STOCK AI
RAG over 5,000+ SEC filings. Each query answered in under 30 seconds. Full research memos in 22 minutes, not 4 hours.
A boutique investment firm was spending 40% of analyst time manually reading SEC 10-K and 10-Q filings. We built a RAG pipeline over their entire filing library — natural language questions return cited, cross-document answers in under 30 seconds. Full investment memos that used to take 4 hours now take 22 minutes.
Financial Research Is a $150/Hour Problem
Financial analysts at boutique firms spend the majority of their working hours reading unstructured documents — 10-Ks, 10-Qs, proxy statements, earnings transcripts. The average S&P 500 10-K filing runs 200+ pages. Cross-referencing multiple filings for a single investment thesis can take days. This creates a brutal bottleneck: analyst hours are the most expensive resource in a firm, yet they are consumed by mechanical reading tasks that AI can handle in seconds.
Manual Reading Was Killing the Investment Edge
The firm's four analysts were covering 60+ companies across three sectors. Each quarterly earnings cycle meant 60+ new filings to read — manually, in sequence, with notes in spreadsheets. Cross-referencing risk disclosures, revenue drivers, and management commentary across multiple companies took days. By the time research was complete, the market had already moved.
No Cross-Document Memory
Insights from one filing could not be easily connected to another. Each document was read in isolation, losing the network of relationships that drives real alpha.
Zero Citation Trail
Analyst notes had no direct links back to source text. Compliance review and investment committee challenges required re-reading the original documents.
Competitive Disadvantage
Larger firms with 20+ analysts and Bloomberg terminals could process filings 10x faster. The boutique firm was structurally disadvantaged on research throughput.
Context Loss Across Quarters
Comparing this quarter's 10-K risk disclosures to last year's required manual cross-referencing — a 2-hour task for every single company.
A RAG Pipeline That Thinks Like an Analyst
We built a semantic retrieval system over the firm's entire SEC filing library — 5,000+ documents ingested, chunked with financial context preservation, and indexed in a vector store. Analysts now ask questions in plain English and get cited answers with exact page references in under 30 seconds. Cross-document synthesis, risk comparison, and trend detection happen automatically.
SEC EDGAR Ingestion Pipeline
Automated daily ingestion of new filings via SEC EDGAR API. PDFs are extracted, cleaned, and chunked with financial table and footnote preservation.
Semantic Vector Search
LlamaIndex-powered retrieval with custom financial entity extraction. Queries understand synonyms — "revenue" finds "net sales", "top line", and "total income".
Multi-Document Synthesis
Ask one question, get answers synthesized across 10 companies simultaneously. Compare risk disclosures, identify sector patterns, track management language shifts.
Citation-First Answers
Every answer includes exact document, section, and page number. One click links back to the original filing on SEC EDGAR. Zero hallucinations — if it's not in the filing, the system says so.
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Research Speed Became a Competitive Weapon
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