SignalLabs + BrassRing = Signals of Attention

On this installment of "HR's Most Dangerous Podcast," Chad Sowash and Joel Cheeseman grill Raj Ronanki, CEO of Signal Labs, on one of the weirdest M&A moves the HR tech space has seen in years. Guided by a childhood obsession with Star Trek, Raj has raised millions to build "systems of attention"—and then immediately used it to buy Brass Ring, an applicant tracking system older than half the engineers currently building LLMs. In a chaotic blend of 1980s comedy references, sci-fi philosophy, and corporate jargon, the boys attempt to figure out if Raj is an AI visionary or if he just bought a ghost ship because 65% of the Fortune 500 forgot to cancel their subscription. Key Highlights & Hot Takes: Spock, Utopia, and Green Aliens The "Nunya Business" Acquisition HR's Forgotten Invoices Interviewing Bots for Jobs Making Compliance Cool Again Tune in for the full episode as Chad and Joel try to decipher whether Signal Labs is building the future of work or just running a very expensive 1999 nostalgia trip!

• 0:00Speaker 00

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• 1:29Speaker 03

  Hi, sir kids. Lock the doors. You're listening to HR's most dangerous podcast. Chad Sowash and Joel Cheeseman are here to punch the recruiting industry right where it hurts. Complete with breaking news, brash opinion, and loads of snark. Buckle up, boys and girls. It's time for the Chad and Cheese Podcast.

• 1:51Speaker 00

  Ah, yeah. It's the podcast that New Wave Chick in social studies class warned you about. It's the Chad and Cheese Podcast, everybody. I'm your co-host, Joel Cheeseman. Joined as always, Chad Sowash, who's riding shotgun, as we welcome Raj Wannaki to the show. He's the founder and CEO of Signal Labs. Who the hell is Signal Labs? Oh, we're going to get to the bottom of that. Raj, welcome to HR's most dangerous podcast.

• 2:20Speaker 04

  Thank you, gentlemen.

• 2:21Speaker 00

  So I'm going to go on a limb and say a lot of our listeners don't know who you are. We'll get to the Signal Labs thing in a second, but give us a sense of Raj. What's the ethos?

• 2:31Speaker 04

  Well, the ethos, gentlemen, is that I grew up watching Star Trek in India.

• 2:38Speaker 00

  I did not have that on my bingo card. Did you, Chad? I did not have Star Trek.

• 2:42Speaker 01

  No, no.

• 2:44Speaker 04

  So that's the root of everything I do in life. What I do at work and my ethos in life in general is one of optimism and tech is the utopia. Of course, there's always going to be good and evil and everything, but for the most part, I believe that technology is going to be making humanity progress faster and further and better than before. And that's sort of the philosophy I've applied to everything that I've done at work, including at Signal Labs, and we'll get into that in a second.

• 3:13Speaker 00

  Good and evil and everything, Chad.

• 3:16Speaker 01

  Yes. I mean, and who didn't want to hook up with a hot green chick? I mean, seriously? Star Trek? That was it. That was it. We have not heard the name Brass Ring in a very long time. We'll get to that. So the acquisition of Brass Ring came out of left field. Joel and I have been in this industry for 25 years. We grew up with Brass Ring.

• 3:38Speaker 00

  And we've covered some weird deals. We've covered some weird shit.

• 3:41Speaker 01

  Yes, yes.

• 3:42Speaker 00

  This is up at the top.

• 3:44Speaker 01

  And this question comes from the heart. Why the hell did you buy Brass Ring?

• 3:52Speaker 04

  Well, I mean, to be honest, other than occasionally using Brass Ring over the years, you know, I didn't really don't much about the company either. And well, the reason we got intrigued by it is as we were talking to some of our customers, and they happen to be in the HR tech space, they just sort of bought up Brass Ring as, hey, this is what we use for recruiting and hiring and talent fulfillment. And the context in which we were having that conversation was, well, what's going to happen when you're no longer just filling headcount, but you have to account for capacity? And you have to think about hiring agents, you know, putting agents, AI agents, side by side with humans? And then

• 4:33Speaker 04

  what if down the road, you're deploying robots at scale? How are we going to manage all of that together on one system, on one ledger? And there he was like, we don't know. Like, we are the HR department. We are happy with Workday and success factors.

• 4:51Speaker 01

  Do you think we're going to hire agents like people, though? I mean, this feels more like a contractor business deal than hiring agents like people. I mean, how do you see that happening? Because that's the first time I ever thought of. I understand this whole agents working in tandem with humans, whether they're created in-house or they're used by a company like a Brass Ring or a Paradox or a Smart Recruiters or something like that. But this whole hiring of agents thing, this is the first time I've ever heard anybody say that. And you get, talk a little bit more about that.

• 5:23Speaker 04

  Well, so Nvidia's CEO Jensen Huang said, I think there'll be like 100 agents to one human or something along those lines. So it would stand to reason, right? So that if we're creating these millions, if not billions of agents, that eventually, and even now they're already on marketplaces like AWS's marketplace, Salesforce's marketplace, ServiceNow's marketplace. So if they're already on marketplaces, how is that any different than like Indeed.com or LinkedIn.com where essentially people are saying, oh, this is what I do. This is what my experience is. This is what I would like to do. Agents probably have a similar spec that says, hey, here's what I was created to do. Here's what I'm good at. Here's my track record. Now, if you want to deploy me into your context, tell

• 6:08Speaker 04

  me a little bit about what I need to do and what systems I need to connect to. And you sort of go through the, you know, security processes to like you would a background check and say, all right, you're ready to go and deploy them into the workforce.

• 6:21Speaker 00

  Does Brass Ring really have 65% of the Fortune 500? And if they do, do those companies know they're still clients of Brass Ring?

• 6:30Speaker 04

  Well, I think many don't because it works so ubiquitously and hidden in the seams of HR tech systems.

• 6:36Speaker 00

  They've just been getting a bill since 2002 and they're just paying.

• 6:39Speaker 04

  Yeah. And when we reached out to them, they're like, what? Wait, we're still paying this bill? Like, what are we paying this for? And then, you know, they check with the HR, you know, tech leaders that are sponsors for the program and they're like, yeah, this works great. Why would we ever change it?

• 6:54Speaker 00

  It's going to be a huge unlock for you, a huge introduction.

• 6:56Speaker 04

  Absolutely, yeah.

• 6:57Speaker 00

  I like that Chad started with Brass Ring because that's where most of our audience are anchored. I'm going to go to Signal Labs. You launch in April of this year, you have 25 million in backing from Lightspeed, and then you buy one of the, I don't know, royal old crusty company. Like, it's just really weird. What is up with Signal Labs? What do you guys do? You have some really weird gobbledygook language around systems of attention, attention infrastructure. How do you buy Brass Ring with 25 million? Like, help me understand, what is Signal Labs? What are you doing?

• 7:39Speaker 04

  Well, let's unpack that. So Signal Labs was formed in January. That's when we created the company. But the idea behind Signal Labs is something that's been percolating in my head for, you know, a few years prior to that. It goes all the way back to when Google wrote the paper, Google Research wrote the paper called Attention is All You Need, which was famously the paper that led to this wild dash that we're seeing with generative AI and all that's happening with the world of OpenAI and Tropic and the open wake models across the world. And in that seam, there's always been sort of a, you know, a kernel there that says, well, if the level of automation sort of increases to the level, you know,

• 8:21Speaker 04

  where humans have to actually think about what they need to do every day, then what institutions, enterprises, companies need to pay attention to becomes almost what differentiates you. Given your unique sort of tribal knowledge of a process and a set of systems that allow you to be competitive, then the institutional attention that's directed to which problems should I solve today, which opportunities should I go after today, is perhaps the most important decision, you know, sort of leaders in a company can make. So to create that, we essentially called out a new category of software that needs to be created that we call systems of attention. And we've got a whole science and IP around how to go create the attention so that if Chad and Joel want to say, what are the three things that I

• 9:06Speaker 04

  should be working on this morning? We don't have to guess at that. We can use science and AI to go create those three things and put it at the top of our inbox. So that's the very mission of our company. And the second part of your question was, well, how does Brass Ring fit into all of that? So if you think about companies now starting to specify missions and objectives for what they want to do versus just hiring 10 people or, you know, filling some butts and seats, then if you start with the mission of, look, this is what I want to get done, then that mission, whatever it happens to be, increase revenue, increase margin, retain more customers, acquire, grow, et cetera, has to be done by a set of people and agents. And we believe that Brass Ring has the people side of it

• 9:50Speaker 04

  covered. We believe we bring a lot of expertise in AI and in Genteek and how that's going to shape the future of work. So we want to put those two things together and say, look, Brass Ring becomes the ledger on which all work is managed, based on a set of mission parameters as opposed to just purely job specs. And then, incidentally, the 60-plus percent of the Fortune 500 that are still on Brass Ring, we can go talk to them and say, look, is this a problem for you? And if so, we can solve it. And so far, all the doors we've knocked on have said, absolutely, yes, come talk to us.

• 10:21Speaker 00

  25 million. Explain to me how 25 million buys Brass Ring.

• 10:25Speaker 04

  So 25 million is in fact the contract value that we launched with. So when we launched, we had, you know, a few marquee customers that said, we want Signal Labs to be our design partner and come, you know, sort of create this for us out of the gate. So that's the contract value that we launched with. Lightspeed and other investors gave us 10 million in seed funding. You know, so you're wondering, you know, how did we make this happen? So it's a combination of cash and stock. And so Infinite, which was selling Brass Ring, also made a bet on Signal Labs, saying, yeah, we believe in this future and we believe that this asset, which, you know, is sort of one of the many things that they worried about, could be front and center in Signal Labs and therefore amplify the value for them as well.

• 11:10Speaker 00

  So terms weren't disclosed in the PR, but I got to ask, what were the terms of the deal?

• 11:14Speaker 04

  Well, they weren't disclosed for a reason.

• 11:17Speaker 00

  I had to do it. Sorry, Raj.

• 11:20Speaker 01

  None, yeah. None your business is what he said.

• 11:23Speaker 00

  None, yeah.

• 11:24Speaker 01

  So in the future of work section on your website, it says the cost of missed signals is skyrocketing. So in the HR space, I mean, what, what are the signals that are being missed?

• 11:35Speaker 04

  You mean, it's, it's like, what talent should we be hiring? You know, where is the workforce going to be in five years from now? Two years from now, you know, 18 months from now. All that requires thoughtful planning ahead of time. So that you're saying, look, you know, these 2,000 job categories have to be classified as something else. And they're going to be automated, but those people that are sort of going to be in those seats are going to be still super valuable. I for one believe that AI will be a net add to jobs as opposed to a subtraction. So if every company is proactively thinking about, look, let's say if 10,000 people are working for this company. And, you know, 2,000 of those jobs, the work they do could be done by AI. Then those 2,000 people, what should they be deployed to do?

• 12:19Speaker 04

  And what skills do they need? How do I retrain them? And then what can we use then to grow the company with that workforce? That's the signal that most people aren't tracking. They're just reacting to, okay, well, you know, I have to do this riff and lay off 500 people. Let's put together the severance. So it's more, more, you know, HR is at the far end of the sort of the spectrum of, you know, strategy has been defined, you know, automation and other AI sort of strategies are being implemented. And now you're saying, all right, look, HR, we've got to, you know, right size the workforce, get to work. So that's really just being handled, you know, sort of an order to go do something versus being proactive thought partners to shaping, you know, the future of the workforce.

• 13:01Speaker 01

  Well, Brass Ring is not going to give you the data on how jobs are evolving. They're just going to show you what jobs are being taken by, by AI slash agent slash whatever. What partners are you working with to actually shape that or find those signals? So that you can provide the necessary L and D elements so that those individuals, you don't want to kick out the door. You want to get them shaped and developed into new workers within your organization. Where are you getting those signals from? Because again, this sounds like a full ecosystem. And there's no way Brass Ring provides that piece of the signal.

• 13:38Speaker 04

  They don't. Yeah. So, so that's where the Signal Labs team complements what Brass Ring does. So we get hired on the front end of a process and that front end of the process could be, look, you know, we've got this, you know, customer relationship management function that has Salesforce on the front end. We're not growing, we're not retaining our customers. Can you read the signals and tell us how to grow and how to retain our customers? So we would go put together a blueprint for all the signals that are necessary in order for a company's CRM system to function at its highest. You know, to ultimately achieve the, you know, the, the objectives of a good CRM system, i.e. growth, retention, profitability. In doing that, we have to put a blueprint of everything upstream and downstream of what needs to happen.

• 14:23Speaker 04

  How the sales teams need to work, you know, what new talent needs to be hired, how the workforce needs to be managed holistically in order to achieve that outcome. So when we put that blueprint together, you know, Brass Ring has a role to play in managing the workforce, documenting the skills and understanding how the workforce is going to evolve. But they're not doing everything. That's where we're building in the missing pieces to, to put that full end to end blueprint together. So that our customer get to those outcomes.

• 14:49Speaker 01

  That's got to be the hardest part though. I mean, we, we're hearing guys like Amman Day and Altman and whatnot. And they're talking about how things are going to change and how jobs are going to change. They have no fucking clue. They can't tell you what job's going to be created, right? Because they don't know where the data is. They've never been in this space before. Two things, right? So the question is, you take a look at like the government data. That's not going to help because that's all looking backwards, right? Where are you going to find the signals? That's the hard part, Raj. If you can find the signals on, I mean, because obviously Altman and Amman Day, the guys who have billions of dollars, they don't know where the damn signals are. Where are the signals?

• 15:27Speaker 00

  They're going to find it in the lab chat because they're Signal Labs. So the lab is where the signal.

• 15:36Speaker 04

  We're cooking it up right there on, on the lab.

• 15:40Speaker 01

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• 16:30Speaker 04

  But no, I think globally, the, the problem is much harder to answer, right? So what is, right, this, the US economy has some $15 trillion that get paid out in wages, right? So enormous amount of jobs. So we're not here to answer sort of the global job question. We're, we're, we're here to answer the much more local question of what happens to jobs as it relates to specific processes within the enterprise? Or with the collective of all, and all processes within an enterprise. So we can take that and we can study that. Then we can say, look, for a billing process or accounts receivable process, a holistic finance, you know, function. We can say, look, what is, what should this look like? What combination of AI, you know, humans, in some cases, robots?

• 17:15Speaker 04

  How do they all need to come together to fulfill this process to its objectives? What quality, what SLAs, what parameters, et cetera? Then you can sort of say, okay, this is how everything changes within that context. Absent that context, it's a much larger, more global problem, which people that have billions of dollars in the bank are going to go try and figure out. But we're solving at a, at a much more macro level with very defined set of outcomes within a matter of weeks as opposed to a global research project.

• 17:44Speaker 00

  Been the rollout to the Brass Ring clients been like? Historically, this is not a real innovative, progressive group.

• 17:52Speaker 01

  Yeah, yeah, they're on Brass Ring.

• 17:54Speaker 00

  My, my assumption would be this shit would scare the hell out of most of them. So how have you sort of slow rolled this? What's been the messaging? What's been the feedback from the Brass Ring folk?

• 18:04Speaker 04

  Yeah. Look, it's, first of all, it's been, there was some trepidation, you know, as Brass Ring has been, you know, sold, you know, a few times, as you both know. And, and so they were like, here we go. One more change. And what is this going to mean? And what, what do we have to change? And, you know, so the first thing we did as we went out to, to do the rounds with our customers is let them, let them know that, look, if you're happy with what you're getting, that'll continue to be the case. We're not changing the, the core promise of what Brass Ring is going to deliver. And, but as we had that conversation, they were like, you know what? If you did these four things better, we'd be much happier. Right? So it was like, okay, what are they?

• 18:42Speaker 04

  And one of the things, incidentally, was compliance, you know, we want to understand all of the compliance requirements locally.

• 18:50Speaker 00

  Like I said, innovation. Yeah.

• 18:53Speaker 04

  But, but, but look, but we can solve compliance with AI, you know? And why hasn't it been solved before? Because there are literally millions of documents you have to sift through to figure out what applies to any one client, any one zip code. And so localize those compliance requirements. Well, fortunately with techniques we can use with AI, we can ingest all of that and efficiently kind of create the compliance requirements for every client and make that available. So nothing sexy. We're going to go do things like that. And then there are a few that are like, okay, so what else are you going to do? So that's when we were talking about, look, there's a future coming, even if you talk to your colleagues down the, down the aisle on strategy or tech or, or finance or, or anything else, they're all talking about AI.

• 19:38Speaker 04

  Everyone's talking about partnerships with the frontier labs. They've got thousands of people working on AI. So where's this all going to go? Look, you know, HR's got to get in front of this and lead the way as opposed to sort of being on the back end, you know, taking orders. So that's, that conversation absolutely perks some years. Others have a roll of the eye, if you will. But, you know, definitely, you know, interested in what we're doing. And as evidenced by, I can't name the customers just yet. Hopefully we can have them on the show in a few months to talk about what we're doing. But the, the design partnerships have started to take place and we're kicking off the work. And there's a release that's planned in October. It's called Marina. That's going to be the first of a future work release where those five design partners are going to shape what we build and how we roll it out to them.

• 20:24Speaker 00

  And how about the employees on this? I mean, according to LinkedIn, you have a whopping three employees. Maybe there's more that aren't on LinkedIn. I don't know. Or LinkedIn's wrong. I mean, Brass Ring is not huge, a couple hundred people. But what was the, like, what? These guys are buying us? Like, I know we've been bought and sold over the years, but what? Was that, was that a, a tough conversation?

• 20:47Speaker 04

  Not really. You know, at first they, they thought they were buying us and then we clarified that we were.

• 20:53Speaker 01

  You don't buy me, I buy you.

• 20:55Speaker 00

  That's what I thought when I read it. Like, wait, is that headline right? Okay.

• 20:59Speaker 04

  Exactly. But at the, the end of the day, it's, it's, it's just good people. They happen to be in a town that's just a hundred miles or so from where I grew up in India. Their, their HQ is in Visakh, in, in, in a state called Andhra. And so I can speak the language and sort of understand the roots of the culture of where, you know, that team was grown up. But very quickly, it became about, look, what can we do? And, you know, for whatever reason, for the last 10 years or so, there's been a lack of a vision and a lack of enthusiasm about what they were doing because they were literally just harvesting what they were doing as opposed to thinking about what the future is. And so what we laid out was, look, this is what we want to do. Are you guys ready?

• 21:42Speaker 04

  And my God, the first town hall we did, we had all the entire compliance base, people are on vacation, wherever they made time showed up. And, you know, we said, right, look, this is how it's going to work. This isn't some spec we need to go create for you and you tell us what you're going to do. It's just like, here's the broad ambition, get to work.

• 22:01Speaker 00

  We're back, baby. We're back.

• 22:03Speaker 04

  That's right.

• 22:05Speaker 00

  Already like it's 1999, Brad.

• 22:07Speaker 04

  Yeah, that's exactly right. 1999, the original internet era.

• 22:11Speaker 01

  Like a little prince, baby. Yeah. Y2K. Um, yeah. So that's a great mo green move, by the way. We don't, you don't buy me, I buy you. So your website also says the data exists, the system to act on it doesn't. Did you see the data inside Brass Ring before you actually bought it?

• 22:30Speaker 04

  Uh, no, well, the confidentiality requirements, you know, precluded us from doing that.

• 22:35Speaker 01

  And here's why I'm asking because job descriptions, garbage data, resumes, garbage data. They're both, they're both unstructured, they're outdated data points. And then there, there's disposition codes which provide hundreds of millions, in your case, probably billions of garbage and bias signals. And you talk about compliance bias scales very quickly. And then you scale that problem for how many companies that you guys support.

• 23:04Speaker 04

  Yep.

• 23:04Speaker 01

  You scale that problem and they all have different hiring processes. They have different disposition code data. They all even have different ways of collecting the data. And I haven't even mentioned what the waves of fake candidate bots profiles that are out there, just garbage. So as you talk about signal and we, and we hear about it all the time, it is so hard, especially in these systems, to be able to discern between what is good and what is bad data. How are you guys going to be able to do that? Magic is, is the easy word, but there's tons of data.

• 23:46Speaker 01

  How can you guys grind on something like that and even understand what's good, what's bad? What can possibly be used?

• 23:53Speaker 04

  Yeah, we should, we should have had you on our diligence team chat on, on the data quality. So.

• 23:58Speaker 00

  He's for sale. He's for sale.

• 24:01Speaker 04

  All right, let's, let's talk.

• 24:02Speaker 00

  He'll, he'll take your money.

• 24:04Speaker 04

  There, there you go. Uh, no, but what's, what's interesting about this is that we're not as interested in the literal data of the job descriptions and the, the job spec, the, you know, the disposition codes, uh, even, you know, the resumes themselves as much as we are in the, the singular act of the signal of you're hiring someone.

• 24:25Speaker 00

  It's commercial time.

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• 26:11Speaker 04

  And the number of people you're hiring and, and then on the, the flip side, what is the outcome of that hiring? How many people left? And how long did they stay? How effective were they in that? Right? So that data is available and that's, that's what we think is gold here. Because what we want to figure out is like, what is causing people to hire? And what is that outcome going to look like 12 months from now, 18 months from now? And is what they're hiring for, should, should it even be a human? And, and that's what we want to start the dialogue with, with the hiring teams saying, look, yeah, you're hiring these five programmers for this company.

• 26:53Speaker 04

  Are you really, the, the job spec says you need to know, you know, Python and Java and, and whatever. Well, guess what? Cursor and, you know, clock code and, you know, these tools do most of that. So are you really going to need programmers to do it? Are you going to need senior level sort of QA people to QA the code that the AI is creating? And rather than five programmers, do you really need one super senior programmer? And is that really what needs to happen? So that's the dialogue that we want to have. And what the brass ring data shows, whether it's noisy and of poor quality, that's all true. But it's got like 25 years of those signals.

• 27:32Speaker 00

  I know.

• 27:33Speaker 04

  Across, right? Across 170 countries. So we go mind that. Like, why did someone hire someone in, in 98? What happened in 2002? What happened in 2010? All these, these technology waves that have happened in the last 25 years have a correlation to, to how jobs have shaped and changed and evolved. So that's what we're looking to understand as opposed to, look, you know, Joe Smith has here five years of experience and, you know, blah, blah, blah, and is applying for this position here at this company. That's interesting, but not as relevant to, to what we're building for the future.

• 28:07Speaker 00

  Chad, I don't want anything from 1998 that I was doing to come back to life.

• 28:13Speaker 01

  Raj, that's a bad signal. That's a bad signal.

• 28:15Speaker 00

  Going to venture partners, when I was, when we reported on this and I looked at, you know, their portfolio has Eightfold, Personio. Um, I know, I'm sure you don't have an opinion on why they didn't buy if Brass Ring was for sale. Now, I think that makes more sense if the, if the press release had been Eightfold buys Brass Ring, I can, I can square that circle. Will you guys be doing anything with Eightfold Personio? We have a conversation on the show that we, you know, there are pre chat GPT companies and there are post chat GPT companies. Both of them are in the pre, you're in the post. Just curious your opinion, like, are you, are you the next wave of those companies? Do you think you'll work or partner with them or businesses like them?

• 28:59Speaker 00

  Just general thoughts on that relationship and how you're in sort of their, their circle.

• 29:03Speaker 04

  More partnerships, Joel, you know, so, so, so we don't necessarily think of ourselves as a HR tech company. Uh, future work happens to be, you know, one of the areas that we want to solve for, but it's one of three areas that, that we're focused on. And so when it comes to the, to the rest of the HR tech ecosystem, you know, I think it's really, we believe that a partnership approach where we're putting together kind of different best of breed kind of point solution and assemble the orchestration around it. That's really our core interest, um, as opposed to necessarily expanding into, you know, more and more, you know, deeper HR tech capabilities per se.

• 29:42Speaker 01

  One of the words that you said earlier that made Joel almost run out of the room was compliance. A lot of the, when we talk about it, the boring stuff is where business gets done. And you take a look at some of the big names, Workday, Eightfold, just talking about Eightfold.

• 29:58Speaker 04

  Yep.

• 29:59Speaker 01

  Talk about HireVue, talking about a lot of these big systems that are getting nailed for compliance because of scaling or, uh, decision shaping versus, you know, AI decision making, those types of things. Um, you, you mentioned that as like one of the number one ways you guys can, can actually go after some of these problems, which I agree with 100%. Do you think that's where your first product set lands is to be able to say, okay, there's tons of AI out there. We're going to be, we're going to be the signal to tell you when it's going right and when it's going wrong.

• 30:33Speaker 04

  Exactly right. And, and the, the root basis of all of that is, is one of the signals. So that is the compliance rule set, right? Uh, you can do this year, the EU AI Act is, is, you know, going live shortly. So you can do certain things in the EU, but you, you, uh, you can't do certain things in the EU and you can in the US and China is a free for all. So how do we, you know, track all of that, right? So that, that's really, I think, one of the, the reasons why. The boring stuff, you know, the compliance stuff also excites us.

• 31:03Speaker 00

  What's uh, what's next? Are we gonna, should we expect more acquisitions in our space? You mentioned going into other industries. Is there some connection to all of them? Like, what's, what's the future hold? What, what PR we'll be looking at from Signal Labs in the next six to 12 months?

• 31:19Speaker 06

  I'm listening.

• 31:21Speaker 04

  Ah, wow. Okay, so.

• 31:22Speaker 00

  We'll be watching, by the way.

• 31:25Speaker 04

  And now I'll be tracking your show. So really, really entertaining. Yeah, so the, the main thing you're gonna, you know, sort of, one is the October release, Marina. We're gonna be doing a lot of PR on that and that's gonna, we're gonna get into to why customers should care, what is it gonna do? And there's some really cool innovative things we're doing and how to, how to construct that particular release. Beyond that, you know, we're getting into a post LLM world, you know? So you said pre, you know, GPT, post GPT. I think there's a, there's a post LLM world that's, that's upon us. And that world is about, how do all of these, you know, fantastic AI capabilities get applied in the real world?

• 32:04Speaker 04

  You know, so Yan LeCun, you know, the former Chief AI Scientist from Meta, you know, and others have, have, you know, jumped on this, this notion of world models, which is model the physical 3D world and then apply AI into that. So for example, if you were to go to a Target down the street and if some company had modeled the entire Target store and is every hour says, look, move these products around. Chad's stopping by, so stock up on popcorn on the exit aisle or whatever his preference may be. So there was a level of dynamic configurations.

• 32:39Speaker 00

  I think is his.

• 32:40Speaker 04

  Avocado toast. Love it. Very, very California view, my friend. So that, that level of simulation can happen in real time. And the reconfiguration of the physical world can happen in real time, and that's what they're betting on. And our view is that there's a corollary to that in the enterprise world. So if we can model all the enterprise data and sort of keep it sovereign to every enterprise, whether it's, it's Disney or Walgreens or it's, it's IBM. Their data can be modeled and we can create a digital twin of that company where you can do lots of simulations. And, and that's really the world that, that's coming in the next few months, if not sooner.

• 33:21Speaker 04

  And a lot of what Signal Labs is going to be doing is, is building out world models for certain problem types that we're interested in. Starting with workforce management. And we're also going to do healthcare productivity. That's a topic we should talk about at some point. And enterprise resilience, which goes back to, you know, the compliance angle. So modernizing global risk and compliance, we want to build so that the entire knowledge structure and the world model around those topics. Then we'll take those models and then intersect it with any company's data. And together, so we're going to reimagine the intelligence stack of, of enterprise and hopefully, you know, create some sovereign AI for the enterprises.

• 34:01Speaker 06

  60% of the time, it works every time.

• 34:05Speaker 01

  Of all the applicant tracking systems, of all the smaller towns in all the worlds, Raj, you picked brass ring. Congratulations, I guess.

• 34:14Speaker 04

  Thank you.

• 34:16Speaker 01

  So, so Raj, if, if somebody wants to reach out to you, maybe connect with you, where would you send them?

• 34:21Speaker 04

  [email protected]. You can also go to our website at www-signallabs.ai and you can find us on LinkedIn and, and Twitter and X as a guest these days and all the other social media platforms.

• 34:33Speaker 00

  It, it, it takes Cajones to come on the show. He reached out after we talked shit and he wanted to come on and explain it. So.

• 34:40Speaker 01

  Of course.

• 34:41Speaker 00

  Cheers to you, my friend. Chad, you heard it here first. Systems of attention, attention infrastructure for the enterprise and the fourth layer, the fourth layer of enterprise software, whatever the hell that all makes, we're gonna unfold it in the coming months and years. Another one is in the can, Chad. We out.

• 34:59Speaker 06

  We out.

• 35:01Speaker 07

  Wow. Look at you. You made it through an entire episode of the Chad and Chase podcast. Or maybe you cheated and fast forwarded to the end. Either way, there's no doubt you wish you had that time back. Valuable time you could have used to buy a nutritious meal at Taco Bell. Enjoy a pour of your favorite whiskey. Or just watch big booty Latinas and bugfights on TikTok. No, you hung out with these two chuckleheads instead. Now go take a shower and wash off all the guilt. But save some soap because you'll be back. Like an awful train wreck, you can't look away.

• 35:44Speaker 07

  And like Chad's favorite western, you can't quit them either. We out.