Ditching Zapier? Building Automated Social Media Matrices with Make.com + OpenAI API

I recently migrated my automation workflows from Zapier to Make.com. The primary driver was cost and complex branching logic. Here is how I built an automated social media content matrix using Make.com and the OpenAI API, saving both time and money.

Why Make.com Over Zapier?

While Zapier is intuitive, its pricing scales poorly for high-volume API calls. Make.com (formerly Integromat) offers a visual, non-linear canvas that makes branching and iterating much easier. I found that I could construct complex JSON payloads and parse responses from OpenAI with fewer steps and lower subscription costs.

The goal was to take a single RSS feed item (like a new blog post) and automatically generate customized content for Twitter, LinkedIn, and a Facebook page, each with different tones and lengths.

The Make.com Scenario

Trigger: RSS Feed module watches for new posts.
Action 1 (OpenAI): Create a Completion module. Send the article text to GPT-4o with a system prompt instructing it to return a JSON object containing three fields: twitter_thread, linkedin_post, and facebook_summary.
Action 2 (Router): A Make router splits the flow into three branches.
Actions 3-5 (Social Posting): Each branch takes the corresponding JSON field and posts it to the respective platform.

The JSON Prompt I Used

Getting the LLM to output consistent JSON is critical. Here's the JSON structure prompt I enforce in the OpenAI module:

{
  "twitter_thread": ["Tweet 1...", "Tweet 2..."],
  "linkedin_post": "Professional post here...",
  "facebook_summary": "Casual summary here..."
}

My Setup Tips

One issue I ran into was OpenAI occasionally returning markdown formatting (like ```json ... ```) which broke Make's JSON parser. The workaround I found was to use Make's replace() function in the mapping to strip out the markdown backticks before passing it to the Parse JSON module.