From Web to Artificial Intelligence: Building the Missing Links

For years, the web intelligence industry has been a reliable support system for major data-powered developments across industries. As big data kept getting bigger, the infrastructure requirements to ensure sustained data flow became harder. In recent years, AI has been taking the biggest leaps forward. The story of how the web intelligence industry responded to the requirements of a constantly increasing scale and complexity is also the story of the most recent crucial steps forward in AI, specifically, and technology in general.

Infrastructure to Handle Everything All At Once

AI companies entered 2025 racing to build multimodal tools capable of reliably and effectively handling audio and video data. Such ambition creates immediate pressure on data infrastructure. Video datasets are exponentially “heavier” than written text, more difficult to process, and demand far greater resources to gather at the scale required for training advanced models.

We have predicted early that multimodal data handling will soon be one of the most important frontiers in AI. Even with preparation, when it was time to power the multimodal AI, there was a lot to juggle.

For example, creator consent has been a heated topic in AI training, especially for complex content such as scripted, well-produced videos. However, even when consent for training is granted, turning licensed videos into ethically sourced, AI-ready datasets requires effort and infrastructure.

We developed the Video Data API to handle the full process: from finding relevant videos and channels, to extracting public data and metadata, without teams needing to build and maintain their own scrapers. Such solutions become the freeway tunnels, allowing public and licensed data to travel fast from the web to AI labs.

That said, moving large video files at scale creates a throughput problem. High-Bandwidth Proxies tackle this with 200+ Gbps of dedicated bandwidth and long-lived connections optimized for video downloads. Conventional infrastructure wasn’t built to handle so much data at a time – this is. 

Sustained data access with headless browsers

The conversation around AI agents shifted quickly throughout 2024, as industry professionals realised that the real question at the moment was not what they could automate but whether they had reliable web access at scale.

As it turned out, the answer was – mostly no. Website complexity increases. It becomes harder to ensure stable automated access, especially on JavaScript-heavy sites. Agentic systems performing user-directed actions online are incomplete without an important link. 

These links are headless browsers that can adapt to dynamic website structures, performing multiple actions that are at once 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. This means that organisations now need to track how their brands appear in these AI responses – a challenge distinct enough that it’s spawned its own category: Generative Engine Optimisation (GEO).

Dedicated Web Scraper API targets for platforms like ChatGPT, Perplexity, and other AI search tools is a way to accept that “online search” now means more than it did just a few years ago. Namely, they extract rich, geo-targeted LLM insights exactly as real users see them, which allows organisations to monitor how their brands are perceived, track how competitors appear in AI responses, and measure their presence in this new layer of search results. 

For AI companies, these scrapers provide additional data sources for prompt engineering and model training. The ability to capture structured data from AI search interfaces at scale signals an understanding that the shape of online information discovery is being rewritten in real time.

Ready-made datasets over extraction tools

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

The E-Commerce Web Data Platform reflects a broader trend: buyers sometimes want finished data products rather than tools to produce them. In other words, organisations increasingly demand clean, structured datasets ready for immediate use, with the 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, however, extracting it at scale requires not only technical skills and fat pockets but also tolerance for ongoing maintenance, as websites continue to change. Platforms that collect data also tend to 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 our day-to-day 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 over time as websites change their structure. Given this, the question was: what organisations could 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 towards 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 organisations can afford.