AI-Driven Expert Content Processing and Personalized Product Recommendation Platform

The client faces challenges with processing vast amounts of expert content, such as videos and podcasts, to extract valuable insights. This content is critical for informing personalized product recommendations but is difficult to process at scale, leading to suboptimal user experience and limited recommendation accuracy. Additionally, integrating extensive product data from multiple sources and ensuring seamless, reliable recommendations is a complex operational hurdle.

A mid to large-sized online retail platform aiming to enhance its personalized shopping experience by integrating expert knowledge and AI-driven recommendations.

• Implement an automated pipeline for transcribing and annotating expert audio/video content to create a rich knowledge base.
• Develop intelligent annotation features to accurately identify speakers and attribute advice to the correct experts.
• Integrate large-scale product data from numerous vendors, including millions of SKUs, into the recommendation engine.
• Enhance the platform's ability to deliver personalized, expert-informed product recommendations based on user queries.
• Manage large datasets effectively to ensure scalable and performant system operations.
• Improve user engagement and satisfaction through targeted, reliable recommendations driven by AI and expert insights.

• AI-powered transcription pipeline to convert expert podcasts/videos into text.
• Intelligent annotation system to identify speakers and associate specific advice with individual experts.
• Advanced knowledge base to store and manage transcribed and annotated expert content.
• Integration of extensive product data from multiple retail sources, including description and image data.
• Social media and pop culture data scraping to identify appearances of products in media.
• A recommendation engine leveraging a Retrieval-Augmented Generation (RAG) system to synthesize expert advice into personalized suggestions.
• User query processing interface to handle natural language questions and provide tailored expert-backed responses.

• Expert content sources (podcasts, videos) via API or ingestion pipelines
• Retail product databases from multiple vendors
• Web scraping modules for social media and media appearances
• Authentication and user profile systems for personalization

• Scalability to process thousands of hours of expert content and millions of products
• High transcription accuracy with a target of >90%
• Low latency for real-time user queries, aiming for responses within 2 seconds
• Robust security measures for data protection and privacy compliance
• System availability of 99.9% uptime

The implementation is expected to significantly improve content processing efficiency, enabling the platform to transcribe and annotate vast volumes of expert content. This will enhance the quality and personalization of product recommendations, leading to increased user engagement, higher conversion rates, and expanded media influence. The scalable architecture aims to handle millions of products with seamless integration, ultimately delivering a more reliable and expert-backed shopping experience with measurable improvements in recommendation relevance and response times.