A comprehensive automation system that scrapes vintage pocket watch data from multiple auction houses, dealer sites, and reference databases, cross-references specifications, and populates a structured database with validated pricing and provenance data.
A vintage watch dealer was spending 15+ hours per week manually researching and entering data for 500+ pocket watches. Each watch required cross-referencing specifications, historical information, and market values across multiple websites. Data quality was inconsistent, pricing was often stale, and the manual process could not scale with growing inventory.
Built a custom Python automation system using Selenium for intelligent web scraping across major watch databases and auction archives. The system extracts watch specifications, historical details, and current market data, handles anti-scraping measures with intelligent retry logic and session management, validates data against known reference patterns, and populates a Google Sheets database via the API.
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