Harnessing Underserved Spaces: How AI and Data Systems Are Closing Market Gaps
Real estate and e-commerce have many digital tools, but they don’t always work well for professionals managing more complex tasks. Activities like handling contracts across different states or making sense of detailed customer reviews can still be challenging. New technology—especially tools powered by AI and enhanced data systems—is beginning to address these gaps, making daily work in these industries faster and easier.
One professional working in this space is Karan Khanna, based at Harmony Venture Labs, who has focused his energy on solving the problems that larger platforms often overlook. Khanna has worked on products that tackle highly specific—and often messy—workflows. Whether it’s simplifying real estate transactions across multiple states or helping smaller online sellers interpret customer reviews, his goal has consistently been to make everyday tasks faster, clearer, and less manual.
In real estate, most digital platforms are built with a single state’s rules in mind. That doesn’t help national teams managing contracts across state lines, where the legal fine print changes from one jurisdiction to another. To address this, Khanna led the development of an AI-powered platform called ListedKit AI. The system translates contract terms from all U.S. states into one consistent format, allowing national transaction teams to manage deals without switching between tools or manually reworking timelines.
A key component of the product is Ava, an AI assistant that helps transaction coordinators handle incoming contracts. It reads purchase agreements and builds out contract timelines in under three minutes. In early testing, Ava reduced data entry time by two-thirds—a major improvement for teams trying to stay on top of critical deadlines.
In the e-commerce space, Khanna worked on a platform called RaveCapture, focusing on something many platforms overlook: the actual written reviews customers leave behind. While star ratings are useful, they often fail to capture the full story behind a product. The tool developed here, called Review Insights, uses AI to detect patterns in customer comments—such as recurring complaints about packaging or unclear instructions. It was designed specifically for smaller online businesses, which often lack access to advanced analytics. Early users quickly adopted the tool, with some even making product improvements based on insights from the dashboard.
None of this came without challenges. In real estate, one of the biggest hurdles was figuring out how to handle 50 sets of legal rules without hardcoding each one. The team solved this by building a flexible system using AI, capable of understanding and adapting to various contract formats.
What ties all this work together is a clear philosophy that Khanna articulated: “Three principles guide my approach: First, follow the messy workflows—if users rely on spreadsheets or email hacks, that’s a product waiting to happen. Second, own the domain data layer—in real estate, controlling acceptance-date logic unlocks every downstream deadline; in e-commerce, owning raw review text powers richer insights than star ratings alone. Third, design for trust, not just features—automation wins only when users see clear guardrails: refund policies, audit logs, explainable AI steps. These turn skepticism into advocacy.”
The lesson to be learned is that innovation doesn’t always come from big changes or flashy features. Sometimes, it’s about identifying the pain points others ignore—and designing practical solutions that truly align with the way people work.
