Property ownership in America runs on 300 years of fragmented county records. The system works—kind of—but it’s held together by brute force and a tolerance for inefficiency.
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The United States real estate market is worth more than $50 trillion. You’d think the system that tracks who owns what would be pretty sophisticated by now. It’s not.
Property ownership records in this country are spread across more than 3,700 counties—each with its own system, its own formats and its own quirks. Many of those systems were established in the 18th century. And while you can trade billions in Treasury bonds in nanoseconds, verifying who actually owns a piece of property still involves a frustrating combination of archived images, manual searches and waiting.
I know this firsthand. Many years ago, I worked in the mortgage lending space, and part of my job was physically going to the county office and scrolling through microfiche to build contact lists and identify property ownership. It was tedious, time-consuming and felt archaic even then. The fact that it hasn’t fundamentally changed is kind of remarkable.
The Gap Is Infrastructure, Not Tools
There are plenty of technology tools out there. The problem is that there’s no underlying infrastructure connecting all of these fragmented county-level systems into something coherent and searchable.
I recently spoke with Tali Gross, co-founder and CEO of Dono—an AI-powered property records platform that just announced $6.5 million in seed funding. What struck me about our conversation wasn’t the company pitch. It was his framing of the problem itself.
“The gap isn’t technology. Plenty of tools exist. The gap is infrastructure,” Gross said. That distinction matters. We tend to assume every problem can be solved with the right app or the right AI model. But in real estate, the underlying data layer is so fragmented that no amount of clever software can compensate for the fact that there’s nothing coherent underneath it.
About 85% of the U.S. population now lives in what Gross calls a “digital county”—one where records are at least accessible electronically, even if they’re just scanned images. That’s progress. But accessible doesn’t mean organized. County clerks get elected every four years, and each one can change how records are stored and indexed. The result is an endlessly messy dataset spread across thousands of jurisdictions with no standard format, no central authority and no real incentive to modernize.
Counties don’t have the budget for it. Even the ones with people who recognize the problem don’t have the resources to fix it. And so the mess persists.
Where the Pain Shows Up
Title insurance is where this infrastructure gap hits hardest. When you buy a house, the title company has to verify your ownership by digging through local archives, collecting records, reading them, interpreting them and drawing conclusions. Today, that entire process is manual. Nothing is automated.
And it shows. Roughly 11-15% of real estate closings get delayed—usually by three to seven days—because of title-related issues. Gross walked me through why. Title searches are typically ordered late in the closing process because they cost a few hundred dollars and nobody wants to pay if the deal falls through. So the search happens near the end, and if there’s a defect—a mortgage that wasn’t assigned properly, a missing data point—it surfaces just days before closing, and now you’re scrambling to resolve something that’s been sitting there all along.
The workforce problem compounds this. More than half of the current title professionals are expected to retire by 2030, and there’s no pipeline behind them. As Gross put it, there’s no school for becoming a title professional. It’s an industry that doesn’t formally train the next generation. Nobody in high school says they want to grow up to be a title clerk. It’s one of those jobs you stumble into—and then you’re the person who holds decades of institutional knowledge in your head. When you retire, that knowledge walks out the door with you.
What Modernization Actually Looks Like
The solution isn’t just digitizing records—it’s building an infrastructure layer that can collect data from disparate county sources, extract and index the relevant information from messy documents, and then apply domain expertise to draw reliable conclusions. That’s three distinct technical challenges, each significant on its own.
Companies attempting this—Dono among them—are finding that the real barrier isn’t the AI. It’s the domain knowledge. You have to understand how each county works, what the internal logic of different record types looks like and how to handle the endless edge cases that come with 300 years of accumulated bureaucratic variation. Several companies have tried to modernize public records before and failed, largely because they underestimated how much specialized knowledge is required.
The other non-negotiable is accuracy. In an industry where millions of dollars ride on every transaction, a platform that’s right 95% of the time is useless. That’s why any serious effort in this space needs human verification built into the process. AI can dramatically accelerate the work, but someone still has to confirm the conclusions.
When it works, though, the difference is striking. Gross told me that current owner searches that traditionally take days can be completed in about 30 minutes—and full transaction searches in under four hours. Beyond speed, the output is more transparent. Instead of a paper document with someone’s handwritten conclusions, you get a platform showing exactly how each determination was reached.
The Bigger Opportunity
Title insurance is the obvious starting point, but the implications go much further. Lenders, mortgage servicers and real estate investment firms all deal with the same fragmented records problem. Portfolio-level questions—”show me every property this person owns nationwide”—simply can’t be answered with today’s infrastructure.
Imagine being able to ask that question and get an accurate answer in minutes instead of it being functionally impossible. That changes how lending decisions get made, how portfolios get managed and how risk gets assessed across the entire real estate ecosystem.
Why This Matters
We talk a lot about AI transforming industries. Most of the conversation gravitates toward flashy applications—chatbots, autonomous vehicles, coding agents. But some of the most meaningful uses of AI are in deeply entrenched industries where the processes haven’t fundamentally changed in generations.
Real estate is a perfect example. The system works—kind of—but it works through brute force and a high tolerance for inefficiency. When you have the world’s largest asset class running on infrastructure that predates the country itself, any improvement in speed, accuracy and cost has enormous ripple effects.
The real estate industry doesn’t need another app. It needs plumbing. And sometimes the plumbing is what matters most.
