Seth: Hey everybody, how's it going? This is Seth Williams from the REtipster podcast.

Seth: And today I'm talking with Brandon Parker, the founder of Land AI. So if you've been paying attention to what's happening in the land investing space lately, especially around acquisitions, you've probably noticed a big shift in how people are qualifying leads and growing their deal pipeline. And Brandon has been hard at work figuring out how to be at the forefront of this shift.

Seth: His team has executed more than 10 million cold calls. And in 2025 alone, his clients closed over 20 million in acquisitions. And along the way, he's logged thousands and thousands of real seller phone calls into an AI training database that powers what Land AI is doing today.

Seth: So we're going to dig into what Brandon is doing with AI and his business and how it works behind the scenes, what he's seeing across thousands of seller conversations, and the big shifts happening in how land investors run acquisitions right now.

Seth: By the way, if you want to try this out as we talk more about Land AI, we've set up a referral link for the REtipster listeners. You just got to go to retipster.com forward slash land AI, and you can get a discount if you go through that link.

Seth: So with that all said, Brandon, welcome to the show. Great to have you here.

Brandon: Thanks for having me, Seth. Appreciate it.

Seth: Let's start the way we usually do. When and how did you get into the land business?

Brandon: I got into the land business through real estate initially. And the most visceral story that I can tell about it is at one point, I had an entire house jacked in the air doing a real estate fix and flip.

Brandon: We were fixing a foundation, market turned, and that deal basically broke even. And I had this thought in my head, just the massive amount of work that residential real estate fix and flips can take and how risky it can be.

Brandon: And I loved real estate, but that nagged at me. When I found land, it was all the beautiful things about real estate that I really enjoy. But without the section one pest inspections, the leaky roofs, the broken foundations. And I fell in love with land instantly.

Brandon: I think that's probably how a lot of people feel about the land business when they find it. It's an entrepreneurial business. There's a lot of different directions we can go in land, and there's a lot of opportunity there, and it's real estate.

Brandon: And so it was just a no-brainer. When I found it, I fell in love with it instantly. It was about three years ago in March when I first came into land. Just started like everyone else, started cold calling manually, and then met a person online, Cole Rude Johnson on Twitter, who builds call centers, went through his program.

Brandon: Built my own call center, and then turned that into an agency for land investors. And the rest is history.

Seth: So did the cold calling thing occur to you like immediately? Or had you tried the normal direct mail approach for a year, then you wanted to try something else? Or what made you even think of cold calling in the first place? Because this was back in a time when it wasn't the norm yet to do that, right?

Brandon: Yeah, I was actually one of the very first cold calling operations that started in land. The reason that I was drawn to it is because I was a caller myself. I worked in biotech sales on a sales floor with a headset on, making 60 to 100 calls per day.

Brandon: And I knew exactly how to operate on the phone. And it made a ton of sense to me. I want to get these people on the phone. If I mail them, I'm just going to end up getting them on the phone anyway. So why don't we just start calling them?

Brandon: And so cold calling just made a lot of sense to me and I had a background in it. And so it was the natural spot for me to start.

Seth: Take me through the normal cold calling cadence. I know some land call centers will sort of start with this initial call that the caller handles. It's like the lowest value call where you're basically just finding out, does this person want to sell at all? That's call one.

Seth: Call two, that's where maybe the land investor or one of their employees would jump in and have a deeper conversation about the property and maybe even start the negotiation process. And then possibly call three, where you actually make the offer and close the deal. That's like one model that people follow.

Seth: When you were doing this, is that a similar process you went through or was it broken up into two calls or four calls or did everything happen on the first call? Tell me how that worked.

Brandon: That evolution is interesting. And yeah, it is similar to lots of sales processes that have an intro and then a setter call and then a closer call. That's the concept to me with land.

Brandon: One of the main goals I feel like in the setter call is to maintain rapport and the initial two calls and not burn rapport with some sounding like a salesperson. And so that's one of the keys there.

Brandon: Our evolution, we started as a call center. What ended up happening is that we would send people too many leads. So that was our main issue as a company was that I started to notice we were churning clients on a familiar pattern.

Brandon: And it was at about 90 days to 120 days. They would talk to me and they go, hey, Brandon, everything's going well. You're doing exactly what you said. We're getting 100 leads a month, but we have too many leads. And we want to pause. We're going to sort through all these leads. We're going to work them, and then we want to restart in two months.

Brandon: And so it became really clear to me that, oh, the issue isn't generating leads. The issue is sorting through leads. And so about six months into running the cold calling agency, I started to try to solve the issue of sorting through leads. Okay, how do we take these hundred leads and make them more manageable for our clients?

Brandon: So the first thing that we did was we hired lead managers, which is that second call. And so we just had humans, and we just started pushing those calls to humans. Humans would have that second conversation and pre-qualify the leads, and then we would push them into the client's CRM with that second conversation already done.

Brandon: Basically, trying to attack that bottleneck that people were hitting. That was the thing that we worked on for over a year. And so that's where we have thousands of recorded calls of what these setting calls look like.

Brandon: And then one day I was just playing with AI like I do as much as possible. And I heard this conversational AI. It was shocking how good it was. This was almost a year ago now. And I thought, oh my gosh, we have to do this immediately.

Brandon: And so we paused the whole cold calling operation. We stopped taking clients. We kept the clients we had, but we put everything on pause. That was in March of this year. And we have five developers.

Brandon: There's a full stack developer, two automations person, our CTO and me. We all just went head down on building conversational AI. And the idea is to replace human setters with conversational AI and use the data that we'd built to do that. And so that's how we got to where we are.

Seth: Sure. And when you say human setters, you mean the people that handle call number two?

Brandon: Yep. The people who handle call number two, lead managers is the way to say it in land terms, human setters is a computer person way to say that.

Seth: Okay, got it. So how does it work? Like, I know when we talked a while back, you mentioned that you call it Maya. Is that still what it's called?

Brandon: That's what it's called. And it's funny because there's probably other Mayas out there, but we think of her almost like a person because she's a voice and she's talking to us.

Brandon: And Maya, the whole pitch was, if we can have this voice answer all the calls, this AI character basically answers the phone, and it talks to the lead and pre-qualifies the lead and does all the heavy lifting, then we can deliver a pre-qualified lead to our clients. And that's all they want.

Brandon: Nobody wants to talk to 100 people. They want to talk to like the five that are worth the time, are qualified, or they want to do the deal. That's the mission. So Maya makes 100 calls or whatever the number is, and then hands off five pre-qualified leads to our clients.

Seth: Is Maya handling call number one and call number two, or is she only handling call two, or is she doing all three? How does that work?

Brandon: So right now she's handling call number two, and the call center is handling call number one. And that's right now while we're in kind of the early innings of this. We're going to merge those. We're going to have Maya and an AI handle call number one also.

Brandon: So we want both of those to be completely automated. It'll still be a human on call number three. So there's a human closer who will take the final call and make the price negotiation and walk them through the transaction. But the idea is the AI will handle call one and call two.

Seth: Got it. And so on that call number two with Maya, is it a two-way conversation? Like, is Maya asking questions and the seller is answering and then asking their own questions and Maya is responding? Or is it more of just a one-way script that Maya is reading?

Brandon: Yeah, it's a full conversational AI. So Maya is listening to what the seller is saying in real time, and she's responding in real time. There's no script. She has a personality and a goal, and she's trained on all of these calls.

Brandon: So she knows what to ask. She knows how to respond. She knows when to end the call. She knows when to hand it off to a human. All of that is built in. And it's a full two-way conversation. You can interrupt her. You can ask her questions. She'll respond naturally.

Seth: So like if I were to call and I said, hey, I got your call about my property. I'm kind of interested, but I'm not sure. What would Maya say?

Brandon: She would say something like, yeah, absolutely. I'd love to chat with you about it. Can you tell me a little bit more about the property? What's the address? And she would start going through the qualification process.

Brandon: She would ask about the property details, like size, location, access. She'd ask about the seller's motivation, like why they want to sell, what their timeline is. She'd ask about their price expectations.

Brandon: And she's doing this in a very conversational way. It doesn't feel like she's reading from a script. It feels like you're talking to a person who's curious about your property and wants to help you.

Seth: How long does a typical Maya call last?

Brandon: They range from like two minutes to 15 minutes. The average is probably around five to seven minutes. It depends on how much the seller wants to talk and how qualified the lead is.

Brandon: If it's a quick disqualification, like the seller says, I'm not interested anymore, Maya will politely end the call. If it's a good lead and the seller is engaged, she'll spend more time gathering information and building rapport.

Seth: And then after that call, what happens? Does Maya automatically update your CRM or send you a summary or what?

Brandon: Yeah, so after every call, Maya generates a call summary. It includes all the key information she gathered, the seller's motivation, property details, price expectations, timeline, all of that.

Brandon: And then she tags the lead based on the qualification. So she'll tag it as hot, warm, cold, or disqualified. And all of that information automatically flows into the client's CRM.

Brandon: So the client wakes up in the morning, they look at their CRM, and they see five new hot leads with full summaries. They know exactly who to call back and what to talk about. It's all ready to go.

Seth: That's pretty cool. So I'm curious, when you were training Maya, what were some of the biggest challenges you ran into? Like, what was hard to teach her or what did she struggle with initially?

Brandon: The biggest challenge was getting her to sound natural and not robotic. Early versions of conversational AI, they sound very stiff and scripted. And sellers would hang up immediately because it was obvious it was a bot.

Brandon: So we spent a lot of time on her voice, her pacing, her tone. We wanted her to sound like a real person who's having a genuine conversation. We added pauses, we added filler words strategically, we worked on her personality.

Brandon: Another challenge was teaching her when to escalate to a human. Like, if a seller starts asking really complex questions or gets upset or confused, Maya needs to know when to say, hey, let me connect you with someone who can help you better.

Brandon: And then the third big challenge was data quality. We needed thousands of real calls to train her on. And even with all the calls we had recorded, we had to clean the data, categorize it, label it. That was a massive undertaking.

Seth: Yeah, I bet. So when you say you had thousands of calls, how many are we talking? Like, what's the actual number?

Brandon: We have over 5,000 recorded calls in our database right now. And we're adding more every day. Every call that goes through our system gets logged and added to the training data.

Brandon: And that's one of our big competitive advantages. Most people who are trying to build AI for this don't have that kind of data. They're starting from scratch. We have years of real conversations with real sellers.

Seth: And when you think about the future of this, like where do you see it going? Are we going to get to a point where the entire transaction is handled by AI from start to finish?

Brandon: I think we're heading that direction. Right now, humans are still needed for the final negotiation and the closing process. But I can see a future where AI handles that too.

Brandon: The technology is already there. It's more about trust and comfort levels. People need to get comfortable with the idea of an AI making financial decisions on their behalf. But I think we'll get there.

Brandon: And honestly, I think AI might be better at negotiating than most humans. It doesn't get emotional. It doesn't get tired. It can analyze thousands of data points in seconds and come up with the optimal offer.

Seth: That's a good point. So let me ask you this. With all the calls Maya is making, what are some of the patterns you're seeing? Like, what are sellers saying? What are their biggest concerns or objections?

Brandon: The number one thing we're seeing is that sellers are more educated now than they used to be. They know about land investors. They know we're trying to buy at a discount. They've probably talked to other investors already.

Brandon: So the days of getting a property for 10 cents on the dollar just because the seller doesn't know any better, those days are mostly gone. Sellers are savvy. They're shopping around. They're comparing offers.

Brandon: The second thing we're seeing is that motivation is still the biggest factor. Sellers who are motivated, they'll take a fair offer. Sellers who aren't motivated, they'll hold out for retail or close to it.

Brandon: And the third thing is that access and utilities are becoming more important. Sellers want to know if the property has road access, if it has water, if it has power. Those things matter more now than they used to.

Seth: Interesting. So given all of that, what's your advice to land investors who are trying to compete in this market? Like, how do they stand out?

Brandon: Speed and follow-up. If you can be the first person to call a seller back and you can move quickly, you'll win deals. Most investors are terrible at follow-up. They'll call a seller once and never call again.

Brandon: But we have data that shows most deals close on the third, fourth, or fifth touch. Not the first one. So if you have a system for consistent follow-up, you'll beat most of your competition.

Brandon: The second thing is personalization. If you can make the seller feel like you actually care about them and their property, that goes a long way. Maya does this really well. She asks questions. She listens. She shows interest.

Brandon: And the third thing is automation. If you're still doing everything manually, you're going to get left behind. The investors who are leveraging technology and automation are going to dominate the next few years.

Seth: Yeah, I think you're right about that. So let me ask you this. If someone's listening to this and they're thinking, okay, I want to try Land AI, what should they expect? Like, what's the onboarding process? How long does it take to get up and running?

Brandon: The onboarding process is pretty straightforward. You sign up, we have a quick kickoff call to understand your business, your criteria, your target markets. We integrate with your CRM, which usually takes a day or two.

Brandon: And then we start making calls. We typically recommend starting with a smaller list to test things out, make sure everything's working smoothly. And then we scale up from there.

Brandon: Most clients are up and running within a week. And they start seeing qualified leads coming in within the first few days. It's pretty quick.

Seth: And what kind of results are people seeing? Like, what's a realistic expectation in terms of leads and deals?

Brandon: It depends on the market and the criteria, but on average, our clients are seeing about 20 to 30 qualified leads per month. And they're closing about 20 to 30 percent of those leads.

Brandon: So if you're getting 25 qualified leads a month and you're closing 25 percent of them, that's six or seven deals a month. And these are deals that you wouldn't have gotten otherwise because you didn't have the bandwidth to follow up on all those leads.

Seth: That's pretty significant. So I'm curious, where do you see the land business going in the next few years? Like, what do you think the biggest changes are going to be?

Brandon: I think there's going to be a lot of consolidation in the land business. I think that there will be people who start leveraging this and kind of stay on the cutting edge, like probably a lot of your listeners are playing with AI. And I think those people are going to have a massive amount of leverage.

Brandon: And that we're going to have a bit of a barbell here where maybe the real small flips keep being easy to do. But I think that a lot of it consolidates with people who are leveraging technology and automations and these transactions start to get really fast.

Brandon: People can do high levels and probably people start getting funding and things like that to fund lots of transactions. This consolidates quickly over the next couple of years would be my guess.

Seth: Let's walk through the current process from start to finish. Now that you have this AI that intervenes and sort of takes over call number two from the human setters or the lead managers. So is it basically follow the exact same process where now there is still a human handling the first call?

Seth: That part hasn't changed. The difference is just that once they determine this person wants to sell, now they move it on to the AI bot?

Brandon: Yeah. So the process is very simple. We make the same cold call that we've always made. We still have our call center. We've made over 10 million cold calls. So we're very good at cold calling. We call. We have a short call. We capture their interest.

Brandon: So if someone says, yes, I might be interested in selling my land, then our cold caller transitions into warming up the handoff. Okay, hey, we've got this really cool AI tool that will talk to you more about your land, see if this might be a fit for us to buy, and we'll even send you a quick report about your land after the call. You want to talk to Maya?

Brandon: Maya. Yes. Okay. It's going to be two rings. Then we warm transfer them to Maya, the conversational AI. Maya has a five to six minute full on sales call with them. And this is Chris Voss tactics, some of AJ Sharma's best moves. Everything that we've learned over thousands of calls in lead managing, Maya does every time.

Brandon: So she qualifies them on pricing, timeline, and motivation. After that call, we are able to sort all of the calls and about 20 to 30 percent of them are pre-qualified and those get sent to our clients. That means they have a reasonable price they've mentioned, they're willing to sell on a fast timeline, or they have some sort of reason to sell.

Seth: That's going to be one of my next questions because I know one of the complaints I've heard about land cold calling services in general is the quality of leads are not that great, meaning like they're not actually that motivated to sell. It's different than what you would get with direct mail, where a person has to open the mail and actually pick up their phone and call you.

Seth: So like they want it. Whereas with cold calling, you're kind of interrupting their day and they may or may not be on the same page with you. With this AI process involved, when the lead actually is dropped into the land investor's lap, what does the land investor still need to do?

Seth: Where does AI stop? Where does the human land investor pick it up? How high quality is this lead? Like, are they ready to go? Like just send some of the contract and we're done, or is there still some more negotiation and conversating that needs to happen?

Brandon: Yeah. The numbers for our cold calling operation, this is standard cold calling. It's about a one to 2 percent conversion rate. So we send a hundred leads, you're going to get one or two contracts, but you have to sort through a hundred leads. So like you said, that's the Achilles heel of cold calling.

Brandon: With our system, we take those 100 leads, they pass through Maya, now they turn into 20 leads. And out of those 20 leads, our clients are getting that one to two deals. So it's five times as effective as our cold calling. There's nuance here that I explained to everyone who uses us.

Brandon: Leads are still cold calling leads. They still need to be called. The deals need to be worked, underwritten, negotiated. The negotiation isn't done. What we do is we eliminate 80 percent of the tire kickers. We eliminate most of the work of sorting through garbage so that you can focus on just talking to people who are possibly going to close.

Brandon: So we take out everything that's not going to work, but you're still in charge of making the stuff work. That's how we look at it.

Seth: So what exactly is happening to the 80 percent of leads that get eliminated? What are they saying that's making them get kicked out? Like if I'm the world's best salesman, what if I could have won some of those?

Brandon: We have full transparency on those. We're sorting the leads and we still push those. We just push them into a different section of your CRM. So we'd push those straight into your nurture system. So people can use whatever CRM they want to use. I know that some people probably love Stride. And so if they're using Stride, we'd push it straight into the Epic CRM like that.

Brandon: What we do is push them either to pre-qualified call now or to nurture. But all the notes, the transcript, the summary of the call all come through. So a lot of our clients will get their eyes and just quickly scan through those other leads that come through and see if there's any gold in there, anything that maybe we missed.

Brandon: We also have a team that as leads come through, gets eyes on them and make sure they were sorted correctly. And so nothing falls through the cracks as far as that goes.

Seth: Correct me if I'm wrong, but there is a big compliance issue right now around AI making outbound calls. So for people who don't know what the rules are, what are the key legal or regulatory lines that investors need to understand? Like, can AI make outbound phone calls or is that like against the law right now? Is that why you have humans doing this first call?

Brandon: Yeah. To be compliant, there needs to be a prior business relationship and needs to be disclosed that you're talking to AI. So that keeps our phones from being outrageous. I mean, could you imagine if we could just call straight outbound with AI, our phones might never stop ringing.

Brandon: They might just ring 24 hours. I can call everyone in the US and press of a button. So what needs to happen is some sort of prior relationship and they need to consent to speaking with AI. And so that's why we leverage our human call center like the thing that we're experts at that we've already done millions of times.

Brandon: That is a really unique opportunity for us because we can reach out at mass. We can get that consent and that relationship started and then instantly pass it over to AI. So that's a big competitive advantage for us. We're so good at cold calling from doing it for so long. And that plays right into passing the calls to the AI and then being able to leverage that AI.

Seth: Now, you kind of alluded to a little bit earlier, but what's different about your process is the live transfer from human to AI, which actually might sound a little backwards to some people. I know when I think of when I call a company and I'm listening to like a pre-recorded voicemail greeting, like that's what I hear first.

Seth: And then I push a button to talk to the human. You're kind of going about a different way, probably because you have to, because you're making the phone call. It's not coming in. And so when the first caller has the person on the phone and when they've determined this person wants to sell, they actually say, you're going to talk to an AI bot now? Like the seller knows they're talking to AI. That's not being hidden from them or anything, right?

Brandon: Yeah, it's really unique. There's a few things that go in here that make this process build a ton of rapport. The handoff to AI, we offer a bit of a lead magnet, and also we tell them it's going to have a conversation that most people won't get to have.

Brandon: They're going to find out some information about their land that most people might not know, and we're going to send them a report after. And it's going to see if this is something we might buy. So there's a value there that we're offering that's part of the warm handoff.

Brandon: The other part of it is that the conversation that Maya has is trained on thousands of these seller conversations. And so all the speed bumps that humans accidentally hit along the way or questions they phrase wrong, or maybe they were tired and your lead manager's having a bad day, Maya never has that.

Brandon: So it's just at scale, asking the same questions in a similar line of thinking with the same strategy hundreds of times just builds more rapport than our human lead managers were ever able to. When a human lead manager nails it, does everything perfect, they're trained up, they're having a good day.

Brandon: They probably do outperform Maya. But the other 90 percent of the time, Maya outperforms a human lead manager. And at scale, we're just building better rapport, having better conversations. One of the interesting things also is that people are surprisingly candid with AI.

Brandon: They're very comfortable talking to it. I don't know if you've had conversations with ChatGPT. I'm sure you have. But you might say things to ChatGPT that you might not say to your family member. You might open up to it and ask for advice or give it some insights that you just wouldn't say.

Brandon: There's not the social barrier. It's just people say stuff. They give their real motivations. And so that happens sometimes also.

Seth: Do people ever react negatively to the AI? Like, this would have been better if they were talking to a human. Or is it pretty much the other way around in most cases?

Brandon: Pretty much the other way around in most cases. We measure that rate. It's under 5 percent. People, hey, I just want to talk to a human. And Maya just very adeptly ends the call. Okay, no problem. We'll get someone in touch with you. I'm going to send this to our team right now. And then it just pushes into the leads as this person want to talk to you.

Seth: Do you add anything to the background of the call when the AI is talking to throw people off and make it feel more natural? Like I was talking to a AI bot the other day, like I could tell it was a bot. I'm a bit of a trained ear. There was that slight delay and the voice itself sounded great, but I could hear this like background.

Seth: It sounded like they were in a call center. I think it was like a fake background ambient noise they put in there. Do you do anything like that? Or does that help to do that?

Brandon: It does help. And we've A-B tested the voices, the tone of voice, male voices, female voices, Southwest, the West, English accent. But yeah, a little bit of white noise in the background, like maybe a key type or like, yeah, a little bit of a call center sound, like they're in an office or something, helps because it just sounds more natural.

Brandon: And then we found with the voice, I think I was talking to you about this, 30-year-old female from Western US is kind of the voice, which is funny because when I went through and I picked the voices that I liked, none of them won out. It was this little bit of almost vocal fry, like that type, like this type of, that type of thing that is a little bit annoying to me, to be honest.

Brandon: But that's the one that has performed the best. So we test things like that.

Seth: And you may have already explained this, but just to confirm, if I'm using your service and the seller has talked to caller number one and then the voice AI thing, and now it's being pushed into my CRM and I'm supposed to call them, just help me understand how far is it taken and then how much further do I have to take it to get the deal closed?

Brandon: So I'll tell you what happens on the call and then the follow-up in between and then what the founder or the owner does. So on the call, pricing timeline motivation. For motivation, it's like, okay, just curious, when did you buy this property? 10 years ago? Oh, what was happening 10 years ago when you bought that?

Brandon: So that's a calibrated question. Then they're going to ask something. Then Maya will generally maybe do a mirroring question to go deeper into their motivation, capture the motivation. Then she'll get into pricing. And pricing will be something like, oh, okay, well, sounds like it's been sitting for a while. Have you had any offers on it?

Brandon: Everything is selling it. If so, what numbers came in? Or what ballpark might seem fair to you? What's the lowest number you would take? Okay, what's something that someone offered that you would definitely reject? And so she asks a bunch of probing questions to get the seller to say a price.

Brandon: Maya doesn't validate the price. She doesn't say yes or no or anything like that. She just extracts information and then continues to move the conversation around. She does all that without losing rapport by the way she asks questions and then does a similar thing with timeline.

Brandon: Those notes are all consolidated and passed into the CRM. So it's just a concise set of notes that's really easy to scan through. This is how much they said they wanted to sell the property for. This is why. This is a timeline and motivation. So it comes through with dense information and context, but Maya doesn't do any negotiating. That's one part of your question.

Brandon: The other part is kind of what happens there. So Maya has the conversation. At the end of the conversation, she does a warm handoff. She says, okay, our founder Seth is going to give you a call. Is today or tomorrow better for you? Okay, I'll give you a call later today. You're in great hands. He's one of the best in this area. He's done a lot of stuff out there. He's a local buyer.

Brandon: So then they're ready for that call right after they get a text message and an email that's automated. The text message just introduces the client. Hey, this is Seth. Just want to let you know that I'm the one you'll be talking to. I'll be giving you a call. That happens instantly. So they're warmed up there.

Brandon: Then they also get an email that's sent. And the email has a link to the person's website. So it would have your REtipster website or your land website, and then also a personalized video from you. And it's just a selfie video. Hey, this is Seth. Just want to personally introduce myself. I'm the person you'll be talking to or a local buyer.

Brandon: And so all of that happens instantly. They get a video, a text message, the website. They've talked to a cold caller. They've talked to Maya. So everything's very warmed up and they know who they're talking to. And that increases our answer rates. Because of all those multiple touches.

Seth: Well, I'm wondering, do you think there is a benefit to the land investor handling this call themselves instead of the AI, since it would eliminate yet another new introduction and give them more continuity in the conversation? Like, if I have nothing else to do with my life and I just have all the time in the world, like, would that have better results, do you think? Or is it always better to use the AI?

Brandon: It would take a really unique person to be better than Maya, you know. Because being able to do the same thing every time at a professional style of how to word the questions, it takes a lot of mental effort to speak that precisely and frame the questions so well and to not validate price when you're doing pricing, to remember to ask mirroring questions, to nail a calibrated question, walk them into their motivation without setting off any red flags that you're trying to sell them.

Brandon: You know, those are all things that, yeah, a human could do it, but it's unlikely. It's unlikely they're going to do it five times every day, five days a week and never miss.

Seth: It's interesting. Just yesterday, I saw this Facebook ad for this vocal imaging app that trains you to sound better with your voice. So you can like have more clarity and have more emotion and speak with more authority and all this stuff. So I got this app and I just did the first lesson yesterday and it says something.

Seth: And then you repeat it back to it and it sort of grades you on how clear did you speak? And how did you speak with authority and all this stuff and inflection? And not surprisingly, I did not that great. I thought I was doing good when I said it, but it just made me realize it actually takes a lot of energy to speak in a compelling way.

Seth: Some people are naturals at it, but other people like me have to work at it. You almost have to put yourself in this character mode. I guess the nice thing about this kind of thing is it's just always turned on, and it just always does it right.

Seth: But when you say the thing about it always saying the same thing again and again, I know with most of these AI knowledge bases, I don't know what or why it does this, but you could ask the exact same question in like different conversations and it will answer a little bit differently. It's not going to give you the exact same word for word answer.

Seth: And so I'm wondering, is there variance in how your AI agent responds? Like, does it ever respond in a way that's unexpected or even saying the wrong thing? Like how well could it be trusted?

Brandon: So we started with a lot of guardrails, and we slowly were able to take those frameworks away as it got better and better. At this point, I haven't heard it get stumped. It changes all the time. It's always doing something. And when I mean the same thing, I mean that it's walking the person down the same path.

Brandon: It's going to get their motivation. It's going to get their pricing. It's going to get their timeline. How it gets there at this point, it can shift a lot. Here's a good example. This one time I was listening to a call and the farmer says we flood that field. It's a crop field and we water that field.

Brandon: And then Maya says like, oh, what type of watering do you do? Do you do this or this or this? And she lists off these three types of like ways that farmers will water their crops. And I was like, I've never heard that. And then I look it up online and like sure enough, there's like these three ways, these different techniques.

Brandon: And she had a conversation about this guy about how he waters his fields and then brought him back into the timeline of selling. And I was like, okay, that's something a human never would have done, I'm sure. Well, that's a cool thing. It's like, you do have a knowledge base and set of instructions for this thing.

Brandon: But don't forget, it still has the knowledge of the entire internet at its fingertips. It's not like it only knows what you told it to do. It knows kind of everything. It's a good example of how it can draw on things like that that are outside the norm. What's unique about it is it has the whole internet, but we're also plugging it into our own personal database where we have information specifically about their property.

Brandon: And so we know who they are, where that property is, how many acres it is. We know all the property details and we're referencing those as well. That's why it's very catered and unique to them also.

Seth: Is there a way I can like test out this AI bot? Like if I want to call it myself and just see what it sounds like, is there a way to do that currently? Or will you ever have a way to do that?

Brandon: By the time this podcast comes out, a hundred percent. Yes. That'll likely be a link in the show notes. You hit that and you call and talk to the bot. Maya, I don't like to call her a bot. It feels rude. Actually, it feels like my baby. I don't like her being called a bot. It's Maya.

Seth: Yeah. Going forward, I will refer to her as that too. So if people want to check that out, whatever Brandon's going to do to make that possible, I'll link to that in the show notes. The show notes for this episode, retipster.com forward slash 252.

Seth: So go there and look for a link or instructions on how to do this. Other than saving time, which that alone is a huge deal, are there other benefits to having Maya handle this call? It sounds like in many cases it can handle the conversation better than even a good salesman could.

Seth: It never calls in sick. It always shows up on time, can handle calls 24-7, eliminates more tire kickers so that your customer doesn't have to. Am I catching everything? Is there any other benefit to this that I haven't thought of?

Brandon: The benefit is the five touches in five minutes type of feel, which is a marketing thing. But the land investor feeling like, yes, I'm a local. This isn't like BlackRock calling you. I'm a local, just normal person, small business, family-owned, but also I'm very professional. I'm going to get this done. We do what we say we're going to do. You know who I am. We're transparent. We're a solid business.

Brandon: That's a very hard line for people to ride. And I think that's the hidden magic of what we do really well is cold caller says, hey, we're a local land buyer. You're going to talk to Maya and you're in great hands. It's a warm handoff. Maya has this five-minute conversation where she keeps rapport, she asks all the right questions, she makes them feel heard.

Brandon: And then, hey, we're a local land buyer, you're in great hands, you're going to talk to Seth, he's our founder. And then they get Seth's website, they get a video from him, they get a text message from him, and then Seth calls.

Brandon: That cohesive, all happening in basically one call and then the next call while you're talking to the founder, is a really solid way to keep rapport, build rapport, and just get on the phone with the decision maker while maintaining all that rapport. I think that's one of the biggest hidden things.

Brandon: Holes in most people's land business is there's things in that pipeline that don't happen. Most of the time, it's not professional enough. The call doesn't come fast enough. They don't remember who it was. They never got an email or a text follow-up. It's just like someone's going to call them. I think it was today or tomorrow. They answer the phone when that person calls. Who knows?

Seth: I'll just mention some listeners out there may have heard me talking about the Stride voice AI agent, which is kind of similar and kind of different from what Brandon is talking about. I think one of the big differences is the Stride voice AI agent handles inbound calls. So it's not meant to call anybody. It's just if somebody responds to mail or a text or whatever, it picks up that phone call and takes the notes and then pushes that seller's information into your CRM directly.

Seth: Whereas Land AI, that's an outbound system. It's meant for finding new leads and going out there and sort of drumming them up along with the whole system that Brandon is talking about. And if anybody does want to use both LandAI and Stride, LandAI can very seamlessly push that seller's information directly into your Stride account when it becomes a legitimate lead. So just be aware, it's possible to use both. They play together pretty nicely.

Brandon: Yep. That's part of our onboarding as we get connected into our client's CRM. And so we can push everything that we're talking about directly in there.

Seth: So regarding the voices, have you tried using a Southern accent when calling people in the Southern US? Do you think that would help at all? Or are you pretty confident this vocal fry, Western US female voice is always the right answer everywhere?

Brandon: Most of the calls that we do are to the South and Eastern US. So that's mainly where it's been tested. The vocal fry is not heavy. It's gentle. It doesn't stick out. It doesn't sound like a Valley girl or like one of the Kardashians or something. It's a very mild vocal fry. But yeah, that's the one that performs best.

Seth: When we talked earlier, you said that you're using 11 labs to grab the voices, which 11 labs is pretty much the undisputed leader right now in voice AI applications. It's got thousands of voices. Most of the ones I've tried sound great. And you can even train your own voice into it if you want. Am I right that if I wanted to, I could train my voice into 11 labs and then have you guys use my voice to talk to people? I'm not saying I would actually do this, but I'm just curious. Is that a thing you could do?

Brandon: I want to test your voice and see how it performs versus Maya. Probably not that well. I haven't experimented with that yet, so I'm not sure. But I would imagine that's possible because I've seen where you can make a video avatar of yourself talking. So if that technology is there, then I'm sure that the voice alone is there.

Brandon: And that would be really interesting. I mean, what I love about the crossover of land and AI is just opportunity like that. We're right at the edge of a lot of these different interesting applications being possible. We're working on new things every day and love hearing ideas like that.

Seth: It's been about a year since I've tried this. It's probably improved since then, but I did try to train my voice into 11 labs. And I didn't think it actually sounded that much like me. Like it did, but it had way more energy to it than I actually talk. A testament to how lazy of a talker I am. It's way too excited when it was talking, but maybe they've improved it since then.

Seth: But on that note, back when I was trying that, it was kind of interesting because I could take English, translate it to Spanish and then plug it into 11 labs. And then my voice is speaking in Spanish or any other language for that matter. I'm wondering, could Maya start speaking in Spanish to people if it needed to? Like if it somehow was tipped off, hey, this is a Spanish speaking person. Could it just go there?

Brandon: That's a great call. Likely. I don't know at this point. And yeah, that'd be really interesting. That sounds like something that she might just do at some point without us even programming her. And she just was like, oh, switch over to Spanish. And it surprised me like that farmer in the Howie Waters' field thing.

Seth: Getting a little bit more technical for the AI nerds out there like me. How is this thing built? Like what technology are you using? I know we talked about 11 Labs for the voices. Like what is the brains behind this thing? Is it OpenAI or is it Anthropic or Gemini or how does this whole thing work?

Brandon: Our tech stack is we use ready mode as a call center. We live transfer and we use webhooks to live transfer to Maya that's built in 11 Labs. Maya engine is OpenAI. And then we push all of our data into Supabase, which is a giant database. And so that database has every seller that we're calling.

Brandon: So if we're calling Seth, it'll have your address and all the information about your property and as much information as we can have is in the database. So Maya connects to that database and connects to OpenAI, has the conversation, and then we transcribe that conversation and then those transcripts all get pushed back to that same record in the database.

Brandon: So now we have everything that Maya talked about and then we can ping that database and get all kinds of summaries and everything and we can push all those notes now into the CRMs. So that's what the flow looks like. It is an intense amount of development on the back end to plug all those pieces together, but it is possible.

Brandon: Someone could technically, if they're a full stack developer and they know what they're up to, build this for themselves.

Seth: Well, I'm wondering if ChatGPT goes down, which does not happen often, but every once in a while it does. Does that mean that Maya stops working during those moments?

Brandon: Yeah, I think the world will stop working if ChatGPT goes down. I'll stop working. I'll stop working.

Seth: Yeah, well, we haven't had any issues with that, but that could be an issue for sure. We still have our human cold callers, so probably should think of a redundancy plan for that.

Seth: Were there any other big technical problems you ran into when getting Maya to hold a real sales conversation instead of sounding like a robotic Q&A? It's kind of a difference between just information gathering and actually like having a meaningful discussion with somebody, like getting them a little further along. Was that hard to do? Like how many times did you have to reiterate and revisit the knowledge base to get it to say the right things?

Brandon: In our version history, we're at version over 200. And so that's how many different iterations that we've done. I did a lot of those personally. I think that I stopped personally iterating it like version 140. And so the biggest technical hurdles were getting Maya to do Chris Voss-style conversation.

Brandon: The active listening, the mirroring questions, the calibrated questions and getting her to do all that in a way that sounds natural because we would get her into things where she would be doing mirroring questions but then she would just mirror every single thing they said. Getting those questions to sound natural took probably the biggest lift because we could get her going through a conversation and asking all the questions, but getting her to ask them in a way that doesn't set off any red flags that keeps rapport that is done well.

Brandon: That's what took the most tweaking.

Seth: When you recognize these things that it's doing wrong, like mirroring everything, for example, you have to like manually listen to this, right? Do you just sort of pick random conversations and listen to those? Or do you listen to literally everything? Or do you only listen when a problem came up? Or I mean, you've spent a ton of time doing this, right? So like, how do you know what to pay attention to and when it's worth reiterating?

Brandon: I approach my business with like a bottleneck theory for my personal time. So I look at what's the biggest thing in the business right now that will take us to the next level. What's the key bottleneck? And then I take all my personal time and I laser focus on that and I let other fires burn. And I just tell my team like, hey, I'm not going to help with that. Sorry.

Brandon: There was a point from March until about October of this year where our bottleneck was a very well-functioning Maya. And so what that means is that I was listening to every single lead that came through and adjusting the prompt every single day and adjusting and tweaking and just being obsessive about fixing that. Because without that, the rest of the company doesn't survive.

Brandon: Yeah, that was kind of a back-to-the-wall experience, to be honest. We shut down the call center. We're not bringing in new revenue. And we can't start selling this until it works really well. And so that was six months of listening to every single call.

Seth: One thing that might be useful to you or anybody listening to this, if you're just curious how things are going, Stride Sales Coach, it's stridesalescoach.co. So it's designed for this exact purpose, where you can take a call recording or a transcript of it and drop it in there. It scans the whole thing and it grades it based on how well it brought the prospect through the conversation, how well it overcame objections, how well it handled closing the call, setting expectations, all this stuff.

Seth: And it'll even tell you like, when the caller said this here, they probably could have said this instead. It's meant to be used with teams or even for yourself. If you're not sure if you're doing the best job on the phone, you can drop your own call recordings in there. It'll grade it for you and help you understand what you can do better. That might be something if you ever want to like take it through that and say, hey, how well is Maya doing? Could we improve this or that? Just an idea. Might be worth checking out.

Brandon: Oh, I would love that. AI coaching AI. That's a dream. Who knows what might happen. We might hit the event horizon if we do that. Just Maya might take over.

Seth: Yeah. Do you ever find that when you try to reiterate or update the knowledge base or instructions does it ever make it worse? Because I've had this lately when I've been trying to build a Claude project and I see it doing it something a certain way and I'm like, no, no, change this. And I have it update the instructions, and then it starts messing something else up or doing it even worse than it was before. Do you ever encounter that?

Brandon: Yes. With Maya, since Maya's not working off of a prior context in my conversation with the prompt type of thing, so the iterations are basically standalone. Not with Maya specifically, but in my ChatGPT projects or Claude projects, sometimes I'll just completely start over. Like, okay, this has gone sideways at this point.

Brandon: It's funny, it gives me like a little like personal, I'm like, oh my gosh, I want to just throw my computer right now. I'll be like, all right, this has gone completely sideways. Give me a full detailed overview of everything we've talked about in this chat. Structure it in a way that ChatGPT readable and your output is basically like a full detailed summary of everything we've done in this chat.

Brandon: And then I take that and I put it into a brand new project. And I just say, hey, here's notes about what we're talking about. So you're up to speed. Let me know if you have any questions. And then I just start again because that gets very frustrating for sure.

Seth: I've wasted many, many hours trying to get a custom GPT or something like that to do what I want it to do. And like, I just can't get there. And maybe it's possible, but like, I'm not understanding what I need to tell it and it's not getting it. It's maddening. It just makes you want to go nuts.

Seth: It almost starts referencing back to like previous things I told it in other iterations that we should have fixed at this point, but doesn't remember the fixes. And I've gone down that rabbit hole many times. I'm sure anyone who's built or played with ChatGPT or Claude has.

Seth: Is it like ChatGPT 4.0 or ChatGPT 5 or like which one of those is it using to handle these conversations? I know some of them work a lot harder to get the information before it talks back and others are just very quick, but it's not thinking that hard when it does it. So like, which one do you have Maya use?

Brandon: I am not sure right now. Yes, we have used ChatGPT 4.0 and then I think 5.0 was faster, but less accurate. And so I think we went back to four for a little while. And I'm talking like a few months in the past because I haven't been doing the prompt myself or been in there myself because I'm not on that part anymore.

Brandon: But now there's 5.2 that just came out. And so we test those as they come out. There's a lot of different things that go into that. But yeah, the tradeoff is speed for reliability. The good thing is that this is a one-way street. What we get out of Maya today is better than we could have got with a perfect prompt four months ago. And it's going to continue to get better.

Seth: And on that note, with speed and accuracy and all that, at the time of this recording, whenever I talk to an AI bot on the phone, if there's anything that's a dead giveaway that I'm talking to AI, it's that it takes a little bit to respond. It's usually not the voice, like the voice sounds great, but there's like this slight delay, you know, like a little bit longer than a human would take to respond, like waits an extra second or so to reply.

Seth: Does yours have that same kind of latency or is there any way to eliminate that? Or is that just kind of what you have to accept right now with AI voices like this?

Brandon: Every once in a while, there's a little latency. There'll be like a half second, too long pause, that type of thing. Hopefully with this 5.2 update, we might close that gap a little bit. But yeah, that's something that we make a lot of our decisions on. When we ping the database for information, that comes at a cost of latency sometimes. So we have to be careful when we ping the database.

Brandon: So for example, in the beginning of the call, we will ping the database while the handoff is being made. So while the ring is being made, we'll ping the database so we can pull that information so that it doesn't slow down the call at the intro because that's a very pivotal time so that we like warm up the intro so it can happen quick. That's one thing that we try to optimize for is try to get that natural pauses down.

Seth: You've captured thousands of real seller calls. You've updated this training database over time. Does anything else stand out in your memory as like the strangest or funniest or most unexpected seller behavior that AI had to learn how to handle? Any other weird, funny stories or anything like that?

Brandon: The weirdest and funniest ones are not podcast appropriate. We just say that. I was on a team meeting. So there was like all the cold callers, like 30 or 40 cold callers on this call. And we had one of the top performers. And I asked her, hey, what's the weirdest thing someone's ever said to you on a cold call? Just curious, you know?

Brandon: And her answer, I was like, oh my gosh, I should not ask that. Anyway, people say really wild stuff on the phone. Yeah. We'll just leave it at that.

Seth: Yeah. So how many clients do you currently have that are using this service? Like, has this been pretty well adopted by the land industry or where's that at?

Brandon: This is brand new. So we've got 20 clients, 20 people using it. The reason we have way more cold callers than that is because a lot of our clients have scaled their volume up. And so they require two, three, four, five callers. One of our clients is at 700 leads per month. So, you know, they take multiple cold callers to get that many leads every month. But yeah, about 20 teams in total.

Seth: So when you say 700 leads in that example, is that after the 80 percent are kicked out, like that's the 20 percent that's left is 700?

Brandon: No, that's the total leads that come in. So what their numbers look like is 700 leads per month, which turns into about 140 pre-qualified leads that they need to actually talk to. So they have two salespeople who follow up with them. And then out of those 140 leads, they are converting about 7 to 10 contracts per month.

Brandon: Their total numbers, as it stands with us, we have a dashboard that keeps track of all their numbers for them. They've pulled in about 4,000 total leads. They've had about 1,000 of those pre-qualified so that they've had to talk to. And then they have 50 contracts that they have signed. So that's what our dashboard shows for them.

Brandon: That's a good representation of the numbers and how they scale. But the math broken down is 100 total leads turns into about 20 pre-qualified leads turns into about one contract. And that's where we look at it as five times as effective as cold calling because it's the 100 leads turns into one contract. Instead, it's 20 leads turns into one contract.

Seth: Let's talk about your pricing. So your pricing is fairly similar to traditional cold calling, maybe even a little bit higher. So if the cost is the same or higher, what's the main reason your clients keep using you? What is the aha moment where they realize this is better than just using a normal cold calling service? If you had to boil it all down, does it just come down to higher lead quality in three words? Is that the answer?

Brandon: Higher lead quality and full done for you operations. For our clients, it feels like they're the CEO of a very well-run land operation. They're just getting on the phone and everything up until that point has been taken care of. And they have this beautiful lead with all the notes, with links to the comps, with transcripts, and they just can press a button and call it.

Brandon: And taking data, we have a data manager that pulls all the data for them, skip traces, it scrubs it. We've got cold callers who make the calls. We've got Maya. We push it into maybe a CRM-like stride. And then we have the follow-ups. We have a CRM manager, and then we have the automated follow-up.

Brandon: So everything that happens up until that point that they get on the phone is all a very well-run operation. So that has a hidden feeling for people where it just feels like, okay, I'm at the head of something serious here. That's nice. What we see with our clients is they end up getting better on the phone because they're having more actual land conversations.

Brandon: They're not having 10 land conversations to get to two conversations that were worth it. They're just having two conversations that were worth it. And that mentally people get more sales reps. They get their sales reps faster. They get more in the zone of like, oh, here's how these calls go. They're not dealing with people like all kinds of this much wild stuff. They're just, here's the people talk to them. So they get good on the phone quicker.

Seth: Is this kind of thing available in a conversation AI chat bot? Like if somebody wanted to use the same knowledge base, but handle these conversations over text or a chat bot on your website or the DMs on Facebook or something like that. Is there a reason they couldn't do that? Or do you have that available?

Brandon: I don't have that available. That's an interesting thought for our roadmap. Yeah. Right now, one of the big things that we're working on next is the nurture sequence. So being able to call outbound to warm leads. So if there's leads that you've contacted in the past, or you have a database of leads being able to take this Maya and call outbound to those people you already have a business relationship with. That's kind of the next big thing on the roadmap.

Brandon: Each of those pieces on the surface, the conversation and the theory is very simple. It's when we start plugging the piping in of how to actually do this at scale and deliver that to people. That's kind of where the bottleneck is. So that's what we're working on.

Seth: I mean, what do you think needs to happen for Maya or a different named AI to be able to actually negotiate and even close deals, get contracts signed? Like, could it really go that far where, like, the land investor doesn't have to do anything? Contracts will show up in their inbox. Like, what would need to happen technically for that to become a reality? And how far are we away from that?

Brandon: I'd say 2027. We already tested Maya negotiating price, and that's something that we did well on. The issue was getting a solid price that we want to be negotiating fast enough. Pulling an API from a land portal or something like that. Those prices are really good, but they're not necessarily exactly what the land investor might negotiate.

Brandon: So let's say that we ping land portal and we know that the price of this property is $100,000. Some land investors might look at that and they want to flip it. And so they want to offer 50 grand on it and they want to negotiate 50 grand. Another land investor might look at that and know, hey, I can subdivide this and they want to offer 100 grand. They'll give a full price for it.

Brandon: Getting that negotiation price to Maya is the main bottleneck, but it's definitely possible. There's some manual workarounds and we'll find a solution to that at some point.

Seth: Looking ahead a few years, 2027 or 28, do you foresee AI replacing every role that there is or every entry level role on the acquisition side, or maybe, I don't know, like how far do you think AI will go in eliminating the need for humans? Like where do humans have to be involved?

Brandon: The biggest thing right now is the quality underwriting and the strategy, you know, getting eyes on a property, deciding what your strategy is to deal with it. I don't want to say that's not going to go away, but that one's got a longer term because someone has to take money personally out of their pocket and pay for something, and that person's going to have an idea and a strategy of how they want to use land.

Brandon: And it's not necessarily because Maya can't make those decisions. It's because the human's going to want to be deciding how to bet their money, basically. I see that kind of as the ultimate thing that will last the longest. The negotiation of the price and all of that, I can see going away in 2027. I mean, we've already worked on that, and that's possible. So that's just some technical hurdles and some brainstorming solutions. We don't have a solution right now to be straight there, but I think we will get one.

Seth: Yeah, that underwriting thing, I think up until yesterday, I would have mostly agreed with you, but not exactly an apples to apples comparison. But I was looking at this parcel of land in Colorado City for potential self-storage development. And I had a lot of information that I had gathered, like the size and the coordinates and the price. I had uploaded like a commercial evaluation, so like I gave it lots of details, but I was using ChatGPT Deep Research and I told it, in addition to those details, what I was hoping to do and other questions I had about it.

Seth: It came back with some clarifying questions and then it did some work for like 20 minutes. It blew my mind how much information it found, including like competitors that were nearby and their pricing and the economic details about that city and the fact that it was kind of going down. It wasn't really trending up and just stuff that like, I don't even think to ask this stuff.

Seth: And I wasn't really asking for a yes or no answer from it. But if I had asked that, it probably would have given it to me. And I guess the point I'm making is even from my experience in the banking world, like it did a better job than most bankers would, than most loan committees would where there's like 10 people around the table and they're all looking at it. It just saw everything.

Seth: And land, I think, is still very tricky because, I mean, you know how hard it is to comp land sometimes when there just literally is no comparable property to the one that you're looking at. So it's a complex issue to figure out. And sometimes there just is no black or white clear answer.

Seth: But if it could do what I saw it do yesterday, I don't think it's that far off in terms of capabilities and the information it can reasonably think through and what to scrape and how to put it together. So I don't know. It's just interesting, and it's a fascinating time we're living in.

Brandon: It is. There's going to be a lot of consolidation in the land business. I think that there will be people who start leveraging this and kind of stay on the cutting edge, like probably a lot of your listeners are playing with AI. And I think those people are going to have a massive amount of leverage and that we're going to have a bit of a barbell here where maybe the real small flips keep being easy to do.

Brandon: But I think that a lot of it consolidates with people who are leveraging technology and automations and these transactions start to get really fast. People can do high levels and probably people start getting funding and things like that to fund lots of transactions. This consolidates quickly over the next couple of years would be my guess.

Seth: Well, for some of the AI nerds out there who are listening to this and they might be tempted to think, I can just build this for myself. If somebody tried to DIY what you've built, where do you think they would hit the biggest walls? Or if they could come up with like a simpler version of this, how far could they take it, do you think?

Brandon: I think our two moats are the call center. So cold calling is tough to manage and build cold callers. And the other is the database of information that we have. So the thousands of real seller calls that we have recordings of in a database, that part is hard to replicate.

Brandon: So those would be the two things. But I mean, the technical lift is probably the easiest of the three of those to get past. Someone who's technically savvy, I mean, I'm impressed with how far low-code people like myself can build things.

Brandon: People can leverage things and build a lot on their own. So I wouldn't put it past people with a lot of work and head down time to build like a little V1 of this themselves.

Seth: If somebody wanted to use direct mail, but not your call center, like say I want to send direct mail out, say, yeah, give me a call. Could I just have that call go directly to Maya that has that conversation?

Brandon: Not yet, but that's something that's on the radar. Did I just put it on your radar right now?

Seth: Maybe.

Brandon: So we have Maya Nurture, which we want her to be able to call outbound to other leads. But then the idea of the number that they're calling outbound from also being able to receive calls is definitely something we'd love to plug in someday.

Seth: One last question, just for fun. If your AI could take over one part of your personal life, not business, what task would you happily hand off forever?

Brandon: Just anything with logistical planning. When I have to book a plane flight to Savannah or something to go to a land investor event, it rips part of my soul out. I hate logistics. I'm a bigger picture person.

Brandon: So when I get into things like that, it's funny how hard it is for me. So on that note, I probably should have a personal assistant. I don't right now. And that's kind of bad on my part.

Seth: Have you used ChatGPT agent mode at all?

Brandon: Actually, I haven't. I use the projects a bunch and I played with Agent when it first came out, but I haven't. Is that getting decent, like where it'll go and do something like that?

Seth: You know, I actually was playing with it a bit this week. I couldn't really get it to give me what I was looking for. Like, I definitely saw it doing a bunch of work, but it was bringing me back stuff that just wasn't useful. It's like I didn't really understand what I was trying to say.

Seth: So it may be a me problem. I don't know. But it looks like there's a future there. I can see a day when it will be awesome, but I've not personally discovered that awesomeness yet.

Brandon: I tried to use it to change the colors in a spreadsheet. So I had like all of this whole spreadsheet and I just wanted all these five sheets to be blue instead of yellow. I like sat and watched it work for a half hour and it came back and like wasn't done. I was like, okay, this isn't there yet. I tried it, but that was like a couple months ago.

Brandon: My favorite thing, and I think the highest leverage thing that I use, is ChatGPT projects. And Claude has projects also. But I have projects for all kinds of different things. Even for things like health and fitness.

Brandon: And I'll ask it, hey, I'm on a plane flight. I didn't eat. I have a headache. What should I eat to keep my macros in place? And it's like, okay, go to the store and buy these things without blowing your diet. I've got all kinds of cool projects for every part of my life.

Brandon: I've probably got 15 of them. So that's my favorite thing to use it for.

Seth: Well, again, folks, we've set up a referral link for REtipster listeners. If you want to try out Land AI and get a discount through our link, you can go find that at retipster.com forward slash L-A-N-D-A-I, Land AI.

Seth: I'll also have a link to that and several other things we talked about in the show notes for this episode. Retipster.com forward slash 252. If Brandon is able to get whatever figured out so that you can test out Maya, if that's able to happen, I'll be sure to throw that in there as well.

Seth: Brandon, again, thanks for coming on. Any closing thoughts or anything else you want to share?

Brandon: Wonderful to talk with you. I'll have that Maya ready so people can test her out and have conversations with her. And I can read all the conversations that come through. So I look forward to seeing what interesting things land investors have to say when they talk to her in those tests.

Seth: Awesome. Sounds great. Thanks again, Brandon. Thanks everybody for listening. And we'll talk to you next time.

Seth: If somebody wanted to use direct mail, but not your call center, like say I want to send direct mail out, say, yeah, give me a call. Could I just have that call go directly to Maya that has that conversation?

Brandon: Not yet, but that's something that's on the radar. Did I just put it on your radar right now?

Seth: Maybe.

Brandon: So we have Maya Nurture, which we want her to be able to call outbound to other leads. But then the idea of the number that they're calling outbound from also being able to receive calls is definitely something we'd love to plug in someday.

Seth: One last question, just for fun. If your AI could take over one part of your personal life, not business, what task would you happily hand off forever?

Brandon: Just anything with logistical planning. When I have to book a plane flight to Savannah or something to go to a land investor event, it rips part of my soul out. I hate logistics. I'm a bigger picture person.

Brandon: So when I get into things like that, it's funny how hard it is for me. So on that note, I probably should have a personal assistant. I don't right now. And that's kind of bad on my part.

Seth: Have you used ChatGPT agent mode at all?

Brandon: Actually, I haven't. I use the projects a bunch and I played with Agent when it first came out, but I haven't. Is that getting decent, like where it'll go and do something like that?

Seth: You know, I actually was playing with it a bit this week. I couldn't really get it to give me what I was looking for. Like, I definitely saw it doing a bunch of work, but it was bringing me back stuff that just wasn't useful. It's like I didn't really understand what I was trying to say.

Seth: So it may be a me problem. I don't know. But it looks like there's a future there. I can see a day when it will be awesome, but I've not personally discovered that awesomeness yet.

Brandon: I tried to use it to change the colors in a spreadsheet. So I had like all of this whole spreadsheet and I just wanted all these five sheets to be blue instead of yellow. I like sat and watched it work for a half hour and it came back and like wasn't done. I was like, okay, this isn't there yet. I tried it, but that was like a couple months ago.

Brandon: My favorite thing, and I think the highest leverage thing that I use, is ChatGPT projects. And Claude has projects also. But I have projects for all kinds of different things. Even for things like health and fitness.

Brandon: And I'll ask it, hey, I'm on a plane flight. I didn't eat. I have a headache. What should I eat to keep my macros in place? And it's like, okay, go to the store and buy these things without blowing your diet. I've got all kinds of cool projects for every part of my life.

Brandon: I've probably got 15 of them. So that's my favorite thing to use it for.

Seth: Well, again, folks, we've set up a referral link for REtipster listeners. If you want to try out Land AI and get a discount through our link, you can go find that at retipster.com forward slash L-A-N-D-A-I, Land AI.

Seth: I'll also have a link to that and several other things we talked about in the show notes for this episode. Retipster.com forward slash 252. If Brandon is able to get whatever figured out so that you can test out Maya, if that's able to happen, I'll be sure to throw that in there as well.

Seth: Brandon, again, thanks for coming on. Any closing thoughts or anything else you want to share?

Brandon: Wonderful to talk with you. I'll have that Maya ready so people can test her out and have conversations with her. And I can read all the conversations that come through. So I look forward to seeing what interesting things land investors have to say when they talk to her in those tests.

Seth: Awesome. Sounds great. Thanks again, Brandon. Thanks everybody for listening. And we'll talk to you next time.