Applicant AI

Applicant AI reads each resume and cover letter and grades how well it matches your job description, then sorts applicants by that score. The ranking is the point: instead of opening applications one by one, you start at the top and stop when the quality drops off. Scores update as new applications arrive, so the list stays current.

The software is built to spot the sameness that AI writing has introduced into job applications. It flags generic cover letters and near-duplicate submissions, so mass-applied, low-effort entries sink to the bottom. For recruiters who used to reject most of a stack by hand, this is the feature that saves the most time.

You can use Applicant AI as your full applicant tracking system, or keep Greenhouse, Lever, or Workable and let it pre-screen first. In pre-filter mode, applicants complete a form through a custom link, get scored, and only those above your threshold pass into your main ATS. You still see everyone who applied, including the ones who did not make it.

The free ATS covers the basics a small team needs: unlimited jobs, a company career page with your branding, clarification questions on each posting, and an application form. You can move applicants between pipeline stages such as reject, pre-select, assessment, and interview, and send automatic rejection emails from a template you customize.

You can give the AI your own evaluation rules, including criteria you would rather not publish on the job ad. That lets you weight the things your team actually cares about, such as specific experience or portfolio quality, without telling every applicant exactly what you screen for.

Applicant AI is positioned as a tool that assists human decision-making rather than replacing it, prioritizing candidates based on how their materials match the job description. That framing lines up with the EU AI Act, which treats hiring AI as high-risk and expects it to support, not overrule, human judgment.