For years, analyzing real estate deals was a slow and often skipped process for many investors. Manual comping—reviewing comparable sales to estimate a property’s value—typically took five minutes or more per property. This bottleneck meant that only the most promising deals received scrutiny, while many potential opportunities were passed over.
Now, artificial intelligence is compressing that five-minute task into just 15 to 30 seconds. Platforms like Bricked AI use machine learning to automate property valuation and underwriting, allowing investors to screen and evaluate deals at a pace that was previously impossible. In an industry that has historically lagged in adopting new technology, this marks an apparent acceleration in how deals are sourced and vetted.
Abhi Bharatham, founder of Bricked AI, entered the real estate world as a typical college investor trying to break in with limited capital. With a master’s degree in computer science from Georgia Tech, Bharatham started wholesaling properties while still a student. He joined a private mentorship group, learned the basics, and quickly began closing deals. But despite his technical background, he soon became frustrated with the tools available for deal analysis.
“I didn’t have much money at the time, so wholesaling seemed like the easiest, lowest barrier to entry,” Bharatham recalls. “I was using other comping software and CRMs, and I didn’t like them at all. Coming from a software engineering background, I was a bit nitpicky about the tools I used. Comping seemed like an issue many new people like me had. Even though the numbers are easy for me, talking and comping and underwriting at the same time was difficult.”
Bharatham saw an opportunity: if comping and underwriting are just math, then software—especially AI—could handle them just as well as a person.
The platform’s main impact is speed. Bricked AI reduces the time to compile from minutes to seconds, enabling investors to evaluate far more deals each day. Bharatham notes that if an investor could previously make five offers daily, they can now make ten, simply because the decision process is faster.
“At the end of the day, every investor is just about the numbers. If they work, they work. If they don’t, it’s not a deal. So getting to that point quicker is the point of the tool,” he says.
Larger teams feel the effect even more sharply. Acquisition and disposition teams can hire new staff and ramp up productivity quickly, since the software handles much of the analysis. “One person can now do the work of five people when it comes to comping and underwriting,” Bharatham explains.
Bricked AI’s users range from individual wholesalers to large teams with sophisticated workflows. Advanced users integrate the platform directly into their customer relationship management (CRM) systems via API. This means that as soon as a lead enters the CRM, it is automatically analyzed and pre-comped in the Bricked dashboard.
“They’re wasting zero time,” Bharatham says. This automation enables teams to segment leads by seller motivation. For example, if the tool estimates a property at $100,000 and the seller is asking $120,000, the team knows there’s room for negotiation and can prioritize that lead. By contrast, if a seller asks $200,000 for a property valued at $100,000, the lead is deprioritized.
This approach helps teams act quickly on the most promising opportunities and avoid wasting time on unrealistic sellers.
Bricked AI’s adoption has accelerated rapidly. The platform grew from 200 weekly active users in November to about 1,500 by January. Its user base includes wholesalers, flippers, realtors, and private lenders. Texas, in particular, has seen strong uptake, driven by Bharatham’s early outreach at a San Antonio real estate convention.
Most deal analysis on the platform focuses on single-family homes, followed by land deals. The average property analyzed is priced below $300,000, with distressed properties drawing the most interest from investors looking to flip or wholesale.
“We have users nationwide. Our tool works everywhere, including non-disclosure states,” Bharatham notes. This broad applicability allows users to analyze deals in a wide range of markets and conditions.
Even experienced investors make errors when analyzing deals quickly. Traditional comping often overlooks key factors beyond bedrooms, bathrooms, and square footage. Bharatham points out that many investors forget to consider year built, number of stories, construction type, water views, or proximity to highways. They also tend to use arbitrary adjustments—adding or subtracting $10,000 for extra bedrooms or bathrooms—without considering specific market dynamics.
Bricked AI’s algorithms account for these variables more precisely. The software can estimate the value of a bedroom or bathroom in a particular zip code, adjust for property features, and recognize local market nuances. “We have heavy algorithms and machine learning that know the exact price of how much a bedroom is worth in this zip code, or how much a bathroom is worth. We adjust all these things when accounting for comps,” Bharatham explains.
This level of detail helps investors avoid the systematic errors that often lead to overpaying or missing out on deals.
Unlike many automated valuation models (AVMs) used on consumer real estate sites, Bricked AI emphasizes transparency. Users can see which comparable properties influenced the valuation and can manually include or exclude certain comps if needed.
“We’re very transparent. We’ll show you the comps, we’ll show you what we used to get our number, and you can actually select and unselect comps,” Bharatham says. This approach allows users to trust the numbers and understand how the valuation was reached.
The platform relies on live data rather than static models, providing more current and accurate market information than traditional AVMs, which may update less frequently.
Bharatham sees interest rates as the main factor influencing future platform usage. As rates drop, he expects real estate activity to increase, with more investors seeking opportunities and deploying capital.
“I think lower interest rates—people have been doing creative deals to get around those rates. As they reduce, the real estate market will pick back up even more, more investor activity, just more money in general,” he says.
For investors with substantial funds, Bharatham recommends focusing on multifamily properties or portfolio acquisitions. Managing a single, enormous multifamily asset is often simpler than overseeing many single-family homes, especially when deploying significant capital.
Bricked AI’s API also serves software providers and service companies, allowing them to integrate property valuation directly into their own platforms. This business-to-business segment is a growing part of the company’s model, extending AI-powered analysis to a broader array of industry tools.
As AI becomes more deeply embedded in real estate operations, tools like Bricked AI are changing how deals are identified and evaluated. Compressing five minutes of manual analysis into 15 seconds doesn’t just save time—it allows investors to screen more opportunities, make faster decisions, and reduce costly mistakes.
In today’s competitive market, speed and accuracy in initial screening can mean the difference between closing a profitable deal and missing out. AI-driven platforms are setting a new standard for efficiency, helping investors and teams adapt to changing conditions and pursue more deals with greater confidence.
For an industry long defined by slow processes and manual work, the shift to instant, transparent analysis is reshaping what’s possible in real estate investment. As technology continues to advance, the expectation for fast, data-driven deal evaluation is likely to become the norm, making AI not just a convenience but a necessity for serious investors.
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