Extract data from sites with logins, CAPTCHAs, and bot defense — at scale and on a schedule.
Get any data scraped.
2-week delivery.
Production-ready scrapers, ETL, and clean datasets — delivered on time, or you get a full refund.

2 weeks
Typical kickoff to delivery
Millions
Records handled when jobs scale
100%
Refund if we miss the deadline
What we do
Built for sources that fight back
Turn HTML, PDFs, images, and docs into structured rows your team can actually use.
Move scrape output into your lake, queue, or warehouse without a fragile glue script.
Process
How it works
- 1
Tell us the source
Share the URL, fields you need, and any login or bot-protection quirks.
- 2
We build the extractor
We review access, scope volume, and implement a resilient scraper or pipeline.
- 3
You get clean data
JSON API, CSV, Sheets, or webhook — on your cadence, with keys in your workspace.
Work
Recent projects

Real estate mapping data
Parcel geometry delivered as structured layers for downstream GIS.

Find businesses by keyword
Location-aware business lists with contact fields for outreach.

Parse PDFs and scanned documents
Scanned documents parsed into typed rows — not raw text dumps.
Guarantee
On time, or your money back
We scope every job upfront with a clear delivery date. If we miss it without a agreed change, you get a full refund — no runaround.
- ✓ Fixed timeline agreed before kickoff
- ✓ Track status and API keys in your workspace
- ✓ Sample data to integrate before go-live
- ✓ Direct access to the team building your scraper
Workspace
Track requests like a product, not a ticket
Create a free account to submit scrapers, preview API responses, rotate keys, and see status — from review through active delivery.
Open workspace- Submit target URLs and field lists
- Test JSON shape before scrapers go live
- Manage API keys and delivery endpoints
- Edit scope while requests are in review
Blog
From the trenches
August 7, 2026
Scraping for AI teams: structure the data before you prompt
Feeding an LLM a zip of raw HTML feels modern and usually wastes money. Extract the stable fields first — then use models where the markup actually fails you.
August 7, 2026
Full re-scrapes are expensive — start with change detection
Most catalogs barely move day to day. Hashing the parts that matter lets you skip the 95% that didn’t change — and catch the 5% that did.