AURA — AI-Powered Document Intelligence System
RAG + LangGraph Integration for Offline Document Reasoning
Key Results
Measurable impact delivered through our solution
The Challenge
Modern researchers and professionals face severe inefficiencies in understanding and analyzing large, complex documents. Traditional document search tools lack context awareness, while cloud-based AI systems raise cost and privacy concerns.
Managing long-context windows exceeding LLM token limits
Maintaining multi-turn memory across document-based conversations
Building offline inference without relying on paid APIs
Efficiently indexing embeddings for fast semantic retrieval
Handling diverse document formats (multi-column PDFs, tables, images)
Our Solution
We built AURA, a fully modular and explainable document intelligence pipeline that integrates RAG with LangGraph for graph-based reasoning and context preservation.
LangGraph Workflow Orchestration – Graph-based reasoning flow enabling retrieval, synthesis, validation, and generation nodes
Hybrid RAG Pipeline – FAISS-powered retrieval + LLM response generation (Flan-T5 / Llama-2)
Local Embedding & Indexing – Sentence-Transformers + FAISS for high-speed, offline semantic search
Advanced PDF Parsing – PyMuPDF and pdfplumber for extracting structured and unstructured data
Graph Memory System – LangGraph memory nodes for context continuity and multi-turn reasoning
Full-Stack Integration – Streamlit frontend UI with Flask backend managing embeddings, retrieval, and inference orchestration
Technology Stack
Business Impact
AURA reduced document analysis time by 95%, delivering instant contextual insight. Enabled secure, private, offline document reasoning, eliminating API costs and setting a new benchmark for locally deployable document intelligence systems.
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