Rawshot.ai

I kept seeing the same pain point across fashion brands: producing product imagery is slow, expensive, and doesn't scale. A single photoshoot means coordinating models, photographers, studios, and stylists—often weeks of lead time and thousands of euros for a handful of shots. For brands launching dozens of new SKUs monthly, it's a bottleneck that kills momentum. The bigger issue? Most AI "solutions" I tested were just glorified face-swaps. They'd paste clothing onto the same handful of preset bodies, and the results looked exactly like that—fake, repetitive, and unusable for any brand that cares about authenticity. I wanted to build something that generates each image from scratch. Real variation in models, poses, lighting, and environments. Results that actually look like fashion photography, not AI templates. Early on, I obsessed over prompt engineering—spending months mapping how different camera systems render skin tones, fabric textures, and depth differently. That became a core differentiator: users can match their existing brand aesthetic instead of getting generic "AI look" outputs. I also learned that fashion teams think in very specific terms—body types, styling details, editorial vs. e-commerce contexts. So I built a system around that language rather than forcing users to become prompt engineers themselves. The goal from day one hasn't changed: make AI photography that brands actually use, not just demo.