Sockets first, intelligence plugged in
Apparel Market was not built by bolting AI onto a marketplace. The deterministic wholesale workflows — catalog, inventory, terms, orders — are the canonical core, and AI surfaces plug into them as advisors. The AI proposes against operating data; it never mutates it directly.
Every output carries its evidence
Import diffs, listing enrichment, search rankings, verification reviews, and appointment briefs all arrive with reason codes, cited evidence, and confidence bands. When a reviewer asks why the model suggested something, the answer is attached to the suggestion — not reconstructed after the fact.
Humans hold every consequential decision
Publishing a catalog change, approving a brand, accepting an order: each runs through a human review queue. Trust automation raises reviewer throughput — verification assist summarizes business evidence, moderation screening flags risk — but the decision count held by people stays at one hundred percent.
Shadow modes, benchmark gates, kill switches
New AI surfaces run in shadow mode against benchmarks before they touch live workflows, and every surface ships with budget controls, rollback paths, and a kill switch. If a model misbehaves, the workflow continues without it — degraded to manual, not broken.
Why this matters to procurement
Established retailers do not ask whether you have AI; they ask whether they can audit it. The governance trail — evidence payloads, review queues, release gates, durable events — exists before the question is asked. That is the difference between AI as a feature and AI as an operating model.