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POST /v1/mob/scan/lookup label + customer → catalogue match, sizes, and every registered store that sells it with live stock, in-stock first / nearest first, one recommended POST /v1/mob/scan/confirm chosen store + size + qty → re-read the ledger; ok, or the next-nearest store with enough of the same product GET /v1/mob/scan/stores registered stores nearest first Recognition is pgvector cosine search over every brand_* table (each with its own index, merged) plus a word match that settles near-ties and works alone when no model is configured. The embedder is chosen by EMBEDDING_PROVIDER (OpenAI-compatible or Gemini) and must be the model that indexed the catalogue: verified 2026-09-15 as all-MiniLM-L6-v2 over search_query, served by the cluster's Ollama as `all-minilm`; the first search refuses a width mismatch by name. Customer, stores and catalogue are read concurrently under a 5 s cap; a slow model degrades to a text answer. Vectors and ranked hits are cached in Redis and in-process; live stock never is. Availability uses the same rules as the customer catalogue (approve, publishedat, ledger balance, outlet price else retail). No stock reservation: confirm re-reads. scratch/cataloguedims reports the catalogue's embedding width and fill. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>