Shopping Assistant App
Budget: ₹12,500 – ₹37,500 INR
I’m building a mobile shopping companion that runs smoothly on both iOS and Android and puts AI-driven, personalized recommendations at the center of the experience. Instead of siloing users into a single niche, the engine should be able to learn from—and suggest across—everyday staples like groceries, the latest fashion drops, and cutting-edge electronics alike.
Here’s the vision:
• The moment a shopper opens the app, an adaptive feed appears, ranking products by predicted relevance. Think “Netflix-style” discovery, but for anything they might want to buy.
• A lightweight onboarding flow captures preferences; from there, the model can refine suggestions continuously from taps, scroll depth, and completed purchases.
• I’d like real-time queries to remain fast, so a hybrid approach (on-device filtering with server-side deep learning—TensorFlow, PyTorch, or similar) will likely be required.
• Push notifications (opt-in) should surface timely deals the user is statistically likely to appreciate.
• Privacy is critical; nothing intrusive, and all data handling must be GDPR compliant.
Deliverables I expect:
1. Fully functional iOS & Android builds (TestFlight / APK).
2. Source code with clear setup instructions (React Native, Flutter, or native Swift/Kotlin—your call, so long as both stores are covered).
3. A modular recommendation engine with documented API endpoints.
4. Brief report outlining training data, model metrics, and how the system guards against bias.
If you’ve shipped cross-platform apps that lean heavily on recommendation systems, I’d love to see examples. Let’s create something shoppers genuinely trust to find what they want before they even know it themselves.
Here’s the vision:
• The moment a shopper opens the app, an adaptive feed appears, ranking products by predicted relevance. Think “Netflix-style” discovery, but for anything they might want to buy.
• A lightweight onboarding flow captures preferences; from there, the model can refine suggestions continuously from taps, scroll depth, and completed purchases.
• I’d like real-time queries to remain fast, so a hybrid approach (on-device filtering with server-side deep learning—TensorFlow, PyTorch, or similar) will likely be required.
• Push notifications (opt-in) should surface timely deals the user is statistically likely to appreciate.
• Privacy is critical; nothing intrusive, and all data handling must be GDPR compliant.
Deliverables I expect:
1. Fully functional iOS & Android builds (TestFlight / APK).
2. Source code with clear setup instructions (React Native, Flutter, or native Swift/Kotlin—your call, so long as both stores are covered).
3. A modular recommendation engine with documented API endpoints.
4. Brief report outlining training data, model metrics, and how the system guards against bias.
If you’ve shipped cross-platform apps that lean heavily on recommendation systems, I’d love to see examples. Let’s create something shoppers genuinely trust to find what they want before they even know it themselves.