BACKGROUND
This project was for a content team aiming to triple SEO output from four to twelve pieces per month without degrading quality. The goal was to design a strategy that allocated AI effort to the parts of SEO production where it added speed without compromising quality, and protected human effort for the parts where quality was the direct product of human expertise.
I built the strategy around a tiered content model that matched production investment to keyword opportunity, with three tiers differentiated by AI use level, review depth, and time budget per piece. The production workflow ran through seven phases with the same quality checkpoints across all tiers, and four explicit safeguards that could not be skipped: a mandatory claim verification pass, a named approver on every piece, a quality floor that subordinated volume targets, and a protected time budget for Tier 1 flagship pieces.
The result was a scaling strategy built around a specific distinction: the activities that make content rank do not compress well, and more content that ranks worse is not a successful outcome. The six-month success metrics included a fabricated statistic incidents target of zero, sitting alongside the volume and ranking targets.
