lazy-loaded content, javascript-driven dom updates, content that appears seconds after initial load. that’s where normal web scraping falls apart. selectors don’t exist yet. elements render asynchronously. automation timing becomes a nightmare.
i’ve spent weeks writing custom wait logic and state checking just to handle pages where content loads dynamically. mutation observers, polling loops, dom state validation. it’s fragile and constantly needs tweaking when the site changes.
thought maybe building the workflow through a visual builder combined with ai copilot could be different. instead of manually coding all that dynamic content handling, describe the extraction task and let the ai generate the workflow.
fed the copilot something like: “navigate to the page, wait for the product list to load, extract product names and prices from each item, handle pagination”. it generated a workflow that actually handled the dynamic content intelligently. built in proper waits, checked dom stability before scraping, retried on timeout.
the generated workflow had safeguards i wouldn’t have coded immediately. it waited for network activity to settle. it validated that extracted data actually matched expected patterns before moving forward. it had fallback logic if selectors weren’t stable.
more importantly, modifying the workflow is faster than modifying code. if the site structure changes, i can adjust the extraction logic visually without rewriting javascript.
has anyone actually built data extraction workflows from plain descriptions without touching code? what was your success rate on pages where content loads unevenly?