A UAE-based company has opened self-serve access to neofashion.ai, an AI platform built to generate product photography, video and campaign assets for fashion brands. Developed by Fipera and launched from Ajman, the service already moved through a private beta in which three global apparel labels used it for live production work, including on-model shots, e-commerce packshots and short campaign videos created from their existing product images.
The system is structured around private workspaces. Each brand uploads its own product references, preferred model looks and style guidelines, then uses those materials to condition every new generation. The company states that one customer’s images and settings remain isolated and are not shared or used to train outputs for another. Modules cover the common production needs of fashion retailers: on-model photography, sketch-to-photo conversion, flat lays and cut-outs, studio-style packshots, campaign video, and batch tools for seasonal collections. A native Shopify integration allows merchants to generate and attach imagery directly from their store catalogs.
Pricing is credit-based with a free tier, removing the need for sales calls or volume commitments. Independent boutiques can begin the same day they register; larger retailers move to paid plans and enterprise customers can add fine-tuning controls, API access and formal service agreements. The platform was designed and engineered in the UAE and is presented as part of the country’s broader effort, under the National AI Strategy 2031, to develop exportable AI tools rather than simply adopt them. Fipera is now speaking with strategic and institutional investors about expanding enterprise reach across the Gulf, Europe and Asia.
AI tools for fashion imagery have multiplied in recent years, promising lower costs and faster turnaround than traditional studio shoots. Consistency of fabric drape, color accuracy and brand-specific casting remains a practical difficulty for many systems, and neofashion.ai’s emphasis on private reference conditioning is an attempt to address that gap. Whether the outputs hold up under close retail scrutiny, especially for high-end or highly textured garments, will determine how widely the service is adopted beyond early users. Cost savings are real for catalog-scale work, yet they come with the usual trade-offs of generative systems: reduced control over every detail and the need for human review before final publication.
Founder Ali Alkan has framed the problem as one of consistency and expense rather than creativity. The commercial launch will test whether a self-serve, reference-driven approach can deliver reliable results for both small labels and larger apparel groups at the speed and price point the market now expects.
