AI resume screening reads each resume, extracts experience, skills and roles, compares them with the job description and produces a relevancy score. It does work, on one condition: the job description must be clear, and a person must review the shortlist, because the score is a ranking aid rather than a final decision.
What does the technology actually do?
The system parses a resume into structured information: education, skills, roles held, companies, and years of experience in each. Good models read context, so they can tell someone who used a technology from someone who led a team using it.
That information is then compared with the job description: what appears in both, what the resume has that the JD does not ask for, and what the JD needs that the resume does not show. The result is a relevancy score for this candidate against this role.
Where does it break down?
It depends on input quality on both sides.
A JD full of jargon gives the model weak signals, and the vaguer the JD, the less reliable the score. Resumes matter too: a keyword-heavy resume can outscore a plainly written one with stronger experience. That is why scores should be paired with human review at the shortlist stage.
Fairness needs checking as well. Compare pass rates across groups, and note that laws such as New York City's Local Law 144 require bias audits for automated hiring tools.
How does GetHirePlus use the score?
In GetHirePlus, the relevancy score routes candidates. You set a minimum relevancy threshold per role. Candidates above it are surfaced for review or invited to the next stage, such as an AI phone interview or an assessment. Candidates below it are not invited automatically, and they are not rejected automatically either. They stay in the ATS, where a recruiter can review them and move anyone forward.
This keeps interview minutes for the strongest matches while making sure a person makes every rejection. Why that matters is explained in Should AI Be Allowed to Reject a Candidate on Its Own?