EasyContent

We turn raw footage into viral content in minutes with our AI workflow

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EasyContent

Inspiration

Creating viral content requires three steps: researching trends, writing scripts, and editing videos.

This process takes 5-6 hours and typically needs 2-3 specialized team members. As a marketer for content creators, I noticed that they usually need to post 2-3 times daily, and this became unsustainable with a small team.

Most creators face a bigger problem: they don't know what makes content engaging. Trial and error takes time they don't have.

We realized these tasks are repetitive and follow patterns. In the AI era, this should be automatable. After watching AI automation tools in action, we thought: what if we could automate the entire workflow from research to final edit?

What it does

EasyContent automates the complete viral video creation workflow through four phases:

Research: AI analyzes top creators and extracts winning scripts from viral videos. Users see a dashboard with creator profiles and analysis of why their content works.

Script Generation: Users select a proven template and input their industry context. AI adapts the viral formula to create a personalized script following successful patterns. Filming: Users record their video following the generated script.

AI Editing: Users paste a Dropbox link to their footage. The system automatically downloads the video, applies cuts, transitions, captions, and music, then returns the edited result in under 5 minutes.

The entire process reduces a 5-6 hour workflow to approximately 6 minutes.

How we built it

I handled product design and interfaces, building the landing page with Rocket.new Agent and designing the complete Airtable structure with three connected databases (Research, Script, Edit).

My partner handled automation, creating n8n workflows that connect ChatGPT for script generation, Apify for web scraping, and AI video editing APIs.

The frontend uses Airtable for the database, interfaces, and forms. The backend runs on n8n for workflow automation, integrated with ChatGPT, Google Cloud infrastructure, and Dropbox for video storage.

We coordinated remotely across different countries, dividing work based on our strengths and iterating through multiple design cycles.

Challenges we ran into

The n8n workflow produced over 100 errors during development. Getting consistent, quality AI output was the most time-consuming challenge.

On the interface side, designing Airtable workflows that felt simple while handling complex data relationships required multiple redesigns.

Working remotely across time zones made real-time communication difficult. My partner's complex n8n setup initially confused me, which meant rebuilding parts of the Airtable structure to match the actual data flow. We learned that better upfront workflow design and async documentation would have prevented these structural mismatches.

Creating a polished landing page also took more refinement than expected. Despite claims about "one prompt and done," making it work exactly as intended required iteration.

Accomplishments that we're proud of

We built a production-ready system in 3 days that achieves a 97% time reduction in content creation. The workflow processes videos end-to-end with no errors in production. We successfully debugged over 100 n8n errors and created an intuitive interface that makes complex automation accessible.

Working remotely across countries, we coordinated a technical project with multiple integrated systems: Airtable databases, n8n automation, multiple AI APIs, and cloud infrastructure. The system works seamlessly from research to final video delivery.

What we learned

This was my first time building Airtable interfaces, creating a landing page, and collaborating remotely on a technical project. I learned that no-code tools can produce production-ready products and that treating AI tools as collaborators rather than magic solutions makes all the difference.

We discovered that errors teach faster than tutorials. Testing, breaking, and fixing taught us more about the tools than documentation could. My partner learned that making AI deliver consistent output requires extensive debugging and refinement.

The biggest lesson: design workflows together upfront. Our initial structural mismatches cost time that better planning would have saved.

What's next for EasyContent

We plan to more features like auto-publish and repurpose to all major social media platforms. Direct posting to social platforms would eliminate the final manual step.

Long-term, our vision is making this infrastructure for the creator economy. We want to help anyone create successful content by combining proven viral patterns with AI automation, democratizing access to high-quality content creation.

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