15 hours per month. That's the figure David Bratslavsky and QuickData.ai have been using to quantify typical time saved for an underwriter, a claim that has become the centrepiece of his industry demonstrations. He showed the platform ingesting offering memoranda, rent rolls and financial statements and returning structured outputs within minutes at a CRE AI Studio event, a tactic chosen to prove throughput on real documents rather than recycle speculative projections. Those live demos, paired with reported accuracy rates and training services, are the immediate route the company is using to drive adoption.

15 hours per month. That number frames the argument Bratslavsky is making to underwriters, investors, brokers and lenders who work on multifamily and commercial deals. It's also the readout he wants decision makers to take away from a live demo: automation that meaningfully reduces repetitive data entry and the several hours of typing manual underwriting typically requires.

Demonstrations, not promises

Bratslavsky has leaned into live demonstrations as the sales motion. At a CRE AI Studio event an account of his appearance described the platform ingesting offering memoranda, rent rolls and financial statements and returning structured outputs within minutes. The sessions emphasised practical accuracy and usability, not abstract speed claims. Attendees pressed on implementation and workflow integration, and Bratslavsky repeatedly underlined a trade off his team factors into product design: speed matters, but accuracy and usability determine whether automation reduces downstream validation time and therefore real profit and loss impact.

Public reporting of the product includes performance figures presented as outcomes on real documents, not laboratory benchmarks. One profile reported rent extraction accuracy of 98 percent and financial statement categorisation accuracy of 97 percent, and the same piece described the 15-hour monthly saving per underwriter. Those numbers are used to argue that QuickData.ai moves rent roll, trailing 12-month statements and offering memorandum data directly into Excel based underwriting models, cutting the manual steps that have long been the banal heart of deal preparation.

QuickData.ai also offers AI training to help firms build in house workflows. A podcast profile of Bratslavsky outlines that combination: software to extract and structure data, and training so teams can fold those outputs into existing models without creating fresh verification burdens. That point reflects the McKinsey analysis cited in coverage, which estimates AI could unlock $430 billion to $550 billion in value across the real estate value chain and argues the most durable gains come from redesigning workflows around people rather than tacking on isolated tools.

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The adoption question

Buyers in real estate are rarely sold by claims alone. The industry context in which Bratslavsky is pitching QuickData.ai helps explain why a demo driven approach gains traction. Consulting analysis from McKinsey provides a strategic backdrop: the largest value pools lie where workflow redesign meets the human agent or decision maker.

That is, firms that can see how a new platform slots into their underwriting models, and that can assess whether it lowers routine labour without creating new validation work, are the ones likely to adopt.

The sessions described in coverage were practical. Attendees asked about integrating automation without disrupting established team processes. Implementation questions dominated the conversation, rather than headline performance metrics. That reaction is consistent with the product positioning reported across write ups and interviews: Bratslavsky presents QuickData.ai as an industry focused automation tool, arguing for smarter data management and training so that firms can extract value from AI rather than chase a novelty.

Bratslavsky’s public profile now runs across conference stages and podcasts. Coverage through 2025 and into May 2026 shows him on industry platforms, using live demos to move conversations from promise to practice. The company’s immediate path to adoption is described as a combination of demonstration and client training, showing prospective users how the software produces structured outputs, and then helping teams fold those outputs into their Excel based underwriting models.

The emphasis on demonstrable throughput and usability points to a larger market truth. Real estate firms have historically measured new tools by whether they create more work downstream in verification. The accuracy claims reported alongside the time savings are therefore central to the sales case. If extraction is fast but produces errors that demand hours of follow up, the P and L benefits evaporate. Bratslavsky’s pitch is that his approach limits that trade off by pairing model accuracy with implementation training.

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The single concrete takeaway is still 15 hours per month per underwriter. It is a neat sales hook; the real test is whether firms actually cut validation time and redeploy staff. Look for integration pilots, vendor audits and published case studies as the next pieces of evidence on durable gains.

This article was created with AI assistance.