BriefGen-AI
Document analysis platform with AI. Uses a pipeline with LangChain and Google Gemini for recursive PDF chunking. Dockerized infrastructure with in-memory processing to guarantee data security.
Technical Overview
BriefGen-AI revolutionizes document processing by leveraging advanced AI algorithms to automatically analyze PDF documents and generate comprehensive briefs. The platform implements a sophisticated recursive text chunking strategy using LangChain to handle large documents intelligently, ensuring optimal context preservation for AI analysis. Built with Next.js and TypeScript, the application features a modern UI with shadcn/ui components, Google Gemini integration for natural language processing, and a vector database for semantic search capabilities. The entire infrastructure is containerized with Docker for scalable deployment and includes automated workflows for document parsing, content extraction, and intelligent summarization.
Problem Statement
Manual document analysis is time-consuming and prone to human error. Organizations struggle with processing large volumes of PDFs efficiently while maintaining accuracy and extracting actionable insights.
Architecture
The platform follows a microservices architecture with: Frontend Layer (Next.js with server-side rendering), Processing Layer (Node.js workers with LangChain), AI Integration Layer (Google Gemini API with vector embeddings), Storage Layer (vector database + file storage), and Infrastructure Layer (Docker containers with Kubernetes orchestration). The data pipeline implements streaming processing for large files and includes intelligent chunking algorithms.
Key Features
- Intelligent PDF parsing with OCR support
- Recursive text chunking for optimal AI context
- Semantic search with vector embeddings
- Real-time processing status updates
- Customizable brief templates and formatting
- Batch processing for multiple documents
Challenges
- Implementing efficient PDF text extraction with complex layouts
- Optimizing chunking strategies for different document types
- Managing API rate limits and costs for large-scale processing
- Ensuring data privacy and security for sensitive documents
Outcomes
Reduced document processing time by 85% compared to manual methods, achieved 95% accuracy in content extraction, and processed 10,000+ documents with a 40% cost reduction in operational expenses.