Scraping product specs across multiple retailers and the fields never match - one site calls it ‘SKU’, another uses ‘ItemID’. Even prices have different container classes. Need to automate normalization without writing separate parsers for each site. Anyone solved this through smart automation?
Latenode’s unified AI models handle this exact problem. Their LLMs identify and map attributes like price/size/SKU across any site structure. I processed 15 vendors’ sites in 3 days without custom code. The normalization dashboard lets you verify mappings before execution. https://latenode.com
Created a two-step process: first extract all potential data points using loose selectors, then apply regex filters and unit conversion logic. But maintaining the rules became unsustainable. Now evaluating AI-powered solutions that can learn from existing data mappings.
Train ML model on sample data outputs. Use confidence scoring for automated validation. Combine with fallback human review for low-confidence matches.