Parsers and data pipelines
Collecting data from websites, exchanges, documents and mailboxes, checking it and putting it where your team already works.
Live demo
Watch it work.
A scenario from a real project, played step by step. Names and amounts are changed.
- Collected 1 284 products, 0 duplicates
- Site C changed its layout: run paused, nothing overwritten
A scraper that runs once is easy. One that runs every night for a year, notices when a site changes and tells you before the data goes bad is the actual job. The same goes for scanned documents and exchange feeds: the hard part is proving the data is complete.
Typical tasks
- Supplier catalogues, prices and stock from websites
- Market data from exchanges with gap detection
- Scanned documents and invoices into structured data
- Statements in different formats into one ledger
- Scheduled exports to Google Sheets, Excel or your database
What you get
- A pipeline that reports its own failures to Telegram
- Checks that compare totals against the source
- Clear limits: what we collect, how often, and what is out of scope
- Backups and a tested restore
Cases.
Certified translation bureau, Lithuania
From a scanned certificate to a finished translation
Construction and aluminium fabrication, Lithuania and Germany
Job costing for a construction company, on its own server
Private crypto trader, Ukraine
Market data a trader can actually trust
Student recruitment agency, United Kingdom
University commission statements, reconciled to the penny
Questions we hear.
What happens when the website changes?
The parser notices that fields went missing and stops with an alert instead of writing empty rows. Fixes after launch are part of support.
Do you collect personal data?
Only when it is your own data or you have the legal basis for it, and then with filtering and storage in the region the law requires.