AI Video Production

50% Faster Video Production: AI Ad Campaigns

Overview

AI-generated video ads using Sora, Runway, and HeyGen for Meta/TikTok campaigns across Italy, Germany, and France. Automated rendering pipeline via n8n cut production time by 50%.

Tech Stack

SoraRunwayHeyGenn8n Pipeline

Connected Projects

N8N

40% Less Manual Ad Work: Campaign Automation

n8n Automation

VID

UGC Ad Creator Platform

AI Video Production

Challenge

Before this build, launching localized video ads across Italy, Germany, and France meant editing every variant by hand. Each market needed its own footage, voiceover, and captions, so one campaign fanned out into a long queue of separate edits. Turnaround stretched across days, and testing a fresh creative angle was slow enough that most ideas never reached production.

Approach

I built an n8n pipeline that takes a campaign brief and drives it to a live ad without an editor in the loop. The brief becomes a scene-by-scene script, and each scene is routed to whichever generator suits its shot: HeyGen renders the talking-head segments, Runway Gen-3 handles b-roll, and Sora covers narrative motion. FFmpeg stitches the clips, syncs the audio, and burns in captions, then the workflow posts natively to Meta and TikTok. Every market gets its own generated script, voice, and captions rather than one master with swapped subtitles.

Result

The pipeline cut production time by around 50%, so work that used to run across days now turns around in a fraction of the time. Because each market is generated rather than re-edited, adding Germany or France on top of Italy costs little beyond the first cut, and testing new angles became cheap enough to do as a matter of routine. Creative stopped being the thing holding a campaign back.

How it was built

1

Brief to structured script

Inside n8n, each campaign brief is parsed into a scene-by-scene script, with every scene tagged by the kind of shot it needs. That tagging is what lets the rest of the pipeline route each segment to the right generator on its own.

2

Model-matched clip generation

The workflow reads each scene's tag and calls the generator that renders it best: HeyGen for talking heads, Runway Gen-3 for b-roll, Sora for narrative shots. Pairing the shot to the model keeps quality even across a spot without anyone choosing by hand.

3

Render and caption in FFmpeg

An FFmpeg step concatenates the generated clips in order, locks the audio to picture, and burns the captions into the frame. The output is a finished, platform-ready cut that no one had to open a video editor to assemble.

4

Native localization per market

For Italy, Germany, and France, the pipeline regenerates the script, the voiceover, and the captions in the local language instead of subtitling one master. Each market ends up with a cut that reads as though it was produced there, not translated after the fact.

5

Multi-platform publishing

The closing nodes push each localized cut to Meta and TikTok in the aspect ratio and format each platform expects. Distribution happens inside the same run, so a brief reaches live placements with no manual handoff between build and publish.