musebridgeWatch an AI shopper decide.
Then check the evidence.
We gave a shopping agent 24 buyer-task hypotheses and four public product listings. It compared the options, made a choice—or abstained—and left a trace we could check against the original pages.
BUYER TASKS
“I need black leggings for leg day, under $65, with a stated squat-proof finish.”
The task is brand-neutral. Hard requirements are fixed before the agent sees any product. This example was written from public buyer themes; no customer has approved it yet.
What the agent chose
from a fixed candidate set
Selections within four preselected products. They are not organic discovery, real platform recommendations, or purchases.
The model sometimes shortlisted products that did not meet hard requirements. A separate rule audit caught those decisions; product-page redirects were held as unresolved. The finding tells us exactly what to improve before a customer pilot.
What a merchant gets: a traceable account of what the agent read, where the decision broke down, and what fact or page path needs attention next.
Method preview from an internal research pilot. Four brands were compared independently; none was a client. No natural-search discovery, authorized checkout, customer feedback, or sales outcome was measured.