Work evidence
Private system. The case describes the pipeline's architecture and controls; I don't publish the niche or the pages to avoid burning the project's SEO edge.
I built a pipeline to generate 500+ pages from structured data. The workflow includes three controls before publication: semantic deduplication across pages, per-token cost controls and human validation. A person reviews, edits or rejects the output.
How it flows
Each page starts from structured niche data, goes through templated generation with controlled variation, gets compared semantically against everything already published, and lands in a review queue where one person approves, edits, or discards. The human reviews; they don't write.
Controls at each stage
- Input: structured data and a template define the content to generate.
- Generation: the LLM produces a draft with controlled variation and token-cost tracking.
- Comparison: semantic deduplication identifies content too similar to existing pages for review.
- Review: a person approves, edits or rejects before publication.
Documented scope: 500+ pages generated with a review workflow before publication. The pipeline connects generation, comparison and approval in a process one person can supervise.
What it demonstrates
Automating content with AI requires connecting costs, quality and approval to the publication workflow. The same pattern can be adapted to product listings, programmatic content or documentation.