I’ve been trying to wrap my head around building scalable web crawling workflows that can handle multiple sites without drowning in code complexity. What I found interesting is how an AI copilot can take a plain-English description of what you need—a crawling task—and then spit out a ready-to-run workflow that actually crawls several sites, extracts data, and aggregates it neatly. This seems like it could save a lot of time and avoid the usual mess of stitching different scripts together. Has anyone here tried describing their crawl needs in plain language and watched AI generate a full multi-site crawler from that? How well did it handle site-specific quirks or layout shifts? I’d love to hear real experiences on how it deals with that complexity before I dive in. What tips did you find useful to get the best outputs out of the AI assistant?
I’ve used Latenode’s AI copilot to do exactly this. You simply describe the sites and what data you want, and it builds a multi-site workflow that runs smoothly. I didn’t have to write any code for selectors or navigation steps. It even manages retries on failures and aggregates data across sources. It saved me days. You can check it out here: https://latenode.com
Tried a similar approach recently—just provided a basic overview of my crawling task and was amazed how quickly the AI mapped out the site traversal and data extraction. It helped especially by automating complex steps like handling pagination and catching subtle DOM changes. The trick I learned: include specifics about the data fields and expected formats in the description to get more reliable workflows.
One tip: always test the generated workflow on a few sample sites first and tweak your description for clarity. The AI works best with precise input. It handled multi-site crawling fine, but edge cases like login-required pages still need manual checkups for now.
I’ve been building web crawlers the old-fashioned way, spending hours on coding and debugging for each site. When I tried an AI copilot feature, it was refreshing how I could just describe the data I wanted and the AI translated that into a working multi-site crawler, including aggregation logic. Of course, it’s not flawless—dynamic or heavily scripted sites still need a bit of manual tweaking. Still, the ability to start with plain language and get a baseline working crawler sped up my process a lot. Over time, I refined my descriptions to improve accuracy and handle exceptions better.
Ai copilot turns simple crawl description into workflows. Good at handling multi-site issues but manual checks still needed sometimes.
use detailed plain english task descriptions so ai builds robust crawler workflows fast