Getting the Right Kind of Data in the AI Era of Web Search

Sometimes we browse the web for in-depth information and multiple perspectives. Sometimes we just jump online for a quick answer and move on. These two ways of searching for information also guide how AI tools are developed and act online. Understanding it also sheds light on what is needed to make AI search tools work well for users and for brands striving for online visibility.

The user perspective: getting the right kind of answers 

AI has been rapidly transforming web search, with users turning to Gen AI tools for answers. These tools can generate quick, clear, concise responses 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.

On the other hand, they can also provide the user with comprehensive answers, citing sources, and listing backlinks to check. They can then answer follow-up questions, diving deeper into the topic or conducting more in-depth research to provide even more sources.

The user decides which tool they want to use or how they want to set its parameters. What matters from this perspective is for tools to be able to provide reliable information in the preferred form.

If I just need a quick, factual answer to move on with my tasks, I want an AI search tool to respond instantly, to the point, and without confusion. And it better turn out to be correct.

For in-depth research, on the other hand, one would rely on AI tools trained on comprehensive data and having broad access across the web. When time and effort are invested in analyzing the topic in detail,  being able to rely on the search tool's ability to identify and access information across various sources is crucial.

Online brands: optimizing for visibility

Businesses need to be visible online. And so do publishers and content creators who want to build their brand. For a long time, this meant clutching a high spot in the list of links on a search engine results page, primarily Google. 

For more than a decade, marketers, content creators, and Search Engine Optimization (SEO) agencies have been devising ways to optimize online content for search engines using keywords, alt texts, structured headlines, and other SEO practices.

Two new extensions of SEO have emerged in the AI search era. They correspond to the two primary ways we use AI tools for online search mentioned above. Generative Engine Optimization (GEO) is for optimizing content to be cited by AI tools when providing longer answers and more context. You want to be among the ones cited on key topics for you, so you create authoritative, helpful content.

Answer Engine Optimization (AEO), on the other hand, is about getting featured in brief AI responses to direct questions. For example, when someone asks for a trusted provider of your product, you want to be among the 3 or 4 companies mentioned in the first sentence. For this, you need your brand mentioned as such in third-party sources trusted by LLMs, and you need content that briefly and clearly answers an often-searched question.

To adapt to the new realities, AEO and GEO professionals are now analyzing how AI search tools present brand and industry-related information in their outputs. LLM scrapers and Fast Search API provide them with the data for insights into how AI uses and cites content for in-depth replies, as well as monitor quick responses to evaluate the brand's visibility.

Building the infrastructure for AI search

Whether users will be able to get the kind of answers they want from AI tools and whether businesses will be able to optimize online content for these answers depends on access to timely web data. The infrastructure that makes this access possible has to be increasingly powerful in the AI era.

Scale alone is incredible. A constant flow of large volumes of data is necessary for AI tools to consistently provide unbiased, reliable answers and context covering multiple perspectives. And you can't compromise on speed in the AI race, which means increasing bandwidth, automatic replacement of infrastructure that fails, and whatever it takes to keep latency low.

In some cases, speed of data acquisition is the priority and needs to be increased even if it means sacrificing something else. For example, completeness, context and metadata can often be left out when users need concise answers without delay. 

Enabling such fast search requires infrastructure built for speed. This means sub-second latency and clean, minimal, structured,  and highly relevant outputs. Essentially, you are building a way for data to turn into answers while traveling faster than it used to be possible at such scale. 

Our name for the tool that enables it is simple but apt -  Fast Search API. If we sometimes need fast answers, so do AI tools and agents acting on our behalf. They just need a different kind of vehicle to be fast.

Summing up

Using AI search is certainly exciting. And so is building infrastructure to make it possible. Optimizing for it might not be as exciting for businesses when competition is fierce. But it can be if you have access to the right kind of data. Just as the right kind of data allows AI companies to provide the right kind of answers to the users, it can help SEO experts and businesses find their way to those answers.