#davidbratslavsky

The path to entrepreneurship is rarely as predictable as it seems. While many startup founders come from engineering or software development backgrounds, David Bratslavsky followed a very different route. Before launching QuickData.AI and becoming involved in the growing field of artificial intelligence for commercial real estate, he spent years studying international affairs, diplomacy, and global systems at George Washington University.

What may appear to be an unusual transition at first actually reveals how valuable diverse experiences can be when building innovative solutions.

A Foundation Built on Systems Thinking

During his time at George Washington University, David Bratslavsky pursued a degree in International Affairs with a concentration in Middle East Studies. His education focused on understanding how governments, institutions, and organizations operate within complex environments.

Rather than viewing challenges as isolated events, students were encouraged to examine the larger systems that influence outcomes. They studied how information moves between organizations, how incentives shape behavior, and why inefficiencies often emerge even when highly capable people are involved.

This approach to problem-solving became one of the most important lessons Bratslavsky carried into his professional career.

Instead of asking what happened, he learned to ask why it happened and what structural factors contributed to the outcome. That mindset would later influence how he approached business operations and technology development.

Discovering Operational Bottlenecks

After graduation, Bratslavsky entered roles connected to investments, business operations, and venture-backed companies. These experiences exposed him to the inner workings of organizations across different industries.

As he observed teams and workflows, a recurring pattern became clear.

Highly skilled professionals were spending a large portion of their time managing information rather than using it to make decisions. Reports required manual updates, spreadsheets needed constant cleaning, and documents were scattered across multiple systems.

The challenge was not a lack of talent or expertise. The real issue was inefficiency in the way information moved through organizations.

Investment professionals gathered data. Analysts organized and formatted it. Managers waited for reports before taking action. Valuable time was being consumed by repetitive administrative work instead of strategic decision-making.

For Bratslavsky, these workflow challenges represented an opportunity for improvement.

Why Commercial Real Estate Stood Out

Among the industries he encountered, commercial real estate presented one of the clearest examples of operational friction.

Multifamily underwriting, despite being critical to investment decisions, often relied on manual processes. Analysts regularly spent hours reviewing rent rolls, offering memorandums, operating statements, and other property documents before they could begin evaluating an asset.

Important information existed, but extracting and organizing it required significant effort.

To Bratslavsky, this situation mirrored many of the coordination problems he had studied during his academic years. The issue was not access to information. The issue was the system connecting people to that information.

Too much time was being spent moving data instead of analyzing it.

That observation ultimately became the inspiration for QuickData.AI.

Building QuickData.AI Around Real User Needs

Like most startups, QuickData.AI did not emerge fully formed. The company evolved through continuous experimentation, customer feedback, and product refinement.

Bratslavsky spent significant time speaking directly with brokers, investors, underwriters, and real estate operators. He observed how professionals worked, identified recurring pain points, and tested different approaches to automation.

Through this process, he discovered an important truth about software adoption.

Most users are not looking for a completely new workflow. They want their existing workflow to work better.

Rather than asking professionals to abandon familiar tools, QuickData.AI focused on automating document extraction and delivering structured data into underwriting models already used by investment teams.

This approach reduced friction while allowing users to maintain the processes they trusted.

As a result, the platform gained traction by solving a specific problem without introducing unnecessary complexity.

A Practical Philosophy on Innovation

As QuickData.AI grew, Bratslavsky developed a philosophy centered on practical execution.

He often emphasizes that successful products solve real problems rather than chasing trends. Technology should create measurable value, not simply attract attention.

His advice to founders frequently focuses on a few key principles:

Solve a meaningful problem exceptionally well.

Stay close to customer behavior and feedback.

Prioritize usability over unnecessary features.

Build products that fit naturally into existing workflows.

He also believes that strong teams are often built on judgment, adaptability, and customer understanding rather than credentials alone.

Connecting International Affairs and Artificial Intelligence

Today, David Bratslavsky leads QuickData.AI, advises businesses as a fractional CTO, and contributes to discussions surrounding AI adoption in commercial real estate.

While his academic background and entrepreneurial career may seem unrelated, the connection becomes clear when viewed through the lens of systems thinking.

International affairs and artificial intelligence both involve understanding how information moves through complex systems. Both require identifying inefficiencies, improving coordination, and finding better ways to connect people with the insights they need.

David Bratslavsky’s journey demonstrates that innovation does not always come from following a traditional path. Sometimes the ability to see problems differently becomes a founder’s greatest advantage.

The commercial real estate industry is experiencing significant changes as global markets become increasingly interconnected. At ReShaped 2025, industry professionals gathered to explore how technology, capital flows, and international investment trends are transforming the way real estate deals are sourced and executed. Among the featured speakers, David Bratslavsky offered valuable insights into the evolving landscape of cross-border real estate and the role artificial intelligence can play in helping investors navigate a more complex global market.

During his presentation, Bratslavsky emphasized that international real estate investment is no longer limited to large institutional firms with extensive resources. Advances in technology and data accessibility are making it easier for investors of all sizes to evaluate opportunities beyond their home markets. However, he noted that increased access also creates new challenges, particularly when it comes to analyzing unfamiliar markets, regulatory environments, and property data.

According to Bratslavsky, one of the biggest obstacles facing cross-border investors is the volume of information required to make informed decisions. Property documents, market reports, financial statements, rent rolls, and legal disclosures often come in different formats and standards depending on the country. Reviewing and organizing that information manually can slow down transactions and increase the risk of missing critical details.

To address these challenges, Bratslavsky highlighted how artificial intelligence is helping investment teams process large amounts of data more efficiently. AI-powered tools can extract information from documents, identify key metrics, organize financial data, and generate preliminary analyses in a fraction of the time required through traditional methods. This allows investors to focus more attention on strategy and decision-making rather than administrative tasks.

A key theme of Bratslavsky’s discussion was the importance of speed in today’s competitive investment environment. Cross-border transactions often involve multiple stakeholders, time zones, and regulatory requirements. Investors who can quickly evaluate opportunities and identify risks may gain a significant advantage when competing for high-quality assets. AI-driven workflows, he explained, are becoming an important tool for achieving that efficiency.

Bratslavsky also addressed the growing importance of transparency in international real estate transactions. Investors need confidence that the information they are reviewing is accurate, complete, and easy to verify. Modern technology platforms can help create more standardized reporting processes, making it easier for buyers, lenders, and partners to understand the fundamentals of a property regardless of geographic location.

Another topic that attracted attention at ReShaped 2025 was the increasing role of data-driven decision-making. Bratslavsky noted that investors are no longer relying solely on local market knowledge or personal networks. Instead, they are incorporating advanced analytics, automation, and predictive tools to evaluate opportunities across multiple regions simultaneously. This shift is creating new possibilities for identifying emerging markets and uncovering investment opportunities that might otherwise be overlooked.

While technology continues to evolve rapidly, Bratslavsky stressed that successful real estate investing remains a people-focused business. Relationships, local expertise, and market understanding still play a critical role in evaluating risk and executing transactions. The goal of AI is not to replace experienced professionals but to provide them with better information and greater efficiency.

His presentation reflected a broader trend within the commercial real estate industry: the growing integration of artificial intelligence into everyday investment workflows. As cross-border activity continues to expand, firms that embrace modern technology may be better positioned to compete in an increasingly global marketplace.

The commercial real estate industry has embraced technology in many areas, yet underwriting often remains surprisingly manual. Analysts still spend hours reviewing PDF organizing financial information, and updating spreadsheets before meaningful investment analysis can begin. David Bratslavsky believes that reality is rapidly changing.

As founder of QuickData.AI, Bratslavsky has built his career around solving one of multifamily real estate's most persistent operational challenges: transforming unstructured property documents into actionable investment data. His goal is not merely to accelerate underwriting but to fundamentally rethink how investment teams interact with information.

According to Bratslavsky, traditional underwriting workflows were created in an era when automation capabilities simply did not exist. Over time firms adapted to lengthy review cycles, manual reconciliation, and repetitive data entry. These activities became accepted parts of the investment process, even though they contributed little strategic value.

The typical multifamily transaction illustrates the problem. Analysts receive rent rolls, T12 operating statements, offering memoranda, and supplemental reports from multiple sources. Before they can evaluate an opportunity, they must extract relevant information, verify figures, normalize categories, and populate financial models. Much of this work involves processing data rather than analysing it.

Bratslavsky argues that modern artificial intelligence can eliminate these bottlenecks. If information follows predictable patterns and requires minimal judgment, software should be able to complete the task automatically. Human expertise should be reserved for evaluating assumptions, assessing market dynamics, and determining investment strategy.

This philosophy shaped the development of QuickData.AI. The platform automates document extraction and categorization, delivering structured information directly into underwriting workflows. Rent roll data can be converted into unit-level analysis, operating expenses can be categorized consistently and offering memorandum details can populate financial models within minutes.

The benefits extend beyond efficiency. When analysts spend less time entering information, they have more capacity to evaluate risk factors, perform scenario analysis, and explore alternative investment strategies. Investment committee presentations become more comprehensive because teams have additional time to focus on insights rather than administration.

One common concern regarding automation is accuracy. David Bratslavsky acknowledges that no technology is perfect, but he believes the comparison should be realistic. Human analysts working under pressure can also make mistakes, particularly when reviewing large datasets late in the underwriting process. Automated systems offer the advantage of consistency, traceability, and continuous improvement. Every extracted data point can be linked back to its original source document, creating an auditable workflow.

Looking forward, David Bratslavsky sees underwriting evolving into a continuously updated environment rather than a static spreadsheet exercise. As new property information becomes available, models will update automatically. Financial documents, leasing activity, and operating performance data will integrate seamlessly into decision-making systems.

This shift has the potential to transform how multifamily investors evaluate opportunities. Faster access to accurate information enables quicker decisions, stronger analysis, and greater scalability across acquisition teams.