Chat-Based Financial Analysis of P&L and Balance Sheets with GPT-4 & PostgreSQL

🧾 Who’s it for

This workflow is designed for finance teams, accountants, and data analysts 📊 who want to interact with financial data from two PostgreSQL databases — one containing Profit & Loss data and another containing Balance Sheet data — using natural language chat.
It’s perfect for those who need quick, AI-powered insights with the correct database automatically selected based on the question.

⚙️ How it works / What it does

1. Chat Trigger 💬 – Starts the workflow when a chat message is received.
2. AI Agent 🤖 – Processes the user’s question and decides:
   • Profit & Loss DB → If the question is about revenue, costs, expenses, or profit.
   • Balance Sheet DB → If the question is about assets, liabilities, or equity.
3. PostgreSQL Query Nodes 🗄️ –
   • P_L_Reports queries the financial_agent_pl_reports table.
   • Balance_Sheets queries the financial_agent_balancesheets table.
4. AI Model (OpenAI) 🧠 – Uses gpt-4.1-nano to interpret results and provide an easy-to-read answer.
5. Memory Buffer 📝 – Keeps recent conversation context for a smoother chat experience.
6. Table Output 📋 – Always formats the results as a clean, readable table with two decimal precision.

🛠️ How to set up

1. Prepare Your Databases

   • Feed your Profit & Loss and Balance Sheet data into PostgreSQL.
   • Ensure the correct table structures are used:
     • financial_agent_pl_reports → P&L data.
     • financial_agent_balancesheets → Balance Sheet data.

2. Configure the PostgreSQL Nodes

   • Add connection credentials for both databases.
   • Link P_L_Reports and Balance_Sheets nodes to the correct tables.

3. Set Up the AI Agent

   • Paste the provided system message into the AI Agent node (already configured in your workflow).

4. Connect the Nodes

   • Ensure Chat Trigger → AI Agent → DB Nodes → AI Model connections match your workflow.

5. Deploy

   • Save and activate the workflow.
   • Start sending finance-related queries to test.

📋 Requirements

• n8n (latest version recommended)
• PostgreSQL databases with:
  • financial_agent_pl_reports table (P&L data).
  • financial_agent_balancesheets table (Balance Sheet data).
• OpenAI API credentials with access to gpt-4.1-nano.
• Active Webhook/Chat Trigger for receiving queries.

🎨 How to customize

• Expand AI Instructions 🗒️ – Add more rules in the system message for different data sources or formatting styles.
• Change AI Model 🧠 – Switch to a different OpenAI model for faster or more accurate results.
• Add More Databases 🗄️ – Connect extra financial datasets, e.g., cash flow, sales analytics.
• Enhance Table Styling 📊 – Use Markdown or HTML formatting for richer outputs.
• Refine Query Logic 🔍 – Modify filtering logic to better match your reporting needs.