Upload a photo, and Snagfit shows you each garment it found plus somewhere to buy it. Sometimes that is the exact piece. Often it is something close, and that is worth explaining rather than glossing over.
What the matching actually does
Snagfit's AI reads the photo and describes each garment: what it is, its color, its cut, its material. Those descriptions are then matched against current shopping listings. So the search is running on what the garment is, not on the picture itself.
That is a different thing from a reverse image search. A reverse image search looks for other pages that used the same picture. It can tell you where the photo appeared; it cannot tell you what the person in it is wearing. Snagfit starts from the clothes.
Why the exact piece often is not there
Three ordinary reasons, none of them a failure of the matching:
It sold out. Fast fashion runs are short by design. A jacket from a video posted two months ago may have had a six-week life on the site that sold it.
It is resale or vintage. A one-of-one item on a resale platform is gone the moment somebody buys it, and a genuinely vintage piece never had a listing to begin with.
It was never sold online. Plenty of clothing is sold in shops and nowhere else, or was made by a brand that has since closed.
In all three cases the honest result is the closest thing you can actually buy, plus alternatives at different price points, plus pieces that go with it. An app that insisted it had found the original every time would just be wrong more often.
What to do with a close match
Treat it as a starting point rather than a verdict. The color, cut and material are what the AI matched on, so a result that shares all three is usually wearable in the same way the original was, even when the label is different.
Check price, size and availability on the retailer's own page before you buy. Tapping through opens that shop directly, and everything after that point, including whether the thing is actually in stock in your size, happens on their site rather than here.