A conversation with Leasey.AI’s COO on AI-powered leasing automation.
The leasing process for residential property has not changed much in twenty years. A tenant submits an inquiry. Someone at the property management company responds, eventually. A showing gets scheduled over email. An application form gets filled in by hand or uploaded as a PDF. A credit check gets run through a third-party tool. A lease gets drafted, printed, signed, scanned, and emailed back.
Each handoff is a place where leads go cold, documents get lost, and qualified tenants move on to a building that responded faster.
That is the problem Carlos Leal, Co-Founder and COO of Leasey.AI, set out to solve. It is the problem he walked through with Anne Cheng, CEO of Supercharge Lab, in Episode 83 of the AIP Podcast.
Why the problem starts with the industry’s own history
Before Leasey.AI was a company, Carlos watched his parents navigate the complexity of managing rental properties in Colombia. The administrative burden of leasing, even at small scale, was disproportionate to the returns. Years later, as a management consultant at EY advising some of the most sophisticated institutional real estate owners in Canada, including Aquilini Investment Group and BCI, he saw the same pattern at enterprise scale. Legacy property management companies had invested heavily in technology, but the technology was fragmented. A platform for listings here. A scheduling tool there. A screening service that does not connect to either. A DocuSign workflow that starts fresh for every new applicant. The tools existed, but they did not talk to each other, and the leasing coordinator was the glue holding all of them together. That is what Carlos described in the podcast as the industry’s technical debt problem: not an absence of investment in technology, but an accumulation of disconnected investments that created more coordination work, not less.Where AI in leasing actually starts
The podcast conversation goes deep on lead qualification: what happens in the seconds after an inquiry hits a property management platform, and why that window is where most leasing teams lose the most ground. Leasey.AI’s AI leasing agent responds to every inbound inquiry immediately, qualifies the prospect against pre-set criteria, and books a showing without any human involvement required. The response is not a template. It is a dynamic conversation that adapts to what the prospect is asking, what unit they are interested in, and what the property’s current availability looks like. For property managers managing dozens or hundreds of doors, that automation compresses what used to take days into minutes. It also creates a consistent first impression regardless of what time the inquiry arrives. Fraud prevention was another topic the conversation covered in depth. Leasey.AI’s qualification layer is not just about speed, it is designed to catch inconsistencies in application data before a showing is ever booked, filtering out low-quality or fraudulent leads before they consume leasing team time.The inbound signal that pointed toward the US
One of the more striking moments in the episode is when Carlos describes how Leasey.AI identified the US market opportunity. The company had grown entirely on inbound demand without any paid marketing. When the team started analyzing where those demo requests were coming from, the pattern in the data pointed clearly south of the border. That kind of signal, product-led and demand-confirmed, is the only kind worth acting on. The US multifamily market is substantially larger than Canada’s, and the same fragmentation problem that exists in Canadian property management exists there at greater scale.What the funding round is actually for
Carlos is direct in the podcast about what the current seed round is designed to unlock. It is not runway for the existing product. It is the infrastructure for a recommendation engine that uses the operational data flowing through the platform to surface insights that property managers cannot currently access from any other source. That data exists inside every Leasey.AI deployment:- Which listing formats generate the most qualified leads in a given market
- Which showing time slots have the highest applicant conversion rate
- Which qualification criteria most reliably predict a tenant who renews rather than churns
- The recommendation engine is how it gets surfaced back to operators in a form they can act on.
