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AI crew to automate fundamental stock analysis - Q&A workflow

Derek CheungDerek Cheung
31495 views
2/3/2026
WebhookContact ManagementSubscriptionActiveCampaignCRM
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How it works:

Using a Crew of AI agents (Senior Researcher, Visionary, and Senior Editor), this crew will automatically determine the right questions to ask to produce a detailed fundamental stock analysis.

This application has two components: a front-end and a Stock Q&A engine.

The front end is the team of agents automatically figuring out the questions to ask, and the back-end part is the ability to answer those questions with the SEC 10K data.

This template implements the Stock Q&A engine.

For the front-end of the application, you can choose one of two options:

  • using CrewAI with the Replit environment (code approach)
  • fully visual approach with n8n template (AI-powered automated stock analysis)

stockaiexplainerdiagram.png

Setup steps:

  1. Use first workflow in template to upsert a company annual report PDF (such as from SEC 10K filling)
  2. Get URL for Webhook in second workflow template

CrewAI front-end: Youtube overview video

  • Fork this AI Agent environment Crew Agent Environment
  • Set the webhook URL into N8N_WEBHOOK_URL variable
  • Set OpenAI_API_KEY variable

n8n AI Crew for Fundamental Stock Analysis - QA Workflow

This n8n workflow leverages AI to automate the process of querying and analyzing financial documents for fundamental stock analysis. It allows users to upload financial reports (e.g., PDF, DOCX) to Google Drive, which are then processed, embedded, and stored in a Qdrant vector database. Users can then ask natural language questions about the documents via a chat interface, and the workflow will retrieve relevant information and provide answers using an OpenAI Chat Model.

What it does

This workflow automates the following steps:

  1. Triggers on Manual Execution or Webhook: The workflow can be initiated manually for testing or via an external webhook, allowing for integration with other systems.
  2. Uploads Document to Google Drive: A "Sticky Note" indicates that the user should manually upload documents to Google Drive for processing.
  3. Loads Binary Document Input: It retrieves the uploaded document as binary data.
  4. Splits Text into Chunks: The document's text content is broken down into smaller, manageable chunks using a Recursive Character Text Splitter. This is crucial for efficient embedding and retrieval.
  5. Generates Embeddings with OpenAI: Each text chunk is converted into a numerical vector (embedding) using OpenAI's embedding model.
  6. Stores Embeddings in Qdrant Vector Store: These embeddings are then stored in a Qdrant vector database, making them searchable and retrievable based on semantic similarity.
  7. Triggers on Chat Message Received: The primary interaction point for users is a chat interface, which triggers the AI Question and Answer chain when a new message is received.
  8. Retrieves Relevant Information: Based on the user's chat query, the workflow uses a Vector Store Retriever to find the most semantically similar document chunks from the Qdrant database.
  9. Answers Questions with OpenAI Chat Model: The retrieved chunks are then fed into an OpenAI Chat Model, which generates a natural language answer to the user's question, leveraging the context from the financial documents.
  10. Responds to Webhook: The final answer from the AI model is sent back as a response to the initial webhook, allowing the integrating system to display the answer to the user.

Prerequisites/Requirements

  • n8n Instance: A running instance of n8n.
  • Google Drive Account: For storing and accessing financial documents.
  • OpenAI API Key: Required for the OpenAI Chat Model and Embeddings OpenAI nodes.
  • Qdrant Instance: A running Qdrant vector database instance for storing document embeddings.
  • LangChain Nodes: Ensure the @n8n/n8n-nodes-langchain package is installed in your n8n instance.

Setup/Usage

  1. Import the Workflow: Import the provided JSON into your n8n instance.
  2. Configure Credentials:
    • Google Drive: Set up your Google Drive OAuth2 credentials.
    • OpenAI: Configure your OpenAI API Key credential.
    • Qdrant: Set up your Qdrant API credentials (host, API key if applicable).
  3. Upload Documents: Manually upload your financial documents (e.g., PDF annual reports, earnings call transcripts) to a designated folder in your Google Drive account. The "Google Drive" node in the workflow should be configured to point to these documents.
  4. Initialize Vector Store:
    • Execute the workflow once (you can trigger the "Manual Trigger" node) to process the documents from Google Drive, generate embeddings, and populate your Qdrant vector store. This step is crucial for the AI to have data to analyze.
  5. Interact via Chat:
    • Activate the workflow.
    • Use the "When chat message received" node (or an external system integrated with the Webhook trigger) to send natural language questions about your uploaded financial documents.
    • The workflow will process your query, retrieve relevant information, and respond with an AI-generated answer.

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