Navigate to the page you want to scrape. Works on any public site.
Select what you want. AI detects repeating structures automatically.
Download structured data as CSV or JSON directly from your browser.
Capabilities
Autonomous CSS and XPath generation that learns from site structures. Selectors adapt when pages change — zero manual maintenance required.
Runs entirely in your browser session. No cloud infrastructure to manage or pay for.
Extracted data is stored on your device. We never see your data or target URLs.
Uses your browser's native connection. No rotating proxy services needed for most tasks.
Use cases
E-commerce
Pull product listings, pricing, images and reviews from any marketplace into a spreadsheet.
Amazon
Walmart
FlipkartLead Generation
Extract names, phone numbers, and location data from directories and map listings in bulk.
Google Maps
Yelp
Yellow PagesReal Estate
Monitor property data, track price changes, and build datasets for market analysis.
Zillow
Redfin
Realtor.comSocial Media
Collect audience data, track competitor mentions, and export engagement metrics at scale.
LinkedIn
X
InstagramWhat people say
Top rated on Chrome Web Store
“Finally, a scraper that doesn't require a computer science degree. I just clicked the prices, hit export, and had my spreadsheet in seconds. It's shockingly simple.”
“I've tried every chrome extension out there for lead gen, but this is the only one that actually handles dynamic content without breaking. Clean data, every single time.”
From the blog
A system design interview walkthrough for an Uber-style dispatch system matching riders to nearby drivers at scale — geospatial indexing, batched matching, real-time location ingestion, and the surge-pricing feedback loop.
A system design interview walkthrough for a real-time messaging system handling 2 billion messages/day — persistent connections, offline delivery, message ordering, and multi-device sync, WhatsApp-style.
A system design interview walkthrough for a distributed rate limiter enforcing per-user and per-endpoint limits across 1M requests/second — token bucket vs. sliding window, sharded Redis counters, and the thundering-herd problem at window boundaries.
