It's not often we get a Y Combinator company to pitch on Firing Squad, so let's start with 'you're welcome, listeners.' So, who's up? CEO Prem Kumar brings his A Game and Combinator credentials to the Firing Squad with Humanly. The company promises candidate screening, scheduling, and engagement at scale. Is this a broken promise?
Gotta listen to this Chad & Cheese gem, sponsored by PandoLogic, to find out.
PODCAST TRANSCRIPTION sponsored by:
Damn programmatic is hot. Yeah, it is hot dude. Pass me a cold PBR. Would ya? Okay. Number one, I wasn't talking about the temperature and number two PBR is a shitty beer time to upgrade to an IPA. Okay. My bad guessing you were talking about programmatic job advertising being hot. Yeah. That shit is everywhere and all the kids are doing it. No man. But there's only one company that's been doing it since 2007. Damn 2007. Hey man, what wife were you on? In 2007? I was on number one. Talk about her focus, dude. I'm talking about panto IQ from our friends at Panda logic, panto IQs, programmatic recruitment advertising platform helps employers source talent faster and more efficiently than ever.
Thanks to predictive algorithms, machine learning and AI Buzzword, overdose alert. Yeah. Panda was on the cutting edge or programmatic while being deeply rooted in the recruitment industry. And do IQ provides an end to end programmatic job advertising platform that delivers a significant increase in job ad performance without any way, social spending to maximize the ROI on your recruitment spend Damn programmatic is hot! Yeah, it is hot Dude, pass me a cold PBR. Would ya? Okay. Number one, I wasn't talking about the temperature and number two PBR is a shitty beer time to upgrade to an IPA. Okay. My bad. Guessing you were talking about Programmatic Job advertising being hot. Yeah. That shit is everywhere and all the kids are doing. I know man, but there's only one company that's been doing it since 2007. Damn 2007. Hey man, what wife were you on? In 2007? I was on number one. We don't talk about her. Focus, dude. I'm talking about pandoIQ from our friends at Panda Logic. pandoIQs, Programmatic recruitment advertising platform helps employers source talent faster and more efficiently than ever thanks to predictive algorithms, machine learning and AI. Buzzword, overdose alert. Yeah. Panda was on the cutting edge of Programmatic, while being deeply rooted in the recruitment industry. pandoIQ provides an end to end Programmatic job advertising platform that delivers a significant increase in job ad performance without any waste spending to maximize the ROI on your recruitment spend.
Joel (1m 18s):
And their AI enabled algorithms use over 48 job attributes and more than 200 billion historical job performance data points to predict the optimal job advertising campaign. The machine does all that shit. That shit sounds expensive! Think again. Cheesman pandoIQ provides an end to end job advertising solution that delivers a significant increase in job ad performance without any wasteful spending. Sold! How do I get started? Go to Pandalogic.com to request a demo and tell him Chad and Cheese sent you. Ooh. They have a chat bot too, that we can talk to.
Joel (2m 0s):
Oh, kill me now.
FIRING SQUAD INTRO (2m 2s):
Like Shark Tank? Then you'll love Firing Squad! CHAD SOWASH & JOEL CHEESEMAN are here to put the recruiting industry's bravest, ballsiest, baddest startups through the gauntlet to see if they got what it takes to make it out alive? Dig a fox hole and duck for cover kids the Chad and Cheese Podcast is taking it to a whole other level.
Joel (2m 24s):
Hell yeah. My trigger finger is little bit nervous today. What's up everybody? You are listening to the Chad and Cheese podcast. This is our Firing Squad episode. I'm your cohost Joel Cheesman as always joined by my trusted cohost Chad Sowash.
Chad (2m 42s):
Joel (2m 43s):
And today we are honored to welcome!
Chad (2m 46s):
Joel (2m 47s):
Prem Kumar don't call it the White Castle movie guy, a CEO and cofounder of Humanly. Prem welcome to the show, man.
Prem Kumar (2m 58s):
Thanks for having me. Thanks to White Castle you pronounced my name, right? So I don't mind that.
Joel (3m 4s):
I'm sure the White Castle jokes are a little old so I at least had to say your name correctly in order to, to make it so a welcome to the show for those who don't know, you let's get into a little bit of your personal background, like in a tweets bit of a bit of length. And then Chad will go into what you've won. So tell us about you Prem.
Prem Kumar (3m 23s):
Awesome. Thank you both for having me. My background is in B2B, SAS, HR technology, people data. I spent about 10 years at Microsoft. I finally escaped and then spent two years at an employee engagement startup, Tiny Pulse here in Seattle before starting Humanly.
Joel (3m 40s):
Chad (3m 40s):
Joel (3m 41s):
Good. That's good. You have practice. Chad tell him what he's won.
Chad (3m 44s):
Well Prem you, my friend, we'll have two minutes to pitch Humanly. At the end of those two minutes, you will hear the bell.
Bell (3m 53s):
Ding, ding, ding.
Chad (3m 54s):
Then Joel and I will hit you with rapid fire Q and A. If your answers start rambling or you get boring Joel's going to hit you with the crickets.
Crickets (4m 3s):
chirp, chirp, chirp
Chad (4m 3s):
That's your signal to tighten your shit up at the end of Q and A, you will receive one of three grades. Number one, being big applause.
Applause (4m 12s):
Chad (4m 13s):
That means you'll be snapping necks and cashing checks, my friend.
Joel (4m 16s):
Back up the Brinks!
Golf Clap (4m 18s):
Chad (4m 21s):
Yeah, we kind of dig what you're doing, but you're whooshu was weak.
Firing Squad (4m 26s):
bang, bang, bang,
Chad (4m 31s):
That's the firing squad! You've been knocked out and carried off the canvas and this probably isn't the game for you so get the hell out. So that's firing squad. Any questions before we go?
Prem Kumar (4m 43s):
No. Ready to go. Thank you.
Joel (4m 45s):
In three, two, ....
Bell (4m 47s):
ding, ding, ding
Prem Kumar (4m 49s):
Our goal at Humanly has helped mid-sized organizations bring efficiency and equity to their hiring processes. We do this by automating job candidates, screening, scheduling, and engagement at scale. For our next act we'll actually be moving down the recruiting value chain into the black box of that interview itself more to come on that. Our sweet spot is organizations with 500 employees or so plus or minus and roles that have high applicant volume, think tons of resumes, high turnover, and repetitive screening processes. So these are entry to mid-level sales, operations, support. For these roles, hiring teams are currently spending about 64 hours screening resumes going back and forth and scheduling parole.
Prem Kumar (5m 32s):
And for the first time ever over these last many years, 72% of candidates who have had negative experiences are now sharing that online so hiring teams are swamped, the result is time wasted, bad candidate experiences bias as well as employee brand hits. You know, there's many tools that have emerged for large enterprises to solve some of these issues. Many of those companies have been on the show. We found that midsize companies just don't have the tools to engage candidates at scale and are being left behind, along with their candidates. If these companies have the tools to engage with their candidate pipeline, the same way marketers and sales professionals can with their leads and prospects, we'd be in a different spot.
Prem Kumar (6m 13s):
We built a tool that will engage with candidates wherever they are spring them and schedule them better yet. You never have to sign into Humanly other than setting it up. We feel any tech company can build a bot, we do have one of those, but just like anyone who can talk can technically ask screening questions. Our focus is more so on what questions are being asked when through what channel getting the right candidates to the right recruiter faster. And as we bridge the gap between people in tech or humans and tech, our next step is launching interview analytics tools to help people become better at interviewing. So moving from addressing the black hole and screening to the black box and the interview.
Bell (6m 48s):
ding, ding, ding.
Chad (6m 50s):
Ooh man, that was right on the button. So quick question Joel loves labels, so we want to be able to help him out here. Is this a chat bot or is this conversational AI?
Prem Kumar (7m 1s):
We consider a conversational AI with chat bots being one kind of delivery mechanism or one channel to deliver it, the conversations through.
Chad (7m 8s):
Okay. So on your website you have kind of a choose your own adventure type of chat bot. Meaning obviously you don't have to, you don't type anything in, you just choose options and it's not conversational at all. Is that a good demonstration of your product?
Prem Kumar (7m 26s):
I think generally the chat bot delivery mechanism is not, not how we get to people. So I think, you know, the SMS might be a better, better demo, but to your point, one big problem in screening is inconsistency. And when you go too deep into natural language processing or open-ended conversations, it might feel better in some degrees, but you're also not having a consistent strain happening with your candidates, which leads to <inaudible>.
Chad (7m 52s):
Gotcha. So what you're talking about is variable data versus non-variable right. You have structured data and then you have all these variable, all this variable data in fields, which you have to crunch. So from your standpoint, it was much easier to get to where you are today by fielding the data in having a choose your own adventure type of scenario versus going really deep and trying to do NLP and all that other happy stuff. Is that, is that what you're trying to do?
Prem Kumar (8m 22s):
So, I think it's not necessarily easier. It's where we chose to invest our time and money. And a lot of that was based on interviews. We did. So while we folk, while we, the bot you see on our website itself yeah. The time to market with something like this as super short, but we've gone really deep on things like integrations, things like what questions are actually being asked in screening processes, how we're using our data. So I think we've made some trade offs, but yeah, definitely I think anyone building a chat bot can get to market very quickly and even the NLP and machine learning algorithms are being commoditized in some cases. So I think it's the data at the heart of it.
Joel (9m 1s):
Interesting. So I've always got asked this Humanly.io was human dot LY taken, does anyone own humanly.com? It's a competitor, how'd you come up with that?
Prem Kumar (9m 12s):
So I keep checking every day and companies like go daddy or are looking at it and it's too expensive for us right now. We're a seed stage company, but yes, yes. humanly.com is taken. I don't think it's being used a whole lot. And then actually they just want something and then a human dot LY. So we went with what we had.
Joel (9m 31s):
Gotcha. Your Tiny Pulse experience. We usually love folks that have some prior knowledge of this space. Talk about your experience at Tiny Pulse and maybe how that helped formulate the current company.
Prem Kumar (9m 44s):
Yeah, absolutely. And you know, I think one of the things I learned at Tiny Pulse, so , we were again addressing this mid market space and I met my co founder, Humanly Emmett, my accumulate co-founder Tiny Pulse. He was in sales. I was in product and we really saw, you know, this mid market space has huge pain. It didn't have the tools they needed to, to screen and schedule at scale. How it informed Humanly, every most, every other hiring tool I've seen there, that's gotten to scale in the mid market as well as staffing agency success is that our role was filled that someone was hired. To us It's not just that that happened, but that you hire the person with the highest impact, the highest employee lifetime value to do that. You need post hire data from tools like Tiny Pulse from performance management tools.
Prem Kumar (10m 27s):
So I think hiring should be looked at more holistically. So having that experience in employee engagement helped me a lot.
Joel (10m 32s):
Gotcha. Now it looks like you've raised $950,000, is that correct?
Prem Kumar (10m 37s):
Yeah. So we're, we're actually in the midst, in process. So I can't go into exact numbers, but yeah, it's a little bit North of that.
Joel (10m 45s):
So, so there is a series A coming, I assume you're raising more money?
Prem Kumar (10m 49s):
Yes, our plan is to go big next year. And right now we're raising to position ourselves. We're seeing, you know, some folks that are traditional players, limping a little bit. So we feel we have an opportunity right now between now and next year.
Joel (11m 4s):
Got you. So what are you, what are you using the current $950 for? I saw that you have a partnership with the football team, the San Francisco 40 Niners, which I'm sure as a Seattleite doesn't play real well with the locals, but what's that money going toward these expensive NFL partnerships?
Prem Kumar (11m 22s):
Okay, good question. So one of the, one of the biggest things we're doing, so product is, is a big area. We're taking, you know, some out of playbooks, like, like Zuora around how they scale outbound sales team so sales and other is another area. From a partnership standpoint. One of the biggest things we'll be doing from a product standpoint, from a marketing standpoint, as well as just from doing the right thing standpoint is investing in making sure our tool is helping people that from underserved groups, find work. So, yeah, we're partnering with Eric Armstead and the 40 Niners who runs a nonprofit focused on that same initiative. We're partnering with Inroads, which is the biggest nonprofit in the country focused on helping underrepresented groups find work.
Prem Kumar (12m 4s):
And there's a lot of things we're doing in our product to eliminate bias in screening, as well as in the interview.
Chad (12m 9s):
Well, that being said, screening engagement and reference checks, that seems to be your lane. Are you doing anything with regard to accepting information for job applications? So being able to do that from a conversational standpoint, as opposed to the attach your resume bullshit that we've been doing for years.
Prem Kumar (12m 30s):
Yeah. Great question. So I think the common theme in those things reference check might kind of seem like an outlier. The common theme in that is we want to define all of those repetitive conversations that are currently happening over the phone, or even in SMS, of course, or emails that recruiters are having. And we wanted to automate those. And then those higher leverage conversations like an actual interview, we wanted to help people be better at those. So automating what we can and helping people be better at the rest. From an application standpoint, the good news is a lot of the tech to like parse resumes and things like that is being somewhat of a commodity. So our, our goal is not to build commoditized solutions and utility series, but to use that data, to ask better questions, less bias questions, and, you know, manage that whole application.
Chad (13m 19s):
So from a, I guess, say from a competitive standpoint, many of the competitors are doing that because they want to gather and field the data themselves. And then it provides that full kind of like full life cycle of engagement. But you're not doing that. Do you see that as a perspective con as you go into sell some of these organizations, because you don't have that full life cycle and you're not engaging them right out of the gate.
Prem Kumar (13m 47s):
So, so it's actually actually, no, and I might have not explained it well, but absolutely I'm at, we're a data company. We're a data company that uses data to solve utility problems in recruiting. So we absolutely are, are collecting that data when I refer to things like resume parsing, being commoditize. It, what I mean by that, it's easy, easy for us to build that now, but really the value we have is not providing that utility in application, but it's in how powerful the questions are based on the data we have. We're not going down the path of as you mentioned, some competitors that are going full cycle post hire in terms of building chats that will help you onboard or do benefits QA. But we have the data because we plug into those post hire systems.
Prem Kumar (14m 30s):
So performance management systems, we don't want to give you a new thing you have to enter data into, but we certainly have, I would say more, more of the data because we're plugging into where people are already putting it. The biggest problem in data, when I was at Microsoft and we acquired LinkedIn, one of my jobs was figuring out how we take this mammoth of data and bring it into Microsoft Solutions. And you don't want to give people my opinion, new ways, new things they have to do to give you their data, you want to grab it from where it is and provide a better selection.
Joel (14m 58s):
Hey, if you're going to name drop Microsoft, you might as well throw LinkedIn there as well. Good on you. Curious, where, where do you guys fit in this, in this ecosystem? Because you know, you have competitors that have raised tens of millions of dollars. Are you sort of the solution for the little guy? Where do you, where are you looking to position yourself or are you looking to raise the kind of money that they have to compete with them? Where do you fit into this ecosystem?
Prem Kumar (15m 25s):
Yeah, that's a really good question. And, you know, I think there's room for a couple of large players to come out at the end, but like you said, absolutely, a lot of them, a lot of them have been on your show. So I kind of answered that from a market standpoint, a business standpoint, and then a tech standpoint. So from the market standpoint, yes, we're aggressively attacking, you know, midsize as well as, as well as SMB. So you know that to us, that isn't just a different set of marketing tooling or a different sales motion, but that's a difference in how we build our products. So who we integrate with, you know, what features we have, you can get set up with us in about a day. It's a very quick cycle. So a lot of those cater to a smaller businesses or mid sized businesses.
Prem Kumar (16m 6s):
So that's kind of one part of the landscape, you know, I think from a technology standpoint, we're very much focused on automating those repetitive conversations. So we're not going to go down the route of turning into an applicant tracking system. We would rather partner with ATS.
Joel (16m 24s):
Yeah. Well, you said the magic word integrations. So talk about where, where you're currently integrated. What's on the roadmap for the future. Who are you going to say to hell with them? What's going on with the integration strategy?
Prem Kumar (16m 38s):
You got him apologize first. Cause I will have to name drop one more time, just cause it's very relevant to this, but what am I, what am I move roles at microsoft was working with startups in helping them build integrations into our ecosystem. And it's been a big, big part of what I did there as well as at Tiny Pulse. I, we have data suggesting that, you know, it's one thing to acquire a customer, but to keep them, you have to be deeply embedded into their existing ecosystem. So from an integration standpoint, you know, it's not just putting ATS logos on our website, but it's, what does integration look like? Is it, you know, is it sinking data by directionally, is it just getting the candidate in there, or are you pulling data out of the applicant tracking system so we're able to ask questions in a better way?
Prem Kumar (17m 23s):
So, so it is a big part of what we're doing. If you have an ATS, you don't have to even sign into Humanly other than setting it up originally or initially. So we act as that recruiting coordinator, interacting with the world where the ATS is more tracking versus interacting.
Chad (17m 39s):
Well, at that point, if you're integrating into applicant tracking systems, I'm trying to understand exactly when, when a candidate is going to be engaged by Humanly. I go in, I click apply. Does it start there? Or does it start after I put my data into this chunky ass applicant tracking system that is really horrible to use on mobile?
Prem Kumar (18m 1s):
Yeah, absolutely, so it's more the first thing. So it's instead of a recruiting coordinator, kind of doing that phone screen and then manually adding the data we do, we engage with you at the point of application. So we're the kind of the most for most of our customers or kind of that first step in between application and your first interview. We take you through a set of questions based on role fit culture, add impact. We add you into the ATS and not just put your record there, but fill out the fields, put in the notes. And the second piece is I think, you know, filling one role is one thing, but with these high volume roles, you're going to have another hire, you know, in a couple of weeks.
Prem Kumar (18m 42s):
So we'll also look at applicant tracking system data to keep in touch with your silver medalist candidates saying, Hey, Chad, are you still in Ohio? Or did you finish this degree? We have another role. So I think there's the ongoing maintenance that's right now happening with people and mid market companies can't afford a lot of people or heavy tech.
Chad (19m 3s):
So you're in a very competitive space. There are names. I mean, we could probably fill the whole 30 minutes with, with names for goodness sakes. Yup. Wow. How, how are you going to differentiate yourself? Not just sector, but how are you going to differentiate yourself from many of these big names who, some of them just got tens of millions of dollars? How do you do that? I mean, it is, it's, it's a very crowded, crowded space. How do you do that from a product standpoint and then from a marketing standpoint?
Prem Kumar (19m 37s):
Yeah. And also go into just also from just a, in addition to marketing, kind of the business side. So on the product side, and I think one advantage we have is, you know, you've had people raise, you know, tens of millions, 30 million, 40 million in many cases. And it's kind of like if I were to take a hundred recruiters and then people train them up to be the best at recruiting for these markets, these demographics, it professionals in Seattle that speaks Spanish for a year. I train them up. So they're the best of the best. And then I get a hundred new people that are joined. They're not going to be as good as the one I trade for a year. I right now that money, that $30 million, that $40 million, that $10 million is, there's an opportunity we have here, or that's not going to this set of roles in these markets for this particular demographic.
Prem Kumar (20m 28s):
So I think we have the opportunity to build the world's largest library of these kind of virtual screening conversations for this market, technologically, with our data and that, you know, we have more data over time. And we also, I find it very difficult. I've seen folks kind of move, try to move down market a little bit. It's pretty difficult to it takes a whole different product. I feel a whole different marketing and sales strategy to get into that segment. So I think we have a little cover. The other piece is, one thing is really important to us and we spend a lot of time on is around how we make these screening conversations, less biased, and that, you know, there are companies like Textio, that'll do that in job descriptions, but I do not see our primary competitors, like the first five names that pop into play, doing a strong, strong enough job there.
Prem Kumar (21m 13s):
And to me, that's a defensible piece of tech that we're building on the business side. Yes. Business marketing on the business side, you know, there's a lot we've learned we've done this before. So I think, you know, like Zuora had a great business differentiator around, you know, having, you know, how have they had their European strategy and had, you know, low cost, outbound SDR models. We want to do the same with our, we consider our CS team a huge differentiator. We're the only company in our space that has CS people with at least five years of experience and I think being prescriptive with mid market five years of recruiting experience. So having a vertical ICS team that is prescriptive with our customers is really important in the mid market, I feel like too.
Joel (21m 54s):
Your website says, and I quote all candidates, have a great experience. How in the hell can you make such a statement?
Chad (22m 3s):
Prem Kumar (22m 4s):
Yeah. So, so, so there's a lot of different ways I can go with that answer. So, I'll give you, my vanilla answer, and then you can push me if you want. But, so yeah, it is our mission to ensure that candidates have a great experience. We publish every single .... Every single candidate goes through our experience can rate our bot and we publish every single one on our website with permission and we will redact personal data. And so we've resulted in an experience score of about around 4.8, 4.9 out of five. I think this way we started as I didn't, we didn't start building this company by thinking, how can we build a creative NLP machine learning bot that can talk to anyone, we started by doing a lot of interviews.
Prem Kumar (22m 55s):
So I interviewed people that had autism. I interviewed people that were blind, that were different races, that were had military backgrounds. And what we found is a lot of these tools are not bringing them along. So what the blind guy interviewed cannot. Yeah, he's awesome. And he cannot interview at companies that are using certain AI video screening tools because they bias against his facial gestures. Folks with autism have more challenges with certain types of interview sites. So I think creating a good candidate experience starts by making it accessible to all. And then there's a lot we do, which I'm happy to tell, I'll save that for later but around the questions we asked specifically to, to reduce bias as well.
Joel (23m 35s):
All right, I'll be nicer in my followup. You're, I think that your reference check technology is pretty unique. Am I right about that? And kind of walk through that, how that works.
Prem Kumar (23m 47s):
Yeah. So, so yeah. Yeah. We're, we're reference checks as one example of a very repetitive process and conversation. There's also compliance issues there. So with, EOC compliance, a lot of times, if people are calling a reference and asking something, they shouldn't there's liability. So we can guarantee that. And this goes back to the consistency thing that was asked at the beginning, because our conversations are structured we can also guarantee compliance to a degree that can't, we can't in open-ended and open-ended scenario so our customers love that. From a reference check standpoint, when a candidate gets to a certain field or certain states or in your ATS, so you've moved them from screens to reference check, we will automate out an email or texts to them saying, Hey, can you give us a set of references?
Prem Kumar (24m 35s):
And we will then contact the references, VR chat in an automated fashion, get asked some questions and sync it back. I think the reason, and I'll make this brief, for the reason why people have really struggled with reference checks is people just pick their friends or people they know. And they'll always say nice things, I think because you can do it at scale now where we actually can let you do reference checks, not just on those five finalists, but on a hundred people at once and ask a set of questions that don't really have a right or wrong answer, but will give you a more holistic view about a person. So then when you go into the final interview, you have the you're equipped with the right questions to ask.
Chad (25m 13s):
So back to the website, Humanly uses predictive learning to measure the potential lifetime value of every candidate, finding employees that will impact your organizations, et cetera, et cetera. So candidates could be coming from a very shitty position with a shitty job, biased job, could be females who were, were passed over that never got a chance to spread their wings into position, bigger positions at their old companies. How can you predict anything around potential, with so many fucking variables?
Prem Kumar (25m 50s):
Yeah and that's almost almost answering the question in the sense that our goal is not to give you those silver bullets, but to eliminate as many of the variables as we can. So, one thing I can tell you definitively, like in our data, for example, we find that one of our customers is hiring for an account manager in Seattle. And, you know, as we ran through their data, they were asking a lot of questions around the difference between four years of experience or five years of experience, or looking at a particular university with a higher degree of, you know, when they were manually doing it, thinking more highly of the candidate, what we were able to show is the most important factor for them in account managers in their market at their company was actually coachability.
Prem Kumar (26m 35s):
As we asked coachability questions, we then saw in their performance data and in their employee engagement data, how these cohorts of candidates were panning out. How long they were staying, where they, you know, engaged? we were able to see that, you know, the ones that have higher employee lifetime value were the ones that was more related to coachability than things that are biased, like maybe years of experience. The difference between zero years of experience and three, obviously is a big difference, but four to five wasn't one. And, you know, we're seeing the same thing with, you know, on a gender scale. So with our products we're beta-ing right now that will listen in two, two interviews.
Prem Kumar (27m 18s):
The next step in the process, we're finding that, you know, female candidates for engineering roles, with one of our customers, which I won't name are actually getting six less minutes, every phone interview than their male counterparts. And that's because the managers are more likely to interrupt, which we can tell by the zoom call transcripts, they're more likely to show up late. So there's I think a lot of things we can uncover without having to find a silver bullet, we're just getting rid of all the other bullets that aren't going to make a difference.
Joel (27m 48s):
Alright, Prem, this thing must cost an arm and a leg, right? talk me through the pricing.
Prem Kumar (27m 54s):
You guys are always getting good at the pricing question. So, so yeah, so yeah,
Joel (28m 1s):
It's an easy one.
Prem Kumar (28m 2s):
I, so I said generally how we don't price as we are not charging based on success. We are not charging based on, you know, amount of candidate volume to address earlier question, our goal is to make, to get as much data as possible and we do that by giving it's charging a flat rate each month. And so we'll sit down and find out, you know, are you to have 50 year olds this year? Do you have between 50 and 200, or do you have 200 or more? And based on those buckets, those aren't the exact numbers. But based on those buckets, we will give you a flat monthly cost. We want you to use us for all your roles so we can collect more data.
Joel (28m 38s):
Were there any numbers in that actual answer?
Buzzer. (28m 41s):
Prem Kumar (28m 41s):
No. Other than the amount of employees.
Bell (28m 43s):
Ding, ding, ding.
Joel (28m 44s):
All right. All right. Prem time is up here. Are you ready to face the Firing Squad?
Prem Kumar (28m 50s):
Joel (28m 51s):
Chad, get him.
Chad (28m 53s):
Prem, Amazon tried to do the whole candidate scoring and Fitz screening and they scrapped the whole project because it was inaccurate and buyers. So I got to say, background, your background obviously allows you to understand a lot of this and obviously very eloquently articulate exactly what's going on, which Joel and I could never fucking do. But this is hard. This is a lot of what you're talking about. You know, you make sound simple, but it's not easy if it was everybody would be doing this, this unbiased tech, right? There's crazy competition out there. We know that you guys are still in seed round.
Chad (29m 33s):