From AI scraping to AI matching — building the data pipeline for competitive analysis
AI scraping collects cleaner data than rule-based crawlers. AI matching processes it beyond what string comparisons allow. Here is how the full stack works.
Extracting matchable attributes from product images: beyond basic categorization
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.
The CPG translation tax: why demand planning breaks at the data seams
Most CPG forecasting failures aren't analytics problems — they're harmonization problems. Internal SKUs, syndicated codes, and retailer GTINs speak different languages, and the cost of translating between them is quietly destroying forecast accuracy.
Matching with images and attributes: the complete file-based matching workflow
Text matching misses products that look identical but are described differently. File-based matching adds images, PDFs, and documents to the comparison — combining visual and textual signals for accurate results.
Product data annotation: why your catalog needs more attributes before matching works
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.
How to match product catalogs from different suppliers
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.