96 products cleaned by hand, 650+ reviews mined and mapped.
●METRICS
Production telemetry, active capacity, and engineering scale measured in live deployment.
01 — What it does
A Shopify store selling into a crowded category, with a catalogue that looked like a supplier feed: third-party watermarks on roughly half the gallery images, inconsistent backgrounds, and no social proof on the product pages at all.
We ran the cleanup by hand rather than by filter. Ninety-six products, five to six gallery images each, every watermark removed and every background standardised to one brand-consistent template. Then we gave the pages the proof they were missing.
02 — The brief
Remove every third-party watermark across the catalogue, by hand.
Standardise backgrounds so the catalogue reads as one brand.
Bring in high-quality, photo-backed social proof and map it accurately.
03 — Technical highlight
Generic review importers assume a tidy source. The best available proof sat on a competitor's site behind a markup structure none of them could read, so we wrote a custom Data Miner recipe against that specific structure instead.
It captured more than 650 reviews with their customer photos, then mapped each one to the right product page rather than dumping them into a single feed. Reviews with images were prioritised, because a photo is the part a shopper believes.
04 — Stack and outcome
Every dependency is chosen deliberately for raw execution speed, strict type safety, zero cold-start latency, and battle-tested reliability under high concurrent load.
"The store went from a standard supplier-looking layout to a catalogue that signals a premium brand, with photo-backed proof on the pages where buyers hesitate."