Paste a cover letter, written application, or take-home assignment and see which passages read machine-made.
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Scores come from an established third-party detection service, not a homegrown scoring trick.
You get the passages that carry the signal, not one number with no explanation.
Revise flagged passages, rescan with the same detector, and compare what changed.
Every saved version records the result and changes without pretending a score proves authorship.
Detection is probabilistic. False positives fall hardest on non-native English writers and on plain, heavily-edited prose. Use the flagged passages to shape a follow-up about the candidate's own work, never as the sole reason to pass on someone.
Reads machine-made
I am excited to leverage my robust skill set to drive impactful results and seamlessly integrate into your dynamic team.
Reads like a person
I spent two years on a support desk before I moved into QA. It made me slower to trust a green test suite.
No, and you should not. No detector is certain on a single document, and false positives fall hardest on non-native English writers. Treat a flagged application as a reason to ask the candidate to talk through their own work, which is the part that actually tells you something.
That is common now, and it is not the same as having AI write the application. The report marks which specific passages carry the strongest signals, so you can tell light editing apart from a draft that was generated whole.
We are not your lawyers, and hiring rules vary by jurisdiction. Because detection can misfire on protected groups, particularly non-native speakers, run anything that affects a hiring decision past your own legal and HR review first.
Yes. Longer writing gives a detector more to work with, and results on very short text are much noisier. A single-paragraph cover letter is close to the shortest text worth checking.
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