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Automate CV screening and applicant scoring from Gmail to Airtable with AI

Fahmi FahrezaFahmi Fahreza
567 views
2/3/2026
AIPostgreSQLForm ProcessingWebhookOpenAI
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How It Works

  1. Trigger Watches for new emails with attachments in a Gmail label.
  2. Extract Data
    • Extracts job code from the email subject (e.g., FN-001)
    • Extracts raw text from the attached CV (PDF)
  3. AI Parsing Uses Google Gemini to parse the CV and extract:
    • Name
    • Email
    • Years of experience
    • Skills
  4. Job Lookup Uses the extracted job code to retrieve job details from Airtable.
  5. AI Scoring
    • Compares applicant data with job requirements
    • Scores from 1–100
    • Generates a brief reasoning summary (in Bahasa Indonesia)
  6. Log to Airtable Saves applicant data, score, and AI notes to the "Applications" table.

Setup Instructions

  1. Prepare Airtable Base
    • Job Posts Table
      • Columns: Job Code, Job Title, Required Skills, Minimum Experience, Job Description
    • Applications Table
      • Columns: Applicant Name, Email, Score, Notes
      • Include a linked field to the Job Posts table
  2. Add Credentials in n8n
    • Gmail
    • Google AI (Gemini)
    • Airtable
  3. Configure Nodes
    • Trigger: Set Gmail filter (e.g., label:job-applications)
    • Extract Job Code: Verify regex format, default is ([A-Z]{2}-\d{3})
    • Airtable Nodes: Select your base and table in:
      • "Find Job Post..."
      • "Save Applicant..."
  4. Activate Workflow
    • Save and enable the workflow
    • New applications will be processed automatically

n8n Workflow: AI-Powered CV Screening and Applicant Scoring from Gmail to Airtable

This n8n workflow automates the process of screening job applications received via Gmail, extracting key information from CVs using AI, and then scoring applicants before storing their data in Airtable. It streamlines the recruitment process by reducing manual effort in reviewing resumes and organizing candidate information.

What it does

This workflow performs the following steps:

  1. Triggers on new emails: Listens for new emails in a specified Gmail account.
  2. Extracts CVs from attachments: Identifies and extracts PDF or other document attachments from the incoming emails, assuming these are CVs.
  3. Extracts information using AI: Utilizes a LangChain "Information Extractor" node to parse the content of the extracted CVs and identify structured data points (e.g., name, contact, experience, skills).
  4. Scores applicants with AI: Employs a LangChain "Basic LLM Chain" with a Google Gemini Chat Model to analyze the extracted information and generate a score or evaluation for the applicant based on predefined criteria (configured within the LLM chain).
  5. Formats output: Uses a "Structured Output Parser" to ensure the AI-generated scores and extracted data are in a consistent, structured format.
  6. Prepares data for Airtable: Transforms and sets the extracted and scored data into a format suitable for Airtable.
  7. Stores data in Airtable: Adds or updates applicant records in a designated Airtable base and table with the extracted details and AI-generated score.

Prerequisites/Requirements

To use this workflow, you will need:

  • n8n Account: A running instance of n8n.
  • Gmail Account: Configured as a credential in n8n for the "Gmail Trigger" node.
  • Airtable Account: Configured as a credential in n8n for the "Airtable" node, with a base and table set up to store applicant data (e.g., fields for Name, Email, Phone, Experience, Skills, AI Score, etc.).
  • Google Gemini API Key: Configured as a credential for the "Google Gemini Chat Model" within the LangChain nodes.
  • LangChain Integration: The @n8n/n8n-nodes-langchain package installed and enabled in your n8n instance.

Setup/Usage

  1. Import the workflow: Download the provided JSON and import it into your n8n instance.
  2. Configure Credentials:
    • Gmail Trigger: Set up your Gmail OAuth2 credentials.
    • Airtable: Set up your Airtable API Key or OAuth2 credentials.
    • Google Gemini Chat Model: Set up your Google Gemini API Key.
  3. Customize Nodes:
    • Gmail Trigger: Specify the mailbox and any filters (e.g., sender, subject line) to only process relevant job application emails.
    • Extract from File: Ensure it's configured to correctly identify and extract content from your expected CV file types (e.g., PDF, DOCX).
    • Information Extractor: Configure the schema for the information you want to extract from the CVs (e.g., name, email, experience, skills).
    • Basic LLM Chain (Google Gemini Chat Model):
      • Define the prompt for the AI to score applicants. This prompt should instruct the AI on how to evaluate the extracted CV information and what criteria to use for scoring.
      • Specify the expected output format for the score.
    • Structured Output Parser: Adjust the schema if necessary to match the output format of your AI scoring.
    • Edit Fields (Set): Map the extracted and AI-generated data to the appropriate fields in your Airtable.
    • Airtable: Select your Airtable Base ID and Table Name, and map the incoming data fields to your Airtable columns.
  4. Activate the Workflow: Once configured, activate the workflow to start automatically processing incoming job applications.

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