X1 — six-layer confidence commerce

Mathematics

1. need = Intent(utterance)

   Natural language, voice, or visual query becomes structured constraints.

2. π* = arg max_p Score(p | need, taste)

   Ranked set with a transparent why — not a keyword listing.

3. inspect = Twin(p)

   True-scale 3D / AR closes listing versus reality before value moves.

4. C = g(inspect, presenter)

   Decision confidence is produced by inspection and demonstration, not by copy.

5. buy ⇔ C ≥ τ

   Checkout is the last step. 3PL is an adapter — no owned fleet.

6. Score_{t+1} ← Outcome(return, fit)

   Returns of 20–35% are a pre-purchase information gap. Feedback trains rank.

Instruction

1. 01Intent

   Shopper states a need. Interface layer writes utterance → need.

2. 02Filter

   AI intelligence ranks π* from the product graph and taste. Every recommendation carries a why.

3. 03Inspect

   3D / AR twin is the product in the user’s space. This is the experiential layer.

4. 04Presenter

   Flipbook, clipbook, catalogue, or live presenter explains. Interactive content, not static specs.

5. 05Decide / train

   If C ≥ τ, Order hands to 3PL. Outcome (purchase, return, fit) writes Score_{t+1}.