AI-Powered Data Extraction

Traditional scrapers use hardcoded CSS selectors or XPath expressions that target specific HTML elements. When the website changes its layout, these selectors break and require manual updates. AI-powered extraction uses machine learning models that understand what each piece of data means based on visual position, text patterns, and structural context — similar to how a human reads a product page. This means our system works on sites it has never seen before and self-heals when sites change, without manual intervention.

Our system achieves 99.4% field-level accuracy across diverse ecommerce sites. For core fields like price, title, and primary image, accuracy exceeds 99.8%. For complex attributes extracted from unstructured text (dimensions, materials, compatibility), accuracy is typically 96-98%. Every record includes a confidence score so you can set your own quality threshold and route low-confidence records for human review if needed.

Our system continuously monitors extraction health metrics for every target site. When a field starts returning empty, unexpected formats, or values that deviate from historical baselines, the auto-healer activates. It re-analyzes the current page DOM using the same ML classifiers used for initial setup, identifies where the data has moved, validates the new extraction against recent historical data, and updates the extraction configuration — typically within 1-2 hours of the site change with zero human involvement.

Yes. Our extraction pipeline uses headless browsers that fully render JavaScript, including Single Page Applications (SPAs) built with React, Vue, Angular, and Next.js. The AI models work on the fully rendered DOM, not the raw HTML source, so they see exactly what a real user sees. We also handle lazy-loaded content, infinite scroll, and dynamically injected product data.

Our neural anti-detection system manages browser fingerprints, TLS signatures, request patterns, and interaction behaviors using AI models trained adversarially against major bot detection platforms. The system generates unique, consistent browser profiles that pass fingerprint checks, simulates human-like browsing patterns including mouse movement and scroll behavior, and adapts request timing to avoid rate-limit triggers. We maintain a 97% access success rate across protected platforms.

Our CV models extract: primary and secondary colors (using standardized color naming), pattern types (solid, striped, plaid, floral, etc.), material texture indicators, product orientation and angle, relative size estimation, logo and brand detection, packaging type, product condition indicators, and image quality scores. These visual attributes supplement text-based extraction to create more complete product records.

Our NLP models support 50+ languages including Chinese, Japanese, Korean, Arabic, and all European languages. The system auto-detects the language of each text field, applies language-specific tokenization and parsing rules, and normalizes extracted attributes into your preferred output language. Measurement units are automatically converted to your target system (metric or imperial), and currency symbols are mapped to ISO codes regardless of the source language.

Our ML classifiers are trained on millions of product page layouts, giving them strong generalization ability. When encountering a genuinely novel layout, the system applies its learned understanding of common data patterns: prices typically appear in larger fonts near buy buttons, titles are prominent headings, images are the largest visual elements, etc. Initial extraction on a completely new site format typically achieves 94-96% accuracy, which improves to 99%+ within 24 hours as the model calibrates.

Absolutely. Our system extracts data from ecommerce sites in any language. The NLP models handle multilingual content natively, and our computer vision pipeline is language-agnostic since it works on visual features. We currently extract from sites across 40+ countries including major platforms in China (Taobao, JD.com, Pinduoduo), Japan (Rakuten, Amazon.co.jp), Korea (Coupang, Gmarket), and Southeast Asia (Shopee, Lazada).