Search behaviour is changing. A growing share of people ask ChatGPT, Google's AI Overviews or another assistant for a recommendation instead of scrolling through search results themselves. That shifts the question from "how do I rank on page one" to "how does an AI decide which business to mention at all." Here's what actually influences that.
The short answer: clear, structured, accurate information
AI search tools generate answers by reading across many sources and extracting facts, rather than sending a visitor to browse a list of links themselves. What gets a business mentioned is content an AI can read confidently and extract without guessing — clear statements of what you do, where you are, what you charge and how to contact you, ideally backed by structured data that labels those facts explicitly.
What AI search tools actually look for
- Clear, factual statements. Direct sentences ("We offer X starting from RM Y") are easier for an AI to extract and repeat accurately than vague marketing copy.
- Structured data (schema markup). Code that labels your business name, services, pricing, hours and location explicitly, rather than leaving an AI to infer it from prose.
- Consistency across the web. The same name, address, phone number and details on your website, Google Business Profile and other listings, so an AI isn't reconciling conflicting facts.
- Content that's actually crawlable and current. Pages that load reliably and reflect up-to-date information, since AI tools work from the same indexed content traditional search does.
How different AI assistants source information
The specifics vary tool to tool, but they converge on the same underlying signals above. ChatGPT answers from training data with a fixed cutoff unless browsing or search is enabled, in which case it pulls in live web results similar to a search engine. Perplexity searches the live web for every query and displays its sources alongside the answer, so being a cited source can drive a direct click. Gemini draws heavily on Google's own search index, so the same signals that help traditional Google visibility carry over. Copilot is built on Bing's search index, rewarding similar fundamentals — clear content, structured data, an accurate Bing or Google business listing. None of this calls for a separate playbook per tool; getting the fundamentals above right once carries across all of them.
How this differs from traditional SEO
Good SEO fundamentals — fast pages, clear content, accurate information — still matter and form the foundation of AI visibility too, so you're not starting from scratch. The difference is that traditional SEO optimises for ranking in a list a person scrolls through, while AI search optimises for being confidently extracted and stated as fact in a generated answer. That puts more weight on structured data and unambiguous, well-labelled information than on keyword density or backlink count alone.
Outdated information is worse than no mention
An AI assistant reading a page with a disconnected phone number, a discontinued service, or last year's pricing may repeat that outdated information with full confidence — which does more damage to a business than not being mentioned at all. Keeping core facts (contact details, services, pricing, hours) current across a website and its listings is one of the simplest, highest-leverage things a business can do here.
Does a Google Business Profile still matter?
Yes, and possibly more than before. AI tools frequently draw on the same business listing data — name, address, opening hours, category, reviews — that powers Google Maps and local search results. Keeping that profile accurate, complete and regularly updated remains one of the highest-value, lowest-effort things a local business can do, whether the recommendation comes from a person searching or an AI generating an answer.
Does an llms.txt file help?
It's an emerging convention rather than a guarantee — a small text file at a website's root that summarises key pages in plain language for AI tools to read. Some AI crawlers reference it; others ignore it entirely, since it isn't a universal standard the way a sitemap is for search engines. It's worth adding as a low-cost extra, but it doesn't replace clear content and structured data as the fundamentals.
Practical steps a small business can take
Most of this doesn't require rebuilding a website: keeping your services, pricing and contact details stated clearly and consistently across your site and listings, adding schema markup so that information is machine-readable, and keeping a Google Business Profile current all move the needle without a major project. Where it gets more involved is comprehensively structuring an existing site's content — which is usually best done as part of a wider redesign or add-on rather than a quick edit.
Where Gotka Technologies fits
Gotka offers Generative Engine Optimisation as an add-on to web design packages — structuring a site's content and adding schema markup so AI search tools and assistants across ChatGPT, Perplexity, Gemini and others can read and represent the business accurately. It's built on top of the basic SEO (meta titles, alt text, sitemap) included in every package — see the full package details on the package builder.
What's different about how AI search engines choose which businesses to recommend?
Traditional search engines rank pages using links, keywords and page authority, then leave you to read and compare results yourself. AI search tools instead read across many sources, extract facts, and generate a direct answer or recommendation — so what matters most is whether your site states clear, accurate facts an AI can confidently pull out and repeat.
Do I need to do anything different for AI search versus normal SEO?
Good traditional SEO — clear content, fast pages, accurate information — is also good AI-search visibility, so you're not starting from zero. The additional piece is structured data that spells out facts like your services, pricing, hours and location in a format machines can read directly, rather than making an AI guess by parsing prose.
What is schema markup, and why does it matter for AI search?
Schema markup is a small block of structured code added to a page that labels information explicitly — this is the business name, this is the price, this is the opening hours — instead of leaving an AI to infer it from surrounding text. It reduces the chance of an AI getting a fact wrong or skipping a business because the information wasn't clearly labelled.
Does a Google Business Profile still matter for AI recommendations?
Yes — AI tools frequently draw on the same business listing data (name, address, hours, reviews, category) that powers Google Maps and local search results, so an accurate, complete, regularly updated Google Business Profile remains one of the highest-value things a local business can maintain.
Can old or outdated information on a website hurt AI recommendations?
Yes — an AI reading a page with a stale phone number, a discontinued service, or old pricing may repeat that outdated information confidently, which is worse for a business than not being mentioned at all. Keeping core facts (contact details, services, pricing) current is one of the simplest ways to avoid being misrepresented.
Does site speed or mobile-friendliness affect AI search visibility?
Indirectly, yes. AI tools rely on the same crawled and indexed content that traditional search does, so a site that's slow, broken on mobile, or difficult to crawl is less likely to be accurately read and summarised in the first place, regardless of how good the underlying information is.
Does Gotka help make a website more visible to AI search tools?
Yes — Gotka offers Generative Engine Optimisation as an add-on to web design packages, which structures a site's content and adds schema markup specifically to improve how AI tools read and represent the business.
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