Large-Scale Product Data Attribution & Enrichment for E-commerce Cataloging
Overview:
A leading US retail giant partnered with us to automate and scale its e-commerce cataloging operations. The objective was to enrich and standardize product data at scale, enabling accurate classification, improved discoverability, and seamless catalog management across 20,000+ SKUs.
Approach:
- Designed structured workflows for large-scale product data enrichment and attribution
- Built comprehensive datasets to support AI-driven product identification and matching
- Integrated Human-in-the-Loop (HITL) mechanisms to enhance classification accuracy
- Focused on standardization across titles, categories, and visual assets
Execution:
- Curated detailed product attributes including titles, descriptions, images, and specification tables to enable precise AI-based matching and classification
- Sourced product images at scale via web scraping and normalized datasets for consistent product display pages
- Conducted taxonomy audits on AI-predicted categories, incorporating HITL feedback loops to correct misclassifications and improve model performance
Impact:
- Automated product metadata creation, significantly improving search optimization
- Enabled accurate and scalable title generation for better navigation and breadcrumb trails
- Improved prediction accuracy for image-based categorization
- Facilitated intelligent product kitting and enhanced catalog structuring.