Building a System That Automates YouTube Post-Production

The team behind Growati has built a system that automates YouTube post-production by analyzing videos frame-by-frame rather than treating them as plain text. To handle complex automation flows reliably, the engineers implemented a queue-based architecture using BullMQ. The project, which aims to reduce manual metadata generation for educational creators and podcasters, is set to launch on Product Hunt on May 28.

Most creator tools today focus on analytics and suggestions. We wanted to explore something different: What if YouTube post-production could actually be automated? We’ve been building Growati, a system that helps creators generate personalised: for YouTube videos. One of the most difficult engineering problems was properly handling video understanding. Instead of treating videos like plain text, our system analyses videos frame-by-frame to extract: The goal is to make metadata generation more contextual instead of generic AI text generation. Another challenge was orchestration. A single automation flow may involve: We ended up using a queue-based architecture with BullMQ to handle automation reliably. One thing we learned quickly while talking to creators: Many educational creators and podcasters are overwhelmed by post-production work after uploading. Most of them are not looking for more dashboards. They want less manual work. That insight changed how we approached the product. Right now, Growati is still early: We’re launching on Product Hunt on 28 May.