How My Scraper Went From 20 Minutes to Under 10 Without Losing a Single Review

This story was originally published on HackerNoon at: https://hackernoon.com/how-my-scraper-went-from-20-minutes-to-under-10-without-losing-a-single-review. Duplicate reviews were quietly multiplying my LLM costs. The two-layer dedup and retry design that fixed it in a multi-tenant Voice-of-Customer pipeline. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #ai, #software-engineering, #deduplication, #hybrid-retrieval, #vector-embeddings, #multi-tenancy, #reviews, #data-science, and more. This story was written by: @hack3t. Learn more about this writer by checking @hack3t's about page, and for more stories, please visit hackernoon.com. I built a Voice-of-Customer pipeline that reads reviews from ~30 platforms and turns them into ranked, actionable insight. This is what it taught me about deduplication (the same review should never pay twice), scraper optimization (20 minutes down to 10), boring-but-winning database patterns, and the AWS bill that comes from buying enterprise infrastructure before enterprise problems.