AI Workflow Automation for Resume Data Extraction and PDF Generation

Manually extracting and organizing information from resumes is a tedious and time-consuming process. This blog post will walk you through an n8n workflow that automates the entire process, from Resume Data Extraction to generating a new, neatly formatted PDF. 

The workflow leverages advanced AI and other tools to create a seamless resume data extraction pipeline. By automating this process, you can eliminate manual data entry errors, save valuable time, and standardize the format of all resumes you receive. 

You’ll learn how the workflow uses an OpenAI chat model to parse resume data and then Gotenberg to convert the final HTML output into a professional-looking PDF.

The Step-by-Step Process for Resume Data Extraction

This automated workflow is designed to be triggered by a user uploading a resume file to Telegram. Here’s a breakdown of the key steps:

1. Trigger and File Download 

The workflow begins with a Telegram trigger that listens for a file sent via Telegram. Once a file is received, the workflow downloads the resume PDF file using the file_id from the message. It also includes checks to ignore a /start message and to ensure only authorized users can trigger the workflow.

2. PDF Text Extraction and AI Parsing 

The downloaded PDF is processed to extract text from PDF. This readable text is then passed to an AI chain that uses the OpenAI gpt-4-turbo-preview model. The AI’s task is to analyze the text and extract all relevant information, such as personal info, employment history, education, and technical skills, into a well-structured JSON format. 

The workflow uses an Auto-fixing Output Parser to handle any JSON formatting issues. This is the core of the resume data extraction.

3. Formatting Data into HTML 

After the AI has parsed the data into a JSON object, the workflow formats each section of the resume (e.g., employment history, education, projects) into HTML chunks. Code nodes are used to iterate through the JSON data and create HTML with bold headings and line breaks for clear readability. 

For example, the education section’s code adds a new line for each entry and formats it with “Institution,” “Start year,” and “Degree” labels. This is a crucial step for the final resume presentation.

4. Merging and PDF Conversion 

All the individual HTML segments are merged into a single, comprehensive HTML document. This final HTML output is then converted into a base64 string and prepared for conversion. 

The workflow uses an HTTP request node to send the HTML to a PDF conversion service, specifically a self-hosted instance of Gotenberg. Gotenberg generates a new, polished PDF document from the HTML.

5. Delivering the Final Document to the User 

Finally, the newly created PDF file is sent back to the user who initiated the workflow via Telegram. The file name is automatically generated based on the person’s name extracted by the AI. This completes the automated resume data extraction and generation cycle.

*Note: For the JSON template, please contact us and provide the blog URL.

Business Use Cases

This workflow isn’t limited to resumes. This robust resume data extraction process can be adapted for various business needs, such as:

• Invoice Processing: Extracting vendor details, amounts, and tax IDs from invoices.
• Legal Document Review: Pulling key clauses, dates, or names from legal papers.
• Financial Auditing: Collecting transaction details or compliance data from reports.

Relevant Reads:

• AI Workflow Automation in 2025: Tools, Trends & Use Cases
• AI Workflow Automation for PDF Data Extraction Using Claude and Gemini

Conclusion

Automating the process of extracting information from PDFs is a game-changer for many businesses. This n8n workflow demonstrates how to build a powerful pipeline that uses AI for structured resume data extraction and other document processing needs. 

By using advanced models like GPT-4-turbo-preview and tools like Gotenberg, you can create a highly efficient system that eliminates manual work and improves accuracy.

FAQs

What software do I need to run this workflow for resume data extraction? 

You need to set up an n8n instance with your API credentials for OpenAI and Telegram. Additionally, you’ll need to have Gotenberg installed for PDF generation, which can be self-hosted or replaced with a similar service.

Can I extract different types of information from the resumes? 

Yes, the workflow is highly customizable. The prompt for the OpenAI chat model can be adjusted to extract any specific data you need from the resume, not just the pre-defined fields. The JSON schema can also be modified to define different data structures.

Why use AI for this task instead of traditional methods? 

Using AI allows for direct PDF ingestion without a separate Optical Character Recognition (OCR) step. This improves accuracy by allowing the model to understand the context of the document, reduces processing time, and handles unstructured or variable formats much better than traditional methods.