David Bratslavsky
David Bratslavsky
David Bratslavsky helps commercial real estate firms put artificial intelligence to work inside their own operations. He partners with investors, brokerages, and lending teams to find where their people are losing hours to repetitive work, then helps clients design and build automations that give that time back, all while keeping each team in control of the tools they end up relying on.
His approach is shaped by years working at the intersection of AI, automation, and real estate technology, including time as a fractional CTO. That operator's view taught him a simple lesson: the best automation fits the way a team already works, rather than forcing the team to change everything around it.
How David works with CRE firms
A typical engagement starts with mapping out how a firm actually operates, step by step, then deciding what genuinely should be automated and, just as important, what should not. From there David builds the solutions, tests them against real cases, and confirms they do exactly what the team expects.
What sets his work apart is what happens next. Rather than leaving a firm dependent on outside help, he trains each department to create and upgrade their own automations, so they can keep adapting as the business changes without calling in a consultant every time. Throughout the process, he keeps data privacy and security front and center, making sure sensitive financial and tenant information stays protected at every step.
The result is not a single tool. It is a team that understands its own workflows, knows which ones AI should touch, and has the skills to keep improving them.
Answers to your questions
David Bratslavsky is the founder of QuickData.AI, a software platform that automates data entry for commercial real estate underwriting. He works at the intersection of artificial intelligence, automation, and real estate technology, and he also consults with commercial real estate firms on building AI capability in-house.
David helps commercial real estate firms build AI capability inside their own teams rather than leaving them dependent on outside help. A typical engagement starts by mapping how the firm operates, then deciding what should and should not be automated, building and testing the solutions, and training each department to run and improve them on their own.
It begins with understanding the firm's actual workflows, step by step. From there David identifies the highest-return opportunities, builds automations, tests them against real cases to confirm they work as intended, and then trains the team to maintain and upgrade them. The goal is a team that can keep improving its own systems long after the engagement ends.
David focuses on the high-volume, repetitive work that quietly consumes a firm's week. That includes underwriting and document-to-data extraction, monthly variance reporting, lease abstraction, invoice processing, due diligence review, and similar workflows where careful automation saves significant time and reduces errors.
He tests every solution against real cases before it goes live and builds in validation so teams can see whether the output is correct rather than taking it on faith. He also keeps data privacy and security front and center, making sure sensitive financial and tenant information stays protected throughout.
Because firms that depend on a consultant for every change stay stuck. David trains each department to create and upgrade their own automations, so the business can adapt as it grows without calling in outside help every time something needs to change.
QuickData.AI is one flagship example. It automates the underwriting bottleneck by extracting data from rent rolls, T12 statements, and Offering Memoranda directly into Excel, with built-in validation, and it saves clients an average of 15 hours per month. It reflects the wider pattern in David's work: automate a repetitive, error-prone process, validate the output, and hand the team the ability to run it themselves.
David works with commercial real estate firms, including multifamily investors, brokerages, and lending teams, along with proptech platforms looking to build AI into their own products. Clients are based across the country, including New York City.