> ## Documentation Index
> Fetch the complete documentation index at: https://help.equip.co/llms.txt
> Use this file to discover all available pages before exploring further.
# What is the Job Fit Score?
> How every applicant gets a 0-100 fit score, and how to tune the ranking with criteria of your choice.
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.
## 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](/bulk-cv-import), their row shows a **Calculate** button so you can score them on demand.
## 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:
| Criterion | What the AI weighs | Requires this application form field |
| ---------------------- | -------------------------------------------------------------- | ------------------------------------ |
| **Skills** | How relevant the candidate's skills are to the job description | Skills |
| **Experience** | Higher weightage for more years of experience | Work Experience |
| **Education** | Degrees and institutions | Educational Background |
| **Previous Companies** | Pedigree of employment history | Work Experience |
| **Seniority** | More managerial positions get higher weightage | Always available |
| **Industry** | Relevance of the candidate's industry experience to the role | Industries |
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](/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.
## 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.
Adjust the selected criteria, their priority order, or the additional free-text criteria for the job opening.
Run the re-rank action to re-score the job's active candidates against the updated criteria.
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](/changing-stages) for how candidate moves work.
## 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](/scorecards), and assessment results before rejecting anyone.
A few places the score shows up beyond the applications table:
| Where | What you see |
| -------------------- | -------------------------------------------------------------------------------------------------- |
| **Kanban cards** | The fit score on each candidate card, so you can scan a stage at a glance |
| **CSV/Excel export** | The score and its AI explanation as columns in exported application data |
| **Insights** | A job-fit score distribution chart, so you can gauge the overall quality of a job's applicant pool |
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](/ats-insights) for the distribution chart and other pipeline analytics.
## Related Resources
* [CV Parsing](/cv-parsing) - How Equip extracts the structured data the fit score is built on
* [Application Form](/application-form) - Enable the fields your ranking criteria depend on
* [Candidates and CVs](/candidates-and-cvs) - Sort, filter, and act on candidates by fit score
* [How Credits Work](/how-credits-work) - What re-ranking and other actions cost