So I’ve been dealing with this repetitive task at work—logging into multiple vendor sites, extracting pricing data, and compiling it into a spreadsheet. I was dreading having to hire someone or learn Selenium or whatever. Then I actually tried describing what I needed in plain language to Latenode’s AI Copilot, and honestly, I was shocked at how it just… worked.
I literally typed: “Log into site A with credentials, navigate to the pricing page, extract the table data, then do the same for site B and C, then compile everything into one dataset.” The Copilot generated the entire workflow. I didn’t have to write a single line of code.
The headless browser integration handled all the navigation, clicks, and form filling. The AI understood the context and set everything up properly. I ran it, found a couple of data extraction quirks that needed tweaking, but even those were just minor selector adjustments.
My team’s been manually doing this for months. Now it runs every morning automatically.
Has anyone else tried this approach? I’m curious if the Copilot works as smoothly for more complex scenarios, or if there’s a point where you hit a wall and need actual coding knowledge.
This is exactly what the AI Copilot was built for. The fact that you went from idea to running workflow in maybe 30 minutes is the whole point.
What you’re describing—data extraction across multiple sites—would normally require you to write browser automation code or hire someone who knows how. With Latenode, you just describe it.
The headless browser integration is doing the heavy lifting. It handles login, navigation, DOM interaction, screenshot capture, all that stuff. And because it’s AI-powered, it understands context way better than traditional automation tools.
If you ever hit limitations with the plain-language setup, you can always drop into JavaScript for one-off tweaks. That’s the beauty of the no-code and low-code combo.
You might also want to check if any of your other repetitive workflows could benefit from the same approach. Most teams find they automate 3-4 things once they realize how fast it is.
Nice win. I’ve run into similar situations where the bottleneck wasn’t really the complexity—it was the time to set it up. The AI Copilot cuts through that.
One thing I’d add: document your workflow once you’ve tweaked it. I’ve found that workflows are easier to maintain if you leave some notes on what selectors you’re targeting and why. When the site updates their HTML, you’ll want to know which steps need adjustment.
Also, if your data extraction needs to validate results or handle edge cases, you might eventually want to throw a data validation step in there. Latenode lets you chain multiple AI models together, so you could have one model extract the data and another verify it looks correct before it gets written to your spreadsheet.
The plain language approach is a game changer for people in your situation. I’ve seen teams automate data collection workflows in a fraction of the time it would take to write and maintain custom scripts. The headless browser handles the messy parts—dealing with dynamic content, waiting for elements to load, handling clicks. You describe what you want, and the AI figures out the execution.
One practical note from experience: vendor sites sometimes change their layouts. Build in some error handling from the start, even if everything works now. Latenode lets you set up retry logic and notifications if something fails. That way you catch issues before bad data gets into your spreadsheet.
Your workflow is a solid use case for AI-driven automation. The key advantage here is that the Copilot generates workflows that you can actually understand and modify, unlike some black-box automation tools. When something doesn’t work perfectly, you can see exactly what steps it’s taking and adjust them.
For multi-site data extraction specifically, consider leveraging the AI’s ability to handle structured data output. You can tell it to format the extracted pricing data in a specific schema before it gets written anywhere. This prevents data format inconsistencies across runs and makes downstream processing easier.
That’s pretty cool. The AI Copilot saves tons of time on setup. Just watch out for site updates breaking your selectors. Set up basic error handling early and you’ll be golden.