HR & recruiting

CV parsing & resume data extraction

CV parsing software that extracts candidate name, contact details, skills, work history, education and certifications from any resume format in seconds — ready for ATS import or direct database storage.

6 min readUpdated April 30, 2026
faster candidate profile creation vs manual entry

What DocPeel extracts

No fixed columns or rigid schema. The LLM reads your document and returns clean, structured JSON automatically — or you define your own template to control the exact output shape.

Smart auto-extraction
Upload any document and the LLM intelligently surfaces all relevant data as clean JSON. No schema setup, no field mapping — it adapts to whatever your document contains.
Custom templates
Need specific field names, data types, or a fixed output structure? Define a template once and every extraction follows your schema exactly.

The resume processing bottleneck in high-volume hiring

High-volume recruiting teams receive hundreds of applications per open role. Manually reading, copying, and entering candidate data into an ATS is a full-time job. When the process takes two to three minutes per resume and a recruiter is processing 50 applications a day, that is two to three hours of non-evaluative work every single day.

Existing ATS parsers help but are notoriously brittle on creative CV formats, non-English documents, and anything scanned from paper. A candidate is passed over not because they lack skills, but because their skills section was in a two-column table that the parser could not read.

What DocPeel extracts — and how

DocPeel reads every CV section — header, professional summary, work history, education, skills, and certifications — and normalises each into a consistent structured output regardless of the visual layout.

Work experience is returned as an array of role objects, each with employer, title, start date, end date, and description. Skills are deduplicated and normalised (e.g. "JS", "JavaScript", and "Javascript" map to a single "JavaScript" entry). Education is similarly structured with degree type, institution, field, and graduation year.

The result can be imported directly into any database or ATS via the JSON output, or exported to CSV and Google Sheets for pipeline review.

Non-English and international CVs

International hiring requires parsing CVs in German, French, Spanish, Mandarin, Arabic, Portuguese, and dozens of other languages. DocPeel handles 60+ languages with no language-specific parser configuration.

Date formats, name ordering conventions, and education system terminology (e.g. "Abitur", "Grande École", "HSC") are understood natively and normalised to a standard output schema.

Privacy and data handling

Candidate data requires careful handling. DocPeel processes documents in isolated per-job sandboxes with no cross-tenant data sharing. Extracted data is never used for model training. Documents are deleted from processing infrastructure immediately after extraction unless you explicitly enable storage.

For teams operating under GDPR, CCPA, or similar frameworks, DocPeel's data retention and deletion APIs allow you to purge individual candidate records on request — meeting right-to-erasure requirements without manual intervention.

Who uses this

  • In-house recruiting teams processing high application volumes
  • Staffing agencies maintaining large candidate databases
  • ATS vendors building smarter import flows
  • HR platforms adding resume parsing to their product
  • Freelance recruiters managing multiple client pipelines

Export formats

JSONCSVExcelGoogle SheetsWebhook

Native integrations

Google SheetsGoogle DriveWebhooksDropboxSlack

Frequently asked questions

Can DocPeel parse two-column and graphically designed CVs?

Yes. The AI model reads the visual layout rather than relying on text flow, so multi-column designs, infographic elements, and creative templates are handled correctly.

Is candidate data stored after extraction?

By default, documents are deleted from processing infrastructure immediately after extraction. You control storage through your workspace settings, and you can purge any candidate record via the API.

Can I extract skills as a standardised taxonomy?

DocPeel returns raw skill mentions by default. With a custom schema, you can map extracted skills to a normalised taxonomy such as ESCO or O*NET before the result is delivered.

How does it handle employment gaps or non-linear career histories?

DocPeel extracts whatever is present on the CV without making inferences. Gaps are represented as-is in the experience array, and freelance / contract work is preserved with whatever dates the candidate provided.

Ready to automate your hr & recruiting workflow?

Start free and extract your first document in minutes. No credit card, no template configuration, no per-document fees to start.