Smart Matching on a Budget: How LLMs Deduped 400K Equipment Records for One Cent

Equipment auction data suffers from persistent naming inconsistencies—the same bulldozer appears multiple times with slight variations in model descriptions. This case study demonstrates how LLM-powered deduplication, combined with intelligent data blocking strategies, can solve this challenge for just $0.01, turning chaotic data into actionable insights. The approach is particularly significant for enterprises managing large datasets where manual deduplication remains prohibitively expensive.