From Search Engine to Category Creation: How This Founder is Reshaping Recruiting Intelligence

Most founders rush to market with their minimum viable product, but in a recent episode of Category Visionaries, Amit Bhatia shared a contrarian approach: he spent four years running an unprofitable job search engine to truly understand the recruiting market before building his product.

"The first four years of this company, I butchered," Amit candidly admits. "I started because I knew I wanted to work in this space. I knew I needed data, and I didn't know how to go about it. So I started by bootstrapping and building a job search engine."

This research phase, which Amit describes as "doing a PhD on the space," led to a crucial insight that would shape his entire product strategy. While most assumed the problem lay with job boards like LinkedIn and Indeed, Amit discovered the real issue was more fundamental.

"I had this epiphany. The same exact job can be written in five different ways with five completely different outcomes," he explains. "Until then, I thought, oh my God, why is LinkedIn so bad? Why is indeed so bad? And then I realized, LinkedIn indeed aren't bad. The jobs are terrible."

When they finally launched their product in 2019, they took a deliberately different approach to market expansion. While most recruiting tech companies were chasing tech startups for quick sales cycles, Amit's team pursued customer diversity.

"One of the things that we've tried to avoid, it was very commonplace in the last ten years in recruiting tech and I think at any part of tech to go and build customers that were other tech startups or other tech companies," he notes. This strategic decision proved crucial during the 2022-2023 market downturn, as their tech customers represented less than 25% of their customer base.

Their go-to-market strategy also challenged conventional wisdom about enterprise sales cycles. "I've been surprised at how much a company can speed up an enterprise sales cycle by making the process data driven and immersive," Amit shares. "Even with our enterprise customers, with the land and expand motion, we're able to keep sales cycles down to 30 days, often 40 days, without any struggle, even in tight times."

The company's approach to category creation is equally thoughtful. Rather than just claiming the "recruiting intelligence" label, they're working to define it through data leadership. "We sit on an enormous amount of data. And if there's one thing that recruiting has always been enormously hungry for and starved off, it's great data sets," Amit explains.

Their research has unveiled surprising insights about the modern workplace. For instance, they discovered that the pandemic's impact on remote work preferences has intensified rather than diminished: "Not only are we seeing that two x hasn't gone back to normal, it's close to three and a half x. More women apply for remote roles than for non remote inequities."

Looking ahead, Amit sees an opportunity to transform recruiting from a cost center into a strategic advantage. The key? Putting hiring managers at the center of the process. "The only reason the hiring manager is not in the driving seat today is because we don't want that hiring manager to be using birthday or we don't want the hiring manager to be using greenhouse. The tools or the processes are too complicated or too arcane and that has to change."

Amit's ultimate vision extends beyond just improving recruiting efficiency. "Today, as an industry, we spend somewhere like $70 billion wastefully on recruitment, marketing and executive search," he notes. By bringing consumer-grade experiences and true intelligence to recruiting, he believes they can help distribute talent more equitably across society while giving companies a competitive edge in the war for talent.

This story exemplifies how patient research and deliberate customer strategy can create lasting advantages in B2B software. Sometimes, the key to building a category leader isn't rushing to market – it's taking the time to truly understand the problem space before building a solution.