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.
- Platform
- n8n
- Category
- Finance
- Price
- $4.99
- Creator
- Zain Ali
- stickyNote
- postgresTool
- agent
- chatTrigger
- lmChatOpenAi
- memoryBufferWindow
How to import this workflow into n8n
- 1Purchase or download the workflow to get the n8n workflow JSON file.
- 2In your n8n instance, open Workflows and choose "Import from File" (or paste the JSON with Ctrl+V on the canvas).
- 3Open each node marked with a credential warning and connect your own accounts and API keys.
- 4Run the workflow once manually to verify the data flow, then toggle it to Active.
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