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
Category
AI Video ProductionConnected Projects
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.
What actually changed
| Before | After | |
|---|---|---|
| Each market | Its own hand edit: footage, voiceover, captions | Generated end to end from the same brief |
| Turnaround | Days per campaign | A fraction of that, without an editor in the loop |
| Adding a market | Another full edit | Little beyond the cost of the first cut |
| Testing an angle | Slow enough that most ideas never shipped | Cheap enough to do as a matter of routine |
Restated from the case above. No campaign performance figures are published.
How it was built
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.
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.
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.
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.
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.
Why generated beats one master with swapped subtitles
Subtitling a single master is faster to build and it is why most teams stop there. It also means every market gets the pacing, the emphasis and the on-screen presence of the market it was shot for. Generating per market costs more to wire up once and then costs almost nothing per market, and the ad reads as though it was made for the person watching it, because it was.
Where it falls down
This is a pipeline for volume and for testing, not for the one hero film a brand builds a season around. Generated footage still has tells, and on the piece that carries the brand those tells are expensive. The honest split is to generate the variants, the angles and the localizations, and to shoot the one thing everything else points at.
Want a system like this?
I scope and build automation like this for other businesses, then hand it over so you own it end to end.
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