How much of this job runs on an AI pipeline versus needs human hands — so you can price it and commit to the timeline with a real number, not a guess.
A Saudi grocery retailer has products already loaded in Blink but no images. They want us to find, resize, rename and hand over the correct image for each SKU. They do not have the images — we source them online, guided by universal barcodes.
Grocery and personal care is the best-case category for automation — Open Food Facts is a free, barcode-keyed, open-license image database built for exactly these products. That's the engine we tested live.
This single decision changes the whole shape of the deliverable — from "a folder of images" to "an AI pipeline that pushes straight onto their live products."
We deliver a folder of correctly named, resized images. Their team runs Blink's bulk import. Clean handoff, but the loop ends on their side.
Map image → product by SKU, then POST to Blink's media endpoint. Source → verify → resize → rename → live, with no manual upload on anyone's side.
Confirm before promising B: (1) does Blink expose a product-image endpoint (likely partner-tier, their team asks Blink support) · (2) auth model + write rate limits for 15k items · (3) exact image spec, so step 05 outputs it right the first time.
No conjuring images that don't exist. These are the buckets that land in the human queue — and why the sample test matters.
500g vs 1kg, flavour differences where the packaging looks the same. Vision helps but isn't perfect — human eyes finish these.
Regional Saudi brands with zero web presence. Nothing to source → skip or ask the client for a link (they already agreed).
Open Food Facts images are clean and licensed; anything pulled from general web search needs a rights judgment call.
The whole price and the 5–10 day feasibility hinge on one number: what share of their 15k SKUs are mainstream (auto) vs. local (human). We don't estimate it — we test it.