From Sales Data to Design Blueprints: How RPT-1 Enabled Our Fashion Inverse Design Engine

The Idea: What If You Could Reverse-Engineer a Best-Seller?
Fashion analytics typically works in one direction: given a product design, predict how well it will sell. We flipped the question. Given that we want a best-selling winter coat, what should it look like?

This is inverse design — and it’s a fundamentally harder question than forward prediction. Instead of scoring an existing product, you need to work backwards from “what sells well” to “what should we design next.”

We built the Fashion Trend Alchemist to explore whether SAP’s tabular foundation model RPT-1 could handle this kind of problem natively, using its in-context learning capabilities. It turns out it can — and what we learned about RPT-1’s tabular capabilities applies far beyond fashion.

 

​ The Idea: What If You Could Reverse-Engineer a Best-Seller?Fashion analytics typically works in one direction: given a product design, predict how well it will sell. We flipped the question. Given that we want a best-selling winter coat, what should it look like?This is inverse design — and it’s a fundamentally harder question than forward prediction. Instead of scoring an existing product, you need to work backwards from “what sells well” to “what should we design next.”We built the Fashion Trend Alchemist to explore whether SAP’s tabular foundation model RPT-1 could handle this kind of problem natively, using its in-context learning capabilities. It turns out it can — and what we learned about RPT-1’s tabular capabilities applies far beyond fashion.   Read More Technology Blog Posts by SAP articles 

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