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Smarter tools and AI-driven automation are rewriting the rules of web scraping. As costs fall and setup barriers vanish, smaller teams can now compete at scale, reshaping how the web’s data economy works.

Acting autonomously on tasks like data gathering, financially-empowered agents could change the economics of the web.

Zyte’s CTO says great tech leaders don’t work on software; they work on systems that make software.

Most web scraping projects that collapse don't fail because of technical incompetence. They fail because teams treat data extraction like a coding sprint rather than a product launch.

From broken parsers to context limits, today’s AI agents have real challenges—but with the right tools and orchestration, they could reshape how we extract web data.

AI-powered web scraping is transforming data collection by making it faster, smarter, and highly scalable. Learn how it overcomes traditional scraping challenges and unlocks new opportunities for businesses across industries.

Ensuring web data quality at scale means moving beyond fragile scripts and spot checks to robust validation that keeps business decisions accurate and reliable.

The proxy era is ending as web scraping shifts from managing IP pools to smarter, API-driven solutions.

From SEO audits to market intelligence, lead generation, and even brand monitoring, structured SERP data can give you the insights you need to make smarter, faster business decisions. But scraping search engines isn't as simple as sending a GET request and collecting some HTML.

There’s a popular archetype in web scraping circles: the heroic engineer who fights CAPTCHAs at 3 a.m., hand-tunes a proxy farm before breakfast, then rewrites four spiders after lunch because the target sites pushed new JavaScript.

The practice of data quality (DQ) is emerging as a key discipline businesses can use to understand and improve the provenance of the content they collect.