Retrieval-augmented generation system that provides accurate, context-aware answers to employee questions by accessing vast enterprise knowledge bases
A large financial services firm struggled with knowledge management across thousands of employees who needed quick access to policies, procedures, regulations, and historical decisions. Information was scattered across multiple systems, databases, and documents, making it time-consuming for employees to find accurate answers to complex questions, leading to inefficiency and potential compliance risks.
We implemented a retrieval-augmented generation (RAG) system that combined large language models with intelligent document retrieval. The system indexed all company documents, policies, and knowledge bases, then used semantic search to find relevant information and generate accurate, contextual responses. The assistant provided source citations and confidence scores for all responses.
The knowledge assistant achieved 91% accuracy in providing correct answers to employee queries, significantly outperforming traditional search systems. Query resolution improved by 76%, with most questions answered instantly without requiring human expert consultation. Employee productivity increased by 68% as workers could quickly access the information needed for decision-making and daily tasks.