Trust before checkout
Toppp separates ranking from monetization, shows cons, and refuses unsupported answers.
OpenAI Shopping-ready demo
Toppp is an answer layer for product decisions.
It proves a shopper can move from messy intent to a trusted product answer, then to a retailer action today and a checkout protocol later.
Query
One product, plain language.
Evidence
Specs, sources, price, tradeoffs.
Answer
Three picks, no sponsored rank.
Action
Retailer link today, checkout-ready later.
Structured product memory
Recommendations, offers, evidence, and click actions use stable IDs rather than page-only prose.
ACP-ready shape
The preview feed mirrors catalog concepts like price, availability, media, seller, and checkout eligibility.
AI as research leverage
AI helps map queries, discover candidates, and summarize evidence, while humans approve winners.
Why it matters
Shopping AI needs decision quality, not just checkout.
Instant Checkout can compress the end of the purchase journey. Toppp focuses on the part before that moment: choosing the right product with evidence, current offers, and honest tradeoffs.
The vertical slice keeps checkout out of scope, but models the data that a commerce surface needs later: stable catalog IDs, price, availability, seller links, media, eligibility flags, and source-backed recommendation context.