How David Bratslavsky Expanded AI From Multifamily Underwriting to CRE Operations

 Commercial real estate has never lacked data. The challenge has always been turning that data into useful information quickly enough to support better decisions. Rent rolls, operating statements, leases, offering memorandums, budgets, and property reports contain valuable details, but analysts and managers often spend significant time moving those details between documents and spreadsheets.

David Bratslavsky saw this challenge firsthand while working on multifamily underwriting workflows. His response was QuickData.ai, an AI-powered approach designed to extract information from real estate documents and place it into the Excel models investment teams already rely on.

The objective was not to replace the underwriting model. Instead, QuickData.ai was designed to reduce the manual work required before analysts could actually use the model.

Solving the Data-Entry Bottleneck

A typical underwriting process can involve reviewing a rent roll, analyzing historical operating statements, studying an Offering Memorandum, and transferring relevant figures into a standardized spreadsheet. Even when the calculations themselves are straightforward, preparing the data can consume valuable analyst time.

QuickData.ai addresses that initial stage by using AI to identify relevant information, organize it, and map it to the appropriate locations within an existing underwriting model.

That experience gave Bratslavsky a broader perspective. The problem was not limited to multifamily acquisitions. Many CRE departments were performing similar information-transfer tasks in different forms.

A Larger Commercial Real Estate Opportunity

Acquisitions teams work with property financials. Leasing professionals review contracts and amendments. Asset managers compare actual performance with budgets. Investor relations teams compile portfolio information for recurring communications.

Each department may use different documents and software, but the underlying process is remarkably similar: information must be located, interpreted, organized, verified, and transferred.

This pattern helped shape Bratslavsky's broader approach to commercial real estate AI automation.

Rather than asking businesses to abandon their existing technology, the focus is on building AI skills around the systems employees already understand.

Teaching AI How a Company Works

Modern AI models can interpret documents, follow detailed instructions, produce structured information, and interact with software systems. That creates an opportunity for businesses to build reusable AI skills around specific workflows.

For example, a real estate company could create one skill for extracting lease dates and another for categorizing operating expenses. A third could compare reported financial figures against historical records and flag discrepancies.

The important component is the company's own process. Employees understand how information should be handled, which exceptions matter, and what a correct result looks like. Those rules can be translated into instructions and tested against real examples.

Beyond Underwriting

For Bratslavsky, underwriting remains a natural starting point. AI can help process rent rolls, T12 statements, and Offering Memorandums before populating an existing Excel model.

The same methodology can extend into lease abstraction, asset management, reporting, and investor communications.

An AI workflow could identify lease escalations, renewal options, termination provisions, and important notice dates. Another could compare monthly property performance with the budget and highlight significant NOI changes.

The value comes from connecting these individual capabilities into a larger operational system.

Starting Small

Bratslavsky's approach emphasizes beginning with one repetitive task rather than attempting to automate an entire organization at once.

A company might start with a recurring report, a spreadsheet update, or a document-processing workflow. Once that process becomes reliable, additional skills can be connected.

QuickData.ai began by addressing a specific multifamily underwriting problem. That focused use case revealed a much broader opportunity: commercial real estate companies can use AI to reduce repetitive information work while keeping human professionals responsible for judgment.

For Bratslavsky, the future of CRE AI is therefore less about replacing expertise and more about giving experienced teams better tools for applying it.