Room for Improvement—Filled
A strategic approach to reducing abandonment, boosting accuracy, and rebuilding trust with smarter spatial AI.
At Brick & Mortar Visions (a fictional virtual-tour startup in the real estate tech space), Sue (the fictional head of client experience) was having a brutal quarter. Her inbox had become a graveyard of apologetic emails: agents complaining about poorly rendered home layouts, clients confused by missing hallways, and one particularly vocal homebuyer who was convinced the bathroom in their future condo had been “erased by AI.”
From the outside, Brick & Mortar Visions looked like it was thriving. It offered immersive, AI-generated 3D home walkthroughs using just a few panoramic photos. The goal was to make property viewings seamless, scalable, and deeply engaging. But inside the company, Sue knew they were inching toward a cliff. Two of their biggest brokerage clients had recently walked away—citing “unreliable tour quality,” and the word on the street was that upstart competitor Roof to Riches Realty was snapping up market share with smarter, more convincing spatial experiences.
Sue’s team had built their pipeline on modern computer vision models trained to stitch together interior photos into virtual tours. But while these models could produce sleek visuals from the views they had, they struggled to understand the space they couldn’t see: hallways around corners, closets behind closed doors, or entire rooms that hadn’t been photographed. The output looked fine at first glance but broke down under scrutiny. And in real estate, where trust, intuition, and visual context drive decisions, those missing details weren’t just bugs. They were dealbreakers.
When Customer Expectations Outpace Your Infrastructure
Behind the scenes, the technical cracks were starting to widen. Virtual tour abandonment rates were climbing—fast. Designers were spending more time manually retouching misaligned rooms than creating new content. And Sue found herself fielding more calls from the CFO, who was increasingly skeptical about the ROI of continuing to fund their internal AI staging efforts.
At the same time, the bar for customer experience in PropTech was rising. Rivals were rolling out virtual staging tools that could reimagine furniture layout with just a click, while others were experimenting with live agent–guided walkthroughs in VR. These weren’t just flashy gimmicks; they were reshaping buyer expectations. When a customer now clicks into a tour, they expect fluidity, context, and a sense of spatial continuity. Anything less is jarring.
Yet the team’s current tooling was working with blinders on. Limited photo sets meant limited input (and their AI models couldn’t infer the “missing” parts of a home). They weren’t just lacking data; they were lacking spatial understanding. As Sue put it during one product standup, “It’s like we trained the system to look at a room, but not imagine what’s around the corner.”
The idea of training AI to infer what it can’t see had been circulating, but few tools on the market offered it at scale or without extensive custom development. Still, Sue knew the status quo wasn’t tenable. Even when her team delivered on time, too many tours failed to meet quality expectations—requiring multiple feedback loops and frustrating their clients.
What Happens When You Leave Buyers Guessing
The deeper problem wasn’t technical; it was emotional. Home buying is inherently personal and spatial. A buyer wants to feel themselves in the space. They imagine their kids running down a hallway or a Sunday morning in the kitchen. When a virtual tour fails to show the full picture (or worse, shows something inaccurate), it breaks that imaginative experience. It creates hesitation, doubt, and a little voice that says, maybe I should wait.
This wasn’t just a design flaw; it was a revenue leak. Each failed tour meant fewer conversions, fewer follow-ups, and less time spent by agents with qualified prospects. Sue ran the numbers: for every 100 virtual tours that users clicked into, nearly 40 were exited before they reached the halfway mark. That abandonment rate, multiplied across hundreds of listings, amounted to a 15–20% drag on projected annual revenue. The ripple effect was real: agent satisfaction was dipping, marketing costs were rising, and the sales team had started asking whether they were pitching vaporware.
And then there was morale. Her design team was exhausted from patching gaps. Engineers were burned out trying to tweak models that couldn’t grasp context. And while Brick & Mortar Visions still had enough momentum to survive, Sue knew survival wasn’t the goal. They had to find a way to move from patching problems to solving them (before the market decided for them).
Curious about what happened next? Learn how Sue applied a recently published AI research (from Stanford, UW, NYU, and Northwestern), rebuilt trust with a smarter spatial strategy, and achieved meaningful business outcomes.