The AI Playbook for Property Managers: Where to Start and What to Expect | Lindsay Liu

A Podcast | Lindsay Liu

Pete Neubig: Welcome, everybody, to the NARPM podcast. I'm your host, Pete Neubig, the voice of NARPM, and I have a distinguished guest with me, Lindsay Liu. She's the co-founder and CEO of Super, the voice AI platform for property management. Super is the early leader in vertical focused AI receptionists capable of identifying intent and managing conversations across departments. Lindsay, thanks so much for being here.

Lindsay Liu: Hey, Pete. Thanks for having me.

Pete Neubig: So I know AI is kind of like the cat's meow right now when it comes to topics, and so I appreciate you jumping on here and helping us learn more about AI and what Super can help us with. So I like to ask this of all my AI experts. What is your philosophy on AI? And where do you see this thing going in the next three to five years? Ten years is way too far out for AI. We know that.

Lindsay Liu: Way too far.

Pete Neubig: The next year or two.

Lindsay Liu: Okay. All right. Next year or two is reasonable because I feel like it's the Wild West right now, but I think the way that I view what's happening right now is AI is a tool, and I think if you think of it that way, it actually becomes a lot simpler, right? It is a tool just like any other tools that we've learned to leverage and to use and to make our work outputs more efficient or more effective. And when you look at it that way, it is something that can be leveraged, it is something that can be used to automate, it can be something that you can use to collaborate with, right? So however people want to leverage that tool, as long as they are thinking about it as a means to solve a problem for them and to spark a conversation that they're maybe stuck on a problem, it can kind of help from that very earliest stage all the way through to a detailed implementation as well. So that's kind of how I'm using it, it's how we're coaching our teams to use it, and that's certainly how we're applying it for our customers as well.

Pete Neubig: So where do you see it, like AI has been changing rapidly, even just the last six months with vibe coding now and all that stuff, where do you see the leverage of this tool over the next year?

Lindsay Liu: Yeah, I think what's really interesting is I actually think that a lot of work actually gets harder in the next year, and I'll tell you why I think that. So all of the lower leverage tasks we will be able to use AI for, I think that's what's on the horizon right now, so really repetitive, lower value activities, an example of that would be a lot of people I'm sure today are using it to write emails for them, or at least draft something for them. So taking that mental load of kind of the basic thing off of your plate. For me, what I'm seeing in my day is that's shifting my work away from some of that. I'm not necessarily gaining more time though, the leverage that I am gaining is I get to do either deeper focused work, and I'm using the tools to help me with that as a brainstorming partner, as a strategy partner for that, and it is enabling us to be able to output at a velocity that we probably just have not been used to before, and what that means is I'm finding, at least for myself, I am using kind of the most challenging parts of my brain to solve problems for more time than I would be before, where you kind of get those breaks to do the smaller, simpler things, or the expectation of your output wasn't quite as much. So I think that's one of the big shifts that we're going to be seeing is kind of not just what work gets done by humans, but how we work, and how we have to think about the pacing and the cadence, and the velocity of our output, and how we're going to balance that as well.

Pete Neubig: I've heard some people say that if you're a problem-managing firm and you're not using AI in the next year, you're going to be out of business. Is there any truth to that statement?

Lindsay Liu: I think it's a little hyperbolic, coming from the AI person, I'm probably anti-selling right now, but I do think, so, look, I think that it's inevitable, right, and I think we have customers that come to us and kind of say, look, I know this is now inevitable, even if I'm not fully comfortable with it, I think I have to try something, right? And so I think that is absolutely the case. In property management, there are a lot of activities that absolutely you're going to gain more leverage by using AI for it, right? Where I don't think it's going to replace anything right away is there's a lot of things that are very relationship-based, it's human to human, right? Do I trust my landlord? Do I trust my property manager, right? Those are things that you can only build through repeated behaviors, and where humans are going to be part of that. And so I do think there are teams that are going to really excel if you're especially in a premium market and you're serving a customer that's expecting a human to be on call for you for every single thing. I think there are some management companies that will be able to at least find some differentiation in the interim with that, but for the most part, I do believe that it's coming, it is, again, as a tool, it's going to help your business overall. So I think it's about finding the right places to apply it first.

Pete Neubig: So I don't have any data to back this up, but my feeling is that most people that own homes, especially investment properties, are a little bit older. The price of homes for these young kids is really just really difficult. Even investment properties is becoming more unobtainable. Do you think that AI would, somebody's going to out there creating a AI property management software where the investor doesn't know, does no longer need that property manager and just bypass the property manager and says, well, I can do this on my own because I have this AI magic software. And I think the answer goes back to probably not because of the relationships, but I think there are going to be some people that probably will use that solution.

Lindsay Liu: I think there will be people that will try. I think one of the things that will make that I do think makes property management quite defensible is it is not one discipline. It's not one thing. Right. So it's not just maintenance, not just leasing. It's not just rent collection. Right. It is a collection of skill sets and specialties that come together to make property management. So I think that's one of the things that makes this industry particularly difficult, frankly, to break into is there's a lot of different areas of expertise as a technology vendor. Right. Thinking about all of those workflows that I have to be an expert in and I have to be great at helping to serve. There's a reason that you see a lot of vendors focus on very specific departments within that. So I think that will probably be kind of near term. Right. I think there will be people that will try. And to be quite frank, I think those are the tech players that are a little naive around what it actually takes to do something.

Pete Neubig: Or people that already are self-managing.

Lindsay Liu: Right. And so maybe if they're self-managing already, it's like my main pain point is this. Sure, you could solve that one part of that for sure with AI tools. But I think if you're saying, hey, I've got a portfolio of 10 units that are scattered in a couple of different locations and I've been using a property manager. Do I as the 10 unit landlord want to take on all of that myself? And can I automate that to the same extent with tools? And how much margin am I really going to save off of that? Right. So I'm saving on some management fees here, but maybe I'm paying more in other ways. I think there's a few questions to answer.

Pete Neubig: No matter how much AI does, there's still time that's needed. Yes. I also think that, again, I bring the age thing up because the kids that are coming up today, they're going to be born with using AI. Like literally AI is just going to be very native to them. AI is not native to a guy like me in his 50s or to somebody even in their late 30s. And I believe these are the people that own the properties anywhere from like 35 to 55 or 65. And my opinion is it's going to be hard pressed for somebody in their 40s and 50s to say, hey, I'm going to go ahead and just lean into all this AI and kind of do it myself. Any kudos to that?

Lindsay Liu: No, I think it cuts both ways, right? So the counterpoint to that is AI tools is making software more accessible than ever before. It's easier to just cat what you want, right? Than to have to go build out a whole workflow and kind of drag and drop things and learn how to use an interface and do all of that, right? So I think one component of it is that, frankly, accessibility of using technology. I think the bar is getting lower for that than ever before because now you have tools, even like with cloud, I just say what I want and it outputs something. And I may not need to know how that's technically done, but I am getting something accomplished there. I do think some of that is changing there. But I think to the earlier points, to stitch together a workflow, we can even just take one example, which is leasing, right? To take it from marketing, so I have to advertise the property, right? I have to price it. I've got to get it syndicated on the places where people are finding it. I then have to field all of those inquiries. I have to make sure that I'm responding in a way that is legally acceptable as well, right? And then I have to make sure I'm screening all of these people. I'm then preparing a lease agreement. I am getting that signed. I'm then preparing their move-in. There's a lot of different pieces to just that one workflow there. I think there's maybe, you know, there's like a golden rule we can probably come up with is, yeah, maybe like 80% of that you could automate to a high fidelity. And there's always going to be that 20% where is that landlord really going to feel equipped to take those two out of 10 steps that aren't able to be automated or maybe, you know, require a human in the loop at that point? And what is the value add to them? I mean, I do think that we may see some management models shift as far as it may not just be, you know, we do everything for your property. It might be that you start to see some unbundling of these things as people are able to say, well, I've actually gotten to this part of that process really well. It's this other half, right? Of it that I actually need the support for. So I do think we may see some shifts there as individuals start to kind of see what's possible with technology as well.

Pete Neubig: If I'm listening to this and I work for a property management firm, you know, the fear mongering out there says, AI is going to take away all of our jobs, right? I mean, not just in property management, but just all of our jobs. Pretty soon, we're just going to be a bunch of just people just hanging out doing nothing all day. Do you see AI taking away all the property management jobs?

Lindsay Liu: I don't see it taking away all of the jobs. Now, I think, again, I think it's the biggest change we're going to see in the next few years is what work people are doing, right? And so it has to be the highest leverage work. It's just got to be the most difficult stuff, the stuff that requires a subject matter expertise to it. Sometimes it's subjectivity, right? As well, just purely, you know what, Mrs. Smith, I know you're going through a tough time at this moment. I'm going to make a judgment call about the situation right now because I know that that's going to pay out, right? Using my experience in the end. So I think that we're going to see the roles shift quite a bit. And I do think you will see a downsizing of the number of roles that are needed, but each role is going to be that much more critical within the organization.

Pete Neubig: Do you see new roles getting created? Like a prompt, you know, I don't know, prompt engineer or somebody like, let's say somebody brings on super. Am I going to need somebody that like, you know, manages that AI or and just takes escalations and is helping train the AI? Do you think you'll see some of those positions get created while other ones may die off?

Lindsay Liu: Yeah, absolutely. I do see those types of roles emerging, especially if you're at kind of a more scaled company, right? At a certain point, the running joke is the AI is going to need managers, right? So you may not be the person doing the work anymore, but you need to know enough about what that work is and what good looks like in order to oversee all of the work that your AI is doing in order to know it's doing a good job. I need to go and or maybe I need to update the training here. I need to improve on that there. Now, our expectation today is not that our customers have to be prompt engineers. So we actually provide that as part of our onboarding team in-house with people that specialize in that and know how these systems work. But I absolutely see, I do see teams wanting more control of that and wanting to be empowered to do that. And so I think that is a direction that things will be headed.

Pete Neubig: You know, as I talk to folks like you and the vendor guys, I'm hearing that there's actually going to be a need for remote team, right? Because they're obviously not as costly as somebody local to be these managers of the AI. Do you see that's a good fit for a remote team to be these managers? Or do you think it needs to be somebody local?

Lindsay Liu: I don't really think actually location matters so much. I think the biggest thing that, at least this is what I'm screening for in candidates now, is curiosity. I think it's like today, knowledge is ubiquitous, right? So it's like I can have knowledge on anything I want just by asking a simple prompt, right? So the ability to understand something has gotten so much lower. But it doesn't matter if you don't know what questions to ask, right? So it's like if you are not inherently curious about what is this problem? Why is this happening? What can I do to improve it, right? Those are the types of things that are going to make really good employees in the future and really good managers of them because they're peeling back the onion, right? They're not just taking things at face value and saying, well, it is how it is, it can't be changed. And so I think it's more of a mindset that you're looking for over location, really.

Pete Neubig: Yeah, that's a great point. So if somebody listens to this and they do own a property management company, why should they invest the time and the money into AI?

Lindsay Liu: Yeah, I think because any investment that you make is going to pay dividends for you. And that is actually very different from what we've been reliant on in the past as far as having to do human-based training, right? So it's like I hire someone, I train them on something, doesn't work out, they leave. I'm literally back to zero and I have to start over again. And I do believe that's one of the strengths of property management knowing that there's a lot of turnover in the industry as well. That PMs have had to have better protocols, SOPs, right? So if you have your training processes down, if you have your information on kind of how to do this, what the expectation is, what the success look like, then by the way, you can also apply that to your AI. So take all of that and you immediately have a training database for your AI system. So I actually think the lift is not as high as people think. It's sometimes just the organization of it there, but you're going to get ongoing dividends from that.

Pete Neubig: That kind of leads me into the next question is like, what should a PM company do to prepare for AI bot like Super?

Lindsay Liu: Yeah, it's almost always, I would say the number one thing is get your processes and SOPs in place. I mean, it's such a boring answer, right?

Pete Neubig: But the majority of that is in the owner's head.

Lindsay Liu: I mean, that is what it is, right? Like we will kind of be like, okay, so you want it to do this. You want it to answer all of these types of leasing questions. Well, do you have answers to that somewhere? And they're like, give me a week, I'll get back to you. And it's like, oh, that took me a while. I had to write it all down for the first time, right? And so if you want to be able to leverage the distribution gains of AI, I do think one of the things is it has to be documented. Unless you're willing to sign up for like Neuralink or something, it's not going to be able to get in your head. So not exactly that answer. I was joking with my team the other day. I was like, I think I'm ready to like have something in my brain to just try to understand what's going on here and put it on the paper for me. But I think it really is. It comes down to just like training an employee, you have to set really good expectations. That's really all it comes down to is what knowledge do you want it to have? How do you want it to handle things? What does good look like? It's the same thing that you would apply to bring on a new hire.

Pete Neubig: Do you think that's the biggest common mistake people make is like they want to turn on AI and they want it to work and they haven't done all the asking of questions and putting the answers down on paper. Is that kind of the biggest common mistake that you're seeing with your clients?

Lindsay Liu: Yeah, and I wouldn't even call it a mistake. It's more like the training wheels, right? So we often talk about like I advocate for a crawl walk run no matter what with customers, which is really just we can start with the basics, right? It doesn't have to know the answers to everything right away, but we do need to know who to route to so that that appropriate person can answer. Or maybe we need to know what the top 10 things it needs to answer and sure, that's going to cover 30 to 40% of things and then the rest, right? So we can incrementally build out on that. So I do believe that getting, kind of just nailing the basics is kind of part one. The mistake that I see is more I expect, I have very high expectations of this because I think what's happening is people are having magical experiences with AI, right? They're like, oh my God, that's amazing, it did this. And so then they go and they apply that to specialized tools and they're saying, cool, go and run with it. But your expectations of a specialized tool, that's operating on behalf of your company is very different than a private chat you and Claude are having, right? And so the level that you need to apply to, what does this mean to make this production ready to release this to my customers looks a little bit different there as well. And so I think it's really just sometimes aligning expectations and understanding there is a tiny bit of work that goes into it, just like any other thing that you have, right? In AI, like if you give it a one line prompt, you're gonna get a type of response that is the quality of a one line prompt. And then as you continue to prompt and you get more detailed, the quality of your response is going to improve. And so I think that's just the way to think about it is there's an iterative way to improve the quality of the responses, but there's also a path where you just start with keeping it simple and then building and layering on it as you go.

Pete Neubig: So let's break this down into bite-sized chunks here. I own a property management firm. Let's say I have 200 units, single family. What's the first step? Like, do I have to determine where I want AI in my business? Like what's kind of like step one?

Lindsay Liu: Totally, yeah. I mean, I think that definitely is step one is what are the biggest problems that I am facing today that could have either an AI or automation applied to it, right? And I think a lot of PMs have, by the way, done a really good job of automating a lot of those things. And so maybe part of the question is even, I have an automation today. What can I do to make that even better, right? So instead of, for instance, having the same email response go out to every applicant, can I now have a tailored response that's AI generated, right? That's going out in response to them. So I think there's ways to think about like biggest problems, where is my time going? And what are the things that, and I think the third part of it is, what are the things that AI can uniquely do well? And that's the interrogation, I think, as far as what's the right partner, which vendors are going to be there to solve that problem really well. But I'd always start with the problem, not the vendor.

Pete Neubig: Do you think like, I kind of lean towards like, hey, this is the biggest problem. Let me go ahead and find a solution. What about like, instead of thinking of the problem, like what about like, hey, I have this system, it's working pretty good, but I think AI would fit much easier, kind of like the first time, my first introduction to AI, to really make it better. Kind of like in your example, like, yeah, we send this email all the time, but like, hey, let's build AI for a process that might be working, but it might be easier to implement. Is there, you know, is there kind of a timeframe or is there like, hey, like when you're talking to your clients, like, hey, this might be easier to implement versus this challenge. Like, what do you think about that as a potential first step?

Lindsay Liu: Yeah, look, I think for a new customer of ours, I always want to start with problem because that's us saying in aligning, there is ROI on solving this, right? So like, if we don't have ROI on it.

Pete Neubig: No problems.

Lindsay Liu: Exactly. Kind of like, it's all, if it's a nice to have, that's not a great place for anyone to be. Now, I think once you have that in place, then you could start to say, well, what are some of the pilots that we could be running here, right? And I think, what are some of the experiments that we could be doing? So we now know what these capabilities are. What if we said even a 10% lift on conversion in this one area would improve our outcomes, right? What could we do to do that? So you're basically applying a very controlled experiment to it. I think that is a great starting point. And I think that teams can do this on their own as well. They don't need a technology partner necessarily to solve that piece for them, right? So if you're kind of a company that's saying, I'm not ready to bring on anything quite yet. I'm not ready to invest all of this time and energy and also the budget maybe, right? Towards this, but I do want to see what it can do. Yeah, find a small thing, right? That's fairly low risk, but that also can give you a sense for the types of incremental gains that you can get. And just see if you can, like you said, everyone's vibe coding out there. See if you can kind of apply it into that one part of it. And then if you're seeing that, then you can go and say, cool, now I understand what would it look like if I had this whole workflow and then go and find the right partner for that.

Pete Neubig: Okay, so now let's say I define the problem that I want to solve using AI technology. What's next step two? Do I have to process flow out the process? If I don't have, because a lot of people have process flows, but they're super outdated. Is that the next step? Or do I look at policy? Like, what do I look at next in your, and it could be 2A, 2B, 2C, whatever.

Lindsay Liu: Yeah, well, I think it's going to depend on where you're applying it first. I'll give you, obviously, this is what I think about all day. So I'll give you the example of phone calls, right?

Pete Neubig: That super solves a big problem. When I owned Empire, phone calls was the biggest challenge for us.

Lindsay Liu: Like, to the point where- We were just a few years too late, Pete.

Pete Neubig: I was a few years too late. Believe me, believe me. It was a problem that I looked at, you know, a big fire that I'd like tried different size hoses and water and sand. And just, you know, it took me forever to kind of figure it out. And technology just wasn't there yet. So let's talk about that. So now, I have this challenge. I'm getting way too many flipping phone calls. By the way, my challenge was every email we sent out, it was like 14 pages long. And at the end, it said, give us a call. So once I cut it down to bullet points and said, stop giving us a call, more actually, the phone- Stopped calling. So I define a problem. So now the next thing is, I'm looking at like for you and phone, I guess the phone tree and like where, process flow, right? That's kind of a flow. Yep. So now we're looking at building a process. Not a lot of people, Lindsay, can really sit down and do that, right? Like just you said, the inquisitor person is a great person for prompting. That person typically is not good for process flows because it's super detailed, right?

Lindsay Liu: It's like the opposite kind of- Yeah, you need like the organizer. I think there are archetypes, right? For these types of folks. I'm sure you do that with your teams. Well, yeah, I mean, I think that for- Okay, so let's just say, okay, I'm having this issue with phone calls. I've figured out the top problem is that the team is just getting the same, let's just take leasing as an example, the same 10 leasing questions over and over again, right? So what do I want to happen there? So maybe it's these nine questions, let AI answer, right? I'm comfortable with that. I think that it can do a good job and it can just kind of take that off of my team's plate. There's one of our top 10 questions that I do want a team member to take. I want a human to take that one. So let's say it's a section eight question, right? Somebody asked about that. I don't even want to, you know, I just want that to go to a human right away and make sure that that gets taken care of in an appropriate way. Great, that's a rule right there. So that's an immediate kind of like a first process kind of a branch, right? Which is what should humans take? What does AI take?

Pete Neubig: Okay, I love that. And then the next layer on top of that would be the actual flow of who gets where.

Lindsay Liu: Exactly, yep. So when it is, and I think that's also, the when is important as well. We set up, you know, rules for after hours versus business hours. And I'll tell you, most of our clients do have some of that in place, but sometimes they're thinking through it for the first time. They're like, oh yeah, do I want like, do I want those types of calls to go to someone after hours? I know sometimes someone looks at their phone for these things, but sometimes they don't, right? Like they're kind of working through some of that process in real time with us. And so I think, you know, one way to look at it is you can start with what's my ideal and see if the tool can help you with that. Or if you're kind of like, great, it's already solving this huge thing for me. Now what's possible with it? And then let me kind of think about based on those possibilities, do I want to have an after hours rule for leasing? Does that look any different than business hours or yes or no, right? So you can kind of start to make some of those detailed decisions.

Pete Neubig: Do you see most people that call in when they, you know, well, I guess first question is when the AI answers the phone, are you a big proponent of saying, hey, I'm an AI assistant or are you not a big proponent of that? So they think that they're talking to a human.

Lindsay Liu: So personally, I would lean more towards disclosure. We actually kind of have some baked in pre-disclosure that the call is recorded with an automated system before that's just legal kind of cover your ass, basically, right? So you don't know what state people are calling in from. You have out of state, right? Owners, I'm assuming the recording consent laws in their states all matter. So we just kind of by default kind of say, you know, we should. And then federally there's mandates now that you cannot intentionally deceive somebody that they're talking to an AI.

Pete Neubig: Got it. Do you have any data? And I know I'm putting you on a spot so I didn't ask you this. Do you have any data on what, you know, I call it off boarding. When like, I know back in the day, you know, we had phone trees. Originally the receptionist answered everything. Then this great, you know, technology came out called phone trees, right? And typically what would happen is as soon as you had a phone tree, everybody dialed out to zero. Do you see a fair number of people still dialing out to zero to talk to a human? And are you seeing that come down over time?

Lindsay Liu: Yeah, so it's really interesting. We look at, we try to get a baseline for every single one of our customers when they come on with their current system. It's fairly typical to see that occur. About 10% of people hang up at the phone tree. So they don't even get past selecting where they want to go. They're just like, and they hang up. And I'll tell you, we secret shop phone lines for property managers. And sometimes these phone trees are two to three minutes long, like no one's getting through.

Pete Neubig: And then they still don't even have the department that you need or whatever.

Lindsay Liu: And you're just like, I don't even know where I need to go. So zero. So I think there's a lot of hidden data that operators actually don't even look at or are aware that they're losing so much along the way. So what's kind of interesting is when they bring us on, we actually create way more visibility for that because we track and we answer every single call. So whether that's a spam caller, whether that's somebody that is just ultimately going to hang up, like we still track all of that. And that was just data that they never kind of were looking at before.

Pete Neubig: And a lot of those phone systems don't even get that data. A lot of those phone systems don't even get that data.

Lindsay Liu: Yeah. And I will say most people are like, I've never downloaded my phone data before. I've never looked at it. So it's like the first time. It's like, I think it's anecdotally, I know I have a problem. I know we're not answering enough. And then the data is usually often worse than people think for that. So we're seeing it's like 50 to 60% of phone calls are not answered. And that is during business hours as well.

Pete Neubig: Yeah. That is a way to not grow your business. That's a way to be out of business in a year. All right. So I find a challenge. I then create kind of like who's going to answer, who's not, what's going to be AI, what's not. I make arrangements for the off-boarding or the zero out. I determine if I'm going to have stuff be replicated for after hours or have a different after hours deal. And then I build my process flow and who is going to get that call, right? Who's going to answer it when it needs a human. Okay. What's next? Do I need to start answering all those questions and putting it into like a Word doc? What's next?

Lindsay Liu: Yeah. Yep. So we will typically ask for your most up-to-date kind of place where FAQs exist. So sometimes that's your website. Sometimes it's not. Sometimes people are like, don't use the website. I know it's still a work in progress. Sometimes that's, hey, do you have a tenant handbook? Do you have a new owner handbook? Do you have any of this already? Right. I'm like a big fan of two birds with one stone. Right. So it's like, if you have things that you're already updating.

Pete Neubig: A lot of times they're outdated, even if they have, right. So they have to go back and basically spend the effort to like, nope, let's scratch this. Let's change this. Oh, we don't even own, we don't even have that product anymore. Right. Things like that.

Lindsay Liu: Yeah. And that's usually where we find the delays in onboarding, right? It's it's usually just, oh, we got to go and figure out all of this stuff. Let us, let us come back.

Pete Neubig: It's like anything, right. Any technology or anybody you hire, there's, there's got to be a download. There's got to be some prerequisite information that needs to be, that needs to be put out there.

Lindsay Liu: Yeah. And the more context you're giving AI, the better it is going to perform. Right. So I think that is the name of the game here is start thinking about these knowledge repositories for your business, right. And start to start to make that part of a regular practice, which is I am grooming, I'm reviewing, I'm updating that over time. And so when you are onboarding additional tools, it just makes it easier for you to get up to speed.

Pete Neubig: Okay. So now I got, I have all my ducks in a row. I got, I got all my policies. I will say what, when I, when I was doing this and I was just process flowing out, like my maintenance, whatever, I realized like it was so many policies that we didn't have. And it would be like whatever Pete felt that day, that was the policy, which obviously you cannot operate on any long-term. So I do all the work. I have all this stuff now. Is there anything else I need to do before I actually, you know, implement with, with super or with any AI company?

Lindsay Liu: We, we always advocate for a customer testing round. So we'll do, we'll do testing for you. So we kind of say, Hey, your goals were that you wanted it to do these types of transfers at this time, you know, for these types of things, you wanted it to email these people about the transcript, yada, yada, yada. Right. So we'll do that type of testing to make sure, but honestly, the profit manager is going to know best what they're expecting there. And so we really advocate for have every person in your team do like five test calls, right? Take the calls that they get.

Pete Neubig: Before I can do the test calls though, I need this, this gnome called the AI bot. Like, do I need a specific phone system? Like if I'm running on like a regular old, you know, old school phone system, can I still use AI?

Lindsay Liu: Yeah, you can. So we recommend like, basically we, we work with, I don't know, at this point, like I've seen over a dozen types of systems, everything from people using hard lines to most people in VoIP, but sometimes it's just Verizon, my cell phone provider, right? Like sometimes it's just that as well. So that should not be a blocker the way that most of the folks in the voice AI space are enabling you to use those tools as you just forward calls. So we make it really, really simple. There's an underlying phone number that the AI is powered by that is able to receive all of those calls, and then it will forward back to you when you need. So it's able to kind of know, I can take this and then I need to send it back. The one thing, the one thing you will need is at least if you need calls to come back in, at least two phone numbers. That's the one thing you need. One number that is forwarding, right? And then another number that is receiving. You can't forward calls back into the, into the line that is the originator, because then you just get a loop of them forwarding back to each other. So that's the one prerequisite there is you can't just be doing everything off your cell phone.

Pete Neubig: Got it. The AI gnome, does it actually live in the super cloud? Like where does this AI gnome live?

Lindsay Liu: Yeah, yeah. So we, so yes, it would be essentially you have your own super AI, it's assigned to its own phone line. So it's got a phone number, it's got an email address, it's got a little training database, right? And all of the settings that you can kind of manage, you can name your gnome as well, if you want, we have different people that have different preferences, you can choose a voice for your gnome, right? All of that. And then what happens is we are doing our best to act kind of like the invisible thread to then get it back to you. So you're not necessarily like I would say success for me is that our customers don't have to log into the dashboard where they're going and managing their gnome on a regular basis. They're really only going in if they want to change something, if they want to like look at the reports, right? Things like that, but everything else will kind of go and live within your existing workflows and systems of record.

Pete Neubig: Do you recommend that they look at their org chart and have the org chart dialed in as well before they build out this technology?

Lindsay Liu: Yeah, I think that's really important. For the reason that we're going to start asking you questions like, well, let's say somebody has a, let's say somebody's really upset and they're just going, they're just, you know, I don't know, yelling profanity, right? And we don't know what to do with it. Who takes that call?

Pete Neubig: Usually the owner, right?

Lindsay Liu: Oh, I don't know. Yeah. So, you know, I think it's like, when it comes to these types of scenarios, like we're going to ask you, where should that go? And so understanding kind of who's responsible for what, and who's going to be last in line, let's say for, let's say an emergency, right? Comes in, we can dial a whole phone tree for you. We can note, send a bunch of notifications, like who's the last one to make sure that that's really, you know, getting answered.

Pete Neubig: Make a rule. If somebody says attorney or something like, yep, everything needs to be right now. Click. Goodbye.

Lindsay Liu: Yeah, absolutely. We have all of that. Like that's a trigger word, right? Like even internally we have for all of our customers, we have kind of like property management, no, no words, but the agent is not allowed to repeat. So it's not allowed to repeat things like black mold, asbestos, right? So those no, no words come in and it's just like, thanks for telling me about the issue. Let me get you to a team member.

Pete Neubig: What's the, so what's the main pain points that Super solves for a PM firm?

Lindsay Liu: It's responsiveness. That's like, if I just boil it down to what do we solve?

Pete Neubig: It's actually getting calls answered, right? Like that's probably the number one thing, right? Just having a call answered.

Lindsay Liu: Having it answered.

Pete Neubig: And then getting that solution really quick at the time that your client wants it and then responsiveness, right? Less stress on the team because they could do their work now versus answering the same, like the same phone call 15 times, right? What are the requirements? What are the requirements? Like it's on the website.

Lindsay Liu: And you don't want a human to be, it's terrible when the human is the one saying, please go to the website. Can you read, right? Like you don't want that happening, right? So that human is going to take the 10 minutes to walk somebody through that.

Pete Neubig: The bot won't say, check the website. The bot will just recite what's on the website for them.

Lindsay Liu: Exactly. The gnome, whatever you want to call it. I like the idea of these little gnomes hopping around. And with that, these gnomes can also take simultaneous calls, right? So whereas a human team can only have the bandwidth for one call per person, right? At a time. We have tested that one single phone line can handle up to 30 simultaneous calls that came in at the exact same time. So like, if you really needed to. Exactly. Exactly.

Pete Neubig: Okay. Lindsay, you broke it down for me. Thank you so much.

Lindsay Liu: Anytime.

Pete Neubig: If somebody's listening like, oh my goodness, that is my problem. I need, you know, I need better reporting on my calls. I need the phones never getting answered because I'll tell you when I owned my property management firm, the number one complaint I got was I can never get in touch with anybody. That's the number one complaint. And the number one reason why people leave you is because of lack of communication, right? And they'll say, well, you didn't get my property leased. It was lack of communication. I didn't get them. It was maintenance. It was lack of communication. You could take the top five reasons why people leave you and really boil it into the number one is lack of communication. So if you can solve 60, 70, 80% of that, it reduces your churn, which now you can, you know, you can justify that you get that ROI from, uh, from investing in, uh, in super and, and, and then the AI known. So if somebody's interested to hear more, Lindsay, how do they get in touch with you?

Lindsay Liu: Yeah. So they can, they can email me. It's just Lindsay with an A, lindsay@hiresuper.com. So that's H I R E S U P E R.com. Um, we've also got some, uh, test lines on our website. So if you want to get a call from one of our outreach agents or make a call to our inbound, uh, receptionist, you can also go and just play around, um, with some of our test nodes.

 

Pete Neubig: I love it. I'm going to, we're going to change the AI about the AI gnome. We're going to, it's way cuter. Right. And if you are listening to this and you're not a NARPM member, shame on you. Go to NARPM, narpm.org, or give them a call at 800-782-3452. NARPM leadership has changed. The NARPM 2.0 is, is, is coming. Uh, they're focusing on, on data. They're focusing on legislation. So this is really, really, uh, the organization for you. If you are a real estate investor and own property and you self manage, or if you actually, uh, run a property management firm and, uh, Lindsay, don't take all my jobs. You're still going to need remote team members. And if you do, please give us a, you know, please check, check us out at VPMsolutions.com, or you can email me direct pete@vpmsolutions.com. Thanks, Lindsay.

Lindsay Liu: Thank you.