Feature: Engineer a 'Fabric Match' cross-sell engine to recommend com…

[pradeep0153]

Closes #402.

Description

This PR executes a massive upgrade to our high-ticket cross-selling strategy by engineering a sophisticated, algorithm-driven "Fabric Match" recommendation engine, completely eradicating the friction of manual catalog searching and accelerating multi-item purchases.

Changes Made

• Backend Metadata Enhancement: Upgraded the global product schema to expose a new fabric_compatibility_matrix. This relational matrix allows merchandising to mathematically define which specific fabric SKUs across different product lines (e.g., Sofas vs. Armchairs) are either exact matches or stylistically complementary (e.g., matching a "Navy Blue Velvet" sofa with a "Cream Boucle" ottoman).
• Component Engineering: Engineered a highly visible <ComplementaryPieces /> React component, strategically injected onto the Product Display Page (PDP) directly beneath the primary fabric selector.
• Dynamic Cross-Sell Orchestration: The component actively subscribes to the user's primary variant selection. When a user selects a specific fabric for the main item, the component instantly queries the fabric_compatibility_matrix. It then forces the cross-sell carousel below to dynamically re-render, strictly surfacing only those items pre-configured in the exact matching or complementary fabrics.

Impact

• Average Order Value (AOV) Multiplier: When a customer buys a $4,000 sofa, there is a very high probability they also want a matching armchair or ottoman. However, if we force them to manually hunt through the catalog and blindly guess if the "Navy" armchair matches the "Navy" sofa they just configured, we introduce massive cognitive friction and lose the cross-sell. By engineering this dynamic matching engine, we provide an "interior designer" level of service automatically. We curate the exact matching pieces for them in real-time, drastically reducing purchasing hesitation, building extreme confidence in the aesthetic coordination, and acting as a massive multiplier for our overall Average Order Value (AOV).

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