The Resolution Layer · Case Study in BriefEKOM
Mineral supplements.
A cold read of the mineral category of an online vitamin and supplement retailer — where every product carries a label panel that says exactly what it is, and the second copy of the product written for machines is supposed to agree. One of a set of anonymized EKOM analyses — what surfaced, and where it leads.
Lead finding · the price a machine is told
On twelve of thirteen discounted products, the structured data carries the "was" price, not the price the page charges.
Every product with a "was" price was checked three ways — the record, the rendered page and the offer price in the structured data that Google Shopping and search read. On ten of the eleven products on sale, the machine-facing copy advertised the pre-sale figure, roughly double what the page charged. Two more carry a "was" price lower than the price, and one of them offered a subscription above its one-time price on the live re-read. Where a product has no second price, the structured data is exact — so it is one rule to correct, not twelve prices to chase.
Also surfaced
Magnesium, filed under cleaning supplies
Twenty-three mineral supplements declare a different category to Google — cleaning supplies, amino acids, bath additives, collagen, hair color — every one of them sitting in the retailer's own minerals category. Fifteen more declare no category at all, all of them products added since spring.
The one iron page without the statement
Of the products whose panel lists iron, all but four display the standard accidental-overdose statement. The highest-dose iron product in the category is the one dedicated iron supplement that does not — its warning line is about antibiotics instead. Held for the retailer: only the printed label can say what the page should carry.
Filled in is not the same as right
The wrong price is populated. The wrong category is populated. The iron page has a warning line. A completeness check scores every one of those fields at a hundred percent; each gives the wrong answer. The fifteen blank categories are the one critical-or-high defect a fill-rate report would catch — and the least consequential of the three category findings.
Three findings that did not survive
Two described EKOM's own reading of the page rather than the catalog — an empty rating on products with no reviews, a label statement "missing" from a field EKOM derives. One was wrong: a days-of-supply figure that matched the label after all. None is counted.
How EKOM read it. The engine read every product page in the category cold — no file, no credentials, no integration — and re-read the headline products live six days later. It joined five statements of what each product is, from the product record to the label panel to the structured data, held them against each other and against each product's siblings, and had an analyst recount every magnitude from the archived pages. Where a finding failed that check, it was withdrawn and says so.
From diagnosis to channel-ready. Most of the eight collapse into four upstream mechanisms — a machine-facing offer bound to the wrong price field, a category mapped from another system's taxonomy, a publish path for new products that skips the category, and label statements that live in free text. Correcting the findings corrects the findings; correcting the mechanisms corrects every product that has passed through them and every one that will. Then the same intelligence holds the line at publish, so the next sale whose structured data carries the wrong price is caught the day it goes live rather than found by Google.