structured-hiring
(4)
Greenhouse vs Lever Is the Wrong Question
The better recruiting platform is the one that matches how your team actually hires. Greenhouse rewards process discipline; Lever puts candidate relationships and pipeline flow at the center.
Daniel Chait
Daniel Chait is the co-founder and CEO of Greenhouse Software, the New York-based recruiting platform he started with Jon Stross in 2012 to help companies make hiring a competitive advantage. A University of Michigan computer engineer who spent more than a decade as a software consultant and previously co-founded the investment-banking tech firm Lab49, Chait built Greenhouse around the idea of 'structured hiring.' He is co-author of the book Talent Makers, a frequent conference speaker, and a vocal commentator on how AI is reshaping recruiting - including his warning about an 'AI doom loop' flooding both job seekers and employers.
Greenhouse Software
Greenhouse Software is a New York-based hiring platform that helps companies run structured, data-driven recruiting. Founded in 2012 by Daniel Chait and Jon Stross, it centralizes applicant tracking, interview scorecards, candidate sourcing, onboarding and reporting into one system used by more than 7,500 organizations. Rather than pushing automation for its own sake, Greenhouse pitches 'structured hiring' - a consistent, comparable process for evaluating candidates - and has layered in AI tools and fraud detection to keep that process reliable as application volumes surge.
Gyfted
Gyfted is a Stanford-connected, remote-first HR-tech company that builds psychometric and cognitive assessments powered by behavioral science and AI. Its free consumer quizzes (Big Five, EQ, DISC, cognitive ability) draw millions of test-takers via organic search, while its B2B software helps founders, recruiters and team leaders screen candidates and hire for role and culture fit. Co-founded in 2020 by CEO Robert Kowalski with a team that includes Stanford GSB psychometrician Michal Kosinski as an adviser, Gyfted has raised roughly $1.3M in early funding.