AIP Podcast EP 83 - AI-Powered Leasing Automation with Leasey.AI

This episode's guest, Carlos Leal, Co-Founder and COO of Leasey.AI, joins host Anne to explore how AI is transforming one of real estate's most manual processes: leasing. Carlos shares how his family's experience managing rental properties in Colombia helped inspire the company, and how his time at EY exposed him to the technical debt and outdated tools holding the real estate industry back. He discusses how Leasey.AI automates the leasing journey, from lead qualification and appointment scheduling to applications, document verification, credit checks, and fraud prevention. Plus, Carlos explains how Leasey.AI uses data and AI to streamline tenant screening while reducing potential bias and building trust in automated decision-making. Tune in to learn how Leasey.AI is making property leasing more efficient and seamless for landlords, property managers, and tenants across North America.

The AIP Podcast is hosted by Anne Cheng, on behalf of AI Partnerships, a Railtown Technologies company.

Follow AIP Partner, Leasey.AI
Website: https://www.leasey.ai/
LinkedIn: https://www.linkedin.com/company/leaseyai/

Follow AI Partnerships
Website: https://www.aipartnershipscorp.com/
LinkedIn: https://www.linkedin.com/company/aipartnerships/
X: https://twitter.com/AIPartnerships

The AIP Podcast is hosted by Anne Cheng, on behalf of the AI Partnerships, a Railtown AI company

Transcript

SPEAKER_01 0:00

Carol's parents wanted to build wealth through wealth through real estate in Colombia, but ran into the same old soul many landlords do. No idea to how to manage tenants and no understanding of the legal exposure around things like non-payment of rent or costs. Now that family history had with its own corporate experience at Ernst and Young, EY, watching legacy real estate companies catch old tools with newer old tools. Oh my god, right? Let Carlos and his brother Juan to build Leasy AI, a leasing automation platform that started as a semi-pandemic project and has now grown into fully inbound funded business, now extending into the US market. In this episode, Carlos joins AI Partnerships Corp, a railtime technologies company to talk about technical debt in real estate, building trust into automated easy. And what's next for EC.ai as it raises its first funding round. Now welcome back to the show, everybody. Good morning, good afternoon, and good evening to all of you wherever you are. I am Anton Jochos from AI Partnerships Corps, a real-town technologies company. Now, today's guest has turned a summer side project into one of North America's fastest growing retail automation platforms. Carl Steel is the co-founder and CEO of Leasty.ai, which he built with his brother Juan out of silver home technology and the DMZ accelerator in Toronto. Now Leasy AI automates the leasing process for property managers and asset operators across Canada and the US, from lead qualification and fraud prevention to reference tax and even scheduling. Well, Carlos, welcome to the show.

SPEAKER_00 2:04

Thanks so much for having me on.

SPEAKER_01 2:07

That's amazing. Let's dive right in. Now, before Lisi.ai was even a business, your parents wanted to build wealth through real estate in Colombia. What did you watch them struggle with and how much of that ended up shaping what you built?

SPEAKER_00 2:23

Yes, that's uh that's an interesting background story, and I think many probably will relate. Where um, you know, my parents were former medical practitioners, and uh essentially they didn't know much about management, so they thought the safest place to store wealth was investing in real estate. But what's interesting is you know, at the time you think it's just a matter of buying a property, putting it in the market, and you'll find a tenant and then just collect rent, nice and easy. But the reality is there were so many little struggles in the process, you know, from knowing where to publish. How do you find the most qualified tenants and make sure that obviously they pay rentment month to month? And especially for most of the individuals like my parents, which obviously do this on the side on top of their already busy careers, this becomes a hassle more than really sort of an opportunity to store wealth. So I think that's what led us to sort of think through how can we make this process easy and simple for everybody who is looking to store wealth in real estate, which is a really good asset class, but recognizing that there is there's things to learn and understand uh in order to do it well.

SPEAKER_01 3:29

Absolutely. So you were at EY before this. I mean, what was it about working with legacy real estate companies that convinced you that the industry had a real technical debt problem?

SPEAKER_00 3:41

Yes, I I spent over a 10 years at EY, and it was interesting because I did jump around sectors, so from government into retail and real estate seemed to be the most interesting one, not just from my background with my family's uh uh history, but also recognizing that it's the largest asset class in the world. Um, and as you know, it's one of those industries that moves slow. So adopting change, using new technology, and especially when we hear terms like AI, frightens people, especially because they fear that there's gonna be a wave of change or people having to be removed from their roles. So when I started kind of my career there, I noticed that most of the solutions that had been launched were back from the 80s. Solutions that literally sort of require data entry and then armies of individuals to actually get into crunching that data to draw insights, understand how to improve the performance of the assets. But the reality is when you sort of think about the retention in the industry, is very low. The amount of innovation is also um not keeping up with all the things that are happening in every other sector. So I saw an opportunity to sort of realize, okay, this is something that is happening everywhere in the world because there is real estate pretty much everywhere. So, how can we leverage the the new innovations that are coming out, technologies and especially now the ability to now have so much data to really enrich those asset classes and be able to bring value to the individuals that pretty much manage those assets and make also their jobs a lot more seamless. So that's how I started getting into the space and seeing how we could um better um you know, like use technology to solve some of the pain points that they were facing uh at the time. And I think right now, even though we're sort of experiencing um a whole new wave with AI, the industry continues to be very reluctant to adopt it. So I think there's a challenge there that is very interesting for us to solve.

SPEAKER_01 5:32

Yeah, that that that's that's really insightful. Now, Carlos, you you know, I think it's a little bit of um the boring things that really make the most sense, right? So, where were you seeing these most manual repetitive touch points between tenants and real estate agents? The the stuff that seemed almost obvious, you know, to be automated.

SPEAKER_00 5:57

Yes, it's interesting when you sort of look at the whole what we call the property life cycle, because usually people think, you know, it's almost like a passive asset once you've rented it out because all you do is collect rent on a month on a monthly basis. And then, you know, the one-off incident that happens where you require a maintenance person to come in and so on. But in the in the whole cycle, what we started learning with my brother when we kind of went deep into figuring out where to play in an in a niche sweet spot, uh, it turned out that the leasing process itself is actually a short cycle, but it's a very cumbersome one because as you pointed out, the number of touch points that happen in that space are in the thousands. So just to give you a very simple example, you know, a company that has, let's say, a portfolio of 100 properties with 10 active listings a month. We've estimated already from the data that we have from our clients that it can easily amount to about 10,000 touch points between the moment a listing goes active into numerous marketplaces, and then you have all of these individuals inquiring through text messaging, calling in, you know, from the marketplace, like a Facebook marketplace, which is a typical one where everybody presses that button, is it available? And then having to move those leads into qualifying them, you know, understanding do they have the right income? Um, are they the right size of a group that actually can accommodate the property? Um, then getting them into actually confirming the appointment, making sure that they actually show up so that the agent on the ground is not wasting their time, then getting them to apply, getting the right documents, verifying that, conducting a credit check, and ultimately getting to a point to signing a lease. What people don't account for that in that is that when you start starting to account in a single individual door or individual unit, there's already quite a bit of interaction. Now imagine this doing it at scale when you have now hundreds, if not thousands, of vacancies on an ongoing basis. So that that really sort of proved to us that there was an opportunity to figure out how we can make this experience not just for the owners, but actually for the tenants as well, to be as seamless as possible. Because at the end of the day, at the end of the day, we're competing against time.

SPEAKER_01 8:03

Yeah, that's that's pretty impressive. Um it's got me a little bit curious, right? You mentioned that Juan, your brother, was working with you, and he came from KPMG, and after that he went on to become a CFO of a startup, and he brought that startup from concept all the way to public, uh going public. How did his path shape the way the two of you work together? And how does that family relation dynamic work out?

SPEAKER_00 8:32

Yes, we've heard that comment many times. We actually have to confirm that we're actually siblings because we have the same last name, so people always inquire where where that relationship come from. And uh Juan's background is interesting because both of us, for some odd reason, as you know, many siblings tend to be quite different. Uh, we actually had the shared passion for entrepreneurship. We just didn't know where to invest our time. And when we sort of went back into, you know, we went into educational technology, we went into marketing, and none of those seemed to sort of create kind of that passion. I think when we hit real estate, we recognized that there was, you know, a common interest and um interesting pain point in the industry that we thought we both would solve. And in Juan's case, um, what what he did through his career, going from auditing to corporate finance and so on, he really sort of understood the nuances of all the financial details that go into operating a business. Um, on top of that, he's a very creative guy. So believe it or not, even though his side is very technical on the financial side, he's actually our head of product. So he's been really sort of the brain behind designing the platform the way it looks like today. Um and it's been sort of a really good partnership because on my side, I bring more so of the operational chops. So understanding how to raise capital, you know, how to um hire people, how to organize teams and so on. And we complement each other quite nicely because he comes on that creative side managing our product team. And then on top of that, has the technical um strengths that helps us make sure that we are obviously managing the business in a responsible way, making sure that our cash flow is always uh, you know, like kept on check to make sure that we're not doing more than what we should. And I feel that has helped us um or organize ourselves quite nicely without sort of stepping in on each other's toes. And most importantly, I think we both share the common vision that we really sort of want to solve these problems that we see in the industry. And I think that that's what helped us kind of kind of motivate each other to push through.

SPEAKER_01 10:25

That's amazing. Now, I I think you I want to shift the the tone right now because um I'm sure a lot of retail landlords are probably have having real risk and real frustrations in the market. Now, one of the biggest problems in this industry is fraud, and worse still, you know, there are even reality TV about squatters, you know, there are real risks in this industry. How does your technology help to qualify the leads the moment they hit the platform? And how does that grow into a wider ecosystem of vendor uh partnerships?

SPEAKER_00 11:05

That's an excellent question, and I think that's one of the biggest hurdles that we've had to overcome in the industry. As you know, right now we're pretty much living in a market where what we call a buyer's market or where there is a lot of real estate out there, uh, which means uh tenants have more choice. And to your point, the reality is companies and individual landlords trying to make this process of finding a tenant as easy as possible, sometimes miss the simple steps of, you know, are you verifying the ID? Are you checking that they have you know a good credit score? Can they actually afford the unit and make sure that they're not gonna miss on rent, you know, after a few months because they don't have enough savings? So what we try to do recognizing that there is a lot of questions to ask, and maybe not everybody has that information, was essentially, well, we did it from two perspectives. One was orchestrating a fairly standard and simple process where from the very beginning, when a prospect is interested in a unit, we pre-qualify them and we take them through a funnel where essentially, depending on the specs of the property, they answer a series of questions and we try to find the most suitable unit for that individual. And this way we at least know that if you're gonna be coming to see a one-bedroom, you don't necessarily have five occupants moving in, for example. Then after the process of the appointment, and when the owner or the property manager decides they want to move forward, the next piece includes the application, which usually you know encompasses a number of pieces of information, such as making sure that they have the right income. Is it verifiable? Or do they have a you know, could be a guarantor or somebody that can back up their rent in case they cannot afford it, making sure that they have you know the correct ID, that they have um good backup or savings in their account, and so forth. And the challenge here is that in many cases, landers don't know what to ask for. Uh, and in some cases, actually, unfortunately, some of this information can be easily um faked. So, what we've done is essentially built an ecosystem with key players in the industry that have already plugged in into uh providers that essentially supply the data needed in order to verify that information. For instance, we work with uh a company called Single Key Out of Toronto that essentially provides uh the credit checks, and they literally verify that directly with Equifax and TransUn, which are the main credit bureaus in Canada. And this way, all we do is amass all that data. And what we do is actually churn through it and simplify it so that we can create a very simple profile with all the check marks that everybody would want to see in a suitable tenant. And at the same time, we try to minimize the biases. As you know, the challenge with obviously putting scores and aggregating information is that you can very easily get into the challenge of you know, like discriminating from either a racial perspective or an ethnic perspective and so forth. And in this case, what we've done is we've baked into our algorithms that essentially all that information that could create those biases is actually kept outside of the model that we trained, so that we really sort of focus on the main pieces of information that matter for that decision, which include financial uh information, employment, um, ID, of course, and credit, for example.

SPEAKER_01 14:13

That's amazing. Well, Carlos, we're unfortunately out of time, but thank you for sharing your journey. Um, it's really, really fascinating from your family's real estate struggles in Colombia to a platform that's now even expanding across the US and you know raising its first round of funding. Now, where should listeners go to learn more about Lisi.ai?

SPEAKER_00 14:38

Yes, they can find us on our website, www.licy.ai, or they can also find us on LinkedIn. Um, we'll be happy to connect and show what the platform can offer you.

SPEAKER_01 14:48

That sounds great. Well, that's a wrap on this episode. Once again, my name is Anne. I am from AI Partnerships Core, and a real it is a railtown technologies company. If you have enjoyed this conversation, follow the show and share it with a colleague or a friend. We'll see you next time.