How a team of Stanford engineers is tackling manual work in commercial real estate\” />
Photo Courtesy of Y Combinator
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A team of Stanford-trained engineers has identified a massive inefficiency plaguing the commercial real estate industry: brokers spending countless hours on manual tasks that technology could automate. Commercial real estate professionals draft offering memoranda by hand, design marketing brochures from scratch, and conduct property surveys using outdated methods.
Two Stanford computer science graduates have developed what industry experts describe as a breakthrough solution to this chronic problem. Chinmay Patel and Anmol Tukrel co-founded Closera, an artificial intelligence platform that automates the most labor-intensive aspects of commercial real estate marketing. Their technology has already attracted attention from Y Combinator, which selected the startup for its highly competitive Summer 2025 cohort with an acceptance rate below one percent.
“Brokers create value through relationships and strategy, not by spending hours making brochures and offering memorandums,” said Patel. “Closera handles the tedious work so they can focus on closing more deals, faster.”
Silicon Valley meets real estate
Patel met his co-founder, Anmol Tukrel, on the first day of freshman year at Stanford University, where they paired up as project partners in the introductory computer science course. They quickly bonded over a shared passion for building technology products and a mutual connection to real estate, with both their families involved in property development.
After earning bachelor’s and master’s degrees in computer science from Stanford, Patel worked as a product manager at Roblox and later at Point72 Asset Management, building AI tools to streamline research. He also spent time at Boston Consulting Group advising Fortune 500 clients on digital transformation, and pursued graduate studies at MIT Sloan and Harvard Kennedy School.
Tukrel earned his bachelor’s degree in computer science from Stanford and began his career at Google. He started as a product manager on the Gmail and Google Photos teams, before going on to work on monetization for Google’s generative AI products, including Gemini, NotebookLM, and Flow.
Their diverse experiences, spanning AI, product development, and business strategy, gave them a unique vantage point on the inefficiencies in commercial real estate. Drawing on their skillsets deploying automation in cutting-edge industries, they saw an opportunity to bring the same level of innovation to a sector still reliant on manual, time-consuming processes.
Redefining industry standards
Closera’s platform represents a fundamental departure from traditional commercial real estate marketing methods. The system employs proprietary AI agents that combine generative models with context-aware inputs from property data, photographs, and client preferences. Unlike generic marketing automation tools, Closera’s technology adapts outputs to match specific target audiences and listing types, which is essential for high-value commercial real estate transactions.
The automation addresses multiple pain points simultaneously. Through automated processes, brokers can generate professional offering memoranda, create compelling marketing brochures, stage spaces virtually, and produce personalized marketing materials.
Commercial real estate brokers typically spend between 30 and 45 hours each week on tasks that artificial intelligence could complete within minutes. These automations save time and significantly reduce marketing costs, enabling brokers to allocate resources strategically.
Early adoption metrics validate these efficiency claims. Brokers from several top brokerages are already piloting the platform, with initial results showing significant improvements in deal flow acceleration. The technology enables individual brokers to scale their output while maintaining quality, establishing new marketing speed and personalization benchmarks.
Transforming a traditional industry
The commercial real estate sector has historically resisted technological change, relying on established relationships and traditional marketing methods. However, the scale of inefficiency has become impossible to ignore. Brokers waste valuable time on repetitive tasks when they could focus on relationship building, deal negotiation, and client service.
The startup’s success reflects broader automation trends sweeping professional services. From Harvey for legal research to Hebbia for financial analysis, AI startups have already begun disrupting traditional industries. With its heavy reliance on document preparation and marketing materials, commercial real estate presents an ideal target for such transformation.
Market conditions further support the timing of this innovation. The commercial real estate industry faces increasing pressure to operate more efficiently while handling larger transaction volumes. Traditional methods struggle to scale to meet current demand levels, creating an opening for technological solutions.
Industry-wide implications
Closera’s technology could catalyze broader changes throughout the commercial real estate ecosystem. Faster marketing cycles accelerate deal completion times, while improved material quality boosts property sale prices. The platform’s ability to generate personalized content at scale enables brokers to serve more clients effectively while maintaining current overhead levels.
These improvements extend beyond individual productivity gains. Widespread adoption of such automation could reshape competitive dynamics within the industry, favoring firms that embrace technological solutions over those maintaining traditional approaches. This technological divide may determine which brokerages thrive in an increasingly efficient marketplace.
Closera’s platform demonstrates how targeted artificial intelligence applications can solve specific professional challenges while creating substantial value for end users. Commercial real estate stands on the cusp of technological transformation, raising questions about how quickly traditional players will adapt to these new realities.
