ÁRSzűrő – Large-Scale Product Feed Processing & Price Comparison Platform
CGAT designed the technical foundation for ÁRSzűrő, a large-scale product feed processing and price comparison platform built around supplier XML feeds, product normalization, partner offers, and scalable background processing.
Initial situation
CGAT designed the technical foundation for ÁRSzűrő, a large-scale product feed processing and price comparison platform built around supplier XML feeds, product normalization, partner offers, and scalable background processing.
Business problem
ÁRSzűrő is a price comparison platform designed to collect and process large volumes of product data from multiple suppliers and online stores. The system is built around partner feeds, product normalization, offer management, and scalable processing logic. CGAT designed the architecture for feed ingestion, queue-based processing, locking, product mapping, and long-term scalability. The system separates supplier-specific offer data from normalized product information, allowing multiple partners to be connected to a shared product catalog structure. The platform is intended to handle XML and CSV feeds, partner-specific product identifiers, product images, prices, stock information, and future AI-assisted product enrichment. The architecture supports background workers, distributed processing, and database structures suitable for high-volume e-commerce data. This case study demonstrates CGAT’s experience with data-heavy e-commerce systems, supplier integration, marketplace logic, and scalable product data processing.
Discovered cause
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Solution approach
ÁRSzűrő is a price comparison platform designed to collect and process large volumes of product data from multiple suppliers and online stores. The system is built around partner feeds, product normalization, offer management, and scalable processing logic. CGAT designed the architecture for feed ingestion, queue-based processing, locking, product mapping, and long-term scalability. The system separates supplier-specific offer data from normalized product information, allowing multiple partners to be connected to a shared product catalog structure. The platform is intended to handle XML and CSV feeds, partner-specific product identifiers, product images, prices, stock information, and future AI-assisted product enrichment. The architecture supports background workers, distributed processing, and database structures suitable for high-volume e-commerce data. This case study demonstrates CGAT’s experience with data-heavy e-commerce systems, supplier integration, marketplace logic, and scalable product data processing.
Implementation
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Next opportunity
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Result
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Project Metadata
The technology stack is shown as secondary evidence, not as a substitute for the business outcome.
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