The Good Tech Companies - How David Bratslavsky Is Helping Commercial Real Estate Operators Use AI to Win Deals

Episode Date: October 9, 2026

This story was originally published on HackerNoon at: https://hackernoon.com/how-david-bratslavsky-is-helping-commercial-real-estate-operators-use-ai-to-win-deals. Discover how David Bratslavsky uses QuickData.ai and AI consulting to automate real estate underwriting, reduce manual data entry, and evaluate more deals. Check more stories related to undefined at: https://hackernoon.com/c/undefined. You can also check exclusive content about #quickdata.ai, #ai-commercial-real-estate, #david-bratslavsky, #ai-real-estate, #investment-analysis, #commercial-real-estate, #ai-consulting, #good-company, and more. This story was written by: @sanya_kapoor. Learn more about this writer by checking @sanya_kapoor's about page, and for more stories, please visit hackernoon.com. David Bratslavsky founded QuickData.ai to help multifamily investors automate rent roll, T12 operating statement, and offering memorandum data entry directly into Excel. The company reports 99.3% accuracy in rent-roll extraction and says users save an average of 15 hours per month. Building on the product, Bratslavsky now helps commercial real estate firms apply AI to lease abstraction, asset management reporting, and investor relations, enabling teams to evaluate more opportunities without proportionally increasing headcount.

This audio is presented by Hacker Noon, where anyone can learn anything about any technology. How David Bratslovsky is helping commercial real estate operators use AI to win deals, by Sonia Kapoor. Multi-family sales volume reached $22, $8 billion in the first quarter of 2026. Deal velocity is climbing, capital is moving, and at most investment firms, the bottleneck is not sourcing or financing. It is the hours and acquisitions team spends transferring numbers from rent rolls and operating statements into Excel before anyone can run a single projection. David Bratslovsky built quick data.

I to eliminate that transfer step. The platform is an Excel ad in that reads multifamily financial documents and places the extracted data directly into whatever underwriting model the firm already uses. The company reports 99. 3% accuracy on rent roll extraction and identification rate for trailing 12-month operating statement line items. More than 800 multifamily professional. use the tool, according to the company, at $99 per user per month. That product was the starting point.

What followed was a consulting practice that now helps commercial real estate firms build AI capabilities across their operations. A speed problem with a dollar value. For a multifamily investor evaluating 20 or 30 acquisition opportunities per month, underwriting speed has a direct relationship to deal flow coverage. Every property requires the same preparation. Pull the rent roll data, categorize the T-12 expenses, normalize the offering memorandum figures, and populate the firm's financial model. The mechanical portion of that work, the part that involves reading a PDF and typing numbers into cells, accounts for the majority of the time and analyst spends before the actual evaluation begins. Quick Data helps multifamily professionals underwrite deals 10x faster by

automating rent-roll, T-12, and own data entry into Excel, said David Bratslowski, founder of Quick Data. I, describing the product's core function, users save an average of 15 hours per month, according to quick data. I, for a three-person acquisitions team, that represents 45 hours redirected from data entry to deal evaluation. In a market where the firm that completes its underwriting first is often the firm that controls the negotiation, those hours carry a measurable cost when wasted and a measurable advantage when recovered. The tool was trained specifically on multifamily document formats, according to the company, which is why generic document processing software tends to produce unreliable results in this sector. A rent roll from one property management company categorizes charges differently than one from another.

T-12 line items vary by operator. Offering memorandums follows no standard layout. The extraction engine was built to handle those inconsistencies rather than guess through them. From a single tool to firm wide AI consulting, working with investment firms on the underwriting problem, Bratislavski saw de Trent roll extraction was one instance of a pattern that repeated across the entire business. Leasing teams read lease amendments to track critical dates. Asset managers compile monthly reports to explain budget variances. Investor relations teams turn portfolio data into quarterly updates. Each of these tasks involves finding, organizing, checking, and moving information from one place to another. That recognition led Bratslovsky to build a consulting practice around what head as

AI skills. Reusable sets of instructions trained on a firm's own documents and terminology that handle specific repetitive tasks. One skill might categorize operating expenses. Another might reconcile conflicting figures across documents. A third might populate a quarterly investor report from reviewed data. The consulting engagements follow a consistent structure. Bratislavski works with the firm's employees to identify a task, document how it currently works, build an AI skill that replicates it, and test the output against real documents. The employees learn the process so they can maintain and extend the automations after the engagement ends. The practice now works with multifamily, office and industrial firms, brokers and lenders, applying the same

method to lease abstraction, asset management reporting, and covenant monitoring. Bratslovsky, who has worked as a fractional chief technology officer, brings a background that combines technology operations with real estate investing. Where the competitive advantage lands, the investor who evaluates 40 opportunities per month and the investor who evaluates 15 are not equally positioned, even if their judgment is identical. The difference is coverage. When underwriting preparation takes less time, more properties get a full evaluation instead of a preliminary screen and a pass. Quick data I addresses the mechanical layer of that preparation. The consulting practice addresses the organizational layer. teaching firms to apply AI to the other repetitive tasks that accumulate across acquisitions,

asset management, and investor reporting. Together, they represent Bratslovsky's answer to a question that most multifamily firms are asking in some form. How do you increase deal coverage without proportionally increasing headcount? The answer, in his framing, is not to replace the people doing the work. It is to remove the hours they spend on tasks that do not require their expertise, and let them apply that time to the decisions that do. This story was distributed as a release by Sonja Kapoor under Hackernoon Business Blogging Program.

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