An AI cataloging methodology where the system identifies what an item actually IS from visual evidence before considering brand names, labels, or text visible on the item. This prevents misidentification errors where AI assumes an item is genuine based solely on visible branding.
How It Works in Practice
Standard AI vision tools read a "Tiffany" label and describe the item as authentic Tiffany. Identify-first protocol analyzes the construction, materials, proportions, and manufacturing quality first — then cross-references visible branding as supporting (or contradicting) evidence. This is critical for auction cataloging where reproductions, fakes, and misattributed items are common. The protocol uses hedging language ('marked Tiffany,' 'appears to be,' 'in the style of') when visual evidence doesn't fully support a brand attribution.
Frequently Asked Questions
Why does identify-first matter for AI cataloging?
How does identify-first prevent misidentification?
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