Theresia Tanzil

Building robust agentic AI workflows with rapid web data

AI agents need access to public web data, right now. Tools connected to web scraping APIs empower agents to return live data quickly.

Enhancing AI model performance with fresh web data

No-one likes an out-of-touch AI assistant. Fortunately, rapid refreshing can keep AI models aware of the very latest public information.

Building superior AI models with quality web data

Training data quality can make or break AI model effectiveness. So how are engineers sourcing the web’s best input?

The great wall of data: The complexities of web scraping in the Asian market

While the technological arms race of web data access is universal, the battleground in Asia has its own unique rules of engagement.

Actually, web scraping APIs are cheaper

Many data teams still think running a proxy-based scraping stack is most cost-effective. Industry pressures and our research disprove that idea.

The science of compliance: Tech tips for a legal data pipeline

New legal and regulatory compulsions for web data have significant business consequences. So, how can technologists engineer their company’s risk profile lower?

Web scraping APIs vs proxies: A head-to-head comparison

Proxies are essential to scraping at scale. So, how do full-stack web scraping APIs compare?

Legal clarity comes with compliance demands

Explore how new regulations like the EU AI Act and California AB 2013 are reshaping AI data compliance in 2026. Learn why provenance, transparency, and lawful sourcing are now critical.

Brand visibility in the digital era: How web data help brands see the full picture

Discover how web data helps brands improve visibility, track competitors, monitor availability, and analyze reviews to win on the digital shelf.

Web traffic is splintering into access lanes

Explore how AI agents are reshaping web traffic into hostile, negotiated, and invited access lanes. Learn what this means for bots, scraping, and the future of data access.

How online retailers use web data to compete on price, promotion, and availability

Discover how retailers leverage web data to optimize pricing, track competitor stock, detect trends, and improve sales performance.

Automation drives power in the data arms race

Anti-bot systems now evolve in minutes, not weeks. Discover why automated, self-healing scraping systems are essential to survive the 2026 data arms race and how to adapt.

How web data turns e-commerce listings into retail intelligence

Discover how web data enables digital shelf analytics vendors to track prices, availability, and product trends at scale—fueling real-time retail intelligence and competitive advantage.

Dawn of the autonomous data pipeline

Discover how autonomous, agent-driven data pipelines are transforming web scraping in 2026, enabling self-healing systems, API discovery, and end-to-end automation.

AI is the new engine for web scraping

From LLM-powered extraction to agentic pipelines, here's how AI is reshaping every stage of the web scraping workflow in 2026 and what it means for your stack.

Is your AI coding assistant stuck in the past?

AI speeds up development - but it might default to past conventions and versions. How can developers ensure they work with the latest features and requirements?

Data outcomes are top of the scraping stack

By 2026, the question won't be "which proxy should I use?" It will be "which API should I call?"

Beyond text: Unlocking value on the multimedia web

The web is about more than the written word. Why companies are racing to harness the power of video, audio and pictures.

Teaching AI to scrape like a pro: how we measure LLMs’ data quality

AI-enabled code editors can now conjure scraping code on command. But is it any good? Here’s how Zyte re-engineered LLMs with Web Scraping Copilot to drive best-in-class output.

Web data for business insights in 2026: Elevate your BI function with quality data

Discover how BI teams in 2026 use AI-powered web data to cut data wrangling, improve quality, and stay compliant. Insights from Zyte’s Web Scraping Industry Report.

Web data for scraping developers in 2026: AI fuels the agentic future

AI is reshaping web scraping in 2026. Learn how agentic systems, scraping APIs, and automated access management help developers build faster, more resilient data pipelines.

Web data for engineering leaders in 2026: Scale scraping without scaling headcount

How engineering leaders can scale web data in 2026 using agentic AI, automated scraping, and compliant platforms, without growing headcount.

The new economics of web data: Smaller scraping just got cheaper

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.

How to Plan Your Web Scraping Project Like a Product Manager

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.

The DQ playbook: How ‘data quality’ fuels business’ pursuit of precision

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.

How price extraction is fuelling insights for modern retailers

Retail pricing has long combined data, experience, and instinct – but today’s market volatility demands a faster, smarter approach.

Four sweet spots for AI in web scraping

Discover how AI and LLMs are enhancing web scraping with smarter crawling, fuzzy data extraction, automated spider generation, and intelligent QA.

From script to system: 10 building blocks to scale web scraping

Scaling your business’ web data gathering – acquiring, monitoring and storing a growing amount of data from a growing number of sources over time – requires holistic planning.

Quality, focus and scale: Three ways data outsourcing benefits businesses

The Strategic Case for Buying Web Data: Quality, Focus, and Scale

What’s your data type? Solving the procurement problem

Engagements with data suppliers break down when buyers don’t have a clear project concept. Understanding and articulating your needs is paramount. Meet the three types of data buyers. Which one are you?