Who is this workflow for? This n8n workflow leverages Multimodal Large Language Models (LLMs) to efficiently parse and extract information from PDF CVs and resumes. By integrating AI Vision, it enhances the accuracy of candidate evaluations, ensuring unqualified applications are filtered out effectively, even when candidates attempt to bypass automated systems with hidden prompts..

What does this workflow do?

  • Resume Acquisition:
  • The candidate’s CV or resume is obtained in PDF format, typically downloaded from Google Drive for demonstration purposes.
  • PDF to Image Conversion:
  • The PDF is converted into a PNG image using a tool like Stirling PDF. This step ensures that any hidden prompts in white font color become invisible in the resulting image, preventing them from bypassing the AI filters.
  • AI Processing with Multimodal LLM:
  • The generated image is sent to a Basic LLM node within n8n for processing. In this example, Google’s Gemini 1.5 Pro model is utilized.
  • Within the Basic LLM node, a User Message is set with the binary type, allowing the direct transmission of the image file in the request.
  • Filtering and Response:
  • The LLM analyzes the image without being affected by hidden prompts, providing an accurate response regarding the candidate’s qualification status.
  • Output and Integration:
  • The processed data can be integrated with other services such as Notion, Google Sheets, or Telegram for further actions like notifications, database updates, or scheduling interviews.

Example Resource:
Access the example CV/Resume with a hidden prompt here.

  • AI Model Access: Google Gemini API Key or an alternative like GPT-4.
  • PDF Conversion Tool: Stirling PDF or a similar service capable of converting PDFs to images. For enhanced data privacy, self-hosting Stirling PDF is recommended over using public APIs.
  • Trigger Integration: Replace the manual trigger with alternatives such as webhooks to incorporate the workflow into existing systems.
  • Extended Data Extraction: Beyond qualification filtering, configure the workflow to extract additional data points like years of experience, previous companies, and specific skills for comprehensive candidate profiling.

This workflow integrates with various tools and services, including:

  • HTTP Request
  • Google Drive
  • Notion
  • Code
  • Google Calendar
  • AI Models (OpenAI, Anthropic, Gemini, OpenRouter)
  • Google Sheets
  • Merge
  • Telegram

By implementing this workflow, organizations can enhance their recruitment processes through automation and advanced AI capabilities, ensuring efficient and accurate candidate evaluations.

🤖 Why Use This Automation Workflow?

  • Enhanced Accuracy: Utilizes AI Vision to detect and ignore hidden prompts or manipulations in resumes, ensuring reliable candidate assessments.
  • Automation: Streamlines the resume parsing process, reducing manual effort and speeding up the hiring workflow.
  • Flexibility: Compatible with various AI models and integrates seamlessly with multiple tools, allowing customization to fit specific recruitment needs.

👨‍💻 Who is This Workflow For?

This workflow is ideal for HR professionals, recruitment agencies, and businesses that handle a high volume of job applications. It is designed for teams seeking to automate the resume screening process, improve the quality of candidate evaluations, and integrate AI-driven insights into their recruitment strategies without extensive technical expertise.

🎯 Use Cases

  1. Automated Candidate Screening: Quickly filter out unqualified applicants by analyzing resumes for predefined criteria.
  2. Data Extraction for Analytics: Extract specific data points such as years of experience, previous employers, and skills for further analysis and reporting.
  3. Integration with Recruitment Tools: Seamlessly connect with platforms like Google Drive, Notion, and Google Sheets to manage and organize applicant data efficiently.

TL;DR

This n8n workflow demonstrates a robust solution for parsing and evaluating PDF resumes using Multimodal Vision AI. By converting PDFs to images and leveraging advanced LLMs, it effectively mitigates attempts to deceive automated screening processes, ensuring that only qualified candidates move forward in the recruitment pipeline.

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