From Morgan Stanley to Market Creation: How This Founder Turned 4 Years of Research into a Recruiting Intelligence Platform

From Morgan Stanley to Market Creation: How This Founder Turned 4 Years of Research into a Recruiting Intelligence Platform

Investment bankers don’t typically become neural net engineers, and neural net engineers rarely spend four years running unprofitable job search engines. But in a recent episode of Category Visionaries, Amit Bhatia shared how this unconventional path led to crucial insights that shaped his recruiting intelligence platform.

The Wall Street View

While at Morgan Stanley, covering companies like Indeed and Monster, Amit had two realizations that would shape his founder journey. “My goodness, recruiting is massive,” he recalls thinking. “Even when you think about the size of these companies, even the company like workday, which is nearly $100 billion, it’s not even a drop in the ocean in terms of how big this market is.”

But size wasn’t everything. Amit noticed a stark efficiency gap between recruiting and other business functions. “You could spend $100 in Google Ads today and you could target with incredible precision… But an average company can spend millions of dollars on hiring and not know how many candidates they’re going to get or when their jobs are going to get filled.”

The Research Phase

Instead of rushing to build a solution, Amit took an approach that would make most VCs cringe. “The first four years of this company, I butchered,” he 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 period, which Amit describes as “doing a PhD on the space,” led to a breakthrough insight that challenged conventional wisdom. “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.”

The Organizational Insight

But the problems went deeper than poorly written job descriptions. Amit discovered that recruiting faced a unique organizational challenge. “Marketing is done by marketers. Sales is done by sales folks. Finance is done by finance people. Recruiting isn’t just done by recruiters,” he explains. “In fact, the vast majority of recruiting activities are done by hiring managers.”

This insight shaped their entire product strategy. Instead of building another recruiter-focused tool, they created a platform that could be used across organizations. “We end up having hundreds if not thousands of users across the company. We end up becoming one of the most used tools in their stack,” Amit notes.

Market Creation Through Data

Rather than just claiming the “recruiting intelligence” category, they’re actively defining 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 revealed surprising insights about modern workplace trends. 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.”

The Future Vision

Looking ahead, Amit sees an opportunity to transform recruiting from a cost center into a strategic advantage. “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, they aim to help distribute talent more equitably across society while giving companies a competitive edge.

This story illustrates how deep market understanding, combined with the patience to truly research a problem space, can lead to category creation in enterprise software. Sometimes, the best preparation for building the future isn’t rushing to market – it’s taking the time to understand why the present isn’t working.