Perplexity, Claude, Gemini and ChatGPT: How to Show Up in Every AI Search Engine

One question, several engines

Ask the same question (“who are the best commercial landscapers in Halifax?”) in Perplexity, ChatGPT, Claude, Gemini and Copilot, and you may get five different answers. Some name businesses; some name directories; some hedge. Each draws on different sources, in different ways, and none of them publish how they weigh those sources.

That variety tempts people into platform-by-platform tactics, often sold with confident claims about what each engine “prefers”. Most of those claims are guesses dressed as findings. What can be said with confidence is narrower and more useful: where each kind of engine gets its information, and what that implies for a business that wants to be found.

The general mechanism (how an assistant retrieves, reads and weighs sources before recommending anyone) is explained in how AI search engines find and recommend businesses. This article is about the differences between platforms and the shared foundation that serves all of them.

Where each engine looks

The products change often, so treat this as a description of their broad design rather than a feature list. The durable distinction is between answers built from live search and answers built from what a model learned in training.

Perplexity

Perplexity is built as an answer engine: for nearly every question it searches the web, reads a set of pages and writes an answer with numbered citations the user can click. That makes it the most search-like of the group. If your pages are not found by the searches it runs, or cannot be read when fetched, you will not appear. It also means visibility here can change quickly when your pages improve, because it is reading the current web rather than a memory of it.

ChatGPT

ChatGPT answers many questions from what its model learned in training and searches the web when a question needs current or specific information, which local and “who should I hire” questions usually do. OpenAI documents separate crawlers for search and for model training, and site owners can allow one while blocking the other.

Claude

Claude, from Anthropic, works in a similar way: answers from training, with web search available for questions that need it. Anthropic also publishes the names of its crawlers and how they respond to robots.txt. Claude is widely used inside workplaces, which makes it relevant for business-to-business questions in particular.

Gemini and Google’s AI features

Gemini, and the AI Overviews and AI Mode in Google Search, draw on Google’s own index and its business data, including Business Profiles and Maps. Google has stated that no special optimization or markup is needed for its AI features beyond normal search best practice. For a local business, this is the engine where the Business Profile matters most. Google also offers a separate control (Google-Extended) that governs whether site content is used for its AI models, which is distinct from appearing in Search.

Microsoft Copilot

Copilot is grounded in Bing search results. Many businesses have never looked at how they appear in Bing, never verified their site in Bing Webmaster Tools and never claimed a Bing Places listing. For Copilot visibility, and for any assistant that uses Bing results somewhere in its pipeline, that is low-effort ground to recover.

What the differences actually mean for you

Three practical implications follow from those designs, and none of them require guessing at secret ranking factors.

1. Be in more than one index. Engines that search live can only read what their search finds. Most businesses check Google and ignore Bing. Verify your site with both, submit a sitemap to both, and check that your important pages are indexed in both.
2. Decide your crawler policy on purpose. Each provider documents its crawlers, and many sites block some of them by accident through a firewall rule, a security plugin or a hosting default. Look at your robots.txt and your firewall settings. Blocking training crawlers while allowing search crawlers is a legitimate choice; blocking everything by accident is not.
3. Training data is slow, live search is fast. What a model learned about you in training only changes when a new model is released, and you cannot submit corrections. What it finds through live search reflects the web today. So work on the current web, and expect answers that rely on training to lag.

The foundation that works across every engine

Because every engine is ultimately reading and weighing the same public web, the work that improves visibility in one improves it in the others. The foundation has five parts.

• Accessible pages. Key information in plain text on pages that load without logins, scripts that hide content, or pop-ups covering the text. Service descriptions in images and price lists only in PDFs are invisible to some systems.
• Clear, specific pages. One page per service that says exactly what you do, for whom and where, with the direct answer near the top. Assistants quote what they can extract cleanly.
• Consistent facts. The same name, address, services and contact details on your website, Google Business Profile, Bing Places, Apple Business Connect and the directories that matter in your field. What business information helps AI search systems covers how to build and maintain that canonical record.
• Independent corroboration. Reviews, association listings, regional press, partner pages and client mentions that describe you accurately. Every engine treats your own site as a claim and other sources as evidence.
• Accurate structured data. Markup that mirrors what the page says, so machines read your facts unambiguously. Useful, but not a shortcut; structured data and AI citations explains what it does and does not do.

Notice how closely this matches good search engine optimization. That is not a coincidence, and it is why we argue for one strategy for search and AI visibility rather than two programs competing for the same budget.

Testing across platforms without fooling yourself

Every business owner who tries an assistant once and sees a competitor named feels the urge to react. One answer is a snapshot. The same question can produce different businesses an hour later, for a different user or in a different location. A useful test is systematic:

1. Write ten to twenty questions a real buyer would ask, in their words: category questions (“best employment lawyer in Saint John”), comparison questions, and direct questions about your business.
2. Include French versions if you serve francophone buyers. In New Brunswick, a buyer may ask in either language and get different sources.
3. Ask each question in each platform you care about, in a clean session where possible, on the same schedule each month or quarter.
4. Record whether you are named, how you are described, which competitors appear, and which sources are cited.
5. Look at the cited sources. They tell you which directories, articles and review sites each engine trusts for your category, which is far more actionable than the answer itself.

Combine that with referral traffic from assistants in your analytics and a simple question to new inquiries about how they found you. How to measure AI search visibility sets out an honest monthly scorecard, including why most “AI visibility scores” deserve scepticism.

A worked example

Consider an illustrative accounting firm with offices in Fredericton and Moncton. A partner asks two assistants for “accountants for small businesses in Moncton” and the firm appears in neither.

A methodical look finds:

• The website is indexed in Google but the firm has never verified it in Bing, and several service pages are missing from Bing’s index.
• A security setting on the host is blocking several AI crawlers, which nobody chose deliberately.
• The Moncton office has a Business Profile, but the website has no page for that office, and services are listed only as a bulleted line on the home page.
• The sources cited for the question are a provincial professional directory, a business association listing and two local review pages. The firm’s listing in the directory shows an old address.

The fix list writes itself: verify with Bing, decide the crawler policy, write a proper page for each office and each core service, correct the directory listing, and ask satisfied small-business clients for reviews. None of it is platform-specific, and all of it helps in Google too.

What to skip

Some advice about AI engines is worth ignoring:

• Platform-specific content. Writing separate pages “for Perplexity” or “for Claude” splits your effort and confuses everyone, including the engines.
• Guaranteed placement. No one can guarantee inclusion in an assistant’s organic answers. There is no submission form.
• Volume for its own sake. Publishing large amounts of thin content to “feed” AI tends to dilute a site rather than strengthen it. A smaller number of genuinely useful pages does more.
• Chasing every product change. Features, partnerships and interfaces change constantly. The foundation above has stayed stable through all of them.

Our SEO and AI visibility work is built on that foundation: make the business easy to find, read and corroborate, then measure honestly across the engines your buyers actually use. For more tactics at the content level, see the LLM optimization guide.