Use Case

Search Indexing & Product Tagging

Large-scale eCommerce platforms continuously classify products, images, listings, and catalog updates to power search relevance, filtering, recommendations, and merchandising systems.

moco reduces the compute required for indexing and tagging pipelines, allowing eCommerce teams to process significantly more catalog data on existing infrastructure while improving index freshness, retrieval quality, and search responsiveness.

Problem

Product classification and tagging systems operate continuously as catalogs evolve. Re-indexing millions of products with deep models is computationally expensive and limits how frequently search indexes can be refreshed.

Approach

moco identifies products and assets that can be classified confidently using partial computation, reserving full model execution only for ambiguous or difficult cases.

Impact

Reduced inference cost enables more frequent indexing cycles, higher-throughput ingestion pipelines, and lower GPU utilization across search infrastructure.

Outcome

Faster and more efficient indexing improves search freshness, attribute coverage, and retrieval quality — helping customers find relevant products more quickly while reducing infrastructure spend.

Business Value Increased indexing throughput and lower GPU cost without reducing model quality