How We Built the Content Automation Pipeline in 3 Weeks

We needed a content engine that could produce, render, and publish video content daily — without a production team. Three weeks, one droplet, and several failed approaches later, here is what we shipped.

The Architecture

The pipeline has four layers:

1. Orchestration (n8n) — Schedules content generation, calls the render pipeline, manages publishing queues across platforms
2. Rendering (Remotion) — Converts structured JSON scenes into MP4 video with per-scene images, text overlays, and voiceover sync
3. Narration (edge-tts) — Microsoft Neural TTS via free cloud API — generates 6 scenes in 6.7 seconds on a 2vCPU droplet
4. Publishing — Cross-platform distribution to Facebook, Instagram, Threads, and LinkedIn with platform-specific format enforcement

What Failed First

We tried F5-TTS first. It worked at import level. It took more than 7 minutes to generate 4 seconds of speech on our 2vCPU/4GB droplet. Transformer-based TTS without GPU acceleration is effectively unusable for a daily content pipeline. The lesson: benchmark on target hardware before committing.

Scene Duration Discipline

Every scene duration must match actual voiceover length plus a 1-second buffer. Fixed durations cause voice cutoff. We measure each voiceover with ffprobe and set scene duration to VO length plus 1 to 1.5 seconds.

Platform Compatibility Matrix

Instagram Reels has a 90-second maximum and requires 9:16 aspect ratio. Threads allows up to 5 minutes. LinkedIn allows up to 10 minutes. We built isPlatformCompatible() as a gate that runs before every publish attempt.

Where It Is Now

The engine runs daily on a DigitalOcean droplet via PM2. Content is deduped by SHA-1 content hash — not just by media URL — to prevent text posts from republishing the same content across cycles.