Project Goals

This project automated the process of scraping, summarizing, and storing content from multiple social media platforms, reducing manual work by over 90%. Using an n8n workflow, the system pulled posts from Facebook, Instagram, YouTube, LinkedIn, and RSS feeds, summarized them using AI, and stored the results in Supabase. The automation saved over 15 hours per week and ensured that content monitoring stayed consistent and organized.

Challenges & Problems

Before automation, she had to manually visit each platform to find new posts, copy and paste text into summaries, and maintain spreadsheets by hand. This process was time-consuming, inconsistent, and prone to human error, making it difficult to keep up with daily updates.

Solutions Provided

An n8n workflow was built to run automatically on a set schedule. It began with a scheduled trigger to start the content capture every seven days. Data was retrieved from RSS feeds for podcast and news, as well as Facebook, Instagram, YouTube, and LinkedIn scraping via APIs. Open AI summarized the content into digestible text, after which Edit Fields nodes cleaned and formatted the results. The final step automatically created new rows in Supabase, with data categorized by platform.

The Result

Demo Here

The results were impressive. Time spent on the process dropped from more than 15 hours per week to under one hour of oversight. Daily summaries became consistent, ensuring that no platform updates were missed. The quality of reporting improved through AI-generated summaries, which provided clearer, more relevant information. The system also proved scalable, with the ability to easily add more platforms in the future.