Ragie successfully ingested over 50,000 pages from the FinanceBench dataset, which consists of complex financial documents, and surpassed benchmarks, notably achieving a 42% higher accuracy in Shared Store configuration. FinanceBench is a rigorous benchmark that evaluates retrieval-augmented generation (RAG) systems' abilities to process dense documents, like 10-K filings, and answer financial questions by retrieving relevant information from a dataset of 360 PDFs. Ragie was evaluated by answering 150 complex financial questions and demonstrated high performance by using advanced ingestion processes, including text and structured data extraction, and hybrid search techniques. Despite the challenges of managing large and intricate datasets, Ragie's scalable architecture ensured efficient ingestion and retrieval, achieving notable results such as a 27% accuracy in Shared Store Retrieval compared to the benchmark's 19%. The system's hybrid search capability combines semantic understanding with keyword-based retrieval, enhancing precision and recall, especially for financial jargon, all contributing to Ragie's ability to maintain high performance across large datasets.