Hairitage by Mindy McKnight. Ground truth at catalog scale.
A 38-SKU haircare line sold across Walmart with a 6M-subscriber YouTube creator behind it. Shopthru.OS ran the deepest ground-truth pass we have on any merchant and drafted the rewrites before the brand had to choose what to ship.
Measured every fact AI said about every SKU.
The Content Optimizer compared 8,407 product fields between AI-quoted facts and the live catalog. By far the largest GT pass run on the platform. 4,160 came back exact-match, 2,482 mismatched, 1,753 fuzzy. Mismatches concentrated on price (205) and availability (53) for the primary catalog.
Simulated 8 rewrites in shadow. Nothing went live until tested.
The OS drafted 8 shadow PDPs (S.O.S. Deep Moisture, Outta My Hair, So Over Your Oil Control Shampoo, and 5 more) each pre-scored for AI-citation likelihood across ChatGPT, Perplexity, and Gemini. The brand sees the projected lift before approving the ship.
- 8 shadow product pages drafted, each split-tested against the live PDP
- 2,710 source recommendations generated (Ulta, Reddit, Walmart reviews)
- Every shadow page carries an EEAT score and a citation-likelihood projection
Shipped the queue: 2,595 action items, prioritised, drillable to the cell.
CONTENT_FIX and SOURCE_OUTREACH lead the queue, focused on category-cohort visibility ("sulfate-free cleansers", "frizz control coastal California"). Every item links back to the exact LLM execution that surfaced it, so the merchant team can verify before approving.
Compounded: the shadow-page primitive is now standard for any catalog over 20 SKUs.
Drafting in shadow before shipping live was a Hairitage-scale necessity that became the platform default. Every multi-SKU catalog now gets shadow rewrites with the same pre-scoring model. The merchant chooses what to push. The OS already knows what will move the needle.
Book a 30-minute Loop walkthrough.
We’ll run the first measurement live. No deck.