TRACE: Agentic Catalog Enrichment with Multi-source Evidence Grounding

TRACE is a new framework that uses agentic Large Language Models to automatically enrich e-commerce product catalogs with missing or buried attributes. It employs a ScoutAgent to gather multimodal evidence from merchant catalogs, syndicated feeds, and web search, and a JudgeAgent to verify and publish the proposed attribute values. In offline evaluation, TRACE achieved 98.2% accuracy with 74.7% coverage, and in production it increased enrichment coverage by 90.4% and boosted checkout conversion by 0.48%.

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• llms
• agents
• multimodal