Sold records
Each estimate starts with nearby sold comparables pulled from the archive.
Shreddy
Sold lots first. Then the estimate.
Shreddy is built around sold art records and clear image references so the result is grounded in the market, not a vague category label.
Image records
The app keeps local image references so the visual match stays easy to inspect.
Simple metadata
Titles, descriptions, prices, currency, and seller data keep the result readable.
Only the useful fields.
Image
The visual record lets the app compare the object against similar sold lots.
Text
Title and description help disambiguate similar forms and patterns.
Price
The price history keeps the output tied to actual market behavior.
No noisy input wall.
The pricing flow does not depend on long forms or extra steps. You can add detail, but you do not have to.
Not required
Long manual descriptions.
Photos are the starting point, not a checkbox list.
Not required
Category-only pricing.
The app does not rely on broad category averages.
More examples.
The archive page mixes random sold objects with compact overlays so the dataset feels active, not static.
See the archive in the app.
Browse the sold lots, then price a piece with the same evidence.