Workflow-Automatisierung
AI content pipeline
From idea to published post, without the busywork.
- less production time
- 80%
- Kunde
- Vertraulicher Kunde
- Rolle
- AI & Data Science Consultant
- Jahr
- 2026
Diese Fallstudie gibt es derzeit nur auf Englisch.
Producing content meant many manual steps, from finding ideas to publishing and checking how posts performed. I built one AI pipeline that covers the whole way, from ideation to scheduled publishing and performance tracking. Content production now takes 80% less time.
- 01Ideas are collected
- 02Drafts are written
- 03Posts are scheduled and published
- 04Results feed the next ideas
Content ate the week
Every piece of content went through the same manual steps: collect ideas, draft, edit, schedule, then check later how it did. Each step lived in a different tool, and each handoff cost time.
One pipeline, end to end
Instead of speeding up single steps, I connected all of them. Ideas flow into drafts, drafts into a publishing schedule, and published posts into tracking, without anyone copying things between tools.
Tracking built in
Performance tracking is part of the pipeline, not an afterthought. What worked feeds back into the next round of ideas.
80% less production time
Content production now takes a fraction of the time it used to. The team spends its time on judgment, not on moving text between tools.