AI-Powered RAG for Stock Earnings Report Analysis

This n8n workflow leverages Retrieval-Augmented Generation (RAG) to analyze quarterly earnings reports from company PDFs. It fetches document links from Google Sheets, downloads PDFs from Google Drive, parses and chunks text, generates embeddings via Google AI (text-embedding-004), and indexes them in Pinecone for semantic search. An AI Agent powered by GPT-4o-mini and Gemini orchestrates querying the vector store to answer complex financial questions.

The workflow delivers actionable reports by compiling AI-generated findings into a formatted Google Doc saved to Drive. Benefits include time-saving automation for investors and analysts, accurate insights from unstructured data, and scalability for multiple companies. It reduces manual review hours, minimizes errors, and enables rapid decision-making on earnings impacts.

Ideal use cases: Portfolio managers tracking earnings seasons, financial researchers querying trends, or BI teams building automated reporting dashboards. Setup requires Google Cloud Vertex AI, Google AI key, Pinecone index, and Drive access for PDFs from investor relations pages.