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Every applicant to a job opening automatically gets a Job Fit Score from 0 to 100, along with a one-line AI explanation of why they scored that way. You can steer the score by choosing up to 3 ranking criteria, and re-rank existing candidates whenever you change them.
The Job Fit Score is part of Equip’s free ATS. It turns a pile of resumes into a ranked list, so you review the most promising applicants first instead of reading applications in the order they arrived.

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How Scoring Works

When a candidate applies, Equip’s AI reads their parsed application (skills, work experience, education, and more) against your job description and produces a fit score between 0 and 100, shown with one decimal place. Each score comes with a short AI explanation summarizing the reasoning. The score appears next to every candidate in the applications table and on Kanban cards, and you can sort the table by Fit Score to bring your strongest applicants to the top. The score and its explanation are also included when you export applications to CSV or Excel. Scoring is relative, not absolute. Instead of grading each resume in isolation, the AI compares every new applicant against benchmark candidates from the same job’s applicant pool. The very first applicants are evaluated individually; as the pool grows, new candidates are placed against those benchmarks, and Equip periodically recalibrates so rankings stay consistent even at high application volumes.
A score of 75 means the candidate ranks well within this job’s applicant pool. Scores are not comparable across different job openings, and the same resume can score differently for different roles.
If a candidate does not have a score yet, for example one added through bulk CV import, their row shows a Calculate button so you can score them on demand.

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Ranking Criteria

By default, the AI uses its own judgment about what makes a candidate a good fit for your job description. To steer it, select up to 3 ranking criteria from these six options: Drag your selected criteria to prioritize them: the criterion at the top carries the most weight. A criterion is only available if your application form collects the field it depends on, so enable the relevant field first if an option appears missing. You can also add additional criteria as free text, up to 200 characters. Use this for role-specific signals the six standard criteria do not cover, such as “Prefer candidates with AWS experience”.
If you select no criteria at all, scoring still works: the AI simply applies its default judgment. Add criteria only when you want to emphasize something specific.

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Re-Ranking Candidates

Changing your ranking criteria affects future applications only. Candidates who already applied keep the scores they were given under the old criteria until you re-rank them.
1

Update your ranking criteria

Adjust the selected criteria, their priority order, or the additional free-text criteria for the job opening.
2

Re-rank existing candidates

Run the re-rank action to re-score the job’s active candidates against the updated criteria.
3

Review the new order

Sort the applications table by Fit Score again. Each candidate’s AI explanation updates to reflect the new criteria.
Re-ranking costs 0.5 credits per candidate re-scored. Initial scoring of new applicants is free; only the re-rank action consumes credits.
Moving a candidate to a different job post does not trigger re-scoring: the application keeps its existing fit score. See Changing Stages for how candidate moves work.

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Using the Score in Your Workflow

The fit score is a prioritization signal, not a verdict. Use it to decide who to review first, then confirm your read with the candidate’s full profile, scorecards, and assessment results before rejecting anyone. A few places the score shows up beyond the applications table:
Sort by Fit Score, review the top of the list first, and use candidate status (Shortlisted, Maybe, Rejected) to record your own judgment as you go. See ATS Insights for the distribution chart and other pipeline analytics.