Most content teams rely on individual memory and subjective judgment. That's the problem.
It leads to:
Inconsistent accuracy
No unified quality standard
Slow revisions and unpredictable timelines
Packaging that varies from editor to editor
Higher exposure to factual or compliance risk
Difficulty scaling without losing control
I build editorial systems that fix this.
My systems are built on a human-in-the-loop model. AI is deployed as an acceleration tool - always monitored and validated by human editorial judgment. I use AI when it improves speed, consistency, or decision-making, but never in place of the editor.
My systems have four layers:
Prompt libraries — structured AI prompts for headlines, QA, and optimization, built on your style guide and brand rules
Golden checks — non-negotiable standards that must pass before publish
Known issues tracking — documented failure modes so mistakes don't repeat
Workflow governance — clear rules for when AI assists and when humans decide
What changes when a system is in place
Faster editing cycles with fewer revisions
Clearer reasoning behind each editorial decision
Less cognitive load across the team
More consistent outcomes from freelancers and internal writers
Stronger accuracy and compliance alignment
Repeatable quality at scale
Examples
Accuracy
Before: Writers relied on outdated details.
After: Accuracy checks built into the workflow surface issues before editors see the draft.
Subject lines and packaging
Before: Headlines lacked structure and consistency.
After: Packaging follows a unified framework that improves clarity and engagement.
Voice and consistency
Before: Tone drifted across articles, newsletters, and landing pages.
After: Clear voice and style rules help writers match expectations and reduce rewriting.
Workflow and speed
Before: Editors spent time untangling drafts that followed different structures.
After: A shared workflow reduces context switching and lowers the editing burden.