People may spend tremendous time and effort on their résumés, but no matter how thoroughly they attempt to present themselves, significant pieces will be missing. There’s only so much you can expect an employer to read, so you may leave out your volunteer work, you may not mention some of the projects you’ve completed on the side, or that you’ve been learning another language.
But these can be essential pieces employers need in order to make the best hiring decisions. Hariharan Kolam set out to solve this problem with his startup, Findem, a talent-search system that pieces together a fuller picture of a person’s history, enabling employees to build more diverse workplaces and find key talent that might have otherwise been missed.
More information: https://www.findem.ai/
Hari Kolam is the co-founder and CEO of Findem, where he is responsible for driving the company’s overall direction and strategic growth, as well as overseeing its day-to-day operations. He’s a serial entrepreneur and accomplished technologist, with nearly two decades of experience building companies and creating trailblazing technology solutions.show more
Backed by AI and all the world’s people data, Findem is transforming how companies make their people decisions. Its People Intelligence platform empowers HR and talent leaders with the data-driven insights they need to architect and develop a best-of-class workforce.
With Findem’s platform, companies can uncover the talent attributes that matter most to their business goals, benchmark their talent internally and externally, identify skills and diversity gaps, and fill those gaps through automatic introductions to exceptional and interested candidates—all without bias.show less
DISCLAIMER: Below is an AI generated transcript. There could be a few typos but it should be at least 90% accurate. Watch video or listen to the podcast for the full experience!
Hariharan Kolam 0:00
People thought is as much of a problem as much as it is a big problem, because it’s a big problem because you don’t want machines to extrapolate and come in and say this is good, because eventually the cost of a wrong pipeline can be wasted cycles everywhere, even enter the wrong person, you will probably hire a wrong person, you’re probably gonna have a lot of damage, right? So it can help. We wanted to extrapolate information, of course, because we wanted to essentially expand the scope of information. So thereby, you can have an educated decision if you don’t want to, we don’t want to essentially make decisions based off. I mean, that’s not how we essentially envision how we will be using people’s
Alexander Ferguson 0:41
Welcome to UpTech Report. This is our apply tech series UpTech Report is sponsored by TeraLeap. Learn how to leverage the power of video at Teraleap.io. Today, I’m joined by my guest, Hari Kolam was based in Redwood City, California. He’s the Founder and CEO of Findem. Welcome Hari, good to have you on.
Hariharan Kolam 0:58
Thank you. Thanks for having me.
Alexander Ferguson 1:00
Absolutely. Now Findem is a people intelligence platform, where you guys are How are focused on helping companies build more engaged diverse teams and close the talent gap faster. So if you’re a people leader out there building and scaling teams, this might be an intriguing platform you want to check out? Have you understand what on your website is reimagine what’s possible with people intelligence? What was the problem you saw initially and set out to solve with find them?
Hariharan Kolam 1:29
A current thought of the conversation and a good question here to break the ice here. So I think one of the like, one of the interesting thing about people search, and we do people search all the time we do people search for hiring great talent, we do people search to essentially identify customers that you want to sell into, right? You do people search to tell somebody a newsletter, eventually, it’s all about targeting somebody and eventually telling them something, you know, so people, service engineers remain unemployed since World War Two. I mean, people used to write documents of resume that actually describe what they did. And companies is to write a job description to describe what they want, right now you have a matchmaker in the middle, doing a matching of what we’ll see
Alexander Ferguson 2:12
who’s gonna who’s a good fit here.
Hariharan Kolam 2:14
Exactly. Right. So, and interestingly, that’s the only tool that made sense a century ago, probably until even recently, right? In the modern era, if you think about as images, no confined, single piece of paper or single document that describes anything and everything about an individual. Usually, people contribute and leave digital footprints all across the internet, both internally and externally, right, coders might actually have a contribution on GitHub or an export might actually have a contributing factor to it, researchers might actually file a patent or a paper, right, you have your company information, both financials essentially, in completely different data sets. Everything essentially is a resume, right? And even within the company, I mean, the contributions of an employee could essentially be part in JIRA part. And then that’s for support person, right? Pardon HR is back home, which the HR team has assembled, right? People fundamentally fragmented and distributed right? Now, in the modern era, when I think there’s so much distribution of information relying on any one single source to, to add the source of people, essentially, is inadequate, right. So the job of a talent personnel recruiter, or any of us, any person that is in the people search becomes arduous, because usually when we think about what we want, or the wish list in our ideal candidate, right, expands way, way beyond what’s present in that single document. I give an example. If I’m building a team, and if I’m essentially hiring for finance, you know, I’m so young startup, right, 35 employees. And I usually look for somebody with a prior startup experience started with a chaotic place. And you need people that that can actually measure that, right. I mean, I look for somebody that’s build enterprise SAS startup, because the enterprise apps products, because that is what I’m building, right? I’m gonna Findem. I look for somebody that can make me more diverse, because I want before lending or right for lending or look for somebody who’s had a good career trajectory, because I need 100 people here as well, right? These are my wish lists. And none of which makes the job of a matchmaker, the good are much harder, because eventually, if I’m true to my belief system, I would want all an ideal candidate. So the hunting becomes much problematic because it becomes a data centric life. So when we were thinking about the people search phase, one of again, the background of the founding team, and the initial team is pretty strong in building data related infrastructure. And we’ve done that for many years in our lives. Now, when we looked at this problem, it actually looked like a quintessential distributed data set bi problem, and it essentially fell right into our alley to actually solve it like an infrastructure problem, right? The motivation essentially was the chance to innovate, which I think is something that our technical founder like myself will always be hunting for solving the hardest problems rather the biggest impact. So this was this fell right into rally.
Alexander Ferguson 4:56
If I can just set a repeat to make sure I get this concept. You saw that the fact that as a person, who I am the experiences that I have, and all that I’ve accomplished is spread across so many different websites and experience, whether it’s on GitHub or LinkedIn, or this site or that site. And as a talent person, someone trying to find good talent, you just look at a resume or one place, it’s not enough, you have to look at all the different sources of where that person’s experience and what they can provide. And what you’re trying to do say, Well, great, let’s automate it, let’s be able to pull together all those sources of truth of who is this person? What are they capable of, and then just allow a leader who’s trying to hire someone find someone to just search almost like the Google search of people, and then it can look through all those different web sites and find out who’s the right candidate based off of all that information in one place?
Hariharan Kolam 5:49
So absolutely, I think you’ve covered it right. I think the only interesting bit here is the fundamental brokenness in the whole people’s ecosystem, is what we want, which is our intent. And what we can express, which is the third is a pretty big gap. Because in my example, what I essentially want is a lot more abstract. I want somebody in a in a third, I want somebody who can make me more diverse, I want somebody with a good career scale, right? That doesn’t exist, you have to extrapolate Is that what you do? Are you extrapolating the concept unable to? So let’s take a simple example of this search of did this person work in a startup, right? Of course, I can’t go to linear type startup, every company, the startup depends on the person actually work there, you know. So, again, the putting a list of companies that were a startup essentially is going to be inadequate, because you’re going to be tapping into only a subset of the pool. Right? Now, the intent here is to center find somebody who will work in a startup. And the final scenario, right? Now, the searchability, of finding that in a resume is gonna be pretty hard, because nobody’s gonna like to start with a keyword, right? It actually, and of course, the data exists, the data exists in a completely different place. Maybe a company or a financial history did have that right. But it is not accessible. When I’m doing a search with Findem, the ability to send a surge of attributes is the power attributes, essentially, are the elements that you seek in your ideal candidate is your wish list, each attribute might correspond to a subset of different data sets behind, some of it comes from might come from profile, some of it might not come from company, some of it might come from details, right? It’s essentially a distributed data set abstracted out in terms of attributes. So the choices that the people either make, it’s collecting data, but that makes more sense to him. So
Alexander Ferguson 7:28
the way that a talent hire or a people hear that they’re looking for someone, are they just having all these options? Or dropdowns? Or are they just like typing in what’s, what’s the interface? How does someone use it?
Hariharan Kolam 7:43
Yeah, so excellent question. So let’s find them. I think the beauty here is the selection that you’re making is very humane, it essentially is how we initially converse about skills and going, what about people you know, so I need somebody who’s been incredibly loyal in their current companion, somebody was essentially seeing possibly a stellar exit before or possibly somebody that essentially can have the right competency to do my job. And right, I mean, essentially have a view around how we communicate about the ideal fit for a role. But Findem the interfaces, the choices that they essentially make on the platform, is attributes that differently, right, they have the ability to build their own attributes, we have a library of articles that recently provide, in fact, they can refer to some of their own superstars within their company, and then say, I need a person like x ray at five years before in their career, and the platform will automatically figure out the attributes that they desire.
Alexander Ferguson 8:33
So as I say, you say, I like this person over here, can I get another one, go find me another one and Findem, your platform would just go Alright, here’s some other candidates that look like this person.
Hariharan Kolam 8:42
Exactly. And I think it’s gonna go even gonna be I could say, I need a person like this five years ago, because I need a person that doesn’t is a much younger version of this, go back in time. Big all the attributes that match the person x, five years ago, right, allows you to select and deselect what you like, and what you don’t like. And let me do five minutes.
Alexander Ferguson 9:01
Now building a platform like this, was it easy.
Hariharan Kolam 9:06
So again, it’s a very later time to talk about easiness. And because we’ve been doing this for about 15 years of our life, and we are experts on the team here in tournament co founder, our experts at building large scale distributed databases. So this is a passion project, primarily because it actually is a we have done this much time for different verticals, we rebuilt it for the line of business with integrated as a large scale and delivery platform right now is our opportunity. Right? And start an after before after leader but also large scale data warehousing solution.
Alexander Ferguson 9:38
And so you basically take that experience, what you built there, like what would you say the biggest lessons learned from your previous experiences that you’re now applying to Findem?
Hariharan Kolam 9:47
Well, I mean, the that’s a that’s an interesting question that you ask Alex because building a company essentially is, is hard work because imagine that I mean, when you are in the shoes of a founder, you Keep something probably early on, and you need to stay true to the passion for probably many years, you know, I mean, and this not every day same, which essentially means you have to be truly, you truly think you have to believe in the mission as well as have to have a passion to wake up every day to live a life that that you think you want to create, you know, I think, I mean, that’s the motivation, you know, so we’re doing it for the first time. Interestingly, the passion of unknown like, what is it is incidental in building a company essentially supersedes many things, because you’re figuring out interesting, for the first time you’re doing for a second time, it is going to be a lot more efficient, but you also have the battle scars that that that usually have accrued in the past life, right? For us, both our previous experiences of early stage startups, we have a whole lot of battle scars, and one of the important battle scars per se, essentially is, is defining the product and the problem statement very, very crisply, very, very crisp, and very, very in a pointed way. So thereby, the definition of a product market fit essentially is to evaluate that is super easy. I mean, I think and that essentially involves cutting down all the variables, including ensuring that the organization is identified, ensuring that the team that you essentially building essentially is is a minimalistic to ensure that the feedback during the early stages of building a company identity is well incorporated, you know, understand the buyer dynamics as well as the motivation in a very, very quick survey, right? So all those things that essentially are super important that when you get into a mode of scaling a business, at this point, you’re forming something and so forming it absolutely right and getting the missionary ability, right, is one of our biggest lessons learned on how do you do it in the most effective way, in the fastest possible way. So thereby, when you actually scale, you’re essentially set up and you have the right norms to actually go and put fuel into the old machine.
Alexander Ferguson 11:50
I liked the terminology say that, as you say, battle scars or whatever from the first one that allow you to have the right experience to do things much more efficiently and faster on the second startup, the the outcome of of instar going into them and Findem. You did you already have the idea for Findem when you were like, I don’t know, what was the exit and started to just Is it
Hariharan Kolam 12:15
good to be a company called Akamai?
Alexander Ferguson 12:18
Gotcha. Gotcha. So did you already have that idea? while you’re still inside? You’re like, oh, man, there’s this this need for people search?
Hariharan Kolam 12:28
Anyone who anyone who has good teams will tell you that the most important job function of any business leader, right? I mean, that is guilty. Right? And it is the highest, it’s the most difficult problem to solve as well. Right? I mean, when we were scaling our last venture, right? I mean, mean, of course painfully learned in the first time as well, right? I mean, it’s, I mean, it’s a, it’s a task to actually bring individuals from different walks of life together, right? I mean, and ensure that the gel as a company and competent in their positions, right? It is the only company actually goes and scales further, right. So the, the easiest thing to do, essentially, is articulate what you have in your head, right? The hardest thing to do, essentially, is to find a fit on what you have in your head, exactly. Sit on the sit on the seat, right? that gap, per se, essentially is littered with a whole suite of human biases, the whole suite of databases, a whole suite of data inadequacy, right? And eventually, you know, nine out of 10 times I think you end up compromising on one thing or the other family, because the the the pipeline essentially, is what essentially drives our decision making, because there is your business that you have to run and there is your idealistic aspirations you have to propel the road, right? I mean, combining both of those, essentially, is the the frustrating part when you’re building a team, because as a leader, you usually know who you want, I mean, usually have a fair bit of idea, right? So the problem of an inherent inability to translate it as it is, maybe through Google Doc, after word doc, essentially, is every people ever listen to you. And your because I think the communicating through docs, essentially is a highly lofty medium, right? Because what you essentially are thinking even in a simple job description might be interpreted in a million different ways, by the person consuming it, right? When we actually I mean, we had the idea of essentially looking at people search into two parts, right? I mean, there’s a part that machines can follow, right? I call it the IQ part of the people search, which is about identification, which is a purely a data and AI problem, right? Then your EQ part, which is about convincing somebody to essentially come and join you, right? I mean, and that, I think, is quite a human touch, right? Which is where I think you’re born. You’re exactly right. I mean, you essentially want somebody to feel good, that they’re essentially joining an opportunity in the bond the company to be represented, right, right. And you essentially want the recruiters most of the time to be talking to them to ensure that they simply do the high value thing with machines can ever do, right. So this intuition of essentially breaking it down, into, into into machine soluble things and, and, and a human only fallible thing, essentially, it was always there. And it essentially was there. Because it has happened in other parts of the ecosystem. It’s not me, right? I mean, if you look at the digital retailing, about before Google And before DoubleClick, most of the digital advertising was sold by humans I mean, example admin to listen to hire a sales rep and give them a yellow pages blog and ask them to call up oil and hire companies to sell their ad slots, right? programmatic ads disrupted that by changing the mechanism of how you express your intent through a protocol called RTP. Right real time bidding. So people have done it in different parts of the ecosystem in the talent search world, it hasn’t been done yet. Because most of the time goes into the work that machines can do most efficiently. Because of the inadequacies of data. That intuition will always the frustration came from scaling businesses. And the idea essentially came from problems that are out there ubiquitously.
Alexander Ferguson 15:39
Now, are you also helping say one is finding them? But are you helping at all with that transaction? Or is it just showing to that people leader that oh, this is a candidate now that they go ahead and reach out to them? Where does the handoff were you for the
Hariharan Kolam 15:54
output of finem, essentially, introductions to diverse qualified candidates in flesh and blood? Right, it is a, it’s an introduction and introduction, essentially, is a touch point where the candidate essentially has expressed that they’re interested in the position, I think that’s where you essentially hand off, right? How do you do that? Eventually, talent and recruiting essentially is a funnel exercise, you have your top of the funnel, your middle of the funnel, your conversion, right. And the faster you can probably at the top of the funnel, the sooner you can convert, attribute centric Search allows us to essentially populate the top of the funnel at wire speed, which is like seconds, we can actually go and express what you want without the needing to breaking them breaking them into keywords and Boolean, and in its entirety. And we have a customized, completely configurable engagement platform, which actually does multi-phase the candidate outreaches, which converts them into people who respond back converts them into a middle of the funnel, which is the interested candidate, which becomes a handoff point to a talented team to go around the process.
Alexander Ferguson 16:48
HRSA lets you make it you just made the point a few minutes ago, the fact that this has already been done in other industries for like the ad industry, let’s put out a whole ad and then it’ll come down the funnel and the people you’d have eventually talked to should be qualified leads. Now the question always is in someone’s mind, is, how do I know the people that come all the way down here? Or who I watch? Whether it’s sales? I don’t know, now and human person for hiring someone? How have you addressed that, that concern of how do I make sure that the right people are coming down that I trust this, this AI that knows what it’s doing?
Hariharan Kolam 17:20
Yeah. So I think when you think about the people search in general, right, there are two components of decision making, when you make right one is a factual, a factual thing. If I’m hiring an engineer, does the candidate person record can the person is entity. So there are verified information, the information that an entity can be triangulated does, it just doesn’t really work in a start up, right. So there is an intellect needed to essentially filter out and figure out whether the persona that we think we are targeting out of fit from a perspective of our aspiration, right, then there’s a cultural fit, which is about ensuring that the people that are interested belong in the argument that they may essentially pass the skill and the competency check, right? What final guarantee through attributes, because attributes of attributes essentially, are highly expected, highly predictable, because they actually have backing data to back them, right, because you’re looking at material, different sources, not just user generated content. So what allows you all it allows you to do essentially, is focus most of the energy on evaluating the cultural fit the competencies, nine or 10 times essentially will be, what you see is what you get, because if you are able to express the set of attributes that he was, indeed desire, we can test it out on the platform to ensure the kind of profiles that show up and then you can click on launch a campaign to see the messenger convert into real applicants. So beyond that, I think the the qualification essentially is going to be around ensuring that they are cultural fit, and they belong in the right. So the the beauty here is, you’re expressing things at a much more abstract level than essentially breaking them down, which allows you to essentially be very creative in terms of describing what you want.
Alexander Ferguson 18:54
Interesting. So spending more time if I understood correctly there on the just a cultural fit that allowing them the the the attributes and the technical side to be just handled by Okay, they know what they’re doing. They have the experience they’ve met met your criteria now are just a as a person, do they fit into your culture at your company,
Hariharan Kolam 19:14
your EQ problem? I don’t think no machine I mean, machine could go only for much too solid. I think that is exactly the person to person company to companies, perfect ones.
Alexander Ferguson 19:26
Where do you see the future of of hiring and building teams, and part of it is maybe the near future, what you see coming up on your own roadmap of kind of the next year or two, but even a little bit longer if you want to pontificate of where we’re headed?
Hariharan Kolam 19:40
Yeah. So, again, people serve differently is interesting. It’s actually how the parameters, you know, biggest, biggest disruption in the last in the last year, you know, I think we’re talking about a whole suite of things around fairness, the whole notion of essentially ensuring that we build a diversity, diversity potentially if possibly, the Top initiative for every company out there, you know, we’re talking about remote work, which I think is it’s something that’s a very reality right now, I think, imagine a year and a half ago, thinking about not working in not working without an office and inconceivable, right. So the modern era essentially has seen the biggest disruption last year, which essentially means you have accessibility of talent anywhere, right? Anywhere through data. And you have the ability to simply execute anywhere, because right now, the whole pandemic hasn’t just taught us on how to include over zoom. So this whole disruption essentially opens up several avenues around mapping talent, because right now, you can actually be absolutely creative about looking at the people with the right competencies, who essentially are well represented in Oregon, and you’re looking for pocket of places where the talent distribution essentially is more available than the other, right. And that data set per se, essentially, it’s gonna, it’s gonna essentially be even more critical as we essentially evolve. Because right now, the barrier of essentially localizing any specific talent is going to go away, it’s already going away, right? I mean, at finally 100% remote, I think we have people essentially, we do have pockets of pockets of places where we have, but we recently have gotten used to the reality that we could hire anywhere and execute. And within three weeks, we’ll be efficient, you know, and that probably is going to be true, is true with most of the orcs that we work with as well, right. So the landscape from a perspective of Findem essentially, essentially facilitate the ecosystem that essentially is going to evolve, and continues to evolve, right? Well diverse, where talent across anywhere and essentially provide the visibility to current leaders to ensure that they meet, they could make an educated decision around where to hell where not to hire by looking at and providing the landscape right in front of them in the on their fingertips by coming from a perspective of evolution of fundamental roadmap, right? So the core thesis of fundamental vision of finance, enable data driven people decisions, right. So one of the things that we are doing with the talent, and the sourcing bit that we’re talking about here is one of the very first application where we are using data, right to make educated decisions and highly efficient decisions around talent and hiring pipeline. Right. Now, if you look at the amount of people decisions that occur within an hour, we take them day in and day out, right and wait about promoting somebody or essentially creating a development plan or essentially ensuring that you are able to identify what doesn’t really make the code our training lab special within a company, you know, eventually, it’s about discovering the set of attributes that essentially solves a particular people problem, right? expanding the scope of, of decision making across the board across all people decisions, and ensuring that we have the backend data delivered as attributes to help there is going to be the way we were going to expand the mission here is to ensure that data driven decisions percolate across the whole people decision tree, right. Like, I think it’s something that is the biggest word in the industry today. Because there are specific workflows and tools that actually are geared towards solving a specific use case using the data set within them, you know, but the decision making essentially cannot be confined to what’s within it similar to talent, the scope here, so can you like find out?
Alexander Ferguson 23:15
For you For you guys? I’d love to just understand a bit more of your own experience of building teams. And that’s one thing is is the future of it, but also your own experience of the need for it. And it’s about two to three years ago or two years ago that you guys started Findem? Is that correct? Yeah. years ago. And and you said, almost now everyone’s virtual. So you’re already experiencing that See that? It works fine. How big is the team for you guys? Today?
Hariharan Kolam 23:40
It’s about 40 employees now.
Alexander Ferguson 23:42
- Gotcha. And if you had to think both from the past, you know, two years here, but also that I think was nine years and starving for lessons learned when it comes to building teams and hiring the right people. Outside the technologist. People tell them finding good culture fit. I’m curious, any advice or thoughts that come to you or building good teams,
Unknown Speaker 24:01
for one of my biggest lesson learned and building teams is waiting for the right hire essentially, is the most prudent thing that the hiring manager all people should do for or because the cost of wrong hire, or I mean, again, around currently, I don’t only mean it from a perspective of competency, but also from a perspective of cultural fit right, essentially creates more work than essentially the wait time that would essentially wait. So one of the interesting thing is entirely that doesn’t that mean, a I’ve learned personally, which I spent a lot of time right now is to articulate the need, right, in clear terms, right. And continue to challenge that because in many cases, when you’re a young company, essentially scaling up, you know, how we articulate the role of continued define on how we’re going to search right, start up the search is exactly where everything, everything is entirely kickstart including the biases including the fact that we essentially are defining it in a particular way, right? I mean, so that definition isn’t really super important, I think one of my biggest lessons learned essentially spend as much time as possible, as much reading as possible as much time as possible, right with people and stakeholders around the table to understand to ensure that that is defined, right? Because once you define it, right, I think finding becomes an exercise that essentially tools and systems and processes continually help, right.
Alexander Ferguson 25:25
If you if when you’re starting building a team, and you want to wait and make sure you find the right person, but I’m curious if you’re, if you’re a little bit newer, and you haven’t filled a new role yet, it’s a it’s a roll you’ve not filled, you’re not even familiar or sure exactly what the right person is. But you see, you see that another company, another business? Oh, that person? I need that person. Yeah. How do you actually curious this, this Findem work that way? Like, is there a way to say that person over there, I want one of those.
Hariharan Kolam 25:53
Exactly. So I think what fundamentally does it better I could attend you say I need this company, I want to go back in time, and look at when the company was say at my stage VP, right. And look at the kind of people they had, right, which essentially will tell you like a dimension, it’s like a people search engine, right. So we can go back in time, look at people that they essentially are looking at an identify on whether this entry applies to a site because eventually a discovery is in level your to your own. All these are data content, people will come people that are made, it will not make it companies that are made it learning from them as it becomes much easier with the best way to find them. Because it essentially has been sorted. You can go back in time, look at the data, look at look at the information, look at the people learn from them, clone them, you know, clone them at a particular point of time, look at more people similar to that, right, a lot of effort goes into discovery. And that’s what we have here as well, we essentially spent a lot of time discovering the real need. And the real because usually when you start a search, and you have a bias that just on one point of view, there’s just one manage contracts. And most of the time, it may be the right one to expand, but it probably is going to be useful to go and look at other vantage points and how other people have done it. And what right I mean, if they say if there’s a peer company that essentially scaled up, and they essentially had a different ratios of engine sales, competition, there may be a reason for that at least listen to open up questionnaire around things that should be asked, you know, not just that. One of the interesting things that when I’m also does is constantly benchmark your company against your peers and competitors, allows you to pick any attribute and look at what are the community care about? I mean, is it a measure around the kind of talent they have is the other kind of example data centers that the company doesn’t do hiring have a particular attribute that we don’t have? So learning on the computer, learning from the peer companies essentially gives you rich insights on how you define the role.
Alexander Ferguson 27:38
I love By the way, the the concept of a time machine that say home that company whether they did really well, who did they have back when they were starting rewind? And we’ll look at which comes to me the thought popped in my head is the data. Was it difficult to to be able to source and have access to all this data and the accuracy of the data like that? That’s a key role here to know that you have good data?
Hariharan Kolam 28:03
That’s a very good question. I like one interesting thing about how we position the company, which is a very important positioning is fundamentally broken platform, people essentially go browse resumes, people go and browse LinkedIn profile via a matching platform. So we essentially are learning attributes about people every single day, right? So think about us, like a people search engine exactly like that. We scrape anything and everything we will not limited to profile, because many times when you’re building a matching platform and building a pipeline, 95% of what you need is hinges on the background of the person, you’re not interested in one in any one person themselves, right. So we essentially have the largest library of attributes, cross different data sets, right? that are out there, right. And it’s all publicly accessible. If you do a Google search of your name, if your name is Intel, PSF and all the different three different pieces in each one of the contracts with different types of information. And the same is true within the company. within the company. Of course, we essentially are guarded by the sandbox and the DPA which is like, which is very limited, very specific to the company. But again, the idea there is similar, that when you integrate integrate with the internet data set, you get different vantage points of individuals from different datasets, which becomes part of the data lake, right. So it allows you to essentially expert attributes, which is like a lot more closer, a lot more real to real world closer to real world.
Alexander Ferguson 29:13
Now, the target market that you guys are focused on is mid market enterprise, like who’s the best fit for you?
Hariharan Kolam 29:20
So the machinery that gets into scaling right now is big market, because we essentially spend money for the mid market is any company that is less than about 1000 employees, right? The machinery that we’re building right now essentially is on enterprise, which is because the it’s mostly not a product questions, mostly a go to market question around ensuring that we essentially stack up the right processes the right security posture, right recipie to essentially ensure that the London CLI actually works, it’s a go to market optimization product. So bear market is something that we think we are scaling, the bootstrapping the enterprise market with the initial set of customers.
Alexander Ferguson 29:56
Cash they start with boots, with enterprise being able to build up and using that. But you’re going to now too for mid market that those who said 1000 sounds great.
Hariharan Kolam 30:06
Yeah, it’s killing up mid market because we didn’t really have a whole lot of customers, including certified enterprises in its infancy, where we are rolling it out
Alexander Ferguson 30:15
data kind of infancy, we’re able to grow it out. But the need for the need for finding good talent is never going to go away. And you’ve actually used the term I don’t know if on purpose or not several times up, well, you just got to find them. And I can appreciate that the the naming choice for you guys. For you. It’s just I’m curious anything you can share of the just the final thoughts of division, where you see find him in 510 years from now.
Hariharan Kolam 30:41
So, again, to the to that rubber definitely is a general verbal people search missionary. Right. I mean, the like, be attentively, difficult previously, I think eventually, the the collection, the collection of data set, essentially, is enabling solving people’s decision problem at various levels, I mean, right now, essentially tackling the talent problem, which is about solving. How does he build the right pipeline? How to build the most right and diverse pipeline, you know, as you think of all the company, solving other aspects of HR problem, including developing talent between two job roles? What does it mean? What are the set of attributes that that an employee needs to accrue quickly go to the next level, what strategies are needed to essentially accrue those attributes, right? I mean, gets into the time element in making that objective by defining productivity in a very objective V, which is about talking about how to integrate productivity into a number, ensuring that the data set directly contribute, for example, engineers contribute and hang out at different data sets and support personal data to support the sentencia salesperson. All right, I mean, defining productivity of the numbers intelligently, super useful, because many of the decisions are on promotion right now is, it’s very subjective, right? I mean, it’s prone to very bad having that having the backend data set will yield and make that from super efficient. But the idea with finem elegantly evolved is to not just expand that public library, but essentially expand the workflows and use and use cases around the problems that we can easily solve with current currently are not data centric, which is mostly open infantry.
Alexander Ferguson 32:11
So that’s a fascinating concept of attaching a number to productivity, which I see the value in wanting to either hire or promote or say, okay, who’s the most productive in this particular field that could add value, though? I also see that the duality of some people like Well, I don’t agree with that number. And that, but but it’s it’s an interesting challenge to try to tackle. And that’s kind of part of the vision, you see, it’s not just help find the people based off of they have this, but it’s expanding the attribute set if I understood correctly.
Hariharan Kolam 32:43
Correct. Yeah. Again, so any, whenever you’re going to present any data out there, it doesn’t even open up for debate, and the debate is a good one, because usually, things come out of it. Without that there’s no debate. Yeah, two opinions.
Alexander Ferguson 32:54
So I’m curious on that fact of because we’re we’re getting into an era of of people are questioning the validity of answers that, you know, ai produces this or is AI? Well, some of this AI is going to take my job, but that’s not in this case. Here. It’s more of it’s producing answers. And are we just letting an AI make decisions? Who’s really in control? And how do you address those concerns?
Hariharan Kolam 33:20
Very, very good question selection. That’s very, very astutely put, you know, I think, interestingly, when you think about AI, the tool that I think we essentially believe is, is a very important tool to essentially get factual answers. Because eventually, the reverse engineering, remove the batteries into the machine and simply go and give you a fact by essentially getting the right data set into getting the right data set, and verifying it in the right way. Right. Interestingly, I mentioned this in the beginning of a conversation that it all starts out with the start of the search, it’s also about what you actually want, I mean, what do you actually want to do is usually going to be driven by what a person essentially wants, in the person that definitely hiring right doesn’t need, right? using the tool to essentially go and get you what you want, is exactly the function of automation, right? So that could the convention, a coding challenge, the current directory, the building machinery, of potentially having the need driven through a job description, which I think was always the case, right, having the inventory described to me, right. And a matching essentially, is being done through a process right? still face, right. I mean, nothing changes. What changes, however, essentially, is an ability to essentially automate and use 32. What do you essentially want in different aspects of, you know, I think the decision making eventually is going to be human to human if it doesn’t, doesn’t ever change, right? I mean, the automation engine will only Eve and make the What do you think you need a lot more accessible? So for us, I think people thought is as much of a problem as much as it is a big problem, because it’s a big problem because you don’t want machines to extrapolate information and say this is good because eventually the cost of a wrong pipeline I think we wasted cycles, every If you’re gonna interview wrong person, you will probably hire a wrong person, you’re probably gonna have a cause a lot of damage, right? So it can help. We wanted to extrapolate information, of course, because we wanted to essentially expand the scope of information. So thereby you can have an educated decision machine. You don’t want to expand, we don’t want to essentially
Alexander Ferguson 35:15
make decisions based off. I mean, that’s not how we essentially envision how we will be using people search. Yeah, it’s the idea that the quantity of data is only increasing about every factor about everything that’s everywhere. So it’s really curation. It’s saying, Here is all the options that we found. But showing the visibility and how it found those and why it’s selected than a human can make that decision saying, Okay, I see all the facts. This is the right choice to make.
Hariharan Kolam 35:43
enrichment with extrapolation. That’s where we categorize it. Usually, enrichment is exactly what the machines doing extrapolation, it’s what something I think you should conditioning the machine to do. But eventually, you’re going to stay true to what you want, you’re gonna stay true to who you want. I mean, I think we make the whole thing in between super efficient.
Alexander Ferguson 36:00
I love it. Well, thank you so much for sharing your insights. And what you’re accomplishing at finding for those that want to learn more, you can go over to Findem. That’s FINDEM.AI. And you can request a demo and be able to explore it yourself. Thank you so much, Hari, it’s great to have you on.
Hariharan Kolam 36:18
Thank you. Thanks for having me. Thanks. And thanks for letting me edit the final story.
Alexander Ferguson 36:22
Absolutely. And we’ll see you all on the next episode of UpTech Report. Have you seen a company using AI machine learning or other technology to transform the way we live, work and do business? Go to UpTech report.com. And let us know