LLM Optimisation: How Your Store Shows Up in AI Answers

“Which standing desk under 500 euros is any good?” – questions like this are increasingly not googled any more, but put to ChatGPT, Claude or Perplexity. The answer names three products and two stores. Either yours – or your competitor’s. Visibility in LLM answers (often called GEO, Generative Engine Optimization) has become a discipline in its own right, alongside SEO.

Key takeaways

• AI answers draw on three sources: training knowledge, live search results, and increasingly structured data that agents read directly.
• Citable content – facts, tables, honest FAQs – gets cited; marketing fluff doesn’t.
• Entity clarity (consistent naming, clean schema markup) decides whether models can even correctly identify your brand and products.
• Blocking AI crawlers in robots.txt means becoming completely invisible in this channel.
• The channel is young – whoever measures and optimises today builds a lead that’s hard to catch up on later.

How LLMs form recommendations

Three sources feed AI answers to buying-advice questions: the model’s world knowledge learned during training, live search results in web-enabled systems such as Perplexity or ChatGPT Search, and increasingly, structured data that agents read directly. You can invest in all three – just on different time horizons.

Training data works slowly, over months and new model versions – what matters most here is whether your content is present in the open web and citable whenever a model gets trained. Search integration, by contrast, works almost instantly: improve your classic ranking today, and it can show up in an AI tool’s very next live search. Structured data, finally, is the future lever, one that gains weight as agent usage grows – more on that in our article on agent-ready commerce.

Creating citable content

LLMs love clear, fact-rich answers and avoid vague marketing language – comparison tables, specifications, honest pros and cons, and substantial FAQ blocks all get cited disproportionately often. The mechanism behind this is simple: a model composing an answer looks for extractable facts, not mood.

That has a direct consequence for product pages: thin, interchangeable content – identical descriptions across colour variants, for example – gets penalised by search engines and ignored even more thoroughly by language models, because it offers no extractable value. Our article on AI product copy at scale describes how to avoid this with copy generated at scale.

Establishing entity clarity

Brand, products and company need to be recognisable as clear, unambiguous entities so a model can correctly place them in context. In concrete terms, that means: consistent name spelling across every channel, clean Organization and Product schema as JSON-LD, and presence in sources models tend to trust – Wikipedia-adjacent sources, trade media, reputable review platforms.

Inconsistent brand names or contradictory product information across different channels don’t just confuse customers – they stop a model from even recognising two mentions of the same brand as belonging together.

In practice, that means writing your company name, brand name and product names identically on your own website, in marketplace listings, in press mentions and on review platforms – a company that shows up sometimes as “blicht GmbH”, sometimes as “Blicht” and sometimes as “blicht.com” makes it needlessly hard for models to map all three mentions to the same entity. A clean Organization schema with name, logo, contact details and social profiles as JSON-LD on every page isn’t a nice-to-have here – it’s the machine-readable confirmation of that identity.

Ensuring machine readability

AI crawlers first need to be technically able to read what’s on the page at all – that sounds trivial, but in practice it fails at the same points as classic SEO often does. GPTBot, ClaudeBot & co need to be allowed in robots.txt, an llms.txt can help as a curated site summary on top of that, and content should be delivered as clean HTML rather than a JavaScript facade that, in the worst case, a crawler sees as empty.

Aspect	Classic SEO	LLM optimisation (GEO)
Target format	Ranking position	Citation/mention in the answer
Most important signal	Backlinks, keywords	Fact density, entity clarity
Measuring success	Search Console, rankings	Manual spot checks, referral traffic
Time horizon	Weeks to months	Instant (search) to months (training)

Collecting mentions, not just links

LLMs weigh where and how often a brand shows up in the context of a topic – not just who links to whom. PR, reviews and community presence therefore pay directly into AI visibility, even with no classic backlink at all. Customer reviews play an underrated role here, because they give models additional, credible mentions that extend beyond your own website.

Content formats that get cited disproportionately often

Certain content formats are demonstrably drawn on as a source by language models more often than others, because they deliver facts in extractable form instead of burying them in flowing prose. Checking your most important pages against these formats raises the odds of being cited, without needing to reinvent the underlying content.

What works particularly reliably: structured comparison tables with clear column headings, numbered step-by-step instructions, FAQ blocks that open with a direct answer, and definitions that pin down a term precisely in one or two sentences before the deeper explanation follows. Plain prose paragraphs with no discernible structure, on the other hand, get cited less often, even when they’re just as factually correct – the model first has to laboriously distil the facts out of the text, instead of lifting them directly.

From experience: a simple test shows whether a page is built to be citable: can you extract a correct, standalone answer to the heading from the first two or three sentences of a section? If not, the actual information sits buried too deep in the text for a model to reliably pick it up.

The most common mistakes in LLM optimisation

Four patterns prevent visibility in AI answers in practice:

1. AI crawlers blocked: out of privacy or control concerns, GPTBot gets blanket-blocked – and the brand disappears from the channel completely. Fix: a deliberate, documented decision instead of blocking by default.
2. Only marketing language, no facts: product pages full of adjectives but without specifications give a model nothing to cite. Fix: add concrete facts and comparison data.
3. Inconsistent brand presentation: different spellings or contradictory information across channels prevent entity recognition. Fix: maintain name spelling and core facts centrally.
4. No monitoring: nobody regularly checks their own buying questions in the AI tools – problems only surface once revenue is noticeably missing. Fix: establish a fixed review cadence.

Measuring what almost nobody measures today

A simple, repeatable audit process makes GEO visibility tangible, even without mature tracking tools:

1. Compile a list of your category’s twenty most important buying questions.
2. Ask these questions in ChatGPT, Claude and Perplexity every month and log whether and how your store gets mentioned.
3. Note the sources cited in the answers – they often reveal which of your own pages, or which third-party sources, actually get read.
4. Track referral traffic from AI sources separately in your own analytics, rather than letting it disappear into “other sources”.
5. Close any noticeable gaps (questions with no mention) with targeted, citable content.

From experience: this audit takes one to two hours a month – small compared with the insight it delivers, because hardly any competitor is currently monitoring this channel systematically.

The bottom line

The channel is young – which is exactly why the lead you build now is so hard to catch up on later. If you’ve already mastered classic SEO, you don’t need to learn a whole new discipline for LLM visibility – you just need to extend your existing strengths with citability, entity clarity and machine readability. The most pragmatic first step: ask your own twenty most important buying questions in ChatGPT, Claude and Perplexity yourself this week, and honestly check whether your store shows up at all.