How QuickData.ai Became the Foundation for David Bratslavsky’s CRE AI Strategy

 Artificial intelligence in commercial real estate does not always need to begin with a massive technology transformation. Sometimes, the most useful starting point is one repetitive process that consumes too much employee time.

For David Bratslavsky, that starting point was multifamily underwriting.

Analysts frequently work with rent rolls, trailing financial statements, offering memorandums, and other property documents before they can complete an investment analysis. Much of the information already exists. The difficulty is transferring it accurately into the models and systems used by the investment team.

QuickData.ai was created to address that bottleneck.

A Different Approach to Real Estate Automation

Many commercial real estate companies have spent years developing spreadsheets and internal processes that fit their investment strategies. Their Excel models may contain customized formulas, assumptions, formatting, and approval requirements.

Replacing those systems is not necessarily the best answer.

QuickData.ai takes another route by helping move information from source documents into the models teams already use. This allows analysts to maintain their familiar underwriting environment while reducing some of the manual preparation involved.

The result is a workflow in which AI handles extraction and organization, while professionals review the output and concentrate on the investment analysis itself.

Discovering the Bigger Opportunity

The experience of building an underwriting-focused product exposed Bratslavsky to a broader reality within commercial real estate: manual information handling is not limited to acquisitions.

Consider the work performed after an investment closes. Asset managers may receive monthly operating statements, leasing reports, occupancy updates, and budget information. Someone has to bring those pieces together, compare results, and determine what deserves attention.

Leasing teams face another form of information overload. A single lease can contain numerous dates, financial terms, options, and obligations that need to be monitored.

Investor relations teams also work with structured information that must be transformed into reports, updates, and communications.

These activities look different from underwriting, yet each contains repetitive steps that can potentially be automated.

Building Skills Around Existing Processes

Bratslavsky's consulting approach builds on the idea that AI automation can be organized into reusable skills.

A skill is essentially a defined workflow that teaches an AI system how to perform a particular business task. The instructions can include company terminology, formatting requirements, examples, exceptions, and expected outputs.

For a commercial real estate firm, that might mean creating a process that reads an operating report, identifies selected financial categories, checks the figures against established rules, and places the results into a predetermined spreadsheet.

Another workflow might compare information from two reports and identify differences for an employee to investigate.

The value comes from making the automation specific to the organization rather than relying only on generic AI behavior.

Human Review Remains Important

Real estate involves financial consequences, contractual obligations, and investment decisions. Automation therefore works best when it supports professional judgment instead of pretending that judgment is unnecessary.

AI can identify information, organize it, reconcile selected figures, and highlight exceptions. Human users can then verify important results and decide what action should follow.

This creates a division of labor: software handles repetitive information processing, while experienced professionals spend more time interpreting the results.

From Product to Consulting Practice

QuickData.ai provided Bratslavsky with a practical example of how AI can solve a real CRE workflow problem. Consulting expanded that lesson to companies with different processes, systems, and priorities.

A multifamily firm may prioritize underwriting automation. An office owner may focus on lease abstraction. An industrial operator may want better reporting around tenant obligations and occupancy.

The technology can vary, but the underlying strategy remains consistent: identify a repetitive workflow, document how employees perform it, build an AI-enabled skill, test it against real examples, and improve it over time.

David Bratslavsky's evolution from building QuickData.ai to developing a broader commercial real estate AI practice illustrates a practical path for technology adoption. Instead of attempting to automate everything at once, firms can begin with one meaningful problem and use the lessons learned to expand from there.