Amber: Catch Gray-Market Diversion

Premium brands lose millions every year to gray-market diversion: a distributor buys cheap in one country and dumps the product in another, undercutting the brand's own market. Today, brand-protection teams hear it from a vendor dashboard, a claim they cannot independently check. Amber turns that gap into evidence. It captures the same product from inside each country on Bright Data's residential network, matches the GTIN, and strips VAT to a net-of-tax floor. A within-country control runs three residential exits per country; when all three agree to the cent, the gap is a controlled experiment, not proxy noise. Every observation is sealed into a cryptographically signed, geo-attributed packet using ed25519 and an RFC 6962 Merkle tree, and anyone can verify it offline with one command. Edit a single byte and verification fails, RED. The architecture is honest by construction. Layer 1 is the deterministic signed spine: no AI ever writes a number into the evidence. Layer 2 is a separate, unsigned advisory that only reads the signed facts, a three-model jury via the AI/ML API, a Cognee temporal memory that shows whether a gap persists, and a TriggerWare workflow that turns a signed catch into an alert. A human draws any legal conclusion. We also gave back: an open pull request to Bright Data's own brightdata-mcp turns a discarded blocked-country error into a first-class signed measurement, closing their issue #104. We say what we do not claim. Requests are dispatched the same instant, not witnessed, and the annual recoverable figure uses the brand's own volume assumption, labeled as one. Every number ships inside a signed packet in the public repo with 324 passing tests, so you can clone it and re-check the proof yourself.