Multi-Site Product Data Extraction with Python and Pydantic

Reliable multi-site product extraction requires a layered pipeline that treats each product field as evidence rather than trusting a single CSS selector. It should define a canonical Pydantic schema first, fetch static HTML before escalating to JavaScript rendering, detect ecommerce platforms such as Shopify, WooCommerce, Magento, and BigCommerce, and prioritize JSON-LD and microdata over presentation-based selectors. Multiple extraction paths should retain provenance and receive field-level confidence scores, with corroborating values increasing confidence and conflicts or normalization failures triggering retries, review, or quarantine. Normalization should standardize prices as decimals, currencies as ISO codes, availability labels, image URLs, variants, and other shared fields before validation, while ambiguous currency symbols require contextual metadata. Production systems also need targeted retry policies, global and per-domain concurrency controls, browser resource limits, and monitoring for validation rates, completeness, confidence drift, and template changes that may otherwise create silently incorrect records. Teams can operate this architecture in-house for maximum control or use managed services such as Context.dev to handle rendering, proxies, anti-bot operations, and structured extraction while retaining their own validation, confidence, and data-quality rules.

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