As Generative AI platforms like ChatGPT become search alternatives, SEO professionals are adopting GEO strategies. While APIs offer structured responses, they often differ from real user outputs. Scraping tools bridge this gap, capturing authentic, geographical results and enabling marketers to source patterns and optimize brand visibility.
Demand to capture and analyze the outputs of Generative AI (GenAI) platforms such as ChatGPT and Perplexity is rising rapidly. Once novelties, these large language models (LLMs) powered tools are fast becoming alternatives to search engines – making them central to search engine optimization (SEO) and the emerging field of generative engine optimization (GEO). Experts now face a pressing question: where do LLMs source their information and how do they shape brand and industry narratives?
Instead of relying on limited APIs, marketers use scraping tools to capture real, user-facing responses. Scrapers mirror the experience, enable geographic targeting, and provide the precision and accuracy APIs often lack.
As the transition from SEO to GEO accelerates, marketers need to get comfortable with these tools, understand how they work, and evaluate which features matter most.
Users shift from search engines to generative
AI’s impact on web searching is becoming more prevalent, with users turning to Gen AI tools for answers. These tools can rapidly compile clear, concise responses that are generated based on their training data and live information retrieval from the web. This saves users the need to click through multiple pages and read long texts in order to find the answer they need.
The outcomes of this are already evident. For example, it is predicted that there will be a 25% drop in volume on traditional search engines like Google and Bing. Apple has also reported that the use of Google search on its browser, Safari, dropped for the first time in 22 years. In response to this, Google has introduced AI-generated summaries in its Search experience, combining traditional search results with Gen AI-powered answers.
LMM outputs
To proactively adapt to this new era and ensure a seamless transition, marketers and GEO professionals are now analyzing how LLMs actually present brand and industry-related information in their outputs. Tracking how often and in what context brands appear for targeted keywords lets marketers gauge visibility and reputation in AI-driven search.
Building full GEO strategies requires more data – like top-ranking LLM responses in a niche – to reveal the formats and approaches these models favor.
Reinforcing all of this work is the understanding that these tools are programmatic. This means there will be predictable trends in which sources are selected for specific queries and how information is presented. However, to uncover such patterns, vast amounts of data are needed.
LLM APIs vs Real User Outputs
One way to acquire LLM output data already exists and can provide enterprise access to the responses of LLMs via API endpoints. The likes of OpenAI, Perplexity, and Claude offer paid packages that enable companies to view generated responses from LLMs for particular queries and prompts. Here, the enterprise simply purchases credits, sends prompts, and obtains responses through the API. Still, these API endpoints often deliver responses that do not match the real-world outputs users are receiving. This is because LLM APIs are set up with specific parameters that guide output generation.
These settings can influence whether the model favors safer, more likely responses or takes creative liberties, which may also introduce more errors. Importantly, API configurations may differ from those used by actual users, resulting in varying outputs.
APIs cannot by themselves mimic requests from various specific locations. However, LLMs can tailor outputs by location, limiting the value of API results for geographically targeted queries.
The pros of scraping LLMs
Hence, the emergence of web scrapers that target LLM responses. LLM scrapers ensure SEO and GEO experts are provided with the same responses that actual users get when making the same queries. Therefore, when compared to APIs, their overarching advantage is that they provide data that reflects actual user experience, not restricted by API parameters.
The most versatile LLM scraping platforms allow for geographic targeting, which provides data on how LLM responses are affected by the user’s location. Such platforms can also be a single source of data from multiple major models, from ChatGPT to Google AI Mode and beyond.
LLM-scraping tools can be a convenient way to acquire data from different generative search experiences. For SEO and GEO experts, they can uncover patterns and factors across various models, regions, and circumstances.
AI and data enterprises can use the data scraped from real-world LLM responses to enrich their datasets and fine-tune AI models. Take a machine learning team, for example, by pulling a variety of responses from LLMs using prompts tailored to a specific field, they can build a dataset to train a custom AI assistant, one that’s fluent in current language, tone, and relevant, up-to-date information.
LLM scraping conditions
While LLM scraping solves crucial shortcomings of API endpoints, its own barriers exist. Building LLM scrapers in-house is an expensive and complicated endeavor, requiring complex and niche technical knowledge. And not every LLM scraper one builds or finds in the market will be effective. There are certain conditions one should meet to expect workable results from LLM output scraping.
- A vast proxy pool: With more proxies, LLM scrapers can achieve higher success rates, broader geographic coverage, and greater resilience against IP blocks and CAPTCHAs.
- Prompting at scale: Companies looking to assemble thorough datasets need the ability to submit thousands of prompts or URLs in a single request, and to extract high volumes of data quickly and efficiently.
- Created for varied modes and outputs: Tools like Perplexity and ChatGPT have web search modes and shopping assistant features, although these must be enabled by users and included in the user’s package. Upon receiving a query, the tool decides whether generating a good answer requires web search or shopping assistance based on the specific prompt. With search modes, marketers can compare AI responses based solely on training data to those enhanced with real-time web results.
- Efficient data parsing: A critical element of any web scraping pipeline is fast and accurate data parsing, the process of converting scraped information into a structured, usable format.
Staying ahead in the era of Gen AI
Looking back at the last two years, Gen AI has completely upended online marketing, resulting in several traditional SEO tactics becoming less effective.
Navigating the shift to GEO requires data. Since LLMs rely on patterns, careful analysis can uncover them. LMM output scrapers have emerged as a powerful alternative to API endpoints for gathering this data, thanks to their geographic targeting and ability to capture precisely what end users are seeing when they use Gen AI tools.
Much as marketers adapted to the rise of search engines years ago, they will now develop new strategies and best practices to ensure their content is effectively optimized for LLM-driven discovery.
About the Author
Juras Juršėnas, Chief Operating Officer at Oxylabs. With over 16 years of experience in the IT field, Juras Juršėnas has established himself as an expert in SaaS product management and large-scale IT business operations. His ability to apply strategic problem-solving, critical thinking, and people management skills led him to become the COO at Oxylabs, a global web intelligence collection platform.
