RAG Workflow for Stock Earning Report Analysis - n8n Workflow

Imagine the chaos of earnings season. Financial analysts are buried under mountains of quarterly reports, spending countless hours manually parsing through dense PDF documents, hunting for key metrics, and crafting comparative analyses that could take days to complete. Meanwhile, somewhere else, an intelligent system quietly downloads the latest Meta earnings report, processes every page in minutes, extracts meaningful insights, and automatically generates a comprehensive analysis that lands perfectly formatted in Google Docs. The difference? One leverages the power of AI-driven workflow automation.

Financial analysis has always been a time-intensive process. Traditional methods involve manually downloading earnings reports, reading through dense PDF documents, extracting key metrics, and spending hours crafting comparative analyses. This workflow changes everything by combining AI-powered document processing with automated reporting to create a system that works around the clock.

This n8n workflow transforms the tedious process of financial analysis into an intelligent, automated system that processes earnings documents, extracts insights using vector search technology, and generates comprehensive reports without human intervention. Let's dive into how you can build this powerful automation.

Prerequisites: What You'll Need to Get Started

Before building this financial analysis automation, you'll need to set up several key integrations. Think of these as the foundation pieces that make the magic happen:

• Google Cloud Project with Vertex AI API - This powers the AI capabilities
• Google AI API key - Get this from Google AI Studio for Gemini integration
• Pinecone account and API key - Your vector database for semantic search
• OpenAI API credentials - For embeddings and GPT-4 processing
• Google Workspace access - For Sheets, Drive, and Docs integration

You'll also want to create a Pinecone index called "company-earnings" and prepare a Google Sheet with URLs pointing to the financial PDFs you want to analyze.

Key Components: The Building Blocks of Automated Analysis

This workflow leverages several powerful n8n nodes that work together like a well-orchestrated symphony. Here are the star players:

• Pinecone Vector Store - Stores document embeddings for intelligent search
• Default Data Loader - Processes PDF documents and extracts text
• Recursive Character Text Splitter - Breaks documents into manageable chunks
• Google Gemini and OpenAI Chat Models - The AI brains behind the analysis
• Vector Store Tool - Enables semantic search across financial documents
• Google integrations - Sheets for data input, Drive for file storage, Docs for output

Step 1: Set Up Your Document Pipeline

The journey begins with your financial documents. Start by configuring the Google Sheets node to read from your spreadsheet containing PDF URLs. This acts as your document inventory, telling the system which earnings reports to process.

Next, connect the Google Drive node to download these PDFs automatically. The workflow uses a "Loop Over Items" node to process multiple documents in batches, ensuring your system can handle entire quarters' worth of reports without breaking a sweat.

Step 2: Transform Documents into Searchable Intelligence

Here's where the real magic happens. The Default Data Loader processes each PDF, extracting text while preserving crucial metadata like quarter and year information. Think of this as converting static documents into structured, searchable data.

The Recursive Character Text Splitter then breaks these documents into optimal chunks for AI processing. This isn't just random cutting - it's intelligent segmentation that maintains context while creating pieces small enough for effective analysis.

Step 3: Create Your AI-Powered Search Engine

The workflow creates embeddings using OpenAI's technology, transforming text chunks into numerical representations that capture semantic meaning. These embeddings get stored in Pinecone, creating a powerful vector database that understands financial concepts and relationships.

This setup enables the system to find relevant information not just through keyword matching, but through semantic understanding. Ask about "revenue growth" and it'll find discussions about "sales increases" or "top-line expansion" automatically.

Step 4: Automate Quarter Detection and Analysis

One of the cleverest aspects of this workflow is its ability to automatically determine which quarters to analyze. The "Determine Quarters" agent uses the current date to identify the last two completed calendar quarters, ensuring your analysis always covers the most relevant recent data.

The system understands that if it's July, you want to analyze Q1 and Q2, not the incomplete Q3. This temporal intelligence eliminates manual configuration and keeps your reports current.

Step 5: Generate Intelligent Reports

The heart of the system is the "Generate Report" agent, powered by Google Gemini. This AI agent doesn't just extract data - it synthesizes information, identifies trends, spots outliers, and creates comprehensive analyses that rival human-written reports.

The agent uses the Vector Store Tool to search through your embedded documents, finding relevant passages and weaving them into coherent narratives about financial performance, growth trends, and notable changes between quarters.

Step 6: Deliver Professional Results

Finally, the Google Docs integration automatically saves your generated reports to designated documents. No more copy-pasting or manual formatting - your professional financial analysis appears exactly where you need it, ready to share with stakeholders.

The Schedule Trigger means this entire process can run automatically at regular intervals, ensuring you never miss an earnings cycle and always have the latest analysis at your fingertips.

Benefits and Real-World Applications

This automated financial analysis system opens up incredible possibilities for various use cases. Investment firms can monitor multiple companies simultaneously without expanding their analyst teams. Corporate strategy teams can track competitor performance across quarters with unprecedented efficiency.

Financial journalists can quickly generate comparative analyses for breaking news stories. Academic researchers can process years of financial data for comprehensive studies. The system's ability to spot outliers and trends makes it valuable for risk management and strategic planning.

Perhaps most importantly, this workflow transforms financial analysis from a reactive, manual process into a proactive, intelligent system. Instead of scrambling to analyze reports after earnings calls, you have comprehensive insights ready before the market opens.

The beauty of this n8n workflow lies in its scalability and adaptability. While we've focused on Meta's earnings, the same system can analyze any company's financial documents. You can expand it to track multiple companies, add different analysis angles, or integrate additional data sources.

Building automated financial analysis systems like this represents the future of data-driven decision making. By combining the power of AI, vector databases, and workflow automation, you're not just saving time - you're creating a competitive advantage that scales with your needs and grows smarter with every document it processes.