The Breakthrough Hiring Show: Recruiting and Talent Acquisition Conversations
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The Breakthrough Hiring Show: Recruiting and Talent Acquisition Conversations
EP 164: Revolutionizing Hiring Processes with Hariharan Kolam, Co-Founder and CEO at Findem
Hariharan Kolam, Co-Founder and CEO at Findem joins host James Mackey to discuss how the advancements in AI are reshaping the hiring landscape.
He shares how Findem's solutions revolutionize recruitment, moving beyond traditional resumes to create a more efficient talent-sourcing process.
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Our host James Mackey
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Hi, this is James Mackey, your host, and today we're joined by Hari Kolam, the founder and CEO of Findum. Hari, I'm glad you could join us today.
Speaker 2:Thank you. Thank you for having me, James.
Speaker 1:Yeah, I'm really excited. I've heard a lot about Findum and I'm looking forward to learning more and then going through your product and telling people about what you're doing. It's really exciting and, just to start us off today, I would just love to learn a little bit more about you. Where are you, where are you joining us from today?
Speaker 2:So I'm right in the middle of the Silicon Valley from Redwood City, California.
Speaker 1:Nice, ok, cool, cool. And you've been working on Findum for what like the better part of five, six years, is that right?
Speaker 2:Yeah, it's close to five and a half years now, right, I mean, we started the company just before the pandemic Saw through the pandemic, saw through a crazy market, saw through the market of the last couple of years. So, yeah, it's an interesting time to build a talent acquisition startup and we've learned a lot through the process.
Speaker 1:It sounds like a great time. It sounds like you probably had a lot of fun that first year Starting a company in the middle of COVID and talent acquisition. I don't envy you, but you found a way to make it work.
Speaker 2:Yeah, the company that Pandem has become possibly is representative of the times that we've endured, so it's a very fun time to actually build right in the middle of the AI wave that we are a part of.
Speaker 1:Yeah, for sure. I couldn't agree more. It's been a challenging time in business, but there's also it's been a time for an incredible opportunity on the market, particularly within recruiting technology, and I'd say, within the last 12 months, we're seeing so much happening. We're seeing a lot of companies make bet on platform plays, creating all-in-one solutions. We're also seeing other companies make bets on AI, and it's interesting to see the different use cases and how folks are incorporating AI into their products. So you know, that's all like. I'm really excited to dive into all of that with you. But yeah, I mean, before we do that, I mean I would love to just get learn a little bit more about why you started Findem. I mean, how did you tell us a little bit about your founding story?
Speaker 2:Sure. So this is my third startup in sequence and second one that I'm co-founding. One of the common themes in pretty much all the companies that I've co-founded before, including Findem, is this deep-rooted data and a deep-rooted infrastructure problem that we're trying to solve. So I consider myself a big data data warehousing distributed systems engineer by training. As an entrepreneur, I get attracted to problems where the complexity lies in solving complex data problems, so I never built anything for talent or HR before FindM. Most of my career has gone into building and solving data problems for CISO or CIO right. I mean. This is different, but the complexity of the problem lies in defining how you think about the talent acquisition challenges.
Speaker 2:One of the observations that led for me to essentially get excited about the talent acquisition problem, particularly the search problem, is the belief that there's a better way to represent a professional right. I mean, for the past century, one of the core invariants of talent workflow has been a resume. A resume is only as good as the person writing it. It's neither complete nor consistent, nor verified, and resume as a data set has been the primary source for all talent workflows. If I'm building a pipeline, I'm searching for information written down by a person. If I'm analyzing the workforce, I'm aggregating information based on a data set of a resume. The problem lies because resume is a very bleak source for capturing the impact of a professional's career Because it's a set of keywords that I can choose to write and, if I'm lucky, it possibly matches the keywords that the recruiter chooses to find me right.
Speaker 2:Many cases when we make hiring decisions, you make it based on what impact a person can create in my company, and that usually relies on the impact one has created in can create in my company, and that usually depends relies on the impact one has created in the past in their career, and those are not searchable because the people don't write things down that they don't believe is important.
Speaker 2:Our intuition when starting FindM was if I were to digitize a professional's career, I would not look at the user-dependent information. But I'm going to essentially marry their profile with the company's information, which I think is when they worked in a particular company, and sort it by time. We call it the 3D profiles, which is person, company and time. That indexes the career in a very digitized way that allows us to build automated workflows on top. It became a fascinating problem because now a four kilobytes of a text file became a 50 megabytes of enriched 3D data, which falls into the era of really what automation grounds up. So it became a problem that I think we got excited about, and we knew how to solve it.
Speaker 1:Well, yeah, I love it. I love it and just fast forwarding to today and I would love to learn a little bit more about your platform, and I would love to learn a little bit more about your platform. I am looking at your website today. Copilot for sourcing, inbound management, candidate rediscovery. What would you say is the core product offering that, as of today, your customers when they come to find them? What is the core use case that you're seeing in 2024 or 2020, as we're leading into Q1 2025?
Speaker 2:Yeah, there are two problems that we essentially solve for talent acquisition professionals. One is building a pipeline and analyzing the workforce. There's a search part and there's an inside part. I mentioned three-dimensional profile, which means the data asset that we create about a professional is very unique and big. It is automation-ready. It is automation ready. It is AI ready. Also, ai on top of a resume is incomplete because it's going to give you garbage in and garbage out by redefining the resume. The data set is big. So the solution that we offer is to leverage data and AI and you mentioned co-pilot for sourcing to build robust talent pipelines.
Speaker 2:When I say talent pipeline, a recruiter typically does three activities to build a pipeline. They hunt for new talent, which means they source, which means they go outside and find new people that they don't know. They farm existing talent, which means they go inside their network, be it their applicant tracking system or referrals within the company, or possibly people that are alumni or people that I think you've been nurturing in the CRM. I mean you go and farm them or you nurture them for tomorrow, which is build a pipeline proactively for tomorrow. All these three elements are different workflows and different tools stored in different places, right.
Speaker 2:So what we built here is to unify all of that. It's a single pane of glass that actually looks at talent across all available channels and provides a single point view for talent teams to build pipeline. And similarly, 3d Profile is built on a large-scale BI platform. It allows rich analytics and insights to further analyzing the workforce a lot more efficiently. So we go to the market with these two major offerings. One is a consolidation of all pipeline building efforts. The other is a consolidation of all data and insights effort.
Speaker 1:Okay, got it. And so for the inbound pipeline or the inbound management, so in candidate rediscovery. So essentially, you're using data to pull from the CRM the most relevant talent and surface, that, and then is that accurate.
Speaker 2:Rediscovery essentially is looking at your applicant tracking system. That and then. Is that accurate? Rediscovery essentially is looking at your applicant tracking system and your network to find candidates that are a match, that may have applied now or applied in the past. It goes and enriches the information and finds them through attributes.
Speaker 1:And then is it how, from a sourcing perspective, what does it are people doing again like they're doing the outreach through your, your product as well? So they're finding, they're doing the rediscovery process, they're finding the folks that have already applied in the past and they're doing the sourcing through Findham.
Speaker 2:They do the whole soup to nut, right From find to engage to inbound scanning, all of those to Findham. It's a centralized place to do it. Again, James, I'm not sure whether it is useful to show and tell the story, but I think if it is useful and if it's a precedent, I can actually do that as well.
Speaker 1:Yeah, for sure. I mean, if you want to do that, I think that'd be awesome.
Speaker 2:Yeah, I think I show a couple of things so that way you actually can visualize what we mean just for everybody tuning in, like in the episode link.
Speaker 1:We also we do post the. We post the video on YouTube so you can actually go on and you can watch the video to see the share screen that Hari is doing right now.
Speaker 2:Thank you, James. So here's an example of a profile on, say, I looked at your profile, right, James?
Speaker 1:Right.
Speaker 2:And on Find them, and this is it actually shows you information that you that right, james Right and on Find, I mean, this is it actually shows you information that is indexed on a search engine based on the information that you put either on your LinkedIn profiles and whatnot, right? So this is only as good as a person searching in a title or English. When you look at it from a 3D profile, you actually can look at timeline of the company, your tenure and the spectrum of where the company was, so thereby you actually get a comprehensive view of your career. Right, so you can correlate that with the financing round, so you actually know what an individual essentially has gone through. And these battle scars are extremely hard to capture in english because people don't just write. People write what they want to write and but search is based on the, the impact that you've endured, not based on the english that you wrote, right? So how do you apply? This is here's an example workflow Supposing I'm actually building a pipeline for, say, an account executive, right?
Speaker 2:So?
Speaker 2:And this is linked to my lever, ats and this is my job description, so we find the way the automation works is in a single click you can leverage the information about the job and leverage the 3D profile of the candidates and go ahead and build a search in a very automated way where it's going to transform and translate the job description into a searchable set of attributes. An attribute works in 3D and it actually goes omni-channel it's not just one channel as a search. If I were to look at it, it automatically went ahead and converted a search for me and shows me talent across my entire pipeline. I look at it. It automatically went ahead and converted a search for me and shows me talent across my entire pipeline.
Speaker 2:If I look at my inbound applicants, it goes into my ATS and finds talent that matches all of these attributes. Yeah, if you look at past applicants, it shows you here. If I'm looking at external candidates, it shows you here. So the warm, hot, warm and cold relationships are all in a single pane of glass. Search becomes an almost simplistic exercise.
Speaker 2:Okay, everyone of these candidates is essentially AI-powered, so the goal here is single pane of glass omni-channel search, attribute-based search.
Speaker 1:Got it and I'm curious too, as you're looking into 2025, where do you see your product heading, your platform heading, Particularly as we're dialing into AI use cases, are you seeing requests from customers in terms of product roadmap in one direction or another, Like how do you think about building AI into your product roadmap into 2025?
Speaker 2:Yeah, ai is a very important topic and it's also a topic where there's a lot of aspirations as well as a lot of fear, right, I mean, we hear there's obvious places where we implemented AI in 2023, messaging or possibly writing candidate notes on hiring manager notes and all those cool things which generate AI is essentially functional.
Speaker 2:We believe and this is something that I think is going to be an important pillar as we start building out the platform for 2025, is the amount of clicks that are dead right now. In terms of building the pipeline, there's a significant automatable part of the job that's going to be AI-enabled, right. So, if you think about a recruiter's lifecycle, there is an IQ part of their job which is searching, building a pipeline and generating that slate of candidates. There's an EQ part of the job which is taking the candidates and then running them through a process and then closing them right. We believe that the IQ part, predominantly, is going to be automated through AI-enabled workflows. Right. Eq part, through the right set of data set, is going to empower the recruiters to actually go ahead and have meaningful conversations with the hiring manager. For us, that's the vision on what does a talent team and a talent tech stack look like in the AI-first first era, and which parts are automatable, which parts are not, and that's a very important driving factor on how we think about a product strategy.
Speaker 1:I totally agree. It's interesting and we saw, you know, 2023, a lot of focus on, I think initially kind of co-pilot. We saw content. It's interesting the different profiles. I mean I started a product company called June and June's focus is essentially knocking out pre-screening, voice AI interviews. So essentially, like all inbound applicants, right as soon as they apply, June gives them a call and, you know, walks through what we consider the knockout questions, where you know, where recruiters are essentially in the first five to 10 minutes, they're disqualifying candidates, which takes a ton of hours and a ton of human horsepower to accomplish.
Speaker 1:And so from an automation top of funnel whether it's sort of like what FINDM is doing right, where you're doing resurfacing, sourcing, end-on-management, I mean there's just where it's like you get into these like high volume aspects that require a lot of time and headcount to do. I think that that's like really where the most we're going to see the most disruption within talent acquisition, right. I mean, because then you get like into down funnel motions. The reality is like the Canada pipelines just aren't quite as big. So it's not that it wouldn't be valuable down funnel towards, like as you're getting approaching, like final rounds and stuff like that. It's just that it's not. You know, there's there's there's a greater need at more of the the top of funnel right For a lot of the automation and AI.
Speaker 1:So I think that those are the use cases that are really going to be the most disruptive and where companies are essentially going to grow the most. Yeah, you got products like yours, you know, start products like popping up like June, like mine, and then you have an interview intelligence right. So you got like Bright Hire and Pillar companies like that, which are essentially doing the co-pilot on Zooms and from there like essentially like packaging data and then helping write custom questions and then recording everybody's answers, seeing if they actually answered everything that they should have, and then they're actually taking almost like a Gong-based approach where they're ranking hiring managers on the quality of the interviews. So I mean that's a pretty interesting use case as well.
Speaker 2:It indeed is right. I think the AI transformation across every function, like you just described, it's going to look and feel differently and I think it's going to elevate the role of what a recruiter will look like for tomorrow, right? So it's very well said.
Speaker 1:Yeah, I mean I think just like the whole, like AI kind of talent operations top of the level workflows.
Speaker 1:You know, we also just had the CEO of Sense on the show and that was a really interesting conversation where he was talking about kind of the automation layers that they're thinking through as well. And then you got even companies like I recommend also for people to check out the episode with Nikos, the CEO of Workable, and they're I mean they're basically implementing AI and automation into like every step of their applicant tracking system, which is pretty cool too. But yeah, I mean I'm curious, like, with some of this automation that you're seeing and like the AI you're incorporating, like what industries are you seeing the most demand from working within the tech sector? Or do you see a lot of demand from industries where it's like higher volume recruiting, potentially like healthcare, retail? You know manufacturing light industrial, I mean, do you see a lot of need for automation there? Or, like, where are you seeing the most demand right now?
Speaker 2:Well, I think we categorize the market into two different buckets. Right, there's a high precision searching and there's a high volume. I mean, in high precision searching, the precision around who you're looking for becomes super important to build a pipeline. Tech, of course, essentially features quite a lot there. I mean healthcare essentially is an interesting one where the other part of it, which is where workflow wins, where you're looking at automating a bunch of tasks to actually get the outcome because it is a conveyor belt approach, so different kinds of things, the precision one on executive search, folks that are doing mid-career professionals.
Speaker 2:On the tech side there's a lot of pull. I mean I think the market rebound essentially is real there, right, healthcare constructions, financial services, right, I mean it I think is slow burn, but I think it is actually the significant amount of nuances around combining either precision or a workflow element to carve out a solution. So we actually have seen in broad strokes, right, I mean we have a partnership with Paychex which I think is tackling a different kind of a company which is doing a different kind of a high volume stuff. The challenge there predominantly is going to be on the workflow side, right. So different pockets of industries and companies. We've seen different elements of the platform essentially shine a lot more.
Speaker 1:Yeah, I hear you on that. Well, yeah, it's definitely a really, really exciting time to be in AI and talent acquisition in general. Curious to get your thoughts just from like on the future of AI, like future use cases, over the next 18 months. So are there any other areas that really interest you? Maybe it's down the pipe product strategy for Findum, or maybe it's like, okay, it's not the right product build out for your team, but that you're just kind of excited to see evolve in the industry.
Speaker 2:I think the whole chatter around agent is a very important advancement. I think it's starting out in a very simplistic way, but I think it is something that I think I'm very excited to look out for, because it's an important tool that actually talks about contextualizing automation, right as a thing. Right. So in this world of identification, I mean, how does many of the tasks? Because, if you think about talent acquisition in the industry that we live in, about 80% of the tasks are fairly menial and automatable, right? So those are things that I think are not even exciting for most of the recruiters. Those are necessarily evil from a workflow perspective, right? So for me, I'm particularly excited about the next wave of action modeling that happens, which is so. You actually had a large language model, and how do you get into this mode of taking actions based on leveraging AI in a framework of an agent? It's something that I think is an exciting next big thing for 2025 in front of us.
Speaker 1:Yeah, for sure, for sure. And, by the way, I saw on the website something I know you had also mentioned, like executive search, and I was wondering is there a services element to FINDM or is it 100% product platform-based?
Speaker 2:It's 100% product platform-based. Findem is very well-suited for exec search because the nature of the search is highly precision right. For example, if I say, hey, find me the CFO in Georgia who's working in a PEVC company, saw through a company from early stage on exit and currently working in a manufacturing sector near impossible search. How do you think you could do that in a one-dimensional resume search way? Right? Finder is geared towards finding that needle in the haystack because 3D profiles capture the attributes. So we found a lot of pull from the exec search side.
Speaker 1:Okay, got it. Yeah, that makes a ton of sense. Well, what else can you tell us about Findum? I mean, I would love to learn even more about the product Future. You know. I think like maybe one direction we can go into is I was kind of interested to learn because I initially did think that more on the like when it comes to recruiting automation. I don't know why, but I assume like my mind kind of went to more like the higher volume bucket that you were discussing, right Like you know companies that are like potentially construction or health care. But when I was speaking with the CEO of Sense and they're kind of like echoing what you're saying too, you know, I think around they said about 30% of their 20 to 30% of their business is like knowledge workers, like within technology. So that's interesting. Do you also? Do you see demand in staffing and recruiting?
Speaker 2:So we don't sell into staffing and recruiting, but there's always demand, right. I mean, I think one of the workflow will win there, because I think it's purely about optimizing the time, because bottom line here in the staffing industry essentially is people time.
Speaker 1:Yeah, yeah, because I think that's another interesting point is like staffing and recruiting companies being early adopters for a lot of the more disruptive recruiting automation.
Speaker 2:Yeah 100%, because it actually ties into the bottom line quite directly Right? So it does, and I think for that reason the AI adoption in staffing and agency has been predominantly high, and I think we've seen that as well.
Speaker 1:Yeah, for sure. I mean that's for June. We're looking at where we're seeing demand for AI interviewing and we're seeing demand in the staffing and recruiting segment. I mean other SaaS companies too, but there's definitely that use case because it can impact top line bottom line, you know, services companies, thinner margins, right, and one of the challenges of scaling a staffing business is, just because you're scaling top line, revenue growth doesn't necessarily mean that you're making more money, right, because, like the overhead, it's so overhead intensive, right, so it's not so appealing, as is the margin Right.
Speaker 1:Because, like the overhead, it's so overhead intensive, right, so it's not so appealing, as is the margin, right. So, yeah, I mean, I think it's like there's. It's interesting to see, like you know, where the where the demand is kind of coming from initially and and yeah, staffing and recruiting. You know, based on a lot of the CEOs I've spoken to, they're they're saying that they're getting a fair amount of demand from staffing and recruiting and then they're also kind of considering the staffing and recruiting early adopters, sort of like ahead of the market, right.
Speaker 2:That's very true. When you think about staffing, definitely right, I mean. When you think about even bigger enterprises, right, I mean, one of the beautiful things. It's an interesting dichotomy. When you think about any recruiter and you tell them that here's the requisition, I need to go close the role, the very first thing that they would say is I need to go source right. Most of the most of them essentially believe that they need to go and build new relationship all the time, which is go and talk to customers.
Speaker 2:Candidates outside 90 of the highest for most enterprises are people that they already know right. So they either applied now or applied previously, or possibly are sitting in their crm somewhere. Or people that I think could be referred by an employee within the company, or folks that I think have nurtured in the past right. So hunting essentially is the most laborious way to essentially go and get a candidate. Imagine this that you're taking a completely cold network and then trying to convince them to take a meeting right, While completely overlooking your warm and hot leads within your network because they're not discoverable. So in reality, when you think about productivity I mean the aspect of combining in-network and out-network is an important manifestation and I think when we think through it from an option and productivity perspective, it's an important beachhead because you're spending less time doing possibly getting better yield, than doing it purely going out and coming in, which is hunting to farm, but that could be farmed in nature.
Speaker 1:Yeah, definitely. Do you have any thoughts on AI and compliance for hiring? I don't know if you've spent a lot of time just going through what, from a regulation perspective, is likely going to happen, but curious to get your take. If you know we don't have to dive into this, I'm curious. I mean, is that something you've given a lot of thought to or have any thoughts around?
Speaker 2:So AI even the New York AI bias audit that, I think, is a mandatory checkbox for every single enterprise contract that we need to deal with.
Speaker 2:Along with the InfoSec, the AI bias questionnaire is an important part of the sales cycle. So we do essentially have interesting experiences because, as we see, the industry essentially go mature I mean, the perspective on what AI can do or cannot do essentially is evolving right. So one of the conscious things that we believe in, particularly in recruiting, which I think is manifesting in a way where the laws essentially are evolving as well, is decisioning, like, who's doing the decisioning for what right? I mean, is it an assisted decisioning or is it a displacement decision? I mean, are we essentially using AI to make a decision on behalf of a recruiter or assisting the recruiter to make better decisions? Right? We firmly believe that, considering the state of affairs with AI I mean considering the stakes at hand I mean assistance is exactly the way to essentially go, which means that AI, automated decision-making, is something that I think is a big no in the design principle in FindUp. I mean it's pretty much all of our workplaces are cognizant of that.
Speaker 2:I truly believe that the compliance part of AI essentially is going to evolve to make sure that the decisioning essentially is appropriately moderated, protected and verified, and I think that is something that we're already seeing in the early tea leaves of the whole industry.
Speaker 1:So when you said on enterprise contracts, you had mentioned that something is typically baked into those. What was that again?
Speaker 2:So when you go through an enterprise contract with any mid-size to large-size enterprise, we used to essentially do InfoSec requirements as part of the contracting, because you used to go and say I need to integrate my email, I need to integrate my applicant tracking system, right. So AI as a next step is we're seeing it more often than not now in pretty much all of our engagements.
Speaker 1:Yeah, yeah, it's interesting. I mean, do you see, like, how risk adverse are you seeing a lot of? Have you? Has that been a prohibitor to move forward with some enterprise customers where they're just basically saying, hey, we want to kick it until next year when we have a better pulse on where the regulation is going to fall?
Speaker 2:I mean, so I think that people are conscious about AI decisioning. So one of the things that we've made sure with Findem as a platform is all the searching is BI first, so we don't essentially let AI make a decision there. Ai decisioning essentially is an overlay on top of the results to assist the recruiters with the right data at the right time not on matching.
Speaker 2:Matching essentially is BI first, which actually eases the nerve. I think there are a lot of anxiety around automated decision making from AI, which I think is what the regulations is also protecting, which is what the company is also concerned about.
Speaker 1:Yeah about. Yeah, that makes a lot of sense. It'll be interesting to see how that continues to evolve over the next year or two, and it's interesting to see how companies are making bets on recruiting tech companies and whether they're looking at AI assistant versus how much of the decision process are they taking over. But, ari, anyways, this has been a ton of fun. I really appreciate you coming on the show with me today sharing your expertise, telling us about Findum. I know our audience is going to learn a lot from everything that you've shared with us today. We really appreciate your support and your contribution to the show.
Speaker 2:I appreciate it and thanks for hosting me here, James. It was a pleasure talking to you as well.
Speaker 1:Yeah, absolutely, and for everybody tuning in. Thank you so much and we'll talk to you real soon. Take care.
