AI Virtual Stylist Platform Development
Budget: ₹1,500 – ₹12,500 INR
I want to launch a cross-platform virtual stylist that feels as effortless as chatting with a fashion-savvy friend. The product must run smoothly on both mobile and web, sharing a single backend so users can switch devices without losing their saved looks.
Core flow
• Users create a profile by either filling out a short form (height, weight, skin tone, style preferences, budget range) or uploading a clear photo.
• The system passes these inputs to an AI layer (OpenAI or a comparable model) that returns a complete head-to-toe outfit: top, bottom, footwear and suggested accessories.
• Recommendations must respect three price tiers—low, mid and premium—and adapt to at least Casual, Date and Party occasions from day one.
• Each generated look can be saved to a personal lookbook for later reference; outfits should be re-rendered instantly when a saved item is tapped.
What I count on you to deliver
• Responsive web app (React, Vue or similar) plus a lightweight mobile build (React Native, Flutter or similar) sharing a common component library for a consistent UI.
• Clean, minimalist interface that loads quickly even on mid-range phones (Lighthouse score 90+ for performance).
• AI recommendation micro-service that I can switch between OpenAI, Google Vertex, etc. via environment config.
• Secure backend (Node/Express, Django or comparable) with endpoints for profile data, image handling, outfit generation history and lookbook storage.
• Admin dashboard where I can review anonymised usage stats and tweak occasion lists, budget thresholds and affiliate links for items.
• Deployment scripts and basic CI so I can push new models or UI tweaks without downtime.
Acceptance criteria
1. A new user can complete sign-up, choose “Casual” and get a full outfit in under five seconds on 4G.
2. Switching the same account between phone and desktop shows the identical saved lookbook.
3. Outfits differ clearly across low, mid and premium budgets when the same inputs are reused.
4. Photo upload route works for JPG/PNG up to 5 MB, masks background, infers body type and skin tone adequately for colour matching.
5. No PII is visible in network logs; OWASP top-10 scan reports zero high-severity findings.
Hand-off items include source code, API keys in .env.example, a short read-me on retraining prompts and a one-hour walkthrough call.
Core flow
• Users create a profile by either filling out a short form (height, weight, skin tone, style preferences, budget range) or uploading a clear photo.
• The system passes these inputs to an AI layer (OpenAI or a comparable model) that returns a complete head-to-toe outfit: top, bottom, footwear and suggested accessories.
• Recommendations must respect three price tiers—low, mid and premium—and adapt to at least Casual, Date and Party occasions from day one.
• Each generated look can be saved to a personal lookbook for later reference; outfits should be re-rendered instantly when a saved item is tapped.
What I count on you to deliver
• Responsive web app (React, Vue or similar) plus a lightweight mobile build (React Native, Flutter or similar) sharing a common component library for a consistent UI.
• Clean, minimalist interface that loads quickly even on mid-range phones (Lighthouse score 90+ for performance).
• AI recommendation micro-service that I can switch between OpenAI, Google Vertex, etc. via environment config.
• Secure backend (Node/Express, Django or comparable) with endpoints for profile data, image handling, outfit generation history and lookbook storage.
• Admin dashboard where I can review anonymised usage stats and tweak occasion lists, budget thresholds and affiliate links for items.
• Deployment scripts and basic CI so I can push new models or UI tweaks without downtime.
Acceptance criteria
1. A new user can complete sign-up, choose “Casual” and get a full outfit in under five seconds on 4G.
2. Switching the same account between phone and desktop shows the identical saved lookbook.
3. Outfits differ clearly across low, mid and premium budgets when the same inputs are reused.
4. Photo upload route works for JPG/PNG up to 5 MB, masks background, infers body type and skin tone adequately for colour matching.
5. No PII is visible in network logs; OWASP top-10 scan reports zero high-severity findings.
Hand-off items include source code, API keys in .env.example, a short read-me on retraining prompts and a one-hour walkthrough call.
Related categories:
Django
Web Development
React Native
Flutter
AI Chatbot
AI Model Development
AI Content Creation
AI Development