How Lucy Built a Source-of-Truth Retrieval Layer for 2.8M+ SEC and DART Filings with Qdrant

Lucy has developed a retrieval layer for more than 2.8 million SEC EDGAR and Korean DART filings, converting complex regulatory documents into searchable text, preserved tables, XBRL facts, and source-linked chunks intended to provide verifiable evidence for financial AI systems. Using self-hosted Qdrant, the company combines metadata filtering with dense semantic and sparse BM25 search, fusing results through reciprocal rank fusion to narrow searches by company, filing type, period, section, and document identifier before ranking. Its multi-lane query process includes narrow, broad, XBRL-focused, and companion-document searches, with the final context linked back to original filing passages for analyst verification. Lucy reports that Qdrant quantization reduced memory use substantially while largely preserving retrieval quality, and that its system achieved 94.7% Recall@10 on FinanceBench; in one evaluation, replacing general web-search context with Lucy RAG context increased answer accuracy from 0.792 to 0.952 using the same answer model. The production deployment contains roughly 52.25 million dense and sparse vectors across 12 collections, and Lucy says separating collections by filing type and moving metadata into indexed fields improved both latency and recall as the corpus grew. Planned additions include expanded filing coverage, a Neo4j relationship graph for amendments and cross-referenced filings, and tools for creating evidence-based financial benchmarks.

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