A trend is only useful to a brand if its own customer will buy it. Trend forecasting that never looks at your sell-through is a report about somebody else’s business.
All Fashion & Apparel →
AI fashion trend forecasting uses models to read signals such as search interest, social video, marketplace listings and runway coverage, and to report which colours, silhouettes and details are rising. That answers the first question: what is the market talking about? It does not answer the second: will our customer buy it, at what price, and in what depth?
The second question lives in your own data. Sell-through by attribute, full-price sell-through versus markdown, returns reasons, and what sold out early. We build the system to put both side by side, so a rising signal arrives with the evidence of whether anything like it has ever worked for you. That pairing is what most trend analysis tools leave out.
Getting depth right has a regulatory edge now. The EU Ecodesign Regulation (EU) 2024/1781 bans large companies from destroying unsold apparel and footwear, which makes an overbuy more expensive to correct than it used to be.
We backtest. The system is run as if it were a past season, using only the signals available at the time, and its brief is compared with what actually sold. That shows which signal sources earned their place for your customer and which were noise, and it is done before anyone uses the brief for a real buy.
Attribute tagging is checked separately against a sample labelled by your merchandisers, because a wrong colour family or fit tag quietly corrupts every comparison built on it.
It counts and compares. Models tag images and text from search, social, marketplaces and runway coverage with attributes such as colour, silhouette and detail, then measure which attributes are growing and how fast. That is pattern detection, not prediction of what a particular customer will buy, which is why we pair it with a brand’s own sales history before anyone acts on it.
External signals, which are available, and your own history, which is the part that matters. At minimum: product attributes or images for past ranges, sell-through by style, and markdown and returns data. Where product attributes are inconsistent, cleaning and tagging them is the first piece of work, and it is useful on its own.
It can inform better depth decisions by showing how similar styles performed and how strong a signal really is. Whether that reduces overproduction depends on buyers acting on it and on supply lead times. We measure it by backtesting against past seasons rather than promising a figure in advance.
What we built, what stage it is at, and a link to the live thing wherever there is one.
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We backtest on one of your past seasons first, so you can see which signals were worth listening to.
30-minute technical call · no deck