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.
Generating one landing page with an LLM is trivial. Generating 500+ that Google doesn't punish as duplicates and that don't blow the token budget — that's an engineering problem. This pipeline solves it with three controls: semantic deduplication across pages, per-token cost control at every stage, and human validation as the final gate before anything ships.
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.
What it demonstrates
That generative AI at scale isn't a prompt: it's cost, quality, and control architecture. The same pattern applies to product listings, programmatic content, or documentation.