We started by trying to make applications less noisy.

Renish and Mann had no background in HR or recruitment. But we could see the gap from both sides: capable people were missing relevant roles, and recruiters were sorting through applications that told them very little.

So we built HeadsIn, a two-way marketplace for candidates and companies, with an AI pre-assessment layer to help the right people and openings find each other.

It worked. In six months, HeadsIn reached 5,000 candidates, 200 companies and 45+ closings.

Then we met the hard part of a marketplace: to grow, we had to bring demand from both sides at once.

Running recruitment ourselves brought us closer to another problem. Applications were rising, but finding the right person was not getting easier.

AI made that gap sharper. Resumes could be rewritten to mirror a job description, which made the first signal easier to game. An AI interview could help later, but it could not rescue a shortlist built from the wrong people. And roles were getting more specific, often asking one person to bring several skills together.

More applications did not mean less work for the teams trying to hire.

That is where Saral began. We stopped asking how to process a larger pile and started asking how to find the people who belonged in it before they applied.

We are building the search, candidate context and outreach that help a recruiter understand fit, choose whom to approach, and start a real conversation. The work we did by hand at HeadsIn still shapes what we build.

We think the next five to ten years of recruitment will be less about collecting applications and more about finding signal: what someone can do, where they might fit, and whether this is the right time to talk. AI can do more of the searching and sorting. The decision to hire, and the relationship that begins there, should stay human.

Renish & MannFounders, Saral AI