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1d (edited) • Business & Strategy 🧠
Built This n8n Resume Parser That Screens 50 Candidates in 10 Minutes 🔥
HR team was drowning in resumes. Manually reviewing 50 applications took 8 hours. Built automated screening workflow. Now takes 10 minutes.
THE PROBLEM:
Startup hiring for 3 positions. Received 150 applications in one week. Each resume needed: skills extracted, experience calculated, fit assessment written. Manual process: 10 minutes per resume × 150 = 25 hours of work.
THE SOLUTION (N8N WORKFLOW):
Manual Trigger → Start batch processing
Google Drive node → Retrieves resume files from folder
PDF Vector Parse node → Extracts all candidate information
Function node → Calculates years of experience per skill
PDF Vector AI node → Evaluates candidate fit and seniority
HTTP Request node → Posts to Airtable candidate database
Slack node → Notifies team with top candidates
Total workflow: 6 nodes. Processes any resume format - PDF, Word, even phone photos.
WHAT IT EXTRACTS:
Personal info: name, email, phone, location, LinkedIn
Work history: companies, dates, technologies used per role
Education: degrees, institutions, graduation dates
Skills: technical and soft skills with experience levels
Certifications: current and relevant to role
Calculated metrics: total experience, skill proficiency scores
THE SCORING LOGIC:
Built custom scoring in Function node:
```javascript
// Calculate experience score per skill
const skillScores = {};
workHistory.forEach(job => {
job.technologies.forEach(tech => {
if (!skillScores[tech]) skillScores[tech] = 0;
skillScores[tech] += job.durationYears;
});
});
// Weighted ranking
const rankingScore =
(totalYears * 0.3) +
(skillCount * 0.2) +
(certCount * 0.1) +
(requirementMatch * 0.4);
```
Assigns tier: A (interview immediately), B (strong candidate), C (consider), D (pass).
THE AI ASSESSMENT:
PDF Vector AI node writes custom assessment for each candidate:
"Evaluates technical depth, leadership experience, culture fit. Determines if candidate is junior, mid-level, senior, or lead material based on scope of past projects."
Returns 2-3 paragraph assessment in natural language. Hiring manager reads assessment, makes decision. No need to read full resume unless strong match.
REAL RESULTS:
Processed 150 resumes in 30 minutes (vs 25 hours manual)
Identified 12 strong candidates (A-tier)
Interviewed 8, hired 2
Time saved: 24 hours = $2,400 value
THE CUSTOMIZATION:
Modified for tech roles by adding weight to priority skills:
React: 1.5x multiplier
Python: 1.3x multiplier
AWS: 1.4x multiplier
Kubernetes: 1.6x multiplier
Sales roles? Extract quota achievements, deal sizes, CRM tools. Healthcare? Check licenses, certifications, EMR systems.
BATCH PROCESSING:
Upload 50 resumes to Google Drive folder. Click trigger once. n8n processes all automatically. Results populate Airtable with sortable columns: Name, Score, Top Skills, Assessment, Action.
Review top-scored candidates first. Schedule interviews. Archive processed resumes.
Template here
THE LESSON:
Resume screening is perfect automation target. Consistent format (mostly), clear criteria, time-consuming manually. One workflow eliminates hours of repetitive work.
Modern document extraction handles any resume format. Schema-based extraction pulls structured data. AI assessment provides decision support.
What hiring process could you automate with n8n?
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Built This n8n Resume Parser That Screens 50 Candidates in 10 Minutes 🔥
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