ChatGPT, Claude and Perplexity all answer questions about businesses, but they do not all find that information the same way. The difference matters because a business well-represented in one engine’s sources can be invisible to another. This guide explains the retrieval logic behind each of the three major AI engines, what signals they reward, and how to make your business findable across all of them.
The short answer: Perplexity searches the live web on every query, ChatGPT blends trained knowledge with optional live retrieval, and Claude draws primarily from its training data. All three, however, reward the same underlying signal: a clear, consistent, corroborated picture of your business across multiple sources.
The question your customers now ask twice
Before looking at how each engine works, it helps to understand why this matters.
Your potential customers now ask “who is the best [your service] in [your area]” in two places. Once, they searched Google and browsed links. Increasingly, they ask one of these AI engines directly and get a short named shortlist back: three to five businesses, no other links to scroll past. You are either in the answer or you are not, and you never see the question being asked. You see only the consequence: the enquiries that did not arrive.
Understanding how these engines find information is not a technical curiosity. It is the difference between being in that shortlist and being invisible to a category of buyer you cannot see.
How does Perplexity work for business information?
Perplexity is the most transparent of the three about how it works. So how does Perplexity work in practice? It performs a live web search on every query, retrieves the current pages its index surfaces, and synthesises them into a cited answer. The citations are visible in the response, so you can see exactly which sources it drew from.
This live-retrieval model has two practical implications for your business:
Freshness counts. A recently updated website, a new review published last month, or a recent article mentioning your business can surface in a Perplexity answer faster than it would influence a model whose training data has a fixed cut-off. If your Google Business Profile was updated this quarter, Perplexity is more likely to include that updated information than ChatGPT or Claude operating without live search.
Coverage counts. Perplexity searches across the web broadly. A business mentioned consistently across its own website, directories, review platforms, and third-party articles gives the engine multiple sources to draw from and cite. A business described only on its own homepage gives it one source to rely on, which introduces uncertainty into any recommendation.
The implication: for Perplexity visibility, the work is keeping your web presence current and consistent. Stale directory listings, an outdated website, or a thin review profile all reduce the strength of what the engine finds.
How ChatGPT finds business information
ChatGPT operates in two distinct modes, which matters for how businesses appear in its answers.
In standard (non-browsing) mode, ChatGPT draws on its training data: a large portion of the public web as it existed up to the model’s knowledge cut-off. Businesses that were well-documented in multiple high-quality public sources before that cut-off are better represented. A business that existed but left no public footprint, or whose information was thin and inconsistent, may simply not appear. The model learned from what the web said about you, so if the web said very little, the model knows very little.
In browsing mode, ChatGPT supplements its training with live retrieval from Bing-indexed pages. This is how the Plus and Enterprise versions often operate for queries about current businesses or local services. Here it behaves more like Perplexity: it searches, retrieves and synthesises current pages, and consistent, current, well-structured pages are more likely to surface as the basis for a recommendation.
One important pattern: ChatGPT is cautious about naming businesses it is uncertain about. If the training signal for your business is ambiguous or thin, its safest behaviour is to name a competitor it has more information about, or to give a generic answer without naming anyone. Confidence comes from the volume and consistency of what it has seen. Our guide on how to get cited by ChatGPT explains the signals that drive this.
How Claude finds business information
Claude, developed by Anthropic, differs from the other two in its standard deployment. It is primarily a language model trained on a large dataset with a fixed cut-off, and in its public form it does not perform live web searches by default. Enterprise integrations can add retrieval capabilities, but for most users asking Claude about local services or specific businesses, the answers come from its training data.
This makes the training signal the critical variable. Businesses that were well-described in reliable public sources before Claude’s training cut-off are better represented. This includes: structured pages on the business’s own website, consistent descriptions across directory and review platforms, and third-party articles or mentions that corroborate the business’s identity and services.
Because Claude draws from training rather than live retrieval, a well-formed llms.txt file carries particular weight. This structured file tells AI models who you are, what you do, and what your important pages contain. For a model that crawls training data rather than searching on each query, that machine-readable structure helps ensure the right information was ingested at training time.
What all three engines have in common
Despite their different retrieval mechanisms, all three engines reward the same quality: a clear, consistent, corroborated picture of your business.
This means that the core work of becoming visible across all three is the same programme of tasks:
Entity clarity. Your website, directories, reviews and third-party mentions should all describe your business the same way: the same name, services, location and differentiators. Contradictions introduce uncertainty, and uncertainty makes an engine hedge or name someone else.
Corroboration. A business described only on its own website gives an engine one source of evidence, which is a risky basis for a recommendation. Consistent mentions across independent sources, review platforms and third-party articles give the engine multiple agreeing sources to draw from.
Structure. Information laid out so a machine can extract it cleanly beats the same information buried in dense prose. Clear headings, concise service descriptions, structured data markup and a well-formed llms.txt file all reduce the friction between your business and the engines reading you. This is where Answer Engine Optimisation (AEO), the practice of making your business visible to AI search engines, earns its place alongside traditional SEO.
Freshness, where it counts. For Perplexity and ChatGPT in browsing mode, recent pages matter. For Claude in training mode, the pattern of what the web said about you over time matters more. Either way, a business described well for a sustained period beats one that updates everything the week it realises the engines are not naming it.
Why each engine produces different results for the same business
Different retrieval mechanisms mean the same business can appear very differently across engines. Perplexity might name you confidently because your recent website update included a clear service page and your Google Business Profile is current. ChatGPT in browsing mode might also name you, but in standard mode it might describe you differently, or not at all, if your pre-cut-off footprint was thin. Claude might give a general answer about your category without naming you if your training signal was weak.
This is why a single manual test, asking one engine one question, gives an incomplete picture. A Visibility Briefing runs the same questions across all four major engines (ChatGPT, Claude, Perplexity and Google AI Overviews) and produces a citation rate for each. That is the real baseline: not “did ChatGPT name me once?” but “across the questions my customers are actually asking, how often is each engine naming me?” Our guide on AI visibility explains how to run this kind of audit.
The practical implication: measure across all engines, not just one
The businesses that perform well across all three engines treat their information consistency as a business asset rather than a marketing task. They describe themselves the same way everywhere, earn reviews, generate third-party mentions over time, structure their pages so machines can read them, and maintain an llms.txt file.
This is not a campaign with a launch date and a results deck. It is a standing set of signals that either exist or do not. The engines read your business every time they are asked about your category, and the question is whether what they find is confident enough to recommend you.
A Visibility Briefing is the starting point: an audit of what each engine currently says about your business, where you are named, where you are not, and which sources each engine is drawing from. It hands you the baseline and the priorities before you commit to any ongoing work. If you want to know where you stand across ChatGPT, Claude and Perplexity right now, Request a Visibility Briefing and we will show you exactly what each engine says about you.