I’m launching a small casual-wear line and want to add an AI layer that suggests the right pieces to each shopper in real time. I already have a basic customer data set; what I need now is a fashion designer who can: • Sketch a compact first collection of casual tops, bottoms, and light outerwear that share a coherent look and feel. • Tag each piece with the key style attributes the AI will use (fit, color palette, fabric weight, etc.). • Work with me to set up a lightweight recommendation workflow—Python or any low-code tool is fine—so the system can match a shopper’s stated preferences to the designs. I’ll provide the customer data, inspiration boards, and access to an existing Google Colab notebook that handles simple similarity matching. You’ll provide: 1. Rough line sheets (hand-drawn or digital) for 6–8 items. 2. A CSV or JSON file listing the agreed-upon attributes for each item. 3. A brief walkthrough (screen share or recorded video) showing the recommendation script in action. Keep things lean; at this stage I’m after a proof of concept rather than a polished lookbook. If the test succeeds there’s room to extend into fuller tech packs, fabric sourcing, and trend analytics.