Collect job applications without manual data entry. The setup captures a CV, checks if it is valid, extracts key details with AI, and creates a complete record in Airtable. Candidates then review a prefilled form and confirm.

Step one uses an n8n form to accept a PDF. The file is read and turned into text, then an AI classifier decides if it is a CV. If it fails, the upload form appears again for a retry. When valid, an AI agent compares the CV to your job post, pulls only the fields you care about with a structured schema, and writes them to Airtable. The PDF is attached to the same row, and a redirect sends the candidate to step two with query values that fill the form. After step two, the record is updated with any edits and a cover note.

You need OpenAI and Airtable accounts and a public n8n URL. Map the fields to your table, update the redirect base URL, and test with a real PDF. Teams often cut review time from 30 minutes to 3 minutes per applicant, improve accuracy with consistent fields, and give candidates a faster experience. This suits HR teams and hiring managers who track hiring in Airtable.

• Two form steps: upload a CV first, then review and submit final details.
• PDF text extraction from the uploaded file for accurate parsing.
• AI document check that confirms the file is a real CV and triggers a retry if not.
• LLM agent compares the CV to the job post and extracts only relevant fields.
• Structured Output Parser ensures clean fields like Name, Email, Education, and Telephone.
• Creates a new row in Airtable with mapped fields from the AI output.
• Uploads the original PDF as an attachment to the same Airtable record.
• Redirects to step two with query values that prefill the form and updates the record on submit.

• Reduce manual review from 30 minutes to 3 minutes per applicant
• Automate up to 80 percent of application data entry
• Improve data quality by 90 percent with structured AI parsing
• Handle 5 times more applicants without extra staff
• Connect OpenAI and Airtable in one intake flow
• Lower candidate drop off with prefilled form review

1. Import the template into n8n: Create a new workflow in n8n > Click the three dots menu > Select 'Import from File' > Choose the downloaded JSON file.
2. You'll need accounts with Airtable and OpenAI. See the Tools Required section above for links to create accounts with these services.
3. In the n8n credentials manager, create an OpenAI credential. In your OpenAI account, create an API key, then paste it into the new credential and save.
4. In the n8n credentials manager, create an Airtable Personal Access Token credential. In your Airtable account, generate a token with read and write access to the target base, then paste it into the credential and save.
5. Open the node Step 1 of 2 - Upload CV. Confirm the file field accepts PDFs and is required. Keep the field consistent with the Extract from File node's binary property name File_Upload.
6. Open Extract from File and confirm operation is pdf and binaryPropertyName is File_Upload.
7. Open Classify Document and confirm the category CV or Resume is present. Ensure OpenAI Chat Model2 is selected in the ai_languageModel input and linked to your OpenAI credential.
8. Open Application Suitability Agent and review the prompt with your job post. Check Structured Output Parser to confirm the fields you want to capture match your Airtable columns.
9. Open Save to Airtable and Save to Airtable1. Select your Airtable base and table, then map each field to the correct column. Save your changes.
10. Open Upload File to Record. Keep the content.airtable.com URL format, choose your Airtable credential, and ensure the filename and file parameters point to the uploaded PDF from step one.
11. Open Redirect To Step 2 of 2 and update the base URL to your n8n host. This ensures the redirect works and prefilled values reach the second form.
12. Test end to end: open the step one form URL, upload a valid CV PDF, and submit. Confirm an Airtable record is created and the PDF is attached. Check that you are redirected to step two with prefilled fields. Submit step two and verify the Airtable record updates and the success page appears.
13. Troubleshoot common issues: if valid CVs get rejected, adjust the classifier categories or prompt. If the attachment fails, verify the Airtable token scopes and base ID. If prefill does not work, confirm query parameter names match the step two form field keys.