FinLens — AI-Powered Financial Document Analysis (Portfolio Case Study)

Overview

FinLens is a production-grade portfolio project that extracts insights from financial documents like SEC filings and annual reports. Instead of spending hours manually reading through 1000+ page documents, analysts can ask questions and get accurate answers with citations. The system can also generate charts and visualizations from your questions, turning financial data into visual insights automatically.

The problem this project targets

Financial analysts spend hours manually reviewing dense SEC filings, annual reports, and regulatory documents. These documents are often 1000+ pages of financial statements, risk disclosures, and regulatory language. Finding specific information or comparing companies requires careful reading and cross-referencing across multiple documents.

What I built

1) Document analysis system

• Upload and process large financial documents (SEC filings, annual reports, regulatory documents)
• Ask questions in natural language (e.g., "Compare Tesla's R&D spending vs Ford and GM" or "Show Microsoft's operating income for the last 5 years")
• Get answers with citations showing where the information came from
• Extract financial metrics, compare companies, and identify trends across multiple documents
• Text-to-charts: Automatically generate charts and visualizations from your questions (e.g., ask "Show revenue trends" and get a chart)

2) Multi-stage analysis workflow

• Retrieval stage: Finds relevant sections across multiple documents based on your question
• Analysis stage: Extracts financial metrics, performs calculations, and compares companies
• Generation stage: Produces structured responses with visualizations and quality validation

Each stage validates its work before moving to the next, ensuring accuracy throughout the process.

3) Document processing and search

• Fast processing of large PDFs while preserving document structure (tables, formatting, financial statements)
• Hybrid search that combines semantic understanding with financial terminology matching
• Filter by company, year, document type, and fiscal quarter for precise retrieval

Technical deep dive

How it works

FinLens works in two separate processes: document indexing and query processing. You upload financial documents, and the system processes and indexes them in the background. Separately, when you ask a question, it searches across all indexed documents, extracts relevant information, performs analysis, and generates a structured answer with citations.

Document Indexing Process:

Query Processing Process:

Technology stack

• Frontend: Next.js dashboard for document management and query interface
• Backend: FastAPI (Python) for document processing and analysis
• AI: Multi-agent workflow for retrieval, analysis, and response generation
• Search: Semantic search with financial document specialization
• Infrastructure: Production deployment with security, data isolation, and error handling

Key technical decisions

• Multi-agent workflow: Specialized stages (retrieval, analysis, generation) ensure accuracy through validation at each step
• Fast document processing: Optimized for large PDFs while preserving critical structure like financial tables
• Financial domain focus: Specialized for SEC filings, annual reports, and regulatory documents with financial terminology understanding