Personalized Gift Shopping with AI & Amazon API Integration
Industries
Retail
Services
Fullstack Development, Cloud Deployment, CI/CD Automation, AI-Powered Recommendation Systems
Tools We used
Python, Flask, Docker, WordPress, LangChain, ChatGPT-4, Redis, PostgreSQL, Amazon API (SDK), Asyncio, Bitbucket Pipelines
30%
Lower Operational Costs
40%
Faster Service Delivery
100%
HIPAA-Compliant Cloud
Challenges We Faced
- Limited Personalization in Online Gift Shopping: Traditional e-commerce experiences lacked intelligent, human-like guidance, leaving customers overwhelmed with product choices.
- Inefficient Recommendation Accuracy: Without contextual awareness, AI systems often repeated suggestions or failed to adapt to user preferences across sessions.
- Amazon API Limitations: Strict request thresholds, incomplete responses, and inconsistent availability data created challenges in delivering real-time product suggestions.
- User Engagement Gaps: Static chatbot designs didn’t replicate natural conversations, reducing trust and engagement in the buying process.
The client required an AI-driven, chat-based solution that could personalize recommendations, handle API constraints, and deliver a seamless shopping experience.
WhizzBridge’s Solution
- Conversational AI Engine: Integrated ChatGPT-4 with LangChain for context-aware dialogues, engineered prompts to ask clarifying questions, and tuned responses with emojis and human-like tone.
- Amazon API Integration: Connected with Amazon SDK for real-time product search, reranking results by reviews and pricing, while caching responses in Redis to optimize API limits.
- Persistent User Memory: Designed session tracking with PostgreSQL and LangChain Buffer Memory to maintain context across conversations, enabling long-term personalization.
- Interactive Chat Interface: Built a WordPress-powered front-end with sidebar features for chat history, liked products, and editor’s choice collections.
- Performance & Reliability Enhancements: Implemented Asyncio for concurrent queries, Redis caching for faster response times, and Bitbucket pipelines for automated deployments.
- Scalable Cloud Infrastructure: Deployed on Docker with CI/CD workflows, secure session management, and backup-enabled PostgreSQL databases.
Results We Achieved
- Enhanced Shopping Experience: Delivered a friendly, AI-driven chat system that guides users to the right gift through natural conversation.
- Improved Recommendation Accuracy: Eliminated repetitive suggestions and provided personalized, session-aware product recommendations.
- Optimized System Performance: Reduced response times with Asyncio + Redis and maintained API compliance while ensuring real-time availability checks.
- Stronger Customer Retention: Contextual personalization and saved product collections fostered higher engagement and repeat usage.
The platform now benefits from a production-ready AI recommender that scales with user demand, increases purchase confidence, and modernizes the online gift-shopping journey.
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