Artificial intelligence has become the focus of the global economy over the last 10 years, leaving the research laboratory behind. Artificial intelligence has become a recommendation engine for products, a fraud detection tool, a language translation system, and a tool for complex pattern analysis in massive datasets. However, the secret behind any amazing AI model is data. Vast amounts of it.
Organizations are turning to one of the most lucrative data sources ever produced—the public internet—as the means to train and perfect algorithms. Product descriptions, customer feedback, news, discussion boards, and social websites all compose a constantly flowing stream of information about customer behavior, markets, and happenings around the world.
However, it is not such a simple task to collect such information on a large scale. Websites are volatile, block automated traffic, and present their content in a format that may be hard for machines to decipher. This has led to an increased demand for businesses that can collect and structure web data with high dependability.
Among the companies that contribute to fulfilling such a need is the technology company Oxylabs, which develops the infrastructure of mass web data capture in the age of intelligence based on AI.
The Data Demand Behind AI
Various datasets are used in real-world applications of artificial intelligence. Machine learning models are enhanced by analyzing patterns in vast amounts of data, which is why organizations need to continuously collect new data to optimize the system.
A lot of this data is not in the company’s databases. To determine consumer preferences, market trends, or shifts in popular opinion, AI developers often draw on publicly available web content to predict the population’s preferences and trends.
A retail firm, such as one, may survey the product descriptions and prices across hundreds of online markets. Financial analysts analyze news websites and discussion boards to monitor market sentiment. Cybersecurity companies scan online communities to identify potential threats.
Such activities need the capability to reach the ordinary web quickly and at all times. Consequently, web data acquisition is currently a very important component of the AI technology stack.
The Data Supporting Web Data
Web data mining, when performed at scale, entails overcoming technical obstacles that are not even apparent to most internet users. Websites usually reject the same IP address request multiple times, identify automated hits, or alter their designs to interfere with scrapers.
To sail through these hurdles, companies use proxy networks. The proxy server is used as an intermediary between a user and a site to enable a user to make a request appear to be made by other devices or locations.
Oxylabs was established in 2015 in Vilnius, Lithuania, and initially offered this infrastructure. It has grown over the years into a global proxy network with more than 177 million IP addresses in more than 230 countries and regions.
This coverage enables organizations to retrieve the web content that people around the world see in various regions, a significant feature for companies tracking prices, search results, or product availability in specific markets.
Closing the Gap Between Proxies and Data Platforms
Oxylabs was a proxy service provider, but soon expanded its product range to include data acquisition tools as the need for web intelligence grew.
Currently, the company uses scraping APIs and automated systems to retrieve structured information from complex websites. These products enable companies to obtain data from sources such as search engines and e-commerce platforms without building and maintaining their own scraping infrastructure.
This change is indicative of a greater trend in the data industry. Companies are increasingly using standardized platforms that provide the technical capabilities for web data collection, rather than writing custom scripts for each website.
AI Changes the Workflow
Web data is also being challenged and transformed by artificial intelligence in its collection.
Oxylabs launched OxyCopilot, an AI-based assistant that helps people create web scraping workflows by answering natural language queries, in 2024. Rather than writing code in detail, users can explain the data they wish to gather, and the system generates much of the required logic.
This strategy reflects a broader trend in the technology sector, where AI applications are making work that previously required specialized engineering know-how easier.
In web data acquisition, it may make large-scale data collection accessible to a broader group of professionals.
Accountability in Data Collection
Ethical concerns about web data have been raised as the industry increases. Even in cases where such information is publicly accessible, the manner of its acquisition should not compromise the law or privacy.
Responsible proxy sourcing and ethical data practices are among the aspects Oxylabs has highlighted. Through relationships with other organizations, such as the International Consortium of Investigative Journalists, and universities, such as Stanford University, the company has also funded research and investigative efforts.
These alliances show that web data technologies are not only applicable to business intelligence but also to journalism and academic research.
The Future of Data Intelligence
Artificial intelligence will continue to need large, reliable data. The ability to retrieve information from the public web will be even more valuable as more industries adopt real-time digital insights.
Oxylabs is one of the companies that act more or less in the background, yet its technology is an inseparable component of the contemporary data ecosystem. The architecture that enables web data access is as essential to AI as the algorithms that process it in an age when AI relies on large channels of information.
