Web Intelligence Fuels Next-Gen AI Infrastructure

For years, the web intelligence industry has quietly underpinned major data-driven breakthroughs across virtually every sector. As the volume of big data swelled, the infrastructure needed to keep that data flowing became increasingly strained. But the most dramatic leaps have come in artificial intelligence, and the industry’s response to surging scale and complexity tells the story of AI’s most critical recent advances.

Infrastructure to Handle Everything All At Once

AI companies entered 2025 racing to build multimodal tools capable of reliably processing audio and video. That ambition immediately strains data infrastructure. Video datasets are exponentially heavier than text, far harder to process, and require vastly more resources to collect at the scale needed for training cutting-edge models.

We anticipated early that multimodal data handling would become a top AI frontier. Even with preparation, powering multimodal AI meant juggling many challenges at once. Creator consent, for instance, has been a heated topic in AI training, especially for complex content like scripted, well-produced videos. But even when consent is granted, turning licensed videos into ethically sourced, AI-ready datasets demands significant effort and infrastructure.

We developed the Video Data API to handle the full pipeline: finding relevant videos and channels, extracting public data and metadata, without teams needing to build and maintain their own scrapers. These solutions act as freeway tunnels, letting public and licensed data travel quickly from the web to AI labs. However, moving large video files at scale creates a throughput problem. High-Bandwidth Proxies tackle this with over 200 Gbps of dedicated bandwidth and long-lived connections optimized for video downloads. Conventional infrastructure simply wasn’t built for this kind of data volume.

Sustained data access with headless browsers

Throughout 2024, the conversation around AI agents shifted rapidly. Industry professionals realized the real question wasn’t what they could automate, but whether they had reliable web access at scale. As it turned out, the answer was mostly no. Website complexity keeps increasing, making stable automated access harder, especially on JavaScript-heavy sites. Agentic systems performing user-directed actions online are incomplete without an important link.

That link is headless browsers that can adapt to dynamic website structures, performing multiple actions simple and complex for machines we want to work for us, such as clicking and scrolling.

Adapting to AI-powered online search engines

Starting in mid-2024, traditional search result pages were supplemented by LLM-generated answers, AI overviews, and conversational interfaces. Organizations now need to track how their brands appear in these AI responses, a challenge distinct enough to spawn its own category: Generative Engine Optimisation (GEO).

A dedicated Web Scraper API targeting platforms like ChatGPT, Perplexity, and other AI search tools acknowledges that “online search” now means more than it did just a few years ago. These tools extract rich, geo-targeted LLM insights exactly as real users see them, allowing organizations to monitor brand perception, track competitor appearances in AI responses, and measure their presence in this new search layer. For AI companies, these scrapers provide additional data sources for prompt engineering and model training. Capturing structured data from AI search interfaces at scale signals an understanding that online information discovery is being rewritten in real time.

Ready-made datasets over extraction tools

Although industry attention has been riveted on AI’s explosive growth, web data remains essential for sectors that were data-dependent long before LLMs arrived. E-commerce, in particular, has always run on high-quality competitive intelligence: pricing data, inventory levels, customer reviews, product catalogues, and more. While that hasn’t changed, expectations around how that data should be delivered certainly have.

The E-Commerce Web Data Platform reflects a broader trend: buyers increasingly want finished data products rather than tools to produce them. Organizations demand clean, structured datasets ready for immediate use, with extraction work already done. For providers, this opens new possibilities to move up the value chain and expand their bottom lines.

Technical barriers, lower than ever before

In theory, public web data is a shared resource equally accessible to everyone. In practice, extracting it at scale requires technical skills, deep pockets, and tolerance for ongoing maintenance as websites continually change. Platforms that collect data also deliberately make access to the public data they control difficult, so only companies with sizable budgets can afford the kind of data collection that drives competitive decisions.

AI presents an opportunity to reverse this dynamic. Oxylabs AI Studio consists of five tools that work through natural language prompts: AI-Crawler, AI-Scraper, Browser Agent, AI-Search, and AI-Map. Users describe what data they need instead of writing scraping code. These tools grew out of solutions we built for our own teams to make daily work easier. Soon, it became clear just how useful they can be across various use cases.

Set it and forget it

Maintenance is the challenge for AI-powered data collection. No matter how well-configured the system is, its effectiveness will inevitably decline as websites change their structure. The question became: what could organizations do to reduce maintenance costs?

Enter self-healing parsers, a significant step toward the “set it and forget it” ideal. With these presets, parsing failures are automatically identified and fixed thanks to the infrastructure’s AI capabilities. This reduces manual maintenance work, improves reliability, and speeds up recovery when problems occur, bringing autonomous extraction ever closer to reality.

The way forward

Restrictions across the web continue to intensify, pushing more use cases toward premium solutions that can maintain reliability despite evolving defenses. Dedicated ISP Proxies, offering fully dedicated IPs from trusted providers like Comcast, Verizon, Orange, and Vodafone, with the unique ability to choose specific ASN providers, represent one response to this reality. As obstacles to automation become more complex, the quality of proxy infrastructure matters more than ever.

But infrastructure is only part of the answer. The larger challenge is ensuring that public web data remains accessible for legitimate business and research purposes, as some seek privileged access in increasingly aggressive ways. The solutions that emerged in 2025 illustrate that the industry is oriented toward building sustainable, responsible, and increasingly autonomous public data collection systems. How well these systems hold up against the next generation of challenges will define whether web intelligence remains a competitive advantage or becomes a luxury only the best-resourced organizations can afford.