Building Preloom: AI Garment Previews for the Textile Industry
By Sprout Team · June 23, 2026 · 6 min read
Preloom is built for the textile industry, where seeing a fabric as a finished garment normally means stitching a sample and booking a photoshoot. That is slow and expensive. Preloom set out to make that preview instant. Here is how Sprout built it.
The problem
A textile brand has a fabric and an idea, but no fast way to see the result. Every design decision that used to require a physical sample and a shoot was a bottleneck: costly, slow, and hard to iterate on. Preloom wanted a founder or designer to upload a fabric photo, describe a style, and get a photoreal preview in seconds.
What we built
Sprout built Preloom in React, Python, and Next.js. A user uploads a photo of a fabric, describes the style they want, and the tool generates photoreal garment previews. It supports South Asian traditional wear and can produce full AI model lookbooks without a single photoshoot.
The hard part was the AI pipeline: taking a real fabric texture and rendering it convincingly onto a garment and a model, quickly enough to feel interactive. That took careful work on the model integration, the image handling, and the flow around it so the experience stayed simple even though the work underneath was not.
The outcome
Preloom turns a fabric photo into a finished garment preview in seconds. Brands can explore far more designs than they could when every preview meant a physical sample, and they save the cost of photoshoots along the way. It is a good example of how Sprout takes a genuinely hard AI idea and ships it as a product people can actually use. If you have an AI concept that needs building, we would love to hear about it.