If you’ve ever wished you could automatically track competitor pricing, feature updates, and strategic changes without spending hours manually checking websites, this playbook is for you.
By following these steps, you’ll set up an AI-driven monitoring system that runs 24/7, costs under $50 per month, and gives you full control over your data.
This system uses a combination of tools including Dakota, Smithery.ai, Google Sheets, N8N, and OpenAI, bringing together real-time data scraping, AI analysis, and automated reporting.
I. Initial Setup (5 Minutes)
1. Create a Dakota Account
Start by signing up for a Dakota account. A 7-day trial with 1,000 free scrapes is usually available.
2. Obtain Dakota Credentials
Once inside the Dakota dashboard, go to “web advanced” and note your User ID and Password. You’ll need these later.
3. Connect to Smithery.ai and Generate MCP Server URL
Head to Smithery.ai and search for Dakota (sometimes listed as “doto”).
- Select Generate URL.
- Enter your Dakota credentials.
- Copy the unique URL generated — this will connect N8N to the Dakota MCP server.
II. Building the Workflow in N8N
Your automation will run inside N8N, an open-source workflow automation platform. Here’s how to set it up.
1. Prepare Your Monitoring Spreadsheet
Create a Google Sheet with columns for:
- Competitor URL (pricing page link)
- Last Updated
- Previous Day’s Pricing
- Difference
This sheet will serve as the central database for your monitoring.
2. Add a Schedule Trigger
In N8N, insert a Schedule Trigger node to decide when your monitoring should run (e.g., every midnight).
3. Configure Google Sheet (Read) Node
Add a Google Sheet node to read competitor URLs from your spreadsheet. Each row will be processed individually in the workflow.
4. Set Up the Dakota MCP Client Node
Use a Dakota MCP Client node (or equivalent).
- Enter the MCP URL generated earlier.
- Configure it to “scrape as markdown”, so data is easy for AI to analyze.
5. Add the Competitive Intelligence Analyst AI Agent
This AI node acts as your pricing analyst.
- Configure it with OpenAI GPT-4.1.
- System message: define its role as detecting pricing changes.
- Input: competitor URL and previous pricing data.
- Output: a JSON file with “pricing summary” and “differences summary.”
A structured output parser ensures consistent JSON formatting.
6. Add a Merge Node
This node waits for all competitor analyses and then combines them into one dataset.
7. Configure Google Sheet (Update) Node
Add another Google Sheet node to update your monitoring sheet:
- Save the new scrape as “Previous Pricing.”
- Update “Last Updated.”
- Insert the differences summary.
8. Add a Summary AI Agent
This node synthesises data across competitors.
- Configure it as a competitive intelligence analyst.
- Output: a polished HTML business intelligence report.
9. Send Report with Gmail Node
Finally, add a Gmail node.
- Input the HTML report.
- Set a subject line (e.g., “Daily Competitor Pricing Report”).
- Email it directly to yourself or your team.
Results
With this setup, you’ll have a fully automated competitor monitoring system that:
- Tracks competitor websites daily.
- Flags pricing and feature changes instantly.
- Summarises data into a clean, professional report.
- Runs for just a few dollars per month.
This approach gives you enterprise-level insights without the enterprise price tag.