SQL JOINs and pandas merges fail on color variants, promotional naming, translated descriptions, and spec formatting differences. AI embeddings and LLMs understand that 'Midnight' means black and 'Violet' means purple. Here's why traditional tools hit a ceiling and how hybrid pipelines break through it.
Product matching accuracy depends on attribute richness. Sparse product data produces weak matches. Here's how to annotate product catalogs — manually and with AI — to make matching reliable.
Product images contain brand names, model numbers, colors, and condition details that aren't in your spreadsheet. AI attribute extraction turns visual information into structured fields ready for matching.
Scraped marketplace data is full of duplicate listings — different sellers, different titles, same underlying product. AI-powered deduplication collapses these into canonical records for reliable analytics and catalog management.
Different suppliers describe the same products differently. Learn how to match catalogs by name, SKU, specs, and AI embeddings to build a unified product taxonomy.
Scraped product data from competitor sites uses different naming conventions, SKU systems, and category structures. AI-powered matching connects equivalent products across sources to build real-time competitive pricing intelligence.