Case study · AI automation · 16x9.ai
AI Content Automation Engine
Cut content production time 95% with four chained n8n workflows.
An end-to-end content pipeline for 16x9.ai, built from four chained n8n workflows for research, generation, media processing and publishing.
- Client
- 16x9.ai
- Role
- AI Automation Engineer (contract)
- Timeline
- Oct 2025 – Feb 2026
The challenge
Every post meant researching a topic, writing a script and assembling media by hand. That was too slow to keep up with what was trending.
What I built
- Four specialised n8n workflows that hand off to each other: research, generation, media processing and publishing.
- Topic discovery with Google Trends, so scripts and assets are generated about three days before posting.
- LLM integrations that write the scripts and prepare the assets for each post.
Also at 16x9.ai: an autonomous AI receptionist on Vapi with real-time speech, an Airtable-backed knowledge base, appointment booking and intelligent call routing.
Results
- 95%less time to produce content
- Research to publishing runs as one pipeline
Tools
- n8n
- LLMs
- Google Trends
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