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RocDoc is an AI-powered document intelligence platform built during the AMD Developer Hackathon to simplify document understanding and knowledge retrieval. The project allows users to upload PDFs, Markdown files, and text documents, then interact with them using natural language queries through a conversational AI interface. Instead of manually searching through large documents, users can ask questions directly and receive contextual, citation-based answers generated from the uploaded content. One of the core goals of RocDoc was to create a practical and privacy-focused Retrieval-Augmented Generation (RAG) application with a clean developer-friendly user experience. The platform processes uploaded documents, manages source references, and generates grounded responses while maintaining a responsive and modern interface. The frontend was built using Next.js and TypeScript with a dark-themed modern UI optimized for usability and readability. The application includes: Document ingestion pipeline AI-powered contextual Q&A Citation-based answer rendering Source management system Live telemetry dashboard Multi-document support Interactive chat interface During development, we focused heavily on stability, frontend optimization, and backend workflow improvements. Multiple build issues, dependency conflicts, and ingestion challenges were resolved while refining the overall architecture of the project. The telemetry panel provides real-time system monitoring, giving users insight into system performance and resource utilization during AI inference workflows. This aligns with the AMD Developer Hackathon’s focus on high-performance AI systems and modern AI infrastructure. RocDoc demonstrates how AI can improve productivity, document comprehension, and information retrieval for students, developers, researchers, and technical teams. The project combines modern web technologies with AI-powered workflows to deliver a fast, interactive, and scalable document assistant experience.
10 May 2026