Stop typing names and emails from PDF resumes
Parse resumes into structured candidate data, name, contact, experience, skills, education, regardless of format. Optionally reformat to a consistent template for client presentation.
Discuss a pilot →Quick facts
- Business size
- Recruiting agencies, in-house HR teams, staffing platforms
- Timeline
- 3-5 weeks test, 1-2 months pilot, 3-4 months production
- Budget range
- Pilot from €20k.
- Hardware
- Cloud or on-prem.
- Data needed
- Sample resumes (200+ typical). Examples of typical format diversity.
- Evolution
-
- Genesis
- Custom-built
- Product
- Commodity
A vendor sells this result ready-made. We set it up and tune it to you.
What this scale means
Further right means more proven and cheaper. Further left means newer and riskier. Here is the test for each step.
- Commodity
- You could get the result yourself from a ready service, with almost no work. We rarely take these on.
- Product
- A vendor already sells this result turnkey, like shelf recognition from Trax or document reading from ABBYY. If one of them fits you, use it. You come to us when it does not: when it has to run on your own servers, cost less, or fit systems the product cannot reach.
- Custom-built
- Vendors sell only the parts. A tool like Tableau hands you charts, but the dashboards and metrics for your business still have to be built. That build is the work, and that is us.
- Genesis
- The approach exists but does not work reliably yet. You are betting on it maturing, so it costs more and carries more risk.
Expected outcomes
The Problem
Resumes arrive in every format imaginable: Word docs, PDFs with odd formatting, scans, design-heavy CV templates that look great but parse terribly, and ATS-stripped plain text. Each format is its own data-entry exercise. Recruiters and HR coordinators retype names, contact info, work history, and skills into the ATS, hundreds of times per role.
The conventional resume parsing tools built into most ATS work for around 60% of resumes. The other 40%, anything with creative formatting, multi-column layouts, photos, or unusual structures, falls through and needs manual handling.
A large language model (LLM: a model trained to read and write text) changes this. A modern parser handles around 90% of resume formats correctly, including the design-heavy ones, and can reformat output to a consistent template for client presentation.
What the Solution Does
A resume-processing pipeline:
- Ingest: a resume in any format (PDF, Word, image scan, plain text).
- Parse: structure-aware parsing extracts text and layout.
- Extract: name, contact, work history, education, skills, certifications, and languages.
- Normalize: dates standardized, job titles mapped to taxonomies, skills pulled from prose.
- Reformat (optional): output in your standard template for client presentation.
- Sync: structured data flows to your ATS or CRM through an API.
Where It Fits
This makes sense if you…
- Process more than 100 resumes per month
- See real cost from manual data entry
- Need consistent client-facing candidate presentation
- Have an ATS or CRM that accepts structured data through an API
This is probably not the right time if you…
- Process a few resumes per month
- Have one resume source (all from one job board with one format), where basic parsing already works
- Need full skill or cultural-fit assessment from the resume (that is a different problem)
Business Value
Data entry removed. Typically 80-95% less manual entry time, depending on format mix. Recruiters get back to recruiting.
Format independence. Design-heavy resumes that used to need manual handling get parsed automatically. Coverage moves from around 60% to around 90%.
Client-presentation consistency. Reformatted output means clients see candidates in your standard template, whatever the candidate’s resume looked like originally.
Talent-pool searchability. Structured candidate data is searchable. “Find me Python developers with 5+ years and SaaS experience” becomes a query. You no longer re-read resumes.
How It Works
1. Multi-format parsing
PDFs are parsed with structure preservation: multi-column handling and table extraction. Word docs are parsed natively. Image scans run through OCR (text recognition on images) first.
2. LLM-driven extraction
Modern LLMs handle the variety of resume formats well. We use GPT-4 or Claude for extraction with structured output (a JSON schema). Self-hosted alternatives cover confidential or GDPR-strict workflows.
3. Normalization
Dates become ISO format. Job titles map to a taxonomy: your client-facing categories or an industry standard. Skills are pulled from prose into a structured list. Education and certifications are normalized.
4. Reformatting
Optional: render extracted data into your standard candidate-presentation template (Word, PDF, or branded HTML).
5. ATS or CRM sync
Output through a REST API to your downstream system.
Stack
LLM extraction (OpenAI, Anthropic, or self-hosted). PDF and Word parsing. Datapipe runs the pipeline. Python services. Optional reformatting through templates.
What You Need to Make This Work
Data. Sample resumes (200+ ideal for testing). Examples of edge cases.
Integrations. Resume ingestion (email, upload, API). ATS / CRM for output.
Hardware. Cloud-only by default.
Team. Recruiting / HR lead. Data engineer for ATS integration.
Implementation Roadmap
1. Test (3-5 weeks)
Build the pipeline with one role type or one client. Validate extraction accuracy. Output: a working pipeline with measured numbers.
2. Pilot (1-2 months)
Production deployment. ATS integration. Output: a working production deployment.
3. Production (3-4 months)
Full rollout. Continuous improvement on edge cases.
Keep in Mind
- Not 100%. Typically 90-95% on standard fields. Edge cases need human review.
- Privacy and GDPR matter. Resume data is personal data with retention rules. We design with privacy by default.
- Reformatting brings a brand-tone choice. Which template is right? We co-design it with your team.
- Skill extraction is approximate. “Python” appearing in a resume does not mean expert Python. The system extracts, and humans evaluate.
- OCR quality matters for scans. Bad scans produce bad parses.
FAQ
Can this handle multi-language resumes?
Yes. LLMs handle multilingual content well; we tune extraction per language.
What about creative / design-heavy resumes?
Modern LLM-based parsing handles them much better than conventional template-based parsers. Coverage is typically around 85% on design-heavy resumes.
How does this compare to off-the-shelf parsers (Sovren, Daxtra, etc.)?
Commercial parsers work well for companies that want a packaged product. Our approach fits when you need custom field extraction, fully on-prem deployment, integration with a non-standard ATS, or heavy customization.
Skills extraction, how accurate?
Around 90% on explicit “Skills” sections. Implicit skills (mentioned inside job descriptions) get extracted with lower confidence, and we surface that.
Ready to Discuss?
If you process resumes at meaningful volume, this pays back fast. We will walk through your formats and your ATS, and tell you what to expect.