Real estate is one of the world’s most operationally intensive industries, yet the work of running a portfolio – answering leasing inquiries, chasing overdue payments, coordinating maintenance, managing renewals – still falls largely on human teams stretched across dozens or hundreds of properties. The problem compounds as operators grow: every new site adds complexity, and the communication volume that sustains occupancy and resident satisfaction scales faster than headcount can. Most software sold to real estate operators has digitized records without automating the work itself, leaving property managers to close the gap manually across phone, email, SMS, and chat, often after hours and across time zones. Uniti AI addresses this gap by functioning as an orchestration layer that sits above the property management systems operators already use, deploying a suite of AI agents – covering leasing, customer service, maintenance, collections, payments, renewals, and reviews – that execute each operator’s existing playbook across every channel, 24/7, without replacing the underlying software stack. The platform deploys in 30 to 45 days, integrates with 15-plus property management and CRM systems, and serves operators across self-storage, manufactured housing, multifamily, senior living, and flexible workspace in more than 10 countries.
AlleyWatch sat down with Uniti AI Cofounder and CEO Francesco De Camilli to learn more about the business, its future plans, recent funding round, and much, much more…
Who were your investors and how much did you raise?
We raised a $12M Series A led by Pathlight, with participation from MetaProp and existing investor Prudence. Angel investors include Gokul Rajaram, Lenny Rachitsky, and Romain Huet. Pathlight Partner Mahdi Raza joined our board as part of the round.
Tell us about the product or service that Uniti AI offers.
Uniti is an AI agent platform for real estate operators. Our agents handle leasing and sales, customer service, maintenance requests, payments, collections, and resident communications across voice, email, SMS, and chat. They sit above the property management systems and CRMs operators already use, reading data and writing actions back, rather than replacing the underlying software. Today we serve operators across self-storage, manufactured housing, senior living, multifamily, and flexible workspace.
What inspired the start of Uniti AI?
My cofounder Emre Altinok and I met as freshmen at Yale, then spent the next decade on parallel tracks: he went deep into machine learning, I went deep into real estate operations at companies like Bisnow, Knotel, and Colliers. When large language models got good enough to hold a real conversation, we both saw the same thing from opposite sides. Real estate is one of the most operationally intensive industries in the world, and most of that work is communication: answering calls, qualifying leads, chasing payments, coordinating maintenance. Operators already know how to run their businesses. They didn’t need new software to learn; they needed their existing playbook executed perfectly, at all hours, on every channel. That’s what we built.
How is Uniti AI different?
Most AI in real estate is a point solution: a leasing chatbot here, an answering service there. Uniti is built as an operating layer. Operators codify their actual process — qualification questions, escalation rules, tone, pricing logic — and the agent runs that process across every channel and every workflow, from first inquiry through collections. We also go deep on integrations with the systems each vertical actually runs on, which is why we can deploy in 30 to 45 days and why our pilots convert to paid contracts at a 94% rate. And we’re deliberately multi-vertical: the same platform serves a self-storage facility, a manufactured housing community, and a senior living operator, each on their own playbook.
What market does Uniti AI target and how big is it?
We target real estate operators in operationally intensive verticals: self-storage, manufactured housing, senior living, multifamily, and flexible workspace. Across those categories in the US alone, operators spend tens of billions of dollars a year on the labor of leasing, customer service, and back-office communication — and that’s before adjacent opportunities in payments and other financial products that sit on top of the same customer relationships. It’s a very large market that has historically been underserved by software because every vertical has its own systems and its own way of operating.
What’s your business model?
Recurring SaaS. Operators pay per site or per community, with pricing that scales by channel and workflow — a customer might start with an AI leasing agent on email and chat, then add voice, maintenance, and collections as they expand. Deals range from a single facility to enterprise portfolios of hundreds of sites.
How are you preparing for a potential economic slowdown?
Honestly, a slowdown sharpens our value proposition rather than weakening it. When operators face pressure on margins, the first thing they scrutinize is labor cost and lost revenue from missed inquiries — which is exactly what we address. Internally, we run disciplined: we closed this round with a long runway, we hire behind revenue rather than ahead of it, and we focus on enterprise customers with multi-year contracts, which gives us durable revenue even in a downturn.
What was the funding process like?
Fast, because we ran it off the strength of the numbers rather than a narrative. We had grown revenue several-fold year over year, our pilots were converting at 94%, and we had enterprise operators expanding across their portfolios. Pathlight understood the operator-first thesis immediately, and Mahdi Raza pushed our thinking on where the platform goes next rather than just validating where it is today. That made the decision straightforward.
What are the biggest challenges that you faced while raising capital?
The biggest was helping investors see past the category noise. ‘AI for real estate’ is crowded on the surface, and a lot of it is thin wrappers around a chatbot. We had to show that the depth of our integrations, the breadth of workflows we execute, and our multi-vertical footprint make this an infrastructure business, not a feature. Once investors talked to our customers, that conversation got much easier.
What factors about your business led your investors to write the check?
Three things. First, retention and expansion: customers start with one workflow and grow into several, and enterprise operators roll us out portfolio-wide. Second, the pilot-to-paid conversion rate — 94% — which tells you the product delivers measurable value quickly. Third, the founding team: a decade of real estate operating experience paired with deep machine learning expertise is a rare combination, and it shows up in how the product is built.
What are the milestones you plan to achieve in the next six months? Roughly quadrupling revenue by year-end, expanding our enterprise deployments across self-storage and manufactured housing portfolios, launching deeper voice capabilities in senior living, and growing the New York team across engineering, sales, and customer success. We’re also beginning our first international deployments in Asia-Pacific.
What advice can you offer companies in New York that do not have a fresh injection of capital in the bank?
Get closer to revenue. Founders spend too much time on things that feel like progress — decks, positioning, partnerships — and not enough time selling. Every dollar of revenue is a dollar you don’t have to raise, and it’s also the single best fundraising asset you can build. And use New York: the density of customers, operators, and capital here means you can compress months of learning into weeks if you’re willing to get in rooms.
Where do you see the company going now over the near term?
Deeper into the operations of our existing verticals — more workflows per customer, more of the operator’s playbook running on Uniti — and broader across enterprise portfolios. Longer term, we believe every real estate operator will run their operations on an AI layer, and we intend to be that layer.
What’s your favorite summer destination in and around the city?
Guilford, CT. Where I grew up. Modest, beautiful shoreline town in Connecticut.
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