AI CV Scoring and Parsing

AI CV Scoring and Parsing overview

AI CV Scoring and Parsing simplifies reviewing and shortlisting candidates by identifying the best matches for jobs and enabling faster CV reviews with AI-parsed data.

Credits and Pricing

The AI CV Scoring feature is available on all plans and is priced per credit:

Recruiter Pro	Teams	TeamsPro
✅	✅	✅

1 credit is used to parse and score 1 CV.
- 0.7 credits for parsing.
- 0.3 credits for scoring.

You can monitor your credit usage in the Settings Page.

If you have exhausted your credits or to enable this feature, please contact us at [email protected] or call to [+971581746200](tel:+97158 1746200).

A. Set up CV scoring criteria

When creating a new job, the AI Scoring Criteria feature automatically generates evaluation criteria based on the job description you provide.

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You can modify the criteria to suit your specific requirements or re-generate them using AI as needed.

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Title: The name of the criterion. This title will be visible on the candidate’s profile page.
Description: A detailed explanation of the criterion, used to score candidates. Ensure the description is precise, as clearer descriptions improve AI performance in CV scoring.
Weight: Assign a weight to each criterion to prioritize its importance during the scoring process. </aside>

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How do I create good CV scoring criteria? Write criteria as if you’re explaining them to junior recruiters. Ensure they are clear, precise, and easy to evaluate.

Principle	Description	Example
Stand-Alone	Write criteria that are clear and understandable without additional context.	Use "Incorporates feedback to improve performance" instead of "Assimilates feedback."
Singular	Focus on one skill or requirement per criterion.	Break "Experience in Python, AWS, and microservices" into separate criteria for each skill.
Clear	Avoid vague terms and specify exact expertise.	Replace "Has technical skills" with "Proficient in React.js and REST APIs."
Specific	Include measurable details like years of experience or specific roles.	Replace "Experienced" with "3+ years in product design."
Unambiguous	Be precise and avoid general terms.	Replace "Good communication skills" with "Experienced in delivering technical presentations."
Verifiable	Ensure the criteria can be verified from the resume.	Use "Designed SaaS platforms with 1M+ users" instead of "Has good taste."
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B. Shortlist candidates based on AI CV score

Candidate list view:
- See whether a candidate is a Best Match, Good Match, or Weak Match directly on the candidate list.
Filter candidates:
- Use the job match filters to shortlist candidates based on their match category.
Detailed view:
- Click on a candidate to view their detailed scoring evaluation in the candidate profile.

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How scoring is calculated?

For each criteria, AI will give score out of 100.
To calculate the overall CV score, we will normalise the scores with weights.
- Let's say there are four criterions (Representing with "C") with their associated weights (Representing with "W") and AI given scores out of 100 (Representing with "S")
  
  Criterion (C) Weight (W) Score given by AI (S)
  
  C1 40 80
  
  C2 20 40
  
  C3 10 30
  
  C4 30 40
- Normalising and calculating overall CV score:
  - (W1S1)/100 + (W2S2)/100 + (W3S3)/100 + (W4S4)/100
  - (4080)/100 + (2040)/100 + (1030)/100 + (3040)/100
  - Overall score = 55 → Good match

Criterion (C)	Weight (W)	Score given by AI (S)
C1	40	80
C2	20	40
C3	10	30
C4	30	40

Score range	Category	Description
70-100	Best Match	Candidate excels in all key requirements and is highly suitable.
40-69	Good Match	Candidate meets many requirements and is a strong contender.
0-39	Weak Match	Candidate does not meet several key requirements.
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