Online Store Parsing

We support online stores built on Shopify, WooCommerce, OpenCart, 1C-Bitrix, and other common e-commerce platforms, as well as custom-built stores that don't run on a standard CMS. Because every store's page structure is different even within the same platform — themes, plugins, and customizations all affect how product data is laid out — we review each target site individually before starting, rather than assuming a generic 'Shopify parser' will work unchanged across every Shopify store. This individual review is part of how we keep parsers reliable over time.

You can receive store data as CSV, XLSX, or JSON, or through a direct API if you're pulling it into your own systems automatically. We can also connect the data to a Power BI dashboard for a visual view of competitor pricing trends, or integrate directly with your ERP if you want competitor data to feed into the same system where you manage your own pricing and inventory decisions.

We use a combination of rotating proxies, request rate limiting, and randomized delays between requests to collect data reliably without triggering a target site's bot defenses. Because bot-detection methods change over time as platforms update their security, we actively maintain each parser rather than building it once and leaving it untouched — if a site tightens its protections, we adjust our collection approach accordingly. The specific combination of techniques we use for your target site is covered in detail in the custom proposal, since the right approach depends on that site's specific defenses.

Development cost depends on how complex the target site is and how much data volume you need collected — a simple store on a common platform is generally faster to build a parser for than a heavily protected or unusually structured site. Typical development timelines run 3 to 14 business days depending on that complexity. We offer a free consultation to review your specific target site before quoting a price, so the estimate you receive reflects your actual situation rather than a generic average.