HR & Recruiting

ERPNext Candidate Ranker AI

Automates candidate shortlisting in ERPNext by evaluating resumes vs job descriptions using AI

Trusted by

AI resume screening can reduce initial screening time by up to 75%, according to industry analyses

Success Story

Runway’s AI resume screening reduced initial screening time by up to 75% and improved candidate quality.

Integrates with

erpnext-apihttp-requests-for-resume-downloademail-or-whatsapp-apis

⏱️

Screening Time

Days per review cycle→Minutes with AI automation

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Volume Capacity

Manual limit ~100 resumes→AI handles hundreds automatically

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Evaluation Consistency

Variable human scoring and bias→Standardized, reproducible AI-based evaluation

Problem

In growing organizations, manual resume screening is tedious, inconsistent, and slow. HR teams waste hours reading resumes, matching them to job descriptions, and making subjective calls

Solution

ERPNext Candidate Ranker AI brings an automated, consistent, and explainable evaluation process: resume extraction, AI scoring, ERPNext update, and notifications — all without human intervention until final decisions

Result

HR teams can cut initial screening time by 70-80 %, increase consistency in candidate evaluation, and focus on interviewing top fits

Use Cases

recruitment-automationcandidate-evaluationhr-aierpnextapplicant-screeningerpnextai-screeningresume-parsingfit-scoringworkflow-automation

ERPNext Candidate Ranker AI streamlines recruitment by automating candidate evaluation. When a new applicant is submitted in ERPNext, it downloads and parses the resume, fetches the related job description, and uses a generative AI model (Google Gemini or OpenAI) to score fit, rate the candidate, and generate justifications. It then writes back to ERPNext custom fields (fit_level, score, justification) and changes status (accepted/rejected) based on thresholds. Notifications (email or WhatsApp) are sent automatically. This pipeline reduces manual screening burden, ensures consistent evaluation, and accelerates decision-making

Integrations

erpnext-apihttp-requests-for-resume-downloademail-or-whatsapp-apis

Connect to your existing tools seamlessly

Technology Stack

Automation

openai-google-gemini-via-langchain

Automation

n8n-or-equivalent-orchestration

Infrastructure

cloud-or-self-hosted-service-handling-webhookshttpai

Implementation Timeline

≈ 2–7 days

ERPNext Webhook & Data Retrieval

~20 min

Configure a webhook in ERPNext to trigger on new applicant creation. Set up logic to fetch applicant details and associated job opening data for comparison

Resume Extraction & Preprocessing

~15–20 min

Automatically download the applicant’s resume, extract text from PDF (using OCR if needed), and clean formatting for AI input

AI Prompt & Model Configuration

~15–20 min

Implement GPT-4o or equivalent model. Design prompts to compare resumes with job descriptions and generate structured match insights

Output Parsing & Scoring Logic

~10–15 min

Parse AI results into structured fields such as skill match %, experience fit, and final recommendation. Validate formatting and scoring consistency

ERPNext Record Update & Notifications

~15–20 min

Update the applicant record with parsed results, assign status (e.g., shortlisted/rejected), and send automated notifications to candidates or recruiters

Testing & Optimization

~15–20 min

Run test cycles with sample resumes, verify matching accuracy, refine prompt logic, and handle edge cases like incomplete data or formatting errors

Support Included

Sample prompt library, code templates, error-handling guides