The Human Hiring Touch

Can AI really recruit humans without turning the process into a bad episode of Black Mirror? Terry Baker from Daxtra says: maybe… if you feed it better than your average intern.

This week, The Chad & Cheese Podcast welcomes a man with more AI mileage than your uncle’s ‘98 Camry. Terry dishes the dirt on training data disasters, resume forgery worthy of a Netflix doc, and why bots still can’t handle the emotional trauma of job hunting (or office coffee).

We’re diving into:

• Resume validation so intense it should come with a blacklight 🕵️‍♂️

• Why “Excel proficiency” might mean yoga for some folks 🧘‍♂️

• And a future where AI writes your performance review and roasts you for using Comic Sans. 💀

  
  

🤖 It’s not man vs machine—it’s man with machine… as long as the machine doesn’t ghost your candidates.

PODCAST TRANSCRIPTION

Joel (00:28.445)

Ohhhhhhhh Yeah, this is the Chad and Cheese Podcast. I'm your co-host, Joel Cheeseman. Join us always. Chad Sos is in the house as we welcome Terry Baker, CEO at Daxtra. Terry, welcome back to HR's Most Dangerous Podcast.

Terry Baker (00:45.003)

It's good to be back with you guys. It's been a while.

Chad (00:47.084)

Not his first time, that's for sure. Was the first time in Ireland, was that the first time you were on the show when we were at the Guinness? It wasn't at the warehouse, so that was the second time, okay.

Joel (00:47.447)

X not

Terry Baker (00:52.727)

No.

Terry Baker (00:56.943)

I think so.

Joel (00:57.021)

I'm glad you added on the show to that was the first time at the Guinness fact. Yeah. On the show. Yes. Yes. Well, Terry, Terry, you know, we're adding fans all the time. So there are people that don't know who you are. Haven't heard you on the, on the show. give them, give them kind of the elevator pitch on you and about Daxter.

Chad (00:59.31)

On the show. On the show.

Terry Baker (01:02.244)

The other.

Terry Baker (01:13.766)

Sure. I've been in HR tech for the hard to say, but 30 years, formerly at Panda Logic, RealMatch sold that company for 200 million. Got a great exit. Thought I was done. Went three months on the beach. And, you know, I communicated with Chad when I was on the beach and I was like, this is really fun. And then...

Joel (01:27.357)

You

Chad (01:29.262)

I hope so.

Terry Baker (01:43.328)

One month went by, two months went by, it's kind of got a little boring. And so I started doing some private equity capital work and the company called Strato pulled me in on a due diligence they were doing with Daxter Technologies, a Muscle Burrow backed company that's been in AI for 23 years, which is amazing. And so they pulled me out of retirement and got me back and I've been enjoying the ride ever since.

I haven't been to the beach in a year.

Joel (02:13.021)

Is, the, I was going to say, the yacht getting gassed up? Is that why you're calling us from a room? You're usually, you know, you usually look like a Duran Duran video yachts and like, you know, caught the cock cocktail shrimp and yeah. So it's, it's nice of you to tone it down for the show, Terry. We appreciate that.

Chad (02:16.142)

That's sad. That's sad.

Terry Baker (02:25.894)

The very

Chad (02:26.496)

He's hungry like the wolf.

Terry Baker (02:31.108)

Yeah, we did have a good day on a yacht in Newport Beach. That was fun.

Chad (02:37.365)

Yeah, I know it's a good time. It's a good time. Hoping for more of those, hoping for more of those, especially God.

Joel (02:38.074)

on a

Terry Baker (02:39.566)

Yeah. Well, let me tell you a little bit about Daxter because they've been doing natural language processing, machine learning, parsing resumes, job descriptions for 23 years. And I think the data that HR tech is based on, you look at any ATS system, if they don't have a good parser, they're not getting good data. So AI is only as good as the data you generate.

Joel (02:44.733)

Yeah, please.

Chad (02:53.88)

Mm.

Terry Baker (03:08.618)

And we've been generating really good data for quite a long time. And now we've moved kind of down funnel. Now we don't just parse resumes job descriptions, but we do grading, scoring, ranking on scaling data, enable recruiters to see who the most qualified applicants are in just seconds. And then we do the engagement part. So if you've found the most qualified candidate, you don't want to send them to a chat pod. You want to...

Chad (03:37.304)

You pivot.

Terry Baker (03:38.414)

you want to engage them directly, pivot. That's a good word. We can now call it Daxter Engage. And that engagement helps solve the black hole, getting conversion of candidates, particularly the most qualified, quickly, effectively, and with little effort for the recruiters. So that's what we do.

Chad (03:58.2)

So let's talk to this real quick. So you get a job description and tell me where I'm going off the rails here. You get a job description, you parse it, you rip it apart, you contextualize it, you know exactly what the requirements are. Then you jump into the databases, whatever those databases are, external, internal, yada, yada, yada. Then you do the matching at the matching points. You hit a certain threshold, whatever the company wants that to be, let's say 90 % match or what have you. And then you engage those individuals.

to be able to either get more information and, or just to apply. What happens after that? Is that first and foremost, is that a good picture? Okay.

Terry Baker (04:33.434)

Well, there's one little piece that is a good picture. There's one little piece that we provide during that process and it's called the DAC agent. lot of companies are getting into antigenic agents, are so much smarter than bots, right? They learn, they take actions, they make recommendations. And so when a recruiter...

Chad (04:47.295)

Agents Cheeseman.

Joel (04:48.775)

Agents.

Chad (04:56.27)

Well, they're specialized.

Terry Baker (04:57.786)

Yeah, when a recruiter runs a search, we're learning from that specific recruiter's work log and building an agent that is making recommendations and then pushing the engagement part directly out so the recruiter doesn't need to worry about it. Everybody that's applying gets a campaign response, but the most qualified applicants, they get to talk to a human, which is what they prefer, right? Because it's what enables

Joel (05:25.053)

Mm-hmm.

Terry Baker (05:27.078)

the hiring manager and the recruiter to humanize the engagement with the most qualified applicants.

Joel (05:35.229)

Here and I know you I know you like baseball, you're a big Padres fan. So I'm gonna start out with a nice little little lob for you to knock out of the park here. Okay, ready? What? What? And you get this as a as a sponsor of the Chad and cheese podcast as well. Who are some companies leverage? Who are some companies leveraging Daxter's technology?

Terry Baker (05:42.444)

perfect.

Chad (05:49.038)

You get one.

Terry Baker (05:53.35)

Just small little companies like I'm gonna play many Machado here and just hit it hit it out So little companies like Google Amazon Apple Metta Ernst and Young We have a lot of people that have taken our technology and then grained it into their recording platforms And when they buy our technology they do the companies

to have the resources to do this, they put it through a trial and they look at the accuracy, they look at the speed, they look at the privacy. And we've won all of those particular clients because of the parsing nature of our application. And a parsing is just kind of the foundation that you build on. All right.

Joel (06:39.869)

All right, Terry, the ad's over, the ad's over. got your list. All right, that's good, that's good.

Chad (06:42.776)

Well, here's the thing, though. But here's the thing, because you've got big tech companies like that. Right. And some of them, like Google, have come into our space and they exited. They got the fuck out. Right. So, I mean, I think that says a lot, first and foremost, from the specialty, right. The focus in this industry is not easy to be able to not just parsing, but we're talking about matching. There's so many different aspects.

Joel (06:47.836)

Yeah.

Chad (07:10.25)

of what we do in our industry that even the big boys can't do. So, I mean, I think that says a lot and we don't have a lot of parsing matching types of tech in this space. Now, quick question around that because of LLMs and because of agentic, do you think that's going to change? Do you think that it's going to be easier for some companies to be able to get massive amounts of information, train those LLMs and then start parsing matching on their own?

Joel (07:23.271)

or list.

Terry Baker (07:25.616)

Mm-hmm.

Terry Baker (07:38.682)

Yeah, I think big companies are going to produce their own LLMs for specific use cases because every LLM is trained differently and they're not necessarily trained for HR tech, right? For qualification of candidates. So to some extent we've seen parsing become a little bit of a commodity because I can take a job description and I can go to chat GPT and dump it in and look at what's coming out of it. But if I have to do, you know, 10,

Chad (07:42.531)

Mm-hmm.

Chad (07:48.451)

Mm-hmm.

Terry Baker (08:08.616)

Resumes a month. It's just not going to work So we're focusing on the high volume hiring highly complex jobs where you need certifications You need not only hard skill qualification, but soft skill qualification and that becomes Really tough to do and so we've doubled down on parsing. We're actually releasing a new parser next month that will be vector based and Will extract even more data out of a job

description and automatically match it to a job posting. So you get that accuracy and speed.

Joel (08:45.585)

Let's, let's touch on Terry. know that this is something near and dear to your heart, sort of the, humanizing of AI through, you know, technology. And you, we've all been in the, at this for quite a while. And you know, the, the, the black hole candidates don't hear back ghosting, like all these things have, have been a real deterrent to the job seeking experience. You're a real, you're really high on how technology is humanizing.

the whole process. Can you talk about that a little bit more?

Terry Baker (09:18.342)

Yeah, think recruiters are a little bit scared right now that AI is going to replace them. And I don't think AI is going to replace humans. Humans will, with AI, replace humans without AI. So you have to have a recruiter that knows how to utilize AI to take care of the right tasks. But we promote the humanization of hiring through AI and automation because

We want recruiters to focus on what they do best. And what they do best is selling candidates and selling hiring managers on the right candidates. All the other tasks.

of looking at qualifications of resumes, we simplify that. We grade, score, and rank them to do engagement. We automatically run campaigns with SMS and WhatsApp. So the team-based approach, so everybody within the organization can see the data that is coming in and that data gets captured. One of the things that we do is ensure

data integrity within the ATS. So we don't run without an ATS. We add value on top of ATSs and ensure that all that data, you know, I came from programmatic job advertising and so many candidates get stuck in recruiters email boxes, right? They run a search, they run back the data, they look at the data, they pick one or two candidates and put them in the ATS.

Joel (10:35.325)

Mm-hmm.

Terry Baker (10:49.018)

We automate that whole process so everything goes in the ATS, gets updated, gets validated. We can even do data validation, right? Because in today's world, so many applicants are going to chat GPT, dropping in the job description, job posting, pulling out the data and putting it in their resume. We're seeing resumes balloon.

because of the use of large language models. So how do you do validation of that for a recruiter who may not know that this candidate, they're ranking really good because they're meeting all the requirements from the job description because they got that from chat GPT.

Joel (11:12.541)

Mm-hmm.

Terry Baker (11:29.294)

So how do you validate that? You do that through third party sources. You can put a notification, this resume was generated by ChatGPD or it's got a lot of large language model content in it. You need to validate this through third party databases.

Joel (11:43.76)

huh.

So the, so the, so the policeman, the validators, if you will, is, technology. So, so Google kind of knows what content is being created by AI, similar to resumes being produced via AI. Does that turn into a game of whack-a-mole? I mean, are we just into for a future that the lazy applies with the world, just get better about hiding whether they're AI or not. And then our tools and the policeman have to get better at policing that. And it just keeps going on and on.

Terry Baker (11:54.085)

Yes.

Joel (12:14.705)

Or is there an end in sight to that dilemma?

Terry Baker (12:17.284)

Well, I think the end in sight is validation through third party sites. And if you can do that in real time, which we do, then the recruiter knows this was in a resume, wasn't in their LinkedIn profile or wasn't in their HubSpot resume that they utilized two weeks ago.

Chad (12:20.867)

Yes.

Joel (12:35.961)

Okay, so it's much different than a Google looking at text and saying that's that's AI produced. You're talking about cross referencing something like LinkedIn to verify that that's that's fascinating. Okay.

Terry Baker (12:43.874)

Exactly.

Chad (12:46.382)

Well, and if it's, and if it's AI produce, who gives a shit as long as it's true, right? The validation part is what Terry's talking about. So that validation part. And then also, I, I don't understand why we're not moving more toward, uh, validation screening testing, right? Like coding tests and those types of things for, for, for many different types of positions. Uh, and I know that you guys are really focused on being able to provide kind of like those, those, uh,

Terry Baker (12:47.93)

Yeah.

Terry Baker (12:52.419)

Yes.

Joel (12:53.479)

Right.

Chad (13:15.95)

technologies and those different pieces to help companies get to that validation point. Are you guys looking at, or do you have partners where you are currently doing those kind of like coding tests, screening, those types of things to not just validate what you're seeing on their LinkedIn and other points of reference, but also actually what they can do.

Terry Baker (13:33.83)

Yeah.

Yeah, that's where the DACS agent comes in. It can look at a resume, look at a job description, say where the gap is on skills. It can say, for engagement with this candidate, ask them the following three questions or give them a test on this particular criteria.

Chad (13:40.087)

huh.

Chad (13:56.536)

The agent has testing capabilities as well. mean, like creating tests.

Terry Baker (13:59.724)

It can, can, eventually we'll get there. Today it's doing recommendations, but yeah, we'll get there.

Chad (14:04.138)

Okay. Gotcha. Okay. Okay.

Joel (14:08.509)

Is this exclusive to Daxter or are you guys like third party? Is there a solution out there that will verify resumes that everyone can plug into? is this exclusive for you?

Terry Baker (14:18.136)

No, we're using a company called PitchMe. And PitchMe does real-time validation. use people data labs. We've used LinkedIn. We have a product called Magnet. A recruiter can go directly and pull down a profile right off of LinkedIn and put it right into the ATS and compare it.

Joel (14:21.169)

Okay.

Chad (14:41.838)

Amazing. So when you're taking a look at the landscape that's out there today, because there's so much that's happening, there are a lot of legacy companies and some could look at Daxter as a legacy company. The only difference is you guys have been doing AI the entire time, right? you're a little bit more fluid. It's a little bit easier for you guys to move. But when you take a look at the landscape today, what do you see happening with some of those

Joel (14:42.077)

Good advice.

Chad (15:08.82)

legacy core platforms like the applicant tracking systems. What kind of disruption disruption do you see happen?

Terry Baker (15:16.346)

Well, I think everybody is looking at Agents to build value into the recruiting process. So if you're an ATS company, what we have to do is add value beyond what they're doing, utilize our AI data to take it one step further than what they're doing. So you guys even promote Winston, right? You promote, you know, there's LinkedIn agents, there's agents across all different kinds of ATSs.

Chad (15:23.714)

Mm-hmm.

Chad (15:32.494)

Mm-hmm.

Terry Baker (15:45.968)

But are they solving the problems that recruiters want them to solve? And are they manually eliminating the tasks that recruiters get bogged down in? And that's our goal, is to ensure that those tasks become easy and an agent can fulfill them.

Chad (16:03.306)

Is there a specific industry that you guys hone in on to ensure that you can really focus, industry and or level? So you've got obviously high volume is much different than mid-level, et cetera, et cetera. Then you've got warehouse, warehouse workers much different than healthcare workers. Do you guys hone in? How does that whole process work?

Terry Baker (16:12.858)

Yeah.

Terry Baker (16:21.392)

Yeah.

Yeah, yeah, Panda logic, we use bots to qualify warehouse workers and delivery drivers. And all you had to do was ask three questions. If they answered them right, you send them a link to, you know, day one start and they show up at a place and they start working. That's not the same for high complex jobs, nursing jobs, right?

Chad (16:33.794)

Mm-hmm.

Chad (16:41.87)

Mm.

Terry Baker (16:47.108)

doctor jobs, trucking jobs even require certifications, right? So we're looking at jobs that have at least 500 applicants per job, which means there's a volume that we can accelerate and highly complex jobs. So it's high volume and highly complex jobs where we can extract all the data and the more data you extract, better the qualifications.

is for these higher level compliant types of jobs. So we're not talking about stand-up jobs, we're talking about sit-down jobs.

Joel (17:22.106)

Any idea?

Chad (17:26.414)

Mm.

Joel (17:27.429)

Any idea why LinkedIn doesn't use LinkedIn to verify applications? I'm kidding. Let's move on to another question. So let's back to...

Terry Baker (17:32.837)

Hahaha

Chad (17:35.47)

Anyways, we could talk about how LinkedIn doesn't use LinkedIn data to actually match shit because I mean they're bad at all of it to be quite frank

Terry Baker (17:36.101)

Yeah.

Joel (17:43.453)

All right, Terry, back to your scary comment. Just something in the news this week that just terrified the hell out of me. Elon obviously is in the news big time with firing federal employees. Write us an email, let us know the five things you did this week. It came out that he said he's going to use AI to throw all the content of the email in an AI and AI will decide whether you can keep your job or not.

Chad (17:52.174)

Joel (18:12.729)

Is this the kind of future that employees can expect going forward that everything you do, everything you write, say produce, et cetera, is going to get chunked into an AI and the AI will recommend whether you keep your job or not. You're nodding your head. Yes. But I want you to expound upon that.

Chad (18:26.926)

You

Terry Baker (18:27.523)

Yeah, well, my wife likes Elon and she asked me to name five things I did in the house last week and I didn't.

Joel (18:35.559)

That's only cause she owns four cyber trucks, but that's a different podcast.

Terry Baker (18:38.454)

Yeah. But yes, I believe that now this one example of five things you did last week to validate and to either make a determination to fire somebody or keep somebody is kind of ridiculous. But I can tell you my last performance reviews that we did all had chat GPT.

Content in them right we sent out questions How did you write on these this and that and everybody went to chat GPT now I'm through performance reviews, and they're all the same answers Right because everybody took these performance questions put them in chat GPT took the content back and that was their performance review So how do you distinguish between? Anybody that's using a large language model? Right, right

Chad (19:27.082)

Outcomes. Outcomes.

Terry Baker (19:31.494)

So yeah, it comes down to what did they provide? What was the quality of data and some measure of timeframe. You can't just do one instance and make a hiring decision or a firing decision over that. It's gotta be over time where this data is being collected and being evaluated. And I think performance reviews already reflect that in the world today. We're using AI to do performance reviews and we should.

Chad (20:01.934)

We had an instance where GoFigure, one of our favorite companies on this show, Amazon, they used AI to advise and also to fire individuals who weren't performing. And many of these people were actually on vacation. So they weren't performing, of course, because they were on the goddamn beach because they were taking their time off. So mean, it's one of those things where we definitely need to, as you talk about humanizing AI.

The shit that Joel's talking about is what everybody's thinking. They're like, my God, they're just going to throw this stuff in a blender and it's just going to go crazy versus being able to take a look at a job. A job, especially recruiting is a list of tasks and being able to see which of those tasks are administrative. And you can actually take off the back of a recruiter so they can do the things that they need to do, like talking to candidates.

Terry Baker (20:44.902)

Yeah.

Chad (20:59.48)

talking to hiring managers, actually talking to people. the whole humanization piece to me really rings true.

Terry Baker (20:59.834)

Yeah.

Terry Baker (21:05.454)

Yeah, here's the positive equivalent to your example with Elon. If you're doing upskill analysis of current employees and you have a new job description and you run that job description, know, most employees are not updating their resumes and providing it to the company, but they're doing tasks each day. And if you look at those tasks, you compare it to a job description, we can analyze the skilling and the gap of that skilling.

Chad (21:13.87)

Mm.

Terry Baker (21:34.54)

and present learning modules to reduce that gap and enable internal employees to actually get higher quality jobs, higher paying jobs within the organization. And I think a lot of this is a positive aspect of using that same methodology to helping companies improve their hiring process by tapping into existing employees. And I think the world, you know,

You guys know the labor market as well as I do, and there's underperformance of jobs. And I think if you can tap into first your existing employee base and identify a skill gap, get them trained up to that quality of job, you save yourself so much time, money, and energy. You have somebody that already appreciates the culture. They want to continue working there. It becomes just a...

really positive approach to hiring candidates.

Chad (22:33.824)

It seems interesting to me that we have had technology that can enrich resumes, right? For years now, where it just goes out on the web, looks at different points, enriches a profile and or resume. There's no reason why we can't do that for employees to be able to collect data on performance, right? To be able to enrich the actual performance profile of that individual so that you know and so that they know.

Exactly what's happening day by day. So, I mean, I think there are some really good aspects and as we take a look at Doge and the stupid shit that they're doing if they could actually analyze performance Outcomes right versus doing this stupid send me an email thing I mean at the end of the day we could be more productive and then obviously the people that are you know, they're they're shaming out They're not doing as much work. They're not gonna be able to hide as easy

Terry Baker (23:02.138)

Great.

Terry Baker (23:29.186)

Exactly. Yep.

Joel (23:30.951)

I want to jump back to, to Amazon again, real quick, Terry. they were, they're sort of well known for a few years back of creating, an algorithm or an AI around hiring that became a really biased product and they, and they killed it. Chad and I talk about companies killing their DEI efforts every week. seems like from the making the technology, talking to the customer's point of view, which you live every day.

Chad (23:33.912)

Jump back, jump back.

Joel (24:00.911)

Are companies still doing it? they still concerned about biased hiring through AI? Are you guys still iterating and improving the technology? Like what's the state of DEI from your point of view and hire?

Terry Baker (24:13.476)

Yeah, that's a good question. So I was on a board call this morning. I'm on the board of ZRG, a top 10 executive search firm. They have a consulting practice. And the consulting business is being impacted by the fact that DE &I is somewhat going away. And there's a push on that, but I don't think it's a positive push. I think we've gone through two cycles of DE &I. We've gone through the Biden version where hire somebody

based upon their whatever, but not their meritocracy. And then we've gone to the Trump model where no DE and I exists. there's two really broad spectrums that are both wrong. And in the middle, you want a cultural that's diverse. You want a cultural that performs. So you have to marry DE and I with meritocracy. I once hired a

a diverse HR rep and she's like, we need diversity in this company. I'm like, yes, we do, but we need diversity with meritocracy. She's like, what's that? Well, it's meaning you can do your job adequately. You can perform. You have the skillset to make this happen. If you have meritocracy and you've got DE &I, it's perfect match, right? Why not make that happen?

but it's been not coexistent in today's world.

Chad (25:45.934)

Yeah, I think that's easier said than done, depending on how large the company is. because obviously there are many areas of the United States where there just aren't the skill sets. and you want to be able to ensure that you can develop the, the, the community that's around you because that is your workforce, right? So being able to actually dip into that community and go through the training process, either with community colleges or, or if you have internal training.

I mean, I think there's, again, there's no one way of doing things. And that's the problem that we're talking about is that, you know, there's this way, there's that way for a company. They have to figure out what works for them. Jamie diamond doesn't want anybody out of the office, right? Okay. Okay. That's their culture versus Daniel leck. Everybody can work from home, adults. They can do whatever they want. We have to stop this trying to do black and white scenario. Dei.

We need to be able to take a look at the actual culture of the organization and then that individual, me, I get to decide whether I want to come work for you or not.

Terry Baker (26:51.802)

Yep, that's the right approach.

Joel (26:56.465)

We talk about different.

Terry Baker (26:56.528)

But not very many people are doing that. And what do you need from a technology standpoint to make that happen? You have to do skill analysis, you have to do hard skill, soft skill analysis, you got it. Great. Yeah, exactly.

Chad (27:03.042)

Well, they...

Chad (27:07.106)

Things that are important to me, the company and the candidate, right? Yeah.

Joel (27:13.021)

Terry, do you ever see a rage against the machine moment with AI? I've mentioned on the show all the time that my 18 year old has more vinyl records than I do. There's a need by humans to want to go back in time or feel a certain way. AI is changing that how we apply to jobs and how we're up skilled is changing that. Any guesses from you or do we, at some point do companies say we have no AI whatsoever in how we hire?

And that's certain attraction for lot of candidates. Are we there yet or do think we'll get there?

Terry Baker (27:48.994)

No, but we got to get there. So one of the initiatives we're looking at, and we've been doing AI for 23 years. Generative AI is new in the marketplace and needs to be adopted. And a lot of the agentic agents are being generated from internal large language model usage. But I think that there's a problem with utilizing this. think about deepfakes, right?

Joel (28:17.863)

Mm-hmm.

Terry Baker (28:18.232)

in stealing somebody's picture, stealing somebody's voice, and propagating that and creating a resume that's not authentic and that's not you, that's a problem. And that will be something that I think over time we'll be able to identify and dismiss. But it's a hard problem to solve.

Joel (28:40.209)

And that's why Chad and I, Chad and I are buying the Kinko's brand and we're bringing back a resume printing and cover letter printing for the, for the kids, for the kids. Cause they, they love, they love all that stuff. That's Terry Baker, boys and girls. Terry, thanks for joining us for those that want to connect with you or learn more about Daxter. Where do you send them?

Terry Baker (28:45.753)

Yeah.

Terry Baker (28:49.574)

Let's not go back there. Let's not go there.

Chad (28:50.03)

Faxing. Faxing. Yes, faxing.

Terry Baker (29:05.67)

Daxter.com. It's pretty easy. D-A-X-T-R-A dot com. And we're here to make recruiter's lives better.

Chad (29:07.982)

too

Joel (29:11.165)

pretty easy.

Joel (29:16.135)

Thank you, Terry. Good to see you again, Chad. That's another one in the can. We out.

Terry Baker (29:17.368)

All right, good to see you guys.

Chad (29:20.61)

Way out.