1The Challenge
Luna's catalog arrived from dozens of brands with wildly inconsistent quality — missing attributes, unstructured descriptions, vendor-specific metadata, and incomplete categorization. Rule-based enrichment couldn't scale, requiring extensive manual effort from merchandising teams just to keep up with catalog growth.
2Our Solution
Built an LLM-driven Product Intelligence Platform centered on a context-aware attribution engine. The pipeline handles multi-source ingestion, normalization, and schema standardization. Advanced language models extract structured attributes (skin type, finish, coverage, materials, fit) by understanding product context rather than relying on keyword matching. Intelligent classification workflows auto-assign taxonomy levels and detect duplicates. Enriched attributes feed directly into search and recommendation systems for immediate impact.
3The Outcome
Products now onboard in minutes instead of days. High-quality structured attributes improved search relevance, filtering, and recommendation quality across the platform. Manual merchandising effort dropped significantly. The enriched product intelligence layer now serves as a reusable foundation for semantic search, conversational shopping, and AI shopping assistants.
Tech Stack
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