Thunderbit

The core move: open a page, click once, and let the AI propose the useful fields. It reads the layout and suggests columns such as name, price, rating, or seller, then fills them from what's visible. That's it. For people who never learned selectors, this is the whole product in one button.

AI isn't the only route. For popular sites like Amazon, eBay, and Google Maps, a template returns the same fields every time, instantly, and without spending AI credits. If your target is a mainstream marketplace or directory, a template usually beats a fresh AI run on both speed and cost. Simple as that.

When a page is unusual, you describe what you want in plain language and the scraper works out which elements match. This is the part that saves time on one-off sites where no template exists, because you're not tracing element paths or guessing at class names before you can pull a single column. It also means the quality depends on how clearly you phrase the request.

Thunderbit can restructure output while it scrapes. Add a summary column, sort rows into categories, or translate fields into another language as part of the same run. Handy when the raw page text is messy and you'd otherwise clean it by hand afterward.

Results go to Google Sheets, Airtable, Notion, or Excel, or you copy-paste them elsewhere. Because the data lands in tools your team already uses, there's less glue work between scraping and acting on it, which is often where manual workflows quietly lose their afternoons.

Beyond the extension, Thunderbit offers an API for piping its scraping into your own applications and internal workflows. This is the path for teams that want the extraction to run inside a product rather than a browser tab, where nobody has to remember to click a button while looking at the right page. Exact limits depend on your plan.