Scaling Editorial Output
2.5× with AI Workflows
How I built an n8n-powered editorial pipeline that automated quality checks, fact verification, and SEO compliance — taking a crypto newsroom from 40 to 100+ articles per week without adding headcount.
The Automated Editorial Pipeline
Click each step to explore what happens at every stage of the pipeline.
Writer Submits Draft
Writer completes draft in CMS and hits submit. This triggers an n8n webhook that starts the automated pipeline.
The Results
Toggle between before and after to see the impact across key metrics.
Weekly articles
~40
Time to publish
4+ hours
Correction rate
Higher
Editorial headcount
15
Writer satisfaction
Mixed
Discover impressions
Baseline
Key Takeaways
Automate the mechanical, not the creative
AI handles SEO checks, fact verification, and style compliance. Humans handle narrative, analysis, and editorial judgment.
Invest in prompt specificity
A 500-word prompt with your exact style rules outperforms a 50-word generic one every time. Detailed context is everything.
Start with your biggest bottleneck
Don't automate everything at once. Identify the single most time-consuming mechanical task and build from there.
Measure beyond output volume
Track correction rates, time-to-publish, editor satisfaction, and Discover performance — not just article count.
Want to implement these strategies?
I help crypto publications build editorial systems that scale.