Nia Bowers
Image courtesy of David Bratslavsky
It is just before 8:00 on a Tuesday morning, and a multifamily acquisitions analyst is staring at a mountain of data that needs to be processed before noon. He has three different offering memorandums open on his desktop, each one stretching to sixty pages of dense financial details. The problem is that every listing broker uses a different layout. Before he can even begin to think about the actual investment value, he has to spend hours manually typing numbers into his Excel model.
This scenario is playing out across the industry as sales volume starts to climb. In the first part of 2026, multifamily transactions hit $22.8 billion. With interest rates stabilizing, more deals are hitting the market, which means more paperwork for acquisition teams. The real bottleneck for these firms is no longer finding deals; it is the sheer amount of time analysts lose to data entry before they can start the real work of evaluation.
David Bratslavsky, the founder of QuickData.ai, believes this is the single biggest drain on productivity in commercial real estate. Having spent his career at the intersection of tech and property investment, he saw that the industry was stuck in a cycle of mechanical work.
Investment teams are built for their judgment, Bratslavsky said. They need to decide which properties to focus on and what assumptions to make. But right now, their analysts are stuck retyping spreadsheets until late at night. That gap determines which firms can actually move fast enough to win a deal.
While AI has transformed industries like insurance and legal discovery, commercial real estate has been a harder nut to crack. The main issue is a lack of standardization. A rent roll from a Phoenix operator looks nothing like one from a boutique owner in Miami. If a general AI tool misses just one number, it can throw off a valuation by thousands of dollars.
To solve this, Bratslavsky developed a tool specifically trained on multifamily documents. QuickData.ai claims to reach 98 percent accuracy on rent rolls and high identification rates for other financial statements. Crucially, the tool operates as an Excel add-in. Bratslavsky argued that analysts do not want to learn new platforms in the middle of a deadline; they want automation that lives inside the models they have used for years.
Document extraction was only the first step for Bratslavsky. He noticed that almost every desk in a real estate firm has repeatable, mechanical tasks that could be automated, from deal screening to investor reporting. Now working as a consultant, he helps firms build their own internal AI workflows.
The shift is possible now because automation has moved beyond coding. New AI tools allow an analyst to describe a workflow in plain language to create a reusable process. Bratslavsky works with departments to build these first automations, teaching them how to be independent and adapt as technology changes.
Security remains a top priority, especially given the sensitive nature of deal terms and investor data. Bratslavsky makes a point to screen tools for privacy and safety before they are deployed. Ultimately, the goal is to make the grunt work disappear so that investors can focus on what they do best: pricing risk and deploying capital.