The Challenge
Global Finance Corp processed over 50,000 documents daily across multiple departments. Analysts spent hours searching through scattered repositories to find relevant information, leading to delayed decisions and duplicated efforts.
Our Solution
We designed and deployed a Retrieval-Augmented Generation (RAG) system that indexed all corporate documents into a vector database, enabling natural language queries across the entire document corpus.
Key Components
- Document ingestion pipeline supporting PDF, DOCX, and HTML formats
- Vector embeddings using OpenAI Ada-002 for semantic search
- ChromaDB for efficient vector storage and retrieval
- GPT-4 integration for context-aware answer generation
- Real-time citation tracking for answer verification
Results
Within 3 months of deployment, the RAG system transformed how the organization handles knowledge:
- 85% reduction in time spent searching for information
- 3x faster report generation
- 92% accuracy in retrieved relevant documents
- $2.4M annual savings in productivity gains
Technologies Used
Python, LangChain, OpenAI API, ChromaDB, FastAPI, React, Docker, Kubernetes