SEC Filings Intelligence System

Context

Public companies publish SEC EDGAR filings (10-K, 10-Q, 8-K) that are extremely long, inconsistently structured, and difficult to analyze manually. Investment research workflows require extracting relevant sections and generating concise, accurate summaries without introducing hallucinations.

This system serves as decision-support infrastructure for investment research—not a document processing utility.

Primary Challenges

Token Limits

• Filings often exceed hundreds of pages
• LLMs have strict context window limitations
• Naive summarization loses critical financial details

Document Variance

• Inconsistent formatting across companies and filing types
• Section headers vary by company and filer
• Tables, footnotes, and exhibits require special handling

LLM Reliability

• High risk of hallucination in financial text
• Investment decisions require deterministic, repeatable summaries
• Need for guardrails that don't compromise usefulness

Architecture

The system follows a retrieval-augmented generation (RAG) approach:

1. Ingestion Layer: Fetches filings from SEC EDGAR API
2. Parsing Layer: Segments filings by logical sections using heuristics and document structure
3. Chunking Layer: Splits sections into LLM-safe token chunks with overlap for context continuity
4. Embedding Layer: Generates vector embeddings stored in ChromaDB
5. Retrieval Layer: Semantic search for relevant chunks based on query
6. Generation Layer: LLM summarization with retrieved context

Key Design Decisions

Section-Based Parsing vs Full-Document Processing

Full-document summarization was explicitly avoided. SEC filings contain boilerplate, legal disclaimers, and repetitive content that dilutes signal. Section-based parsing allows:

• Targeted extraction (Risk Factors, MD&A, Financial Statements)
• Reduced token usage
• More focused summaries

Chunking Strategy

Chunks are sized to fit within token limits with 10-15% overlap. This ensures:

• No information loss at boundaries
• Context continuity for the LLM
• Predictable token consumption

Embedding-Based Retrieval vs Keyword Search

Semantic search via embeddings handles:

• Synonym variations across filers
• Conceptually related content
• Query expansion without explicit rules

Hallucination Guardrails

• Source citations in every generated summary
• Confidence scoring for generated claims
• Human-in-the-loop review for high-stakes outputs
• Explicit "unknown" responses when context is insufficient

Explicit Tradeoffs

What This System Does NOT Do

• Real-time streaming of new filings (batch processing only)
• Quantitative financial modeling or valuation
• Legal or compliance advice

Known Limitations

• Heavily formatted tables may lose structure in parsing
• Very long filings may require multiple retrieval rounds
• LLM latency makes interactive use slower than search

Failure Cases Considered

• Malformed or corrupt filings: graceful skip with logging
• Embedding service downtime: queue for retry
• LLM rate limits: backoff and batch scheduling

Frontend Integration

A minimal React interface supports research workflows:

• Filing Search — Filter by company, filing type, date range
• Summary Viewer — Display generated summaries with source citations
• Chunk Explorer — Review retrieved sections that informed the summary

The frontend is intentionally simple—its purpose is to surface backend-generated analysis, not to showcase UI complexity.

Outcome

The system enables:

• Faster analysis: Hours of manual reading reduced to minutes
• Repeatable summaries: Same filing, same query, consistent output
• Clear correctness boundaries: Users know what the system can and cannot reliably answer

This is not a magic "understand everything" tool. It's infrastructure that makes human analysts more efficient while maintaining transparency about its limitations.