about your background so that, you know, ⁓ our audience can understand.

Leví (01:34)
Yeah.

Maulik Sailor (02:00)
the context behind this, you know, fairly well.

Leví (02:03)
Totally. Thank you. Thank you so much for the introduction and the time and the invitation, right? Yeah, like you mentioned, I have almost 20 years working on this. Actually, I started in a really tiny startup ⁓ based here in Mexico. And it was really strange because at that moment, we were using a Google spreadsheet to source candidates based on yellow pages. Remember the yellow pages? We literally were taking off pages.

Maulik Sailor (02:28)
Yes. Yes.

Leví (02:33)
copying numbers and names into a spreadsheet and then doing that. But at that moment, I think from there I was always like, I'm so lazy that I want to find out a way to make this automatic.

And then through the years, I find ways of doing that. Even at Novartis, for example, one of the companies that I work, yeah, I fully automated a headcount process, payroll process, many other things using macros, because that was what we had at that moment. No, there were no automation tools or these things. And then throughout these other years, I learned a lot about the no code, low code tools, and then starting to program now.

So yeah, going from the most basic Excel formulas that we could make to now then doing custom automations. Yeah, I think that's the experience now I have now.

Maulik Sailor (03:34)
That's pretty good. So you have done all the way from, you know, the basic VBA style automation to the age of AI that we are currently living in.

Leví (03:47)
Exactly, yeah, yeah, yeah. And I think the most interesting thing is that we jumped, I mentioned a lot of these, we jumped from basic descriptive analysis that we are doing to now augmented analytics that barely few people understands. Augmented analysis is now supported by AI.

Really few people know how to use that and how to ⁓ go to a further step beyond that. is like the, you know the D-I-K-W pyramid, you that it's you transform data into information, knowledge and wisdom. Wisdom is the key in this aspect and it's really few people that ⁓ is going through that.

Maulik Sailor (04:35)
Yeah, that's pretty interesting, right? Now, generally, like, ⁓ you know, when we talk of HR, you know, people, talent, you know, they would normally come within, like, you know, either people or a placement department of a company, right? And the very nature of, you know, the things that we need to do ⁓ is fairly manual with a lot of human touch.

uh, overall from top to bottom, right? Now, you know, with AI, the whole purpose is to replace a manual, you know, operations, the manual task, uh, initially from repetitive tasks to now more intelligent, uh, based tasks, right? But before going into the AI side of it, you know, I would like to know more from your experience, like, you know, what are the top

challenges or problems or inefficiencies you see within the HR or people ops departments in different companies at different sizes that you you have worked at.

Leví (05:43)
You know, it's really interesting that we all have the exact same issues and most of them are data related, but the fundamental thing in all of this is like process.

Companies are really like goal driven, let's say like that, in that way. They just go for the numbers and they just do everything possible to just achieve the number, but they don't see the process. So they skip a lot of steps and then data is skilled, no? So.

The same issue I see in all companies is that whenever, for example, a new VP of talent joins a company, they don't have data or it's a mess or processes. They don't have any processes. They don't even know, for example, what is a custom field, who created them. No, there is no documentation around all these things. So they don't have anything basically. And they have to work from scratch on everything. Even somebody...

did something, it's lost in that transition. And then there is another thing when people also changes, sometimes they measure things differently. So in some companies, time to feel is one thing and then time to feel is another. And then you just measure that by different fields or different things. And that is really difficult. So I've seen that.

most of the people is really really stuck in data specifically and I think data literacy is one of the most important things today to then go and try to measure AI literacy you know I think data is fundamental for that

Maulik Sailor (07:35)
Yeah, data is the king, Sorry. ⁓ We, at least from last 15, 20 years, ⁓ just as the social media exploded, ⁓ know, data suddenly became the new currency, right? Or the new gold that every company ⁓ tried to create and protect and monetize, right? ⁓ But having said that, ⁓ you know, a lot of things

⁓ within the people ops and the HR ops in general, ⁓ could be fairly manual processes, right? ⁓ Let's say for example, ⁓ recruitment could be a very good example of this, right? ⁓ Recruitment over all these years, in my opinion, has largely remained the same, as in you have a speculative job, which is fairly static document,

You have people applying with a CV, which is also fairly static document. Historically, know, somebody manual will be doing all the filtering. Then came the whole, ATS who are filtering all the keywords. ⁓ you know, things like that to eventually get to a state now where there are AI agents, are, you know, auto applying to hundreds and thousands of jobs.

And similarly, you have AI agents on the other side filtering out all those thousand plus applicants on every single job you have. Which in my opinion, it's like on one side is creating a lot more junk, the other side is creating a lot more filtering of those junk to actually not get the right output in my opinion. What do you think? Is this the way the HR ops is?

heading into with the AI that, you are just trying to scale a process which was designed maybe 20, 30 years ago, but no longer makes sense anymore with AI. Or it is a time for us to almost get rid of the whole as these workflows and the processes and instead, we look at this whole people ops with an AI first.

⁓ intelligence and more and workflows basically. Right. What's your opinion?

Leví (10:07)
It's crazy how we start to use so much AI lately that right now it's one AI on application side, one AI talking to the other AI on the APS's. And basically humans are just like supervising, I get an interview? Did I get an offer? Blah, blah, Because that's what they now care, no?

It's really interesting that you get your resume into ChatGPT, then you have like a new complete ATS focused resume. You get an interview because of that. But then on the other hand, the ATS system is telling you, so I measure all these things and that matches with your job. So it's a hundred percent match. But in reality, I think the...

We will have to revisit and check how the recruiting process is done because now that's the reason why we have so many assessments and maybe on-site interviews because that's the reason why we need to see if the candidate is doing what he's saying in the paper. We need to reevaluate how the recruiting process in that aspect because since now AI is talking to another AI, it's...

So difficult, no? An AI is doing a perfect resume and then the other AI is checking, ⁓ this matches with everything, so go ahead and schedule an interview. And that is a problem.

Maulik Sailor (11:45)
I think it's a

massive problem in my opinion, right? The way all this ATS is work and the way, you know, you, the employer asked for a CV ⁓ to rely on that. Now, in my view, the biggest problem of both is effectively the static nature of your job description and the CV, right? Which is normally historical, right? So a CV is normally is an historical perspective of what I, what a candidate has done in past.

But past performance is no indicator of future performance, as we all know. Similarly, from the employer's point of view, when they're creating a JD or the hiring managers, when they create the JD, they're thinking of what they want the candidate to do today or in short term. But as we know, with companies becoming more agile and with a lot of uncertainties around us, we need to be able to evolve.

the job description or the actual response roles and responsibility in a fairly dynamic manner, right? As the company grows or as the macro environment changes and so on. What do you think? That could be an opportunity for, I don't know what the solution could be, but could be something fairly dynamic, which is not a JD and not a CV based, something different. I don't know what it could be, but you know, something different.

Leví (13:07)
Yeah, totally. think there are a couple of companies this year at the Unleash conference, for example, in Las Vegas. I was talking to a lot of people, not in the companies. I was just going to the tiny booths where they have a ton of different companies there, but they are really tiny companies that they are now exploring ideas. And the job board was one of them.

I met this person that they are really creative on this they Want to create like a job or like Instagram? Similar to this experience, but also make it playful so you can upload photos you can upload about you specifically

Yeah, professional, but it's more like you play more with that and you are actually what they want to inspire you in that sense is be original, be you. We really don't want the professional fall that you have on your resume. We really want you to be what you are really. And then from this, we can check if other things match professionally. That was one.

there are other people exploring ideas on how we, how can they use the AI avatars to do assessments at the beginning, but to all candidates, like all the candidates. And based on that, I have measured some things that can give you a clue to then schedule interviews. But this is an exploring phase, I think it's the early years of this, think, and humans, I think they are not that fast as AI.

Maulik Sailor (14:34)
Mm.

Leví (14:55)
That is the main reason why today we have really few people exploring these ideas and it's just, have to shift to a new complete process because yes, today is really skilled.

Maulik Sailor (15:10)
I've got a questions there actually, know, ⁓ what you said. Now, talking of AI out there, right? I had an opportunity to work with a recruitment firm here in the UK and build like a video-based recruitment platform for technical roles, like software engineers and DevOps and all that. ⁓

Now this is like about 10 years ago when, you know, video calling was like really, ⁓ like, know, zoom and all, were like, just starting to come up and everybody wanted to do videos first. Right. And the whole theory that, okay, you know what you can do instead of having this see to info questions, you can just literally have a video Q and a, and I think there are a lot of other, it is also leverage that where you can pre-record the questions and the answers. And then you will do it that, ⁓ for the applicant.

Now the challenge we faced there was most like, you know, technical people were just simply not comfortable doing the videos. You know, when we talked to them in person or when you are having a live zoom call, then they were very confident in how to answer that question and so forth. But recording that answers to, you know, pre like, you know, pre questions filters or sorry, pre-filter questions, right? ⁓ They were just simply not comfortable.

So a lot of people will just simply not apply, ⁓ even when they were like a really good fit for the job, right? they couldn't apply just because they, they don't feel comfortable recording themselves, right? talking to themselves in a mirror in a way. so what do you think with, ⁓ you know, with that, I feel that creating an AI clone or a virtual avatar of talents might, especially in the technology, tech talent, right. Might be incredibly difficult.

Not because of technical limitations, but because of behavioral or psychological ⁓ limitations that the person may have.

Leví (17:06)
Yeah.

Can I ask, when did you create this? Which year specifically?

Maulik Sailor (17:21)
This was

2016, I believe, 16 or 17.

Leví (17:26)
Yeah, yeah, yeah. So I'm asking that because I saw something similar at Microsoft 2016 to 18, I was working there. But then after we work, we had the same thing with this system called HireVue. So HireVue is a platform, you just have a virtual interview with pre-recorded questions. But then we saw a reduction of maybe 40 % people applying. So they were applying

Maulik Sailor (17:42)
Hmm.

Leví (17:56)
then we saw that they were receiving the links but nobody was taking the interviews and it was really strange because everybody was used to have like on-site interviews at that moment and then

a virtual interview was so strange for people that I actually had an interview I think also like 2017 and with Nissan I think and it was so incredibly bad interview I felt so so bad in that interview that I stopped trying to apply into these type of interviews no but then we had this shift we were forced not to now have a virtual calls

and everything through the pandemic and today people now is more used to that and I think that that was some people or if you apply that ⁓ idea again people will be more used to today specifically and

Maulik Sailor (18:57)
Okay, so you think that,

you know, you know, the pandemic behind us, what people are got more used to this video based remote calling and interviewing. They would be more comfortable in creating their AI clones, for example, who could do the initial like, you filtering.

Leví (19:15)
Yeah.

Yeah, yeah, yeah, exactly. And more if you play with that. Because today, for example, you apply to a job, and that's it. You might have an answer, and the answer is we rejected you.

Maulik Sailor (19:33)
Mm, yeah.

Leví (19:34)
But if today you can play, people applying, five minutes later you can do an interview, wow, that will change the candidate experience a lot. And then if they are good, they continue. If not, then you can offer these things, you can be really creative. Hey, you are not a fit for this job, but we have these other five jobs.

Maulik Sailor (19:46)
Mm.

Leví (19:59)
Please apply to one of them and then continue with the first. It's just a matter of doing things like that.

Maulik Sailor (20:06)
Do like, you know, on the other side of that, you know, a lot of companies, particularly the ones that grew in nineties and early two thousands, that ATS systems generally are really old school. Like, you know, I can name a few in my opinion. Again, this is my personal opinion. Okay. ⁓ Talio, for example, I find it's really, really outdated. I see IMS, right. That's really outdated system, right. ⁓ that Oracle, I think people soft is called, right.

Uh, really old school, right? Um, and you know, when, before starting my company, when I, me as a talent, I was applying, I'm again, I'm talking almost 10 years ago. If I see Talio based application, I would like simply not apply, you know, because I can't be bothered with filling out the whole application process. Right now, fast forward 10 years now, companies are still using those outdated system. Right. Do you think.

This comes with our change, like, know, one thing which I keep hearing that companies now really change their HR systems, right? Or people system, because they've got so much data behind that, that it's impossible to change, right? But at this moment, given all this technological revolution that we are seeing around us, you know, do we think it will be a right time now for companies to change or they can still, you know, continue defending themselves that no, no, no, this system is good for us.

Leví (21:34)
Yeah, I think ⁓ something that is happening there, we saw, I think two weeks ago maybe, SAP, I think they acquired Paradox. I don't remember the news, but these massive systems that they are like.

horrific, with success factors for example, they are now acquiring systems that will make them better. I don't know the strategy that they have if it's just literally merging the systems that are good.

with their own system or if they are doing something else, you should know. But I think that's the next step that we will see in many systems. Workday also is doing something similar by acquiring other companies. And we are seeing, like Oracle, acquiring other systems that they can also put them in their own environment. But the thing is that...

I've seen so many companies, massive massive companies, know, a hundred thousand, eight hundred thousand employees even, and they keep the systems and it's really strange that how that forces people to create workarounds using Excel spreadsheets or SharePoint, you know, that it's also horrible. But even with that, you have something that

makes your process really bad and then you have to find works around in all of them. I don't know if companies, I asked so many times why you have this system if there are so many other things better than this out there. And it's just because it do works.

most of the times, you know? Like, this monster's company is having seven different systems interacting with all of them, and at the end, the worst system is an HR system.

Maulik Sailor (23:33)
Yeah, exactly. So from,

you know, that raises some interesting point, right? Again, as an HR leader, right? You know, I think in your current role with your current company, you advise companies on their talent and people's right? ⁓ How do you really convince your clients or employers to change their HR system? Like, who's the decision maker there, right? know, and what are the

like typical objects and you keep hearing from them.

Leví (24:04)
Yeah, so thankfully now I have also with my company, I'm going with CHROs, VPs, senior VPs and senior directors. Now I go directly with them just to check the needs. Once we find out that information, I think it's just a matter of comparing. Now current solution is costing you this. New solution will reduce 70 % of the cost, but also the experience from all these people.

And that way you are getting an ROI in a week, basically. Now, most of the times it's like that. actually have a client, they were spending also like 30,000 in a system, and we reduced that to 10,000 only. So we reduced 20,000 every month that they were spending on this system. Because we are now using a custom solution

Maulik Sailor (25:01)
Mm.

Leví (25:04)
for them, I actually programmed like a web application for them with all the other custom things that they needed. And we are just sourcing data from these other systems. And at the end, the cost reduction was incredible today, but how we are still ⁓ adding more and more features to these applications. And they're really satisfied with that instead of going with a massive system that is not going to be as custom as they want.

At some point what I'm seeing and now with all these AI agents also is that creating custom applications will be also maybe like a future state for everybody because you can now create custom things. You only need somebody that knows how to do that.

Maulik Sailor (25:52)
Okay, so talking of AI then, know, this customer agents and all, within the full people ops, the HR stack, which areas you think are the ones that the, this kind of AI agents could disrupt the most, or could be better than by this kind of agents?

Leví (26:16)
I've been discussing with different people since last year. Now we are seeing that the top of the funnel is the one that takes more time and is the most cost.

I think you have a lot of costs because you spend so many time with recruiters at that moment. then moving into the interviews stage, it's somewhat difficult because most of the times recruiters, don't filter correctly or they filter in a way that you have to redo again the process one or two times in some cases or more. And that is where you spend a lot of money. But I think that will be completely unfolded out of

in the next two years maybe. I've seen for example Juicebox, this company they are doing so many crazy things in the top of the funnel and they now have this agent that they are incredible like they are really accurate they have I know this algorithm where you can see like what really good results but not only them there are other companies now like find them a gem that are doing pretty similar

tools. It's just a matter of time, you know, what we are going to see. Once that is fully automated, I'm sure we are going to see the full onboarding part also fully automated, improving the candidate experience, employee experience, you know, and from there, payroll is the next. So yeah.

Maulik Sailor (27:49)
Yeah,

payroll is a behemoth, right? You know, let's not go there, right? But just talking of this top of the funnel, right? I think, yes, I understand a lot of like, know, open and junk happens at the top of the funnel, you know, because all the job was like LinkedIn and Indeed and, you know, Monster and anything else that designs for quantity, not quality. You know, the full purpose is to get, you post a job,

Within 24 hours, you're going to have 1000 CVs. So it's all about how quickly can they deliver the most number of CVs as possible. That in itself creates a big problem for you to get directed. Then it comes to second level, which is the filtering. Again, AI filtering AI. So candidates are using AI to game the AI filtering on the other side. So is AI versus AI happening?

Leví (28:27)
⁓ huh. Yep.

Maulik Sailor (28:46)
Again, in my opinion, an area where needs to be completely rethought. But that comes to the middle of the funnel. So let's say you have your shortlisting done from HR point of view that, here are the few candidates who are most suitable for this particular job. Then comes the middle of the funnel where you are going back to the hiring manager. And, you know, now the hiring manager is like, okay, who are these people? You know, what is their background? Right.

⁓ Do the hiring managers have all the context about these people? Why they are shortlisted? ⁓ What are the concern areas? What are the positives about each candidate? ⁓ And things like that. And then the whole interview process, you need to have to in setting up the interviews with the candidates. And then the technical vetting that goes behind the scene. Again, vetting has a big problem nowadays that you can have...

a lot of technical tests, but on the other side, the candidates are using a lot of AI to answer that. They're gaming the whole thing, right? So again, does vetting make any sense in this world? ⁓ You know, the only option is to bring the candidates to the office and do an in-person ⁓ evaluation, but that's not scalable, right? So you need to be absolutely sure about the people that you want to bring in, right? So, you know, it sounds really complicated and really...

like, you know, ⁓ nightmare to be dealing with all this, right? The question for you, like, you know, given your, like, you know, incredible experience, right? What do you think, you know, what could be done here, you know, from hiring managers point of view, from, you know, your talent acquisition managers point of view, you know, what could be done here so that the hiring manager is good, is happy with the outcome, the...

talent manager or HR manager is happy with the outcome. And at the same time, the candidates are also having a positive experience without really falling prey on either side on this whole AI gaming.

Leví (30:57)
Yes, yes, you make me think a lot on this. Yeah, yeah, at some point this has to change because this is like schools, like schools, basically. We have like a process that has been done since what, like 200, 300 years, you know, and never changed. And people still study the same way as we did 200 years ago.

Maulik Sailor (31:17)
You

Leví (31:26)
And the recruiting process, we have almost 30, 40 years doing the exact same process. Now we have the top of the funnel interviews, offer onboarding. It's the exact same thing. And now with all these tools, we are just trying to accommodate everything into that or fit everything into that process exactly the same as it is.

I'm trying to also find works around. I think the process must be ⁓ considered in different points of views because yes, it's not scalable. We are now going to need more people.

Maulik Sailor (31:51)
Yeah.

Leví (32:03)
I think this morning I reading this article, when we see that people were saying, hey, software engineers, going to decline as we progress with AI. And it's not like it's doubling. It's going beyond that. And people are actually hiring more more more AI engineers now. And now people are starting a lot for being AI engineers.

this and we are going to be more people that can maintain all these things. So at that moment, like what are we going to do? Continue interviewing them one by one, individually and then doing debriefs because that is the next step. Now you interview five people, then you do a debrief with everybody that interviewed them. So it's also like time, no? It should be...

Maulik Sailor (32:57)
Exactly.

Leví (33:00)
And I'm thinking, sorry, sorry, just to finish this, I'm thinking, find them humanly. These companies, are using not the exact same way as everybody. They are using these data points of the candidate profile. That is the skills or they name differently, but they are using something different to source people more effectively. But then at the end of the process, that should be used also against

the decision, no? Because you can use all these data points to then take the decision and then say hey, also my intuition is telling me this. And then just take the decision, but something that should be written here.

Maulik Sailor (33:45)
You know what, once you were talking all that, I thought came to my mind. ⁓ You know, generally when you are making that, you know, purchase decision, whether you want to continue with your existing system or purchase new tools and the new tools, ideally should be, you know, have a better ROI and a lower cost. ⁓ But you know, one of the costs that often people don't consider is the indirect cost, right? So,

Let's say with bad filtering, your hiring managers, for example, engineering manager or VP of engineering, they are interviewing all these candidates and evaluating all the profiles, which means they are spending their valuable time in doing all of that. And they are not cheap employees. know, they are one of the most expensive employee ⁓ you could have. So then there is an indirect cost associated with poor matching, poor filtering, and so on. Right. ⁓

Secondly, let's say if you end up hiding talent which are not really ideal or they look ideal for you for your right now requirement, but in six months time, they may not be as relevant as your roadmap changed or priorities changed. ⁓ So that is another indirect cost that you generally don't account for.

Ask you like you know if you were to build ⁓ a business case here or the use case for a potential client. What kind of things would you would you consider would you ask them?

Leví (35:26)
Well, this is really interesting because this is what I did last year and continue doing with a client. They wanted from the beginning to start measuring quality of hire. That is what you just mentioned. If somebody stays more than six months at the company, and what is their performance? then measure that against the score. ⁓

It's so many things at the same time that you have to have correct in your process and that is the thing with them, no? I told them from the beginning, we will have to shift our mindsets to a process-driven mindset here because we need to follow all these steps and capture all this data to then get these metrics, KPI, whatever you want. So we can get...

10 of these to then measure quality of higher and then get that score. That number is going to give us a measure of how good is the quality. But then quality is also getting us back.

Is the hiring manager the problem? Is the employee? Is the team? What is the team? Why people are not staying at the company? Or why they are staying and they are more like high performance compared to the other team? I think it's a matter of going back now again data is ⁓ data in the process. It's what you have to, what you need at this stage.

Maulik Sailor (36:56)
Mm-hmm.

Yeah, cool, wonderful. ⁓ Just mindful of our time. I think we had good discussions and I think this can carry on for many more minutes or many more hours ⁓ if we start unpacking all this. ⁓ But just mindful of our time. I want to ask you parting thoughts or tips ⁓ for our audience.

If our audience are looking to deploy certain kind of AI tools ⁓ to optimize our streamline their people ops, right? What are the key areas you would ask them to consider and possibly recommend any tools that you think are really good?

Leví (37:55)
So, you know, I think the first thing is to define goals. I've seen this a lot and people are just like, I want to measure quality of hire because it's what my CEO is asking. Because the CEO heard that in a dinner and then everybody wants quality of hire. But I think it's just trying to create a roadmap of all the goals that you need to achieve the most high level goal.

start working on your data mainly, that's the key thing right now, your processes because if you don't capture data the way you need, it's going to be a mess and you are going to have really skewed results. I've seen people that have like 20 days of time to fill when it's actually 120 days because they are capturing data incorrectly or the input in the system is not correct.

Maulik Sailor (38:28)
Mm.

Leví (38:56)
Once you get into those goals, I think it's just achieve at least the first three, five to then measure something that is global for them and then continue with the others. And for the systems specifically, you really need to understand what is the thing that you want to achieve with the system. If you need, for example, you are a startup from zero to 200 people or

to even more but what is your goal on just sourcing people for a specific industry? Once you have that defined then you see how okay so we have 12 different systems that can do the same but the interface is different this one is more friendly, the cost and in how much time I'm getting my arrow agno those are the things that I would recommend and just evaluate as much vendors as possible

Maulik Sailor (39:36)
you

Leví (39:55)
I think take a couple of hours on Fridays and just schedule calls to learn more, to hear more, to understand their algorithms. That is something that is really important. Some people is saying that they have AI when it's automating the steps in the process. AI is not involved or the types of AI that they have. They have generative AI, but they don't have machine learning or they have machine learning.

or what other types of AI they integrate. So it's really important to also see that and understand. That's the reason that we have all the data literacy, but AI literacy also is so important in that aspect.

Maulik Sailor (40:40)
Great, great. Thanks a lot, Now, it has been a wonderful talk with you today, right? And I think your experience really speaks for itself, right? Giving this insight for our audience. But unfortunately, I think we are coming up to our time and I think I need to wrap up our session today, right? So ⁓ thanks a lot, Levi. You know, that was some wonderful insights.

⁓ I hope the audience that is listening in ⁓ will consider the key things that you have said. Of course, we will produce the summary and also post in the video in the description section so that you can read ⁓ through it. But let me thank you for your time today and for your insights and joining us for this wonderful discussion today. Thanks a lot, Levi.

Leví (41:36)
Thank you so much and you already saw my dogs playing so yeah, they are so happy over there. Thank you so much for the time and experience. Thank you.

Maulik Sailor (41:39)
I know, I know.

Heheh, cool.

Yeah,

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