In this video, I talk about a real backend problem that almost every developer faces: after building the application, we still test with fake JSON or we do the classic “copy data from random websites and paste it as dummy data.” That works once, but it doesn’t scale. If you want to simulate real scenarios—pagination, sorting, performance with 10,000 records—you need real data, consistently, without manual effort. So I show you BrowserAct, an AI-powered web automation platform that helps you extract structured data from real websites without writing brittle scraping scripts. I walk through ready-made templates (like Amazon bestsellers) and show how workflows run on a separate machine (your IP isn’t exposed). Then I demo the AI Workflow Builder where you define steps like visit page, click, loop, scroll, extract, and export as CSV/JSON. Finally, I show Agents—where you just type what you want in natural language (example: “extract first 10 wireless headphones from Amazon”) and it generates a downloadable file. I also give an honest take on limitations: it’s credit-based, not meant for extremely high-frequency scraping, and it’s not a replacement for official APIs. My practical tip: test with 5 items first, validate the workflow, then scale—otherwise you’ll burn credits for no reason. For backend ingestion, you can load JSON via a REST endpoint into Spring Boot entities + DB, or import CSV directly into many databases.