Automate Job Applications with n8n, OpenAI, and Airtable

1. Opening Problem Statement

Meet Emma, a busy HR manager at a mid-sized manufacturing company. Every week, Emma receives hundreds of job applications, each accompanied by a CV in PDF format. Sorting through these files manually to check if they’re even resumes, extracting relevant candidate details, and entering the data into their Applicant Tracking System (ATS) takes Emma hours — sometimes entire workdays. Not only is this process time-consuming, but human errors in data transfer lead to candidates slipping through the cracks or being wrongly rejected.

Emma’s company urgently needs to accelerate the hiring process without sacrificing quality. They want to automatically validate if a file is a proper CV, extract only the relevant information based on the job post, and have candidates confirm or update their details in a prefilled form. Doing this manually wastes resources, delays hiring decisions, and frustrates applicants who have to repeatedly submit the same info.

2. What This Automation Does

This unique n8n workflow automates the job application intake process with rich AI integration and smart data handling. When candidates submit their CV via form, this automation:

• Validates the uploaded PDF to confirm it is a CV or resume using an AI-powered text classifier.
• Extracts relevant candidate data such as name, email, address, education, skills, and years of experience directly from the PDF content, tailored to the exact job post using OpenAI’s language model.
• Automatically generates a professional cover letter draft for the applicant tailored to the job, saving human effort on follow-up communications.
• Saves all extracted information and CV file into Airtable as the Applicant Tracking System, attaching the original PDF for reference.
• Redirects the candidate to a second form prefilled with extracted data for verification or amendment, reducing duplicate data entry and errors.
• Handles invalid file uploads gracefully by prompting the user to re-upload a valid CV PDF if the classifier detects an incorrect document type.

Combined, these steps cut down Emma’s manual processing time from hours per candidate to just a few minutes of oversight, drastically reducing errors and speeding up hiring cycles.

3. Prerequisites ⚙️

• n8n account with workflow editing permissions (self-hosting possible via Hostinger guide)
• OpenAI account with API key for access to ChatGPT-like models
• Airtable account with a base configured as an Applicant Tracking System and Personal Access Token for API access
• Modern browser for form access
• Basic PDF CVs from applicants (password-free PDFs)

4. Step-by-Step Guide

Step 1: Setup the Initial Application Form to Upload CV

Navigate to n8n and create a Form Trigger node named “Step 1 of 2 – Upload CV”. Configure the form with these fields:

• Name (required text input)
• File Upload (accept only PDF, required)
• Acknowledgement of terms (checkbox, required)

Set the path to job-application-step1of2 and customize the button to “Submit.” This is the first interaction point for candidates.
After submission, you should see the form data captured in n8n execution.

Common mistake: Forgetting to set acceptFileTypes to pdf can cause users to upload unsupported files.

Step 2: Extract Text from Uploaded PDF

Add an Extract from File node connected to the previous form. Choose the operation pdf and specify the binary property name as the uploaded file field File_Upload.

This extracts text content from the PDF for analysis. Expect the node to output plain text.

Common mistake: Not matching the binary property name exactly will cause extraction to fail.

Step 3: Classify Document Type Using AI

Insert a Text Classifier node (n8n-nodes-langchain.textClassifier) after text extraction. Configure the categories with “CV or Resume” as the desired category and “other” as fallback.

Connect the node to OpenAI Chat Model 2 to call the AI for classification. This validates if the uploaded document is a genuine CV.

Common mistake: Omitting the fallback may cause failure or unexpected results on non-CV documents.

Step 4: Retry Upload for Invalid Documents

If the classifier rejects the document, branch to a form node titled “File Upload Retry” with a simple PDF file field to prompt users to try again.

Expected result: Only valid CV documents proceed in the workflow, maintaining data quality.

Step 5: Extract Relevant Data Based on Job Post Context

Use the OpenAI Chat Model 1 along with a Chain LLM node named “Application Suitability Agent” to extract specific applicant details relevant to the open role.

The prompt includes the full job description to guide the AI in focusing extraction on key data.

The extracted fields include Name, Address, Email, Telephone, Education, Skills & Technologies, Years of Experience, and a tailored Cover Letter draft.

Connect a Structured Output Parser node to ensure the AI output is converted into a structured JSON matching your ATS schema.

Common mistake: Not defining the input schema correctly in the parser results in data mapping errors downstream.

Step 6: Save Extracted Data to Airtable

Add an Airtable node and configure it to the ATS base and table. Map fields from the parsed AI output and form data, including attaching the original uploaded PDF.

Since Airtable supports file attachments via API, use an HTTP Request node to upload the user’s PDF file to the record after initial creation.

Common mistake: Forgetting to use the Airtable API for file upload instead of storing a URL causes files to not attach properly.

Step 7: Display Submission Success and Redirect

Use a Form node titled “Submission Success” to tell the applicant their CV was accepted with an acknowledgement field and a continue button.

Connect to another Form node set as a redirect to the second form job-application-step2of2 with prefilled query parameters from the AI extracted data.

Common mistake: Ensure you update the redirect URL to match your n8n instance hostname or redirection will fail.

Step 8: Second Application Form to Amend Details

Add another Form Trigger node titled “Step 2 of 2 – Application Form”. Populate fields matching extracted data: Name, Address, Email, Telephone, Education, Skills & Technologies, Years of Experience, Cover Letter, and terms acknowledgment.

Since the URL carries pre-filled query parameters, fields will auto-populate for the user to verify or change.

On submission, connect this node to an Update Airtable node to update the candidate’s record with final details.

5. Customizations ✏️

• Change Job Post Context for Extraction: Modify the detailed job post text in the prompt of the Application Suitability Agent node to tailor data extraction for different roles or industries, ensuring relevant info is captured.
• Add Additional Validation Categories: Enhance the Classify Document node by adding more classification categories (e.g., cover letters, portfolios) so you can route documents differently or reject specific types.
• Integrate with Other ATS: Swap out the Airtable node with API calls or nodes for other Applicant Tracking Systems like Greenhouse or Lever, adapting the payload and file upload accordingly.
• Customize the Cover Letter Format: In the prompt to OpenAI, adjust the tone, length, and style to match your organization’s branding or tone guidelines.
• Embed Notifications: Add a Slack or email notification node post-submission to alert HR or hiring managers instantly of new applicants.

6. Troubleshooting 🔧

Problem: “Extract from File node returns empty or errors.”
Cause: Uploaded file is encrypted, password protected, or not a valid PDF.
Solution: Ensure applicants upload only unprotected PDFs, and check the binaryPropertyName matches the upload field exactly.

Problem: “Text Classifier misclassifies CV as other.”
Cause: Insufficient training categories or ambiguous document text.
Solution: Refine categories in the classifier, add fallback, and improve training examples in the prompt configuration.

Problem: “Airtable file upload fails or doesn’t show attachment.”
Cause: Using a normal Airtable create operation without separate file upload.
Solution: Use the HTTP Request node with Airtable API to upload the PDF file attachment after creating the record.

7. Pre-Production Checklist ✅

• Verify API credentials are correctly set up for OpenAI and Airtable.
• Test the form submission with a variety of PDF CVs (valid and invalid).
• Confirm that the classifier node properly distinguishes CVs from other files.
• Check the structured output parser schema matches extracted fields.
• Test Airtable record creation and attachment uploads.
• Validate redirect URLs are correctly formed and accessible.
• Run the entire workflow in test mode to simulate a full candidate submission.

8. Deployment Guide

Activate your workflow in n8n by enabling it. Customize the public form URLs to match your hosted instance’s domain. Monitor executions via the n8n dashboard to catch errors or bottlenecks.

Encourage your recruitment team to use the prefilled forms for candidate follow-up, increasing data accuracy and speeding up decisions.

9. FAQs

• Can I use a different AI provider than OpenAI? Yes, however, you’ll need an AI node compatible with n8n and a prompt designed for your AI provider’s interface.
• Does this workflow consume many OpenAI API credits? It uses one classification & one extraction call per application. Optimize usage by reusing model sessions or upgrading your API plan.
• Is applicant data secure in Airtable? Airtable offers secure cloud storage, but ensure your account uses strong passwords and 2FA.
• Can this handle hundreds of applications per day? Yes, n8n and Airtable scale well for typical mid-size recruitment volume.

10. Conclusion

By following this detailed workflow, you have automated a complex multi-step job application process that used to require hours of manual work. Emma’s HR team can now swiftly validate, extract, and store candidate details while offering a refined applicant experience with prefilled application forms.

This automation saves countless labor hours, improves data quality, and speeds the recruitment lifecycle. Next, consider adding AI-driven resume ranking or automatic interview scheduling for even more powerful hiring pipeline automation.

Happy automating with n8n, OpenAI, and Airtable!