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TRACE Uses Agentic LLMs for Catalog Enrichment

Rohan Kumar, Steven Xu, Kyle MacDonald, Matthew Long, Bernice Chow, Mac VanRenterghem, Sudeep Das· August 24, 2026 View original

Key takeaways

  • TRACE automates product catalog enrichment using agentic LLMs and multi-source evidence.
  • It achieves high accuracy (98.2%) and significant coverage (74.7%).
  • Production deployment led to a 90.4% increase in enrichment coverage.
  • Enriched attributes boosted checkout conversion by 0.48% in online experiments.

Who benefits

E-commerceRetailSupply ChainMarketingData Management

Summary

TRACE is a new framework that employs agentic Large Language Models (LLMs) to automate product catalog attribute enrichment by triangulating multimodal evidence from various sources. It achieved 98.2% accuracy and significantly increased impression-weighted enrichment coverage in production, boosting checkout conversion.

E-commerce product catalogs are fundamental for search, discovery, and recommendations, but they often lack detailed attributes, which are crucial for both shoppers and downstream systems. Manually enriching these catalogs is not scalable due to their immense size and rapid growth. To overcome this, a novel framework called TRACE (Agentic Catalog Enrichment with Multi-source Evidence Grounding) has been developed. TRACE leverages agentic Large Language Models (LLMs) to automate the enrichment process. It features a "ScoutAgent" that gathers and triangulates multimodal evidence from diverse sources, including merchant catalogs, syndicated feeds, and identity-matched web searches. This agent proposes candidate attribute values along with supporting evidence. A "JudgeAgent" then verifies these proposed values against their evidence, deciding whether to publish them directly or route them for human review. In offline human evaluations, TRACE demonstrated impressive accuracy, with 98.2% of its proposed attribute values being correct, achieving 74.7% attribute coverage. When deployed in a production environment on an industry-scale catalog, TRACE led to a 90.4% increase in impression-weighted enrichment coverage across four business verticals. An online experiment further confirmed its business impact, showing a 0.48% increase in checkout conversion rates due to the enriched attributes being surfaced on product detail pages.

Why it matters

For e-commerce and retail professionals, automated, accurate catalog enrichment directly translates to improved product discoverability, better customer experience, and measurable increases in conversion rates and sales.

How to implement this in your domain

  1. 1Assess the current completeness and accuracy of product catalog attributes.
  2. 2Pilot TRACE or similar agentic LLM frameworks for automated catalog enrichment in a specific product category.
  3. 3Integrate multi-source evidence grounding to ensure high accuracy and reduce manual review.
  4. 4Monitor key e-commerce metrics like search relevance, conversion rates, and customer satisfaction post-enrichment.
  5. 5Establish a feedback loop for the JudgeAgent to continuously improve its verification process.

Original post by Rohan Kumar, Steven Xu, Kyle MacDonald, Matthew Long, Bernice Chow, Mac VanRenterghem, Sudeep Das

"arXiv:2608.20844v1 Announce Type: new Abstract: Product catalogs underpin search, discovery, and recommendation in e-commerce, yet they are often attribute-sparse: the attributes shoppers and downstream systems rely on are either buried in unstructured content such as titles and…"

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Originally posted by Rohan Kumar, Steven Xu, Kyle MacDonald, Matthew Long, Bernice Chow, Mac VanRenterghem, Sudeep Das on X · view source

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