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.

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⚙️ 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 🗄️ – - PLReports queries the financialagentplreports table. - BalanceSheets queries the financialagentbalancesheets 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.

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🛠️ 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: - financialagentplreports → P&L data. - financialagentbalancesheets → Balance Sheet data.

2. Configure the PostgreSQL Nodes - Add connection credentials for both databases. - Link PLReports and BalanceSheets 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.