AI search gives direct answers rather than lists of links, citing websites it considers trustworthy
Large language models combine trained knowledge with real-time web searches
AI crawlers like GPTBot and PerplexityBot scan websites for training data and real-time answer generation
Google AI Overviews, ChatGPT, and Perplexity each select sources differently, but all favour accurate, well-structured content
Trust signals (E-E-A-T) influence which sites get recommended
AI search engines generate direct answers to questions instead of showing lists of websites. When someone asks ChatGPT, Perplexity, or Google’s AI Overview about your industry, they receive recommendations pulled from sources the AI considers trustworthy.
If your business isn’t among those cited sources, you’re invisible to that searcher.
This scenario plays out thousands of times each day as more people turn to AI tools for recommendations.
If these systems aren’t finding and citing your business, you’re hidden from a growing audience. According to Semrush’s 2025 AI Search study, AI search traffic could overtake traditional organic search by 2028. ChatGPT already processes over 800 million queries weekly, making AI platforms a significant discovery channel for businesses.
This article explains how AI search engines actually find, process, and recommend websites. You’ll understand the mechanics behind AI recommendations and what influences whether your business gets cited or ignored.
Table of Contents
How Is AI Search Different from Traditional Search?
Traditional search engines built their business on showing ranked lists of websites. You’d type “best accountant in Birmingham,” scan through the results, and visit several sites to piece together your own answer.
AI search flips this model.
Instead of pointing you toward information, it delivers the information directly. Ask ChatGPT about accountants in Birmingham and you’ll get specific recommendations with explanations.
The AI has already done the comparing and summarising for you.
The Shift from Links to Answers
This change fundamentally affects how people find businesses. When someone asks Google’s AI Overview a question, they often get what they need without clicking anything.
Research from Ahrefs analysing 300,000 keywords found click-through rates for position one drop by 34.5% when AI Overviews appear on the page.
The main AI search platforms include ChatGPT (with over 800 million weekly users as of late 2025), Perplexity (processing around 30 million queries daily), and Google AI Overviews (appearing in around 13-16% of UK searches).
For your business, the challenge has shifted. Ranking highly in search results used to be the goal. Now you need AI systems to actually cite your content in their generated answers.
The question isn’t just whether people can find your website; it’s whether AI will mention your business when answering questions about what you do.
How Do Large Language Models Find and Process Information?
Large language models (LLMs) like GPT-4, Claude, and Gemini power most AI search tools. Understanding their mechanics helps explain why certain websites get cited while others don’t.
Training Data and Knowledge Cutoffs
LLMs learn by processing enormous amounts of text from the internet, books, and other sources.
During training, they develop understanding of language patterns, facts, and relationships between concepts. Picture it as intensive study that gives the model broad knowledge across many topics.
The limitation is that this knowledge has a cutoff date.
An LLM trained on data up to 2025 doesn’t inherently know about events in 2026. This explains why AI assistants sometimes give outdated answers when asked about recent developments.
Real-Time Information Retrieval
Retrieval Augmented Generation (RAG) addresses this freshness problem. Rather than relying solely on training data, RAG allows AI systems to search the web and retrieve current information before generating answers.
Every sentence you write in a paper should be backed with a citation… We took this principle and asked ourselves, what is the best way to make chatbots accurate, is force it to only say things that it can find on the internet, and find from multiple sources.
Aravind Srinivas, CEO of Perplexity AI
So the AI has very broad knowledge from training, but for specific or current questions, it looks up fresh information and weaves those findings into its response.
Perplexity uses this approach for every query. ChatGPT searches when it determines current information is needed, such as for recent news or time-sensitive details.
This creates a real opportunity for your business.
If your website contains accurate, useful information about your industry, RAG-based systems can find and cite it in their responses. Your content doesn’t need to have been part of the original training data.
Large Language Models (LLMs) Explained
Large Language Models are AI systems trained on massive amounts of text to understand and generate human language. They learn patterns, facts, and relationships between concepts by processing billions of web pages, books, and documents. When you ask a question, the model predicts the most helpful response based on everything it learned during training. Popular examples include GPT-4 (ChatGPT), Claude, and Gemini. The key limitation is that their knowledge has a cutoff date, which is why AI search tools now use real-time web retrieval to provide current information.
How Do AI Crawlers Index Your Website?
Just as Googlebot crawls websites to build Google’s search index, AI companies use their own crawlers to collect web content. GPTBot (OpenAI), ClaudeBot (Anthropic), and PerplexityBot are among the most active.
Training Crawlers vs Search Crawlers
AI crawlers serve two distinct purposes.
Training crawlers like GPTBot collect content to train and improve language models. This data becomes embedded in the AI’s underlying knowledge.
Search crawlers like PerplexityBot and ChatGPT-User fetch information in real-time to answer specific queries.
Cloudflare data shows GPTBot increased its crawling activity by 305% between May 2024 and May 2025, reflecting growing demand for AI training data. If you’ve noticed more bot activity in your server logs recently, this explains why.
What AI Crawlers Look For
AI crawlers differ from traditional search engine bots in several important ways. Most don’t execute JavaScript and they also favour fast-loading pages with clean HTML structure.
Factors that help AI crawlers access your content include well-organised page structure with clear headings, accurate and detailed information, schema markup that helps machines understand context, and fast server response times.
Content appearing in the initial HTML response (rather than loading via JavaScript) has much better chances of being found and used.
Website owners can control AI crawler access through their robots.txt file. A newer standard called llms.txt is emerging specifically for AI crawler permissions, though adoption is still developing.
How Do ChatGPT, Perplexity, and Google AI Find Sources?
Each major AI platform approaches source finding and citation differently. Understanding these differences reveals where your visibility opportunities lie.
Google AI Overviews
Google AI Overviews combine Google’s existing search index with the Gemini language model. When an AI Overview appears in search results, the system pulls information from multiple websites Google has already indexed and considers authoritative.
AI Overviews typically cite between five and eight sources per answer.
Pages with strong E-E-A-T signals and proper schema markup have significantly higher chances of being cited. Ahrefs research found that 76.1% of URLs cited in AI Overviews also rank in the top 10 of Google search results, but that still means nearly a quarter of cited pages rank outside the top ten.
Google also uses a “query fan-out” technique, issuing multiple related searches to build more complete answers. Ranking for related questions can be as valuable as ranking for the main query.
According to seoClarity’s analysis of 432,000 keywords, 97% of AI Overviews cite at least one source from the top 20 organic results. However, being highly ranked doesn’t guarantee inclusion.
ChatGPT and Perplexity
ChatGPT draws primarily from its training knowledge but searches the web when it detects a need for current information.
Recent news, specific details that might have changed, or questions about current events trigger web searches.
Perplexity takes a different approach, always searching before answering.
This makes it function more like a traditional search engine with AI-generated summaries layered on top. Every Perplexity response includes inline citations showing exactly where information came from. The system prioritises well-structured content from authoritative sources that directly answers user questions.
Both platforms use RAG architecture, meaning fresh, accurate content on your website can be retrieved and cited regardless of when the AI was originally trained.
Why Does AI Recommend Some Websites Over Others?
These systems rely heavily on recency and repetition. If your brand is mentioned often across the web – especially in public, crawlable formats – it’s more likely to show up in ChatGPT, Gemini, or Perplexity results.
Rand Fishkin, Paid Media Lab podcast (November 2025)
When AI systems decide which sources to cite, they look for signals indicating trustworthy, accurate, and useful content. These factors overlap with traditional SEO principles but carry some important differences.
Trust Signals AI Systems Look For
E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) plays a major role in AI source selection. Content demonstrating first-hand experience, genuine subject matter expertise, and credibility gets prioritised over generic information.
Specific trust signals include author credentials displayed on the page, proper citations supporting claims, and consistent brand information across the web.
Your presence in business directories, social profiles, and industry listings all contribute to what’s called “entity SEO.” AI systems cross-reference these signals to verify you’re a legitimate, established business.
Third-party mentions matter significantly. Being cited in reputable publications, discussed in industry forums, or mentioned in community discussions strengthens your authority.
Digital PR activities that generate brand mentions across trusted sources directly influence AI recommendations.
Detailed, genuine reviews that mention specific aspects of your service provide stronger signals than generic five-star ratings. SE Ranking research found that businesses with profiles on review platforms like Trustpilot and Google Reviews have three times higher chances of being cited by ChatGPT compared to those without.
Why Structure Matters
AI systems extract information in chunks rather than reading entire pages. Content broken into clear, focused sections with descriptive headings performs better because the AI can identify and extract relevant passages more easily.
FAQ sections work particularly well. They’re already structured as questions and answers, matching how people interact with AI tools. Statistics and specific data points also get cited more frequently than vague statements.
Research from Princeton University, Georgia Tech, and IIT Delhi (published KDD 2024) found that including citations, quotations, and statistics can improve AI visibility by up to 40% compared to content without these elements.
Clear, quotable passages that directly answer common questions give AI systems something concrete to reference. If your content reads like a helpful expert giving a straight answer, it’s more likely to be cited.
What Does AI Search Mean for Your UK Business?
Understanding AI search mechanics isn’t about gaming the system, these platforms are specifically designed to reward helpful, accurate content from genuine experts.
The businesses that perform well in AI search are typically those already creating quality content for their customers.
The opportunity for UK small businesses is real.
Many larger competitors haven’t adapted to AI search yet.
This creates a window where smaller businesses with genuine expertise can establish authority before the field becomes more competitive.
If your website clearly demonstrates what you know, provides accurate information, and remains technically accessible to AI crawlers, you can appear in AI-generated recommendations alongside or instead of bigger players.
Your first step should be auditing current visibility.
Search for questions your customers commonly ask on ChatGPT, Perplexity, and Google. Note whether your business or competitors get mentioned. Pay attention to what kinds of sources get cited and how they present their information. This gives you a baseline for improvement.
For practical strategies to improve your AI search visibility, see our complete guide to AI Search Optimisation.
Frequently Asked Questions
An AI search engine answers questions directly rather than showing website lists. Tools like ChatGPT, Perplexity, and Google’s AI Overviews understand your question, search for relevant information, and generate a response with cited sources. You receive a synthesised answer instead of browsing multiple websites.
ChatGPT combines knowledge from its training data with real-time web searches when needed. General knowledge questions draw on what it learned during training. Current events, specific details, or time-sensitive information trigger web searches that incorporate fresh results into responses. This combination approach is called Retrieval Augmented Generation.
RAG allows AI systems to search for and retrieve current information before generating answers. The AI has broad knowledge from training, but for specific or recent questions, it looks up fresh information from the web and incorporates those findings. This addresses the limitation of training data having a cutoff date.
Google AI Overviews pull from Google’s existing search index using the Gemini language model. They typically cite five to eight sources per answer. Pages with strong trust signals, clear structure, and demonstrated expertise have higher chances of being cited. You don’t need to rank number one; research shows many cited pages rank outside the top ten traditional results.
E-E-A-T stands for Experience, Expertise, Authoritativeness, and Trustworthiness. AI systems use this framework to evaluate source credibility when deciding what to cite. For your website, this means displaying credentials, citing sources for claims, keeping information accurate and current, and demonstrating genuine first-hand experience with your subject matter.
Read more: E-E-A-T Optimisation for AI Search
Perplexity always searches the web before answering, functioning like a search engine with AI-generated summaries. ChatGPT only searches when it determines current information is needed. Perplexity shows sources inline with every answer, making verification easier. Both use similar underlying technology but apply it to different user experiences.
You probably don’t need a complete overhaul. AI search rewards helpful, accurate, well-organised content from credible sources. Focus on clear structure with descriptive headings, demonstrating your expertise through detailed content, keeping information current, and ensuring your site loads quickly. These improvements benefit both AI and traditional search visibility.
Common reasons include: weak signals of expertise and trustworthiness on your website, content structure that makes extraction difficult, technical issues preventing AI crawlers from accessing pages, or competitors having stronger authority signals from third-party mentions and citations. Manual testing on AI platforms helps identify specific gaps.
Search for questions your customers commonly ask on ChatGPT, Perplexity, and Google with AI Overviews enabled. Note whether your business gets mentioned and which competitors appear. Pay attention to how cited sources present information. This manual testing establishes your baseline visibility before making improvements.