Shopping Assistant App

@Microlent

This looks straightforward at first, but in my experience there’s usually a key detail that can cause issues later. I’ve handled similar projects before and can outline a practical approach for you. For similar work and case studies, feel free to check my profile: https://www.freelancer.com/u/Microlent Let me know if you I'd like me to walk you through the plan. – Rajesh Rolen

@joomextensions

Hi, I've read your vision for the Shopping Assistant App — this is an exciting project. Building a personalized AI-driven shopping companion is exactly my strength. My approach: ? Mobile App (Flutter — iOS & Android): - Adaptive product feed (Netflix-style discovery) - Lightweight onboarding + learning from user behavior - Push notifications for personalized deals (GDPR compliant) ? Recommendation Engine: - Hybrid: on-device filtering + server-side ML (TensorFlow/PyTorch) - Collaborative filtering + content-based model - Modular REST API endpoints - Optimized for real-time speed ? Privacy: GDPR-compliant, no intrusive data collection, bias mitigation documented. Deliverables: - iOS + Android builds (TestFlight / APK) - Source code with setup instructions - Recommendation engine with documented API endpoints - Report: training data, model metrics, bias safeguards I have experience with ML-integrated mobile apps. Happy to discuss training data and product catalogue. Regards, Harsh Thoriya

@rahuliitm69

Hi there, I can develop your AI-powered shopping companion for both iOS and Android with adaptive recommendation feeds, personalized product discovery, behavioral learning, smart push notifications, and scalable AI recommendation architecture across groceries, fashion, electronics, and other shopping categories. Once you share the detailed feature scope, preferred recommendation flow, and expected user scale, I can structure the complete application with a modular backend recommendation engine, including GDPR-compliant data handling, documented APIs, and production-ready iOS & Android builds. I would request to connect on a quick call or chat to discuss the recommendation strategy, AI model expectations, timeline, and deployment flow in detail. Thanks, Rahul A.

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@mrresearcher99

Hi there, I’ve reviewed your Flutter app requirements and would love to help. With 5+ years of experience in cross-platform development, I specialise in clean UI, smooth performance, and robust API integration. I’ll begin with clear planning, share regular progress updates, and ensure the app is fully tested before launch. Let’s connect to discuss your vision — I’m ready to get started! Best, Bhargav Flutter Developer | Android & iOS Expert

@Srashtasoft

Hi, I can build a scalable AI-powered shopping companion for iOS and Android with a personalized recommendation engine that delivers real-time “Netflix-style” product discovery across groceries, fashion, electronics, and more. I have experience developing cross-platform mobile apps with AI/ML integration, recommendation systems, behavioral analytics, and cloud-based architectures. The app will intelligently learn from user interactions such as clicks, scroll behavior, searches, and purchases to continuously improve recommendation accuracy while maintaining fast performance and strong privacy standards. My proposed solution: • Flutter or React Native app for unified iOS & Android deployment • AI recommendation engine using TensorFlow/PyTorch with modular APIs • Hybrid architecture combining on-device personalization with server-side deep learning • Adaptive product feed with ranking based on behavioral predictions • Smart onboarding and continuous learning pipeline • Push notification engine for personalized deals and engagement • GDPR-compliant data handling, encryption, and privacy-focused tracking I can also help design a scalable cloud infrastructure using Firebase, AWS, or GCP to support future AI expansion and analytics. I would be happy to share relevant AI-powered app and recommendation system experience during discussion. Best regards, Kumar

@point2solutions

--- AI-Powered Cross-Platform Shopping Companion App --- Hi, I can develop a scalable AI-driven shopping companion app for iOS & Android with personalized product discovery, real-time recommendations, and privacy-focused architecture. Key features: AI recommendation engine, adaptive product feed, user behavior tracking, onboarding personalization, push notifications, cross-category recommendations, fast real-time queries, and GDPR-compliant data handling. Lets connect... Thanks

@pinkeegupta

Hi I will be able to help you. Please message me so that we will have detail technical discussion. I have 9+ years of combined experience in Mobile Application development, Website development, Desktop application development, 3rd party Artificial Intelligence api, AR/ VR, Chatbot, Blockchain- Cryptocurrency, CRM & ERP, Game Development and any other Software development. I am having expertise in Native on Android Java, kotlin and IOS Swift, and For Hybrid Cross platform on Flutter Dart & React- Native, and for web and backend on react js and node js, Python Django and php CodeIgniter mvc and Laravel. Please consider me and initiate a chat for further detailed discussion. Regards, Anju Logical Soft Tech Pvt Ltd, Indore(M.P)

@Rajoriadares

Hello Client, Greeting of the day!!!! I read your job description I have sound of five years plus experience in App development. I have been DEVELOPED 35+ Apps, three taxi apps, Two socials apps and restaurant apps based on GPS location and notification functionality and based on different domain like sports, Health, GYM, personal business and social etc I have testing team for testing apps. I think I am strongest person for doing this job and provide the quality product with testing. I am always available for your support. Thanks Sr. Mobile Developer 

@vipinjain1213

Hey, I like that this is focused on recommendations across categories instead of limiting users to just fashion or groceries. The difficult part here will really be making the recommendation engine feel fast and relevant without overwhelming users with random suggestions. We can build this in Flutter with: • personalized recommendation feed • behavior-based learning (clicks, scrolls, purchases) • AI/ML recommendation engine integration • realtime product ranking • push notification workflows • scalable backend APIs • GDPR-conscious data handling Quick question — will products come from your own inventory/database initially or aggregate from external ecommerce APIs/affiliate sources as well? Can start immediately.

@TechizeBuilder

Hi, I came across your project "Shopping Assistant App" and I'm confident I can help you with it. About Me: I'm a full stack developer and agency owner with over 8+ years of experience in Mobile App Development, React Native, Flutter, Android, iOS Development. , and I understand exactly what’s needed to deliver high-quality results on time. Why Choose Me? - ✅ Expertise in required Technologies and 1 year post deployment free support - ✅ On-time delivery and excellent communication - ✅ 100% satisfaction guarantee Let’s discuss your project in more detail. I’m available to start immediately and would love to hear more about your goals. Looking forward to working with you! Best regards, Deepak

@abhishek060

Hello, This is a strong fit for my experience building cross-platform mobile apps with recommendation-driven architectures and scalable backend systems. I can build: iOS + Android app (Flutter or React Native) Adaptive home feed (Netflix-style product discovery UI) AI-based recommendation engine (hybrid server + client filtering) Lightweight onboarding preference system Real-time personalization based on user behavior (clicks, scrolls, purchases) Push notifications for personalized deals (opt-in) GDPR-compliant data handling + privacy-safe tracking layer REST/GraphQL API for recommendation service Recommended approach: Mobile app handles fast UI filtering + caching Backend runs recommendation logic (Python/TensorFlow or Node + ML service) Event tracking pipeline for user behavior signals Modular API so recommendation engine can evolve without app changes A few quick questions: Do you already have product data available, or should it be ingested from external APIs? Do you prefer rule-based + ML hybrid first, or full ML model from day one? Any existing backend, or is this greenfield? I can share similar app architecture examples (personalized feeds + e-commerce recommendation systems) once we connect. Thanks, Abhishek

@NEHABHAT92

With nearly a decade of experience in web and mobile development, I, Neha, am confident that my team and I possess the skills and know-how to bring your vision to life. Building cross-platform shopping apps is an area where we have extensive experience, particularly developing recommendation systems that prioritize personalization without compromising on speed. Our ability to leverage frameworks such as React Native and Flutter will ensure seamless performance across iOS and Android devices. One key value of our work is ensuring maximum user trust through GDPR-compliant data handling. My team understands the importance of maintaining user privacy while providing recommendation services efficiently. We have also previously delivered projects with a similar hybrid approach which balances on-device filtering with server-side deep learning using robust technologies like TensorFlow and PyTorch. In conclusion, our adaptability, extensive skillset in recommendation system development, previous experience in creating fully functional cross-platform apps, and more make us the perfect fit for your project. We are committed to delivering a well-documented modular recommendation engine complete with training data reports, model metrics, and a robust system to guard against bias. Let's turn your idea into reality!

@iimtpradeepdash

Hi! The bit about real-time, Netflix-style discovery across very different categories is smart—that’s tough to pull off without users feeling boxed in or getting junk suggestions. Most shopping apps stick to one vertical because that’s easier for the data. I shipped a cross-platform app last year that relied on user behavior to drive recommendations—tracking taps, scrolls, and purchase events like you described. That one had a modular feed with on-device filtering (Flutter, Node.js backend), so I know where the snags usually are. For yours, I’d use Flutter so one codebase covers both iOS and Android. I’d split the engine: a small, privacy-focused filter on the phone, and a deeper model (Python, Node, or Go) for real ranking and bias checks. GDPR and explainability would get real attention; users should feel safe, not watched. Quick one—what data (if any) can we use at signup, or should the AI start cold every time? That changes onboarding flow. Happy to sketch out a 1-page plan on how I’d architect this. No cost, no commitment. You can also see similar work at work.techindika.com. — Pradeep

@chiragardeshna

Hi there, Strong alignment with this project comes from experience building AI-powered mobile applications, recommendation-driven platforms, and scalable cross-platform systems with personalized feeds, behavioral analytics, and secure cloud architecture. Clear understanding of the requirement to develop a shopping assistant app with adaptive recommendation feeds, onboarding-based preference learning, AI-powered product ranking, push-notification personalization, and GDPR-compliant data workflows across iOS and Android. Hands-on expertise with Flutter, React Native, TensorFlow, PyTorch, Node.js, PostgreSQL, recommendation engine architecture, behavioral analytics pipelines, and scalable API systems ensures fast user experiences and maintainable AI infrastructure. Risk is minimized through modular AI architecture, hybrid on-device/server-side recommendation workflows, privacy-focused data handling, scalable notification systems, optimized feed ranking pipelines, and detailed API/model documentation support. Available to start immediately happy to discuss recommendation-engine strategy, model training approach, milestone planning, and similar AI-driven mobile application experience. Recent work: https://www.freelancer.com/u/chiragardeshna Regards Chirag

@otusonellp

As a seasoned IT and digital product development firm, OTUSONE is more than equipped to bring your vision of a cross-platform Shopping Assistant App to life. Our mobile app development expertise spans Android, Flutter, iPhone, and React Native - whatever technology suits the unique demands of your project. We've built similar apps before, empowering businesses to offer rich personalized experiences. At OTUSONE, we always put user preferences at the heart of our projects. Your imagined adaptive feed with Netflix-style discovery very well aligns with our client-centric approach. We prioritize building modular applications that can learn from each user's interactions, just as you envision for your app's recommendation engine. Finally, we recognize the high-stakes nature of your project when it comes to data privacy and GDPR compliance. Our thorough understanding of this allows us to build robust and secure systems while handling data. Not only will we deliver fully functional iOS & Android builds with comprehensive source code and API documentation as you've requested, but also a transparent report outlining training data and model metrics. Let's work together to create a shopping companion shopper's trust to find what they want before they even realize it themselves!

@shibbuquadir

With our extensive skills in Android, iOS, and mobile app development, particularly with React Native, we are well-equipped to deliver your vision for the Shopping Assistant App. We have a solid understanding of front-end and back-end technologies like React and Node.js so ensuring your app is both visually appealing yet robust and scalable is within our wheelhouse. Moreover, at Paper Perfect, creating user-friendly applications is imperative to us. Your app's onboarding flow and personalized feed will be designed to maximize user engagement while minimizing any intrusive data handling that could compromise its GDPR compliance. We appreciate the importance of protecting your users' privacy while still delivering cutting-edge technology solutions. Finally, one of our strengths lies in creating apps that integrate an AI-driven recommendation system - a key component of your project description. Our deep knowledge in coding APIs and using frameworks like Flutter will enable us to build the modular recommendation engine you need while maintaining real-time query speed. Let's work together to bring this vision to life: a shopping companion that's fast, smart, but above all, trustworthy and fitting - just as you intend it to be!

@KrisuTechkul

Hi, we are a team of 20+ AI/ML Engineers based in Delhi - have completed 300+ projects with 100% client satisfaction & long term association. I As an AI-driven software developer, I have built my career around creating solutions similar to the one you are seeking. I have successfully employed machine learning and deep learning algorithms to develop robust predictive models and personalized recommendation systems. My extensive experience with React Native will ensure seamless compatibility with iOS and Android and guarantee an efficient, user-friendly shopping app. What sets me apart from other contenders is my deep understanding of algorithms like TensorFlow and PyTorch that you've specified for this project. I have used these extensively in the past, ensuring quick responses through the on-device filtering with server-side deep learning method you anticipate for your real-time queries. This hybrid approach also guarantees privacy - an aspect that is absolutely essential to this project as evidenced by your emphasis on GDPR compliance.

@arshig8

Hello, Your app vision combines two critical challenges—building a seamless cross-platform shopping experience and delivering fast, intelligent recommendations users can genuinely trust. That balance between performance, personalization, and privacy is exactly where my expertise fits. I can develop your mobile shopping companion using Flutter or React Native (based on your scalability preference), paired with a modular AI recommendation engine powered by TensorFlow or PyTorch. What I will deliver: • High-performance iOS and Android apps with a personalized “discovery feed” built for smooth user engagement. • Smart onboarding and continuous learning from user behavior (clicks, scroll depth, purchases). • Hybrid recommendation architecture combining on-device logic with server-side deep learning for fast response times. • Intelligent push notifications for highly relevant offers and deals. • GDPR-compliant data handling with privacy-first architecture. • Fully documented source code, setup guide, API documentation, and deployment support. • ML documentation covering training datasets, recommendation metrics, and bias mitigation strategy. I have strong experience in cross-platform development, AI-integrated applications, and scalable backend systems, and I’m ready to help turn this into a production-ready product. Best regards.