Thunderbit is an AI web scraper and Chrome extension for turning websites, PDFs, images, and documents into structured spreadsheet data. It is built for teams that repeatedly collect web data but do not want to write scraping scripts, inspect page HTML, or maintain CSS selectors. A user can open a page, ask Thunderbit to suggest columns, adjust the fields in plain language, and run the extraction from the browser. The resulting table can be exported to Google Sheets, Airtable, Notion, CSV, or JSON.
The product is useful for sales, marketing, operations, recruiting, ecommerce, and research workflows. Sales teams can collect company, contact, and listing data from directories or search result pages. Ecommerce and marketplace teams can monitor product names, prices, ratings, availability, and seller details. Researchers can turn article pages, PDFs, and long documents into structured tables. Operations teams can use it for recurring data collection tasks that would otherwise require manual copy and paste.
Thunderbit supports more than single-page extraction. It can follow pagination, visit subpages, and enrich each row with extra details from linked pages. It also includes AI actions for cleaning and transforming results while scraping, such as summarizing long text, categorizing records, translating content, formatting values, or calculating derived fields. This makes the output closer to a usable spreadsheet instead of a raw scrape that needs a separate cleanup step.
A key design choice is accessibility for non-technical users. Thunderbit runs as a Chrome extension and lets users describe the data they want rather than configure selectors. It also provides prebuilt templates for popular sites, plus options for API and CLI use when teams need more repeatable workflows. The product is freemium, so users can test smaller extraction tasks before choosing a paid plan.
Teams can also use Thunderbit when the shape of a data source changes over time. Instead of rebuilding a scraper every time a page layout changes, users can revise the requested columns, rerun the task, and review the table output directly. That makes it a practical bridge between lightweight manual research and heavier engineering work. It is especially helpful when the same team needs to handle many different websites rather than one fixed source.
For a launch audience, the main problem Thunderbit addresses is the gap between manual web research and developer-managed scraping infrastructure. Many teams need fresh data from websites every week, but their tasks are too varied for one rigid integration and too frequent for manual copying. Thunderbit gives those teams a faster way to collect, structure, enrich, and export web data without starting a custom engineering project.
