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Power a comparison site without running a collection team

Comparison sites live and die on two things: whether the products are actually the same, and whether the discounts are real. Both are matching-and-history problems, not front-end problems.

Canonical productsIndexPercentile, not %-offDepthPublished match qualityTrust

Price comparison & deal sites

Discounts you can defend in public

“40% off” against an inflated reference is the claim that gets comparison sites into trouble. Percentile against a product's own history is a defensible statement about the same product at the same seller.

Price comparison & deal sites

Matching you can show

Every product page can display how its offers were matched and how confident that is. It is a trust signal, and it costs nothing because the number already exists.

Endpoints that do this

Where it lands in the API.

GET /v1/search

Search commerce

Keyword and structured search across every connected seller. Returns canonical products with live offers, spread and a verdict — not raw listings you have to deduplicate yourself.

→ reference
POST /v1/compare

Compare

One product across every seller that carries it, ranked by landed cost, with the spread. Server-side work is worth more than four raw reads the caller has to join.

→ reference
GET /v1/products/{id}/context

Price context

Is this actually a good price? Percentile against the product's own history at that seller, time-weighted, with low, median and high.

→ reference

Questions

Common questions.

How many products are in the index?

The index is built from published catalogues rather than a fixed list, and coverage is reported per seller with median record age rather than as a single headline count.