This is a classic example of why generic ATS filters and keyword-based AI fail both candidates and EMs. When the screening process is a black box, you lose high-signal candidates before a human even sees their CV.

This is a classic example of why generic ATS filters and keyword-based AI fail both candidates and EMs. When the screening process is a black box, you lose high-signal candidates before a human even sees their CV. I'm building Taknut (https://taknut.com) specifically to solve this for Engineering Managers. Instead of auto-rejecting, it uses a framework to generate interview guides rooted in the candidate's actual projects. It identifies specific technical proof points (like that nuclear engineering degree + specific stack experience) to help the EM ask deep questions rather than relying on a "Pass/Fail" bot. If you're tired of "over-zealous filters" killing your pipeline, it might be worth a look to bring the human evaluation back into focus without losing time.