1 credit per CV · 56 structured fields
Rule-Based CV Parsing for High-Volume Recruitment
Use deterministic extraction when standardized candidate records are enough. Keep AI parsing available for documents that need more context.
From document to structured record
A rule-based parser uses extraction patterns and normalization logic to structure candidate information without an AI model call for every document.
1. Read the CV
Extract text from the uploaded document.
2. Find candidate data
Apply deterministic rules to identify data such as contact details, skills and experience.
3. Normalize
Put detected values into consistent candidate fields.
4. Use the result
Review the structured record and pass it to your ATS or database.
Extraction rules can be reviewed and refined when an output needs correction. Rule changes are deliberate; this is not automatic model training.
When each parsing level fits
| Mode | Credits / CV | Fields | Use case |
|---|---|---|---|
| Rule-Based | 1 | 56 | High-volume ATS ingestion |
| AI 56 | 3 | 56 | Complex CVs |
| AI 96 | 5 | 96 | Detailed candidate records |
Populated fields depend on the information in the source CV.