Build an LLM‑Powered Filings Copilot

An effective filings copilot is a retrieval‑augmented generation (RAG) pipeline tuned for financial documents. The core idea: store high‑quality text chunks from official filings, then answer questions by retrieving only the relevant excerpts and asking the LLM to reason within those bounds.

Data ingestion

1. Source: Pull documents via curated EDGAR endpoints and/or nightly bulk archives (for scale).
2. Scope: 10‑K, 10‑Q, 8‑K, S‑1/F‑1, proxy statements, and earnings call transcripts (from licensed providers).
3. Parsing: Convert HTML/ASCII/PDF to clean text; preserve headings (e.g., Item 1A. Risk Factors).
4. Chunking: Split by section/paragraph with overlap; generate embeddings per chunk.

Index & metadata

• Store vectors + metadata (ticker, CIK, filing type/date, section, page, source URL).
• Pre‑compute deltas vs. prior filings (e.g., newly added risk paragraphs).

Query pattern

• Prompt template includes: question, retrieved chunks, citation style, disclaimer.
• Force the model to answer only from retrieved text; if not found, return “insufficient evidence.”

Key features to implement

• Change tracking: Automatic “what changed” between successive 10‑Ks/10‑Qs.
• Risk extraction: Label risk themes (supply chain, regulatory, litigation, customer concentration).
• KPI harvesting: Detect new or updated KPIs and guidance language in MD&A.
• Footnote awareness: Extract significant accounting policy changes and one‑off items.
• Audit trail: Export a research memo (with timestamps and citations) for your records.

Lightweight stack sketch

• Ingestion: Python workers pulling EDGAR + licensed transcript feeds.
• Storage: Object store for raw; Postgres for metadata; vector DB (FAISS/pgvector) for embeddings.
• LLM: General model for reasoning + smaller embedding model for retrieval.
• UI: Simple search + chat with pinned sources, export to PDF/Markdown.

Quality & compliance guardrails

• Strict source whitelist; no web‑wide scraping without rights.
• Every answer includes quote + link/section reference.
• Log prompts, retrieved chunks, and model versions to keep research reproducible.

Affiliate hooks (placeholders)

• Data/API: {{aff_alpha_vantage}} (fundamentals, technicals), {{aff_sec_api_vendor}}
• Visualization & notes: {{aff_koyfin}}
• Backtesting platform: {{aff_quantconnect}}
• Charting/screening: {{aff_tradingview}} / {{aff_trendspider}}

Bottom line: A filings copilot won’t pick stocks for you, but it will shrink hours to minutes, letting you test better hypotheses and maintain a defensible research trail.

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