Scraping Data Automation

A scraping and document-processing pipeline that collects, parses and structures data from many portals every day.

The problem

Useful data is published every day across many portals: tenders, listings, prices and documents, often as PDFs and scanned files. Finding the relevant records and copying the important details by hand is slow and easy to get wrong.

What I built

A scraping and document-processing pipeline that runs every day on its own:

Collection – scheduled scrapers gather new records from the target portals.
Parsing & OCR – text is extracted from PDFs and scanned documents.
Structuring – key fields such as dates, values, locations and categories are pulled into clean records.
Delivery – the data is stored in PostgreSQL and exported to Excel or an API for the team.

How it works

The pipeline is written in Python, using Selenium for portals that need a real browser, OCR for scanned documents, and PostgreSQL for storage. Failed runs are retried and logged, so missing data is caught early.

Result

100K+ records processed into a searchable, structured dataset.

Want something similar?

I can build scrapers and data pipelines for tenders, listings, prices or any other public data your business depends on.