Quantitative research increasingly relies on unstructured financial content such as filings, earnings calls, and research notes, yet existing LLM and RAG pipelines struggle with point-in-time correctness, evidence attribution, and integration into research workflows.
To tackle this, QuantMind introduces an intelligent knowledge extraction and retrieval framework tailored to quantitative finance. It adopts a two-stage architecture:
Knowledge Extraction – transforms heterogeneous documents into structured knowledge through multi-modal parsing of text, tables, and formulas; applies adaptive summarization for scalability; and uses domain-specific tagging for fine-grained indexing.
Intelligent Retrieval – integrates semantic search with flexible strategies, multi-hop reasoning across sources, and knowledge-aware generation for auditable outputs.
A controlled user study shows that QuantMind improves both factual accuracy and user experience compared to unaided reading and generic AI assistance, highlighting the value of structured, domain-specific context engineering for finance.