n8n Resume Parser: OpenAI GPT-4o Fit Score to Postgres + Slack

Automatically parse resume submissions into scored candidates—save to Postgres and alert HR in Slack

This n8n workflow captures PDF resumes from an n8n Form, extracts and structures candidate data with OpenAI (gpt-4o-mini), computes a simple fit score, writes the results to PostgreSQL, and posts a concise update to Slack for your HR team.

What this workflow does

• Receives a candidate submission via an n8n Form trigger, including a `PDF` resume attachment.
• Uploads the resume to a selected Google Drive folder and extracts resume text from the uploaded PDF.
• Sends the extracted resume text to OpenAI (gpt-4o-mini) to return key candidate fields as JSON.
• Parses, cleans, and normalizes fields—such as name casing, email formatting, and numeric experience years.
• Calculates a candidate fit score based on experience and the presence of specific skills: React, Node.js, and PostgreSQL.
• Inserts the structured candidate record and score into a PostgreSQL table (e.g., public.candidates) with columns matching the workflow mappings.
• Posts a Slack message to an HR channel containing candidate details and the computed score.

Use cases

• Turn inbound recruitment form submissions into a structured candidate pipeline without manual copy/paste.
• Score applicants consistently using the same resume-parsing prompt and skill checks (React, Node.js, PostgreSQL).
• Give HR an immediate Slack notification with a ready-to-review candidate summary and fit score.

Technical details

• n8n Form trigger for resume intake
• Google Drive for storage (folder selection via OAuth)
• OpenAI (gpt-4o-mini) for structured JSON extraction
• Code steps for parsing/normalization and fit score calculation
• PostgreSQL insertion into public.candidates
• Slack OAuth + HR channel notification
• (Includes workflow helpers such as a Sticky Note)