Secrets to Effective Competitor Research in Product Management

In the increasingly crowded and competitive world of AI-driven SaaS, product innovation often comes down to how well companies understand and act on the competition. One expert quietly making waves in this space is Karan Khanna, a product strategist and builder whose data-backed insights are reportedly influencing go-to-market strategies across industries—from real estate to review analytics.

As per the reports, Khanna’s recent work has centered on product differentiation through competitor intelligence and accelerated MVP cycles. Notably, his research-driven contributions to platforms like RaveCapture and ListedKit are said to have translated directly into faster adoption, higher feature accuracy, and reduced product lead times.

Coming from the expert’s table, Khanna remarked, “In today’s AI SaaS market, a competitive edge doesn’t just come from what you build—but how quickly and intelligently you build it. Feature gaps are closing fast, so signal mining is now as important as engineering.”

Between 2023 and 2025, Khanna reportedly played a pivotal role in shaping the direction of three major AI products. While developing RaveCapture—an AI-powered review analytics tool—Khanna conducted a systematic tear-down of leading platforms. His findings led to the creation of the “Review Insights” dashboard, a signature feature that reportedly achieved a 35% adoption rate within just 24 hours of release.

Additionally, while working with ListedKit—a real-estate transaction coordination platform—Khanna’s research into state-specific workflows of major incumbents like DotLoop and SkySlope helped eliminate a major operational bottleneck. His decision to standardise milestone terminology across all 50 states reportedly resolved key adoption barriers for transaction coordinators (TCs) working across jurisdictions.

According to internal benchmarks, these improvements contributed to a 300% increase in contract-reader accuracy and a sub-3-minute timeline generation speed in the company’s AI product, ListedKit AI.

One of Khanna’s most striking accomplishments came in early 2025, when he reportedly designed and shipped the MVP of ListedKit AI in under two weeks. This rapid turnaround, according to sources familiar with the project, was fueled by a targeted feature-gap study that identified Gmail-monitoring and timeline auto-generation as high-impact, underserved capabilities among competitors.

“We realised no one was doing real-time contract ingestion and building timelines for TCs,” Khanna shared. “That insight moved us from research to prototype to demo in less than 14 days—and it’s now one of the top-requested features from early users.”

Reportedly, Khanna’s edge lies in his ability to work with messy or opaque market data. The fragmented nature of competitor feature sets—often gated behind demo calls or hidden pricing models—was one of the earliest challenges he tackled. To reconstruct this intelligence, Khanna combined open-source API analysis, user community feedback, and even sample contracts to reverse-engineer feature offerings.

This approach not only informed ListedKit’s roadmap but also fed into Ravecapture’s development, proving that cross-industry insights—from e-commerce to real estate—could be strategically adapted.

Khanna believes that traditional approaches to competitor research are quickly becoming outdated. “Static feature matrices are giving way to signal mining,” he explained. “Job listings, changelogs, API docs—those are your leading indicators now.”

Looking forward, he predicts that data flywheels, not individual features, will define market leaders. “In AI SaaS, whoever controls transaction-level data—like TCs in real estate—or multi-channel sentiment—like commerce platforms—will win on iteration speed. That’s your real moat.”

For product teams, Khanna offers a clear takeaway: “Translate signal into roadmap. If customers don’t feel the difference within weeks, not quarters, you’ve missed the window.”

In a space where speed and precision now define product success, Khanna’s trajectory illustrates how lean, insight-driven innovation can rival traditional scale. And as AI reshapes the very structure of digital workflows, experts say his brand of practical foresight may prove to be a blueprint for how the next generation of products gets built.