Real shopping is rarely one clean search.
People add constraints, reject choices, and speak vaguely.
That is why we built Shopping Copilot.
The model understands; the system remembers, retrieves, ranks, and explains.
When rule confidence is low, local Qwen interprets intent and action.
It does not generate products, scores, or ranking decisions.
A vague request becomes structured intent.
Item request · Tanks and Camis · adjustable as a soft preference.
Free text stops at the boundary; versioned state records the change.
SQLite FTS5 recalls candidates from fifty thousand frozen products.
Transparent rules rerank the recalled candidates.
The policy asks only the most discriminative follow-up.
Then the shopper changes direction: forget adjustable; use polyester.
The model identifies remove, add, and retain—not isolated keywords.
Version two marks adjustable as superseded and adds polyester.
A bounded rewrite with history—not append-only conflict or reset.
The updated state drives the same retrieval and ranking engine.
Violating products leave; better matches enter; the list reorganizes.
The verified replay places the target at rank one.
Category and material match; the old preference is gone.
The large model sits only at the entrance.
Multi-turn consistency comes from inspectable, versioned state.
Real commerce needs more than task completion.
Sponsored supply, budgets, and relevance require a separate ad layer.
Advertisers configure keywords, bids, and budgets independently.
Those values never enter user state, ranking, or official evaluation.
A relevance floor rejects off-topic inventory—even at a higher bid.
Eligible products compete by bid times relevance; spend is recorded.
The winner enters a clearly labeled sponsored position.
Every organic product remains in exactly the same order.
Public 200: Technical Score 0.8665; private performance remains unknown.
The model and ads are product extensions outside this evaluation contract.
Users experience a conversation—not a metric dashboard.
They can revise, inspect changes, understand ranking, and continue.
The local model may polish replies, but cannot alter facts or ranking.
From vague intent to explainable recommendations and transparent ads.