Speed of an index, reach of a scraper: Introducing Oxylabs Web Intelligence Index

Why scraping alone isn’t enough for AI agents

For years, scraping has been the default way to access web data. And for deep, targeted extraction, it still is. However, AI agents work quite differently. They need:

• Sub-second responses for natural conversations

• Clean, structured context, not raw HTML

• Fresh data, without managing thousands of scraping jobs

In practice, many teams end up building complex pipelines: search APIs for discovery, scrapers for extraction, custom parsers for cleanup, and fallback logic when something breaks. It worked this far, but it’s slow, brittle, and expensive to maintain.

We believe AI systems shouldn’t have to dig the whole web every time they need to understand it.

Our unique approach

One of the biggest challenges with web indexes is obsolescence. The moment data is indexed, it starts aging. This is where Oxylabs takes a different approach with a hybrid model. The Web Intelligence Index combines the speed of a cached index and the reach of live web scraping.

If the data you’re looking for is missing, outdated, or untrustworthy, you can always trigger a real-time operation using our existing scraping infrastructure and return fresh results instead of a dead end. Never get “zero results” – take the best available answer, as fast as possible.

Designed for AI

The Web Intelligence Index is built with developer workflows in mind. Instead of raw HTML, the Web Intelligence Index responses are optimized for LLMs:

• Clean Markdown or JSON

• Clear metadata and citations

• Lower token usage and less post-processing

• Easy integration with tools like LangChain and LlamaIndex

From query to usable context, the entire flow is designed to fit directly into RAG pipelines and real-time agents – without custom parsers or brittle logic.

Why use Web Intelligence Index?

The Web Intelligence Index is designed for teams that need fast discovery, not deep page-level extraction. Some of the most typical use cases include:

• AI engineers connecting chatbots to current web information without hallucinations

• Market research platforms monitoring news, trends, or competitor changes in real time

• Financial or analytics agents tracking mentions, events, or updates as they happen

For deep, highly specific extraction tasks, the Web Scraper API remains the core tool. The Web Intelligence Index builds upon it by handling discovery and retrieval – fast.

Why this matters for Oxylabs and AI workflows

The web is increasingly being consumed by machines, not humans.

As the “agentic web” grows, all AI systems need infrastructure designed for them – not just retrofitted classic tools. That means cleaner data, predictable structure, and sub-second retrieval speeds that match real-time interactions.

With years of experience in large-scale web crawling, compliance, and infrastructure, Oxylabs is ready to offer an enterprise-grade index built for teams that care about data quality, compliance, and long-term reliability.

What’s next?

The Oxylabs Web Intelligence Index is in active development, and we’re exploring it together with teams building AI-first products.

If you’re working with scraping latency in RAG pipelines, relying on search APIs that weren’t designed for LLMs, or simply looking for a safer and up-to-date way to retrieve web knowledge, we’d love to hear from you.