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.
LLMs like Gemini, ChatGPT, and Claude can read PDFs, understand tables, extract text from images, and interpret graphs. Here's how multimodal AI enables granular PDF document classification — and where it still needs help.
ChatGPT, Gemini, Claude, and other LLMs can absolutely do fuzzy matching. They're just not built for it. Here's what works, what doesn't, and when you need a dedicated matching tool.
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.
Thousands of images sitting in folders with meaningless filenames. AI image categorization extracts structured labels, categories, and descriptions — turning visual assets into matchable data.
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.
A comparison of rule-based and AI embedding approaches to record matching — strengths, weaknesses, costs, and why the best systems use both.
PDFs contain structured information trapped in unstructured format. AI extraction turns invoices, contracts, reports, and spec sheets into matchable data rows — no manual data entry required.
Deterministic matching compares exact values. Probabilistic matching uses statistics, embeddings, and LLMs to find likely matches. Here's how each works, where each fails, and how combining them produces faster, cheaper, more accurate results.