XCrawl vs. Extralt

XCrawl is an advanced web scraping and data extraction platform built to deliver structured, real-time web data for modern applications. It provides a comprehensive set of APIs, including Scrape API, Crawl API, SERP API, and Map API, allowing users to extract information from single pages, search engines, or entire websites. The platform returns clean, structured outputs such as JSON, Markdown, and headless browser screenshots, making it easy to integrate data into analytics systems and AI pipelines. XCrawl is specifically designed to support AI-driven workflows, including LLM training, RAG pipelines, and intelligent automation. Its infrastructure includes auto-rotating residential proxies, browser fingerprinting, and CAPTCHA handling to ensure reliable access to protected and JavaScript-heavy websites. The platform integrates seamlessly with tools like n8n and supports Model Context Protocol (MCP) for connecting AI assistants to live web data. XCrawl is widely used for SEO monitoring, competitor analysis, sentiment tracking, lead generation, and price monitoring. It also enables businesses to collect and process large volumes of data in real time, improving the accuracy of predictive models and decision-making. With its unified API approach, users can manage multiple data extraction tasks without building custom scrapers. The system is built for scalability, handling thousands to millions of requests daily with consistent performance. XCrawl reduces development time and maintenance costs by eliminating the need for in-house scraping infrastructure. It also enhances productivity by delivering ready-to-use structured data without additional processing. Ultimately, XCrawl empowers organizations to harness the full potential of web data for innovation and competitive advantage.

A considerable portion of ecommerce data is trapped within proprietary platforms or selectively filtered through merchant feeds, causing sellers to exhibit only what they wish to highlight; in contrast, Extralt grants access to the raw data that actually exists. Our system captures structured product information from any ecommerce site, normalizes it into a consistent format, and detects identical products sold by various vendors. This operation unfolds in four key stages: Extract, which scrapes multiple websites to create standardized structured data; Enrich, which converts product information into English, categorizes it using the Shopify taxonomy, emphasizes specific characteristics, and synchronizes items from different sellers; Extend, which finds the same product across various platforms, reveals alternatives, and links related products; and Explore, which empowers users to search, compare prices, and analyze the entire dataset. Users incur charges during the Extract and Enrich stages, while the Extend and Explore features are provided free of charge. To address the complexities of scraping ecommerce sites, we developed our extraction engine, as conventional scrapers are often difficult to maintain due to frequent changes in website layouts. While AI-based scrapers offer flexibility, they can be excessively costly to deploy across every page. Thus, our solution not only guarantees reliability but also significantly improves accessibility to vital data, making it easier for sellers and consumers alike to navigate the ecommerce landscape efficiently. This ultimately fosters a more transparent marketplace where users can make informed purchasing decisions.