Parsing structures the information
CVReaderPro extracts useful CV and job data such as skills, experience, education, languages, occupation and location.
CV to job matching for recruiters
Compare CVs with your jobs and prioritize profiles for review
Structure the CV and job, then review the overall score and its components to organize your shortlist. The score helps compare profiles; recruiters make the decision.
- Available from the trial
- 0 credits per comparison
- Active jobs vary by plan
Senior Java Engineer
Published synthetic example
69.03
/ 100
- Skills
- 100
- Experience
- 83.2
- Career trajectory
- 75
- Profile / role
- 60
- Semantic match
- 52.75
Result from a public synthetic dataset. This score is not a general performance measure or a probability of success.
Parsing, then matching
From structured CV and job data to a reviewable match
Parsing extracts information from the documents. Matching then compares the candidate profile data with the job criteria and calculates available components based on the configured setup.
Matching reviews the fit
The engine compares structured profile data with job requirements. Recruiters can inspect the score and its components before continuing their review.
Missing or unusable information does not prove that a candidate lacks a skill. Check the source CV when that information matters to your assessment.
Published, verifiable example
Senior Java Engineer
The score and its components come from the public JSON result. The CV, profile, job, people and companies in this example are fictional.
This example represents one CV, one job and one setup. It is not a customer result or a general performance measure.
Published synthetic example
69.03/ 100
Skills100
Experience83.2
Career trajectory75
Profile / role60
Semantic match52.75
Result from a public synthetic dataset. This score is not a general performance measure or a probability of success.
Explore a synthetic example
Download the CV inputs and captured API outputs. All people, employers and vacancies are fictional. The sample documents are in English; results depend on the input and parser and require human review.
Which components can contribute to a match score?
The engine reviews several dimensions. Their calculation depends on usable information in the CV and job, as well as the matching setup.
- Profile and occupation
- Compares the candidate’s occupation or profile with the role being sought.
- Skills
- Compares structured candidate skills with the job requirements.
- Experience
- Reviews the candidate’s experience against the role requirements.
- Education
- Compares available education information with the requirements.
- Languages
- Compares the candidate’s language data with requirements stated in the job.
- Distance
- Uses available location data for the geographic component.
- Semantic match
- Compares job and profile content beyond identical word use.
- Career trajectory
- Reviews recent experience in relation to the target role.
Not every component is necessarily calculated for every match. The API response contains the available fields and may include supporting evidence.
Semantic matching adds context to term comparison
Lexical and semantic comparisons offer two ways to examine the text in a CV and job.
Lexical comparison
Looks for matching or similar terms. It serves as a fallback when the required vectors are unavailable.
Semantic comparison
When stored vectors are available, it compares meaning even when the wording differs.
If vectors are unavailable, the engine falls back to a lexical comparison.
Semantic similarity is a matching signal, not proof that two statements are fully equivalent or a human assessment of a candidate.
Weights are configured in the setup
Set the importance of criteria for each role
A criterion with a configured weight of 0 is removed from the calculation. It contributes neither positively nor negatively to the score.
Illustrative example only. These values are not a CVReaderPro default setup.
- Skills
- 90%
- Experience
- 80%
- Semantic match
- 55%
- Education
- 0%
Example of an active criterion
Example of an active criterion
Example of an active criterion
Removed from the calculation
How should you read a matching score?
The overall score summarizes selected components using the available data and configured weights. Review its details instead of reading it in isolation.
- 01
Use it as a reference
It helps organize the review of profiles associated with a job.
- 02
Inspect the components
Check active criteria and the information contributing to the result.
- 03
Return to the documents
Review the CV and job before deciding what to do next in the recruitment process.
- There is no universal threshold that automatically labels a candidate good or bad; the score is not a probability of success.
- Scores calculated for different jobs or setups may not be directly comparable.
From an open role to recruiter review
Matching fits into the recruitment workflow and leaves assessment and decision-making with the recruiter.
- 1
Structure the job
Import or select the role and its criteria.
- 2
Structure the CVs
Organize the candidate information needed for comparison.
- 3
Run matching
The engine applies the active setup to available data.
- 4
Review the result
Inspect the score, its components and related evidence.
- 5
Prioritize the review
Use the result to organize the profiles to examine.
- 6
Decide what happens next
The recruiter chooses whom to contact, assess, present or decline.
A comparison tool for recruitment teams
Matching supports different hiring workflows, from shortlist review to ATS integration.
Recruitment agencies
Compare profiles with client requirements and organize shortlist reviews.
IT services and staffing
Review how a consultant’s experience and skills align with a client need.
HR teams
Add a structured reference when reviewing applications for a role.
ATS vendors
Integrate matching into your product and display results in your own workflow.
Web app and API
Use matching in CVReaderPro or integrate it with your ATS
For recruitment teams
Work with structured CVs and jobs, review matching results and continue your review in your process.
For ATS and HR applications
The API calculates a score for a stored job and CV. A separate endpoint accepts files directly for processing.
Example request for stored resources
POST /api/v2/recruiter-scores/match
Authorization: ApiKey YOUR_API_KEY
Content-Type: application/json
{
"jobId": "JOB_UUID",
"candidateId": "RESUME_UUID"
}This endpoint matches the IDs of a stored job and CV. The docs also describe the direct file matching endpoint.
The API documentation also covers refreshing scores and sending the top 10 candidates for a job, or top 10 jobs for a CV, by webhook.
The score helps review profiles; recruiters decide
Configurable weights
The setup adjusts the relative importance of criteria for a role.
Reviewable result
The score and its components give recruiters points to check in the CV and job.
Human decision
Matching does not decide whether to hire, reject or contact a candidate.
CV to job matching FAQs
What is CV to job matching?
It compares structured CV information with job criteria and presents a score with the available components.
Is matching available during the trial?
Yes. Matching is available from the trial period.
Does matching use credits?
No. A CV to job comparison does not use credits. Parsing documents may use credits depending on the selected engine.
What changes by plan?
For matching, the number of active jobs varies by plan. Matching is not reserved for a particular plan.
Which criteria are compared?
Depending on available data and setup, the engine can calculate profile, skills, experience, education, language, distance, semantic and career trajectory components.
Can I configure the weights?
Yes. Weights are set in the matching setup to adjust the relative importance of criteria in the calculation.
What does a weight of 0 mean?
The criterion is removed from the score calculation, so it cannot increase or reduce the result.
Is the score a probability of success?
No. It is a comparison reference calculated for a particular CV, job and setup. It does not predict success in the role.
Can I integrate matching with my ATS?
Yes. The API supports matching stored resources and has an endpoint for processing two files directly. The documentation describes requests and responses.
Does the score make the hiring decision?
No. Recruiters review the evidence and source documents, then make the final decision.
Explore related features
Rule-based CV parsing
Structure CV data before comparing it with a job.
Rule-based CV parsingCV parsing API
Integrate candidate data extraction with your ATS.
CV parsing APICompany-format CVs
Prepare CVs in your organization’s presentation format.
Company-format CVsTechnical documentation
Review matching, parsing and webhook endpoints.
Technical documentationTry matching with your own data
Compare profiles with your jobs and inspect each result
Configure your criteria, review the score and its components, then continue the review with your team.
Matching is available from the trial · No credits used per comparison