We implemented an AI-driven listing enrichment engine that reads raw catalog data at scale and turns it into structured, searchable metadata. The system automatically generates tags, categories, and short abstracts so buyers can discover the right item faster, even when source descriptions are messy or sparse.
Using NLP, entity extraction, and semantic clustering, the optimizer standardizes attributes across 80k+ items and feeds them into search, filters, and recommendation modules. Moderation rules and QA sampling ensure the system stays safe, on-brand, and accurate over time.
The AI Listing Optimizer reduced manual tagging effort, improved search/filter precision, and made catalog pages easier to scan with clear, concise highlights.
Large catalogs suffered from:
We introduced an NLP-driven enrichment engine:
The AI Listing Optimizer delivered:
The optimizer runs as a FastAPI-based service that reads listings from PostgreSQL, enriches them with NLP, and writes back structured metadata and summaries ready for search and UI consumption.
Handles tens of thousands of listings per run, with incremental updates for new and edited items.
Outputs structured tags and fields optimized for semantic search, filters, and ranking signals.
Flags or blocks listings that contain prohibited terms, off-brand claims, or other rule violations.
Random and risk-based samples are routed to human reviewers, and their decisions feed back into future model and rule updates.
The pipeline is built to be extensible: new languages, new verticals, or new attribute schemas can be added with minimal changes to the core architecture.
