Our Flutter-based e-commerce platform is ready for a major upgrade: a Virtual Try-On layer that lets shoppers see garments on their own photos with photorealistic accuracy. The goal is to bolt this capability onto the existing stack rather than build a separate app, so tight integration with our current Flutter front end and backend services is essential. Key success factors • Realistic rendering of clothing on users — minimal artefacts, correct cloth drape, accurate lighting and skin-tone preservation. • Support for a broad catalogue (tops, dresses, outerwear, accessories) without retraining for every new SKU. Scope of work – Design or adapt an AI/ML pipeline for body segmentation, pose estimation and garment warping. – Implement the inference service (Python/TensorFlow, PyTorch or similar) behind a clean REST or gRPC API. – Create Flutter widgets that call the service, handle image upload, and display the composite result in under three seconds on a mid-range device. – Provide a simple admin routine so new product images can be added in bulk and automatically prepared for try-on. – Work with our team to slot the module into production, respecting our existing auth and analytics. Acceptance criteria 1. A user uploads a front-facing photo, picks any in-stock item, and receives a try-on preview that scores at least 4.0/5 in our internal realism benchmark. 2. Latency (upload → rendered image) ≤3 s on a 4G connection. 3. New SKUs appear in the VTON carousel within one hour of being pushed to the catalogue, with no manual image editing. 4. Complete build/run documentation plus a one-page inference cost estimate for scaling to 10 k daily sessions. All source code and model weights produced during the project remain our property.