ZYNG AI is a visual operating system for the business domain, designed to use automation and AI technology to rapidly transform raw photographic material into commercial-grade image assets suitable for all channels at scale. It is positioned as an enterprise's visual infrastructure, emphasizing API-first design, auditability, and scalability.
Main capabilities
- Smart Standardization: body- and margin-aware cropping, support for omni-channel resizing, content- and context-aware cropping/generation, compatible with all aspect ratios and image formats.
- Vision QC: automatically detects intellectual property issues, blur, hallucinated text, incorrect cropping, and more at image ingestion, and blocks undesirable assets from entering business systems according to custom rules.
- Real-Time Virtual Try-On: users upload one selfie and the target garment is mapped onto the user within 2 seconds, matching folds, shadows, and pose.
- Generative Styling: mix and match garments to create multiple looks without reshoots, enriching the catalog for different target groups.
- Infinite Combinations: generate new looks by mixing and matching garments without additional shooting.
- Visual Similarity: vector-based garment retrieval; upload a reference image to match style, fabric, and cut across the entire catalog.
- Image Search: users can search the entire catalog by taking a photo with their phone or uploading an inspiration image.
- Other capabilities: including changing the base model while preserving physical fit, fabric drape, and lighting; automatically isolating the subject and replacing the background; dynamically changing background colors based on SKU data; extracting color profiles and physical texture samples from source garments; smart upscaling, outpainting, and recomposition; automatically detecting and isolating body parts (such as shoes, bags, collars) to generate macro close-ups.
How to use ZYNG OS provides automated pipelines; users can link any steps into a single pipeline and branch-route based on the content of each image. For example, full-length model photos are cropped with a 5% margin and 3:4 ratio, while still-life and packaging photos use a 10% margin and 2:3 ratio. Users can also access individual visual intelligence endpoints directly via API and build custom workflows in a sandbox. After uploading assets, users can view the transformation results in real time.
Applicable scenarios Suitable for the e-commerce and fashion industries, especially teams that need to process product images at scale. Case examples include: India's largest fashion e-commerce platform processedmore than 450,000 images and 80,000+ SKUs in one day through the standardization API; a global DAM provider embedded QC checkpoints into its ingestion layer to automatically scan millions of images; an Australian retail brand used the mix-and-match feature to generate thousands of new outfit combinations from existing catalog assets.
Limitations and pricing The materials do not explicitly mention specific platform limitations, usage thresholds, or system requirements. In terms of pricing, a credit-based model is used, with no subscription fee and billing by credits used (1 credit = 1 output image); the larger the processing volume, the lower the rate. Different quality tiers are offered: high-quality editing at standard resolution, high-quality editing at premium resolution, and ultra-high quality for professional content. Specific price figures are not provided in the materials.
