AI-Powered Personal Knowledge and Content Management with Big-Brain

Abstract

One of the major challenges of the digital era is information overload, which makes it difficult for users to structure information and find actionable insights based on dispersed sources of knowledge. Conventional, static knowledge management systems are ineffective in handling the dynamic nature of contemporary information streams. With the integration of AI, the process transforms into an engaging personal assistant. This AI-powered system serves as a "Big-Brain" by tapping into multiple streams of data through a web interface or browser extension in order to construct a rich, interconnected knowledge base. When user pose natural language questions to the system, the AI responds with contextually aware answers based on vector-based semantic search combined with LLM-driven reasoning, providing a personalized overview of knowledge. We evaluated the AI system using real-world data from digital platforms and web content, achieving a retrieval accuracy of 92% for semantic relevance (F1) and 73% for exact matches. Usability tests yielded a high user satisfaction rating of 4.6 out of 5. Unlike existing tools restricted to keyword-based search or rigid categorization, our method mimics human associative memory, effectively bridging the gap between fragmented data sources and actionable knowledge. This work advances personalized AI by providing a scalable, user-friendly, and privacy-centric solution for managing digital information, ultimately boosting productivity.