How My Scraper Went From 20 Minutes to Under 10 Without Losing a Single Review
SHA-256 hashing, bounded retries, chunked Postgres writes, and the cloud bill that forced me to treat cost as a feature.
Every product team drowns in feedback the same way.
A review lands on the App Store. Someone vents on Reddit. A G2 comparison goes up. A support ticket gets resolved, a tweet gets posted, a Play Store rating drops from four stars to two. Each one of those is a signal about your product, and each one lives on a different platform, in a different format, behind a different wall.
Reading them all is impossible. Acting on them is harder.
So I built FountainData VOC: a multi-tenant Voice-of-Customer engine that connects to roughly 30 feedback sources, pulls everything into one pipeline, strips out the noise, runs AI extraction over the clean signal, clusters it into themes, ranks those themes by business impact, and pushes the results straight into Jira, Linear,...
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