AI search optimisation is the practice of structuring and positioning your content so that AI platforms — ChatGPT, Google AI Overviews, Perplexity, Gemini, and similar tools — can find it, understand it, trust it, and cite or reference it when answering a user's question. It overlaps heavily with traditional SEO but adds a new layer: writing and structuring content so it can be lifted cleanly into an AI-generated answer, not just ranked on a results page.
We started paying close attention to this a couple of years ago as more of our clients' potential customers began getting their first answer from an AI assistant rather than a page of blue links. If you want to see how we apply this practically for clients, our AI search optimisation agency page covers our process end to end.
Why we treat this as a distinct discipline, not a footnote to SEO
We're occasionally asked why this deserves its own name rather than just being "modern SEO." Our honest view: it's related closely enough to SEO that the two should be planned together, but different enough in what it actually optimises for that treating it as a mere subset undersells the shift. Traditional SEO optimises for a link being clicked; AI search optimisation optimises for a claim being trusted enough to be synthesised, without a click, into someone else's answer. That's a genuinely different unit of success, even though many of the underlying tactics — depth, clarity, technical accessibility — overlap substantially.
How AI search differs from Google search
Traditional Google search returns a ranked list of links; the user clicks through and reads. AI search — whether that's a chatbot answer, an AI Overview, or a generative search result — synthesises an answer directly, often citing a small number of sources, and many users never click through at all.
That changes the goal. With traditional SEO, ranking #1 is the win. With AI search, being one of the two or three sources an AI platform actually pulls from and cites is the win — and that's a different, more competitive filter, because the AI is selecting a handful of sources rather than presenting ten.
What AI search optimisation involves
In practice, we think about it across four layers:
- Discoverability — can AI crawlers and retrieval systems actually access and parse your content (not blocked, not buried behind JavaScript that doesn't render for crawlers)?
- Clarity — is the answer to a specific question stated plainly and early, rather than buried under three paragraphs of preamble?
- Authority — does your business have enough of a footprint (mentions, citations, consistent entity information) that an AI model trusts your content as a source?
- Structure — is content organised in a way that's easy to extract cleanly — clear headings, direct answers, well-labelled data?
AEO, GEO and LLM SEO explained
You'll see a few overlapping terms used almost interchangeably:
- AEO (Answer Engine Optimisation) focuses on optimising content to directly answer specific questions, the way it would be surfaced in a featured snippet or a voice assistant response.
- GEO (Generative Engine Optimisation) is the broader term for optimising content specifically for generative AI tools that write novel answers by synthesising multiple sources.
- LLM optimisation / LLM SEO refers more narrowly to improving how large language models like ChatGPT or Claude represent and cite your brand when asked about your category.
In practice, the tactics behind all three overlap heavily, and we've written a full comparison in SEO vs AEO vs GEO if you want the terminology untangled in one place.
How AI platforms select sources
Based on what we've observed testing hundreds of queries across different platforms, AI systems tend to favour sources that are: topically comprehensive (covering a subject in real depth, not a thin 300-word post), structurally clear (answer-first content with headings that map to actual sub-questions), consistently corroborated (the same facts appear across multiple credible sources, not just yours), and technically accessible (not blocked by robots.txt, not hidden behind heavy client-side rendering).
Content factors that improve visibility
The content changes we see make the biggest difference:
- Answering the core question in the first sentence or two of a section, before adding nuance
- Using descriptive, question-style headings that match how people actually ask AI tools things
- Including specific facts, figures, and definitions that are easy to lift as a standalone answer
- Structuring comparisons and processes as tables or numbered steps rather than dense paragraphs
- Keeping content genuinely comprehensive on a topic rather than splitting it thinly across many short pages
Authority and entity signals
AI models draw on more than just your own website — they weigh how consistently your brand, expertise, and claims are represented across the web. That means your Google Business Profile, third-party directory listings, press mentions, and even how other sites describe your business all feed into whether an AI system treats you as a credible source. Consistent naming, consistent service descriptions, and genuine third-party validation matter more here than they typically did for classic keyword-based SEO.
A practical example of applying this framework
Consider a business auditing its own visibility for the first time. Running a dozen realistic customer queries through ChatGPT and Perplexity reveals the business is rarely mentioned, while two specific competitors appear consistently. Looking at those competitors' content, a clear pattern often emerges: their pages open with a direct answer in the first sentence, cover the topic in genuine depth rather than a shallow overview, and are corroborated by mentions on a few relevant third-party sites. Reworking the business's own priority pages to match this pattern — not copying the competitors' content, but matching the structural and depth standard they've set — combined with pursuing a small number of genuine third-party mentions, is typically the fastest path to closing that visibility gap.
Why this matters even before it shows up in direct traffic
A reasonable objection to investing in this now is that AI referral traffic is still a relatively small slice of most businesses' overall visitor numbers compared to traditional organic search. We'd argue the trend line matters more than the current snapshot: the share of research happening through AI assistants is growing steadily, and the content and authority-building work involved in AI search optimisation strengthens traditional SEO performance at the same time, since the two disciplines reward largely the same underlying qualities. Waiting until AI referral volume is large enough to feel urgent means starting from behind competitors who built this foundation earlier.
How to measure AI search performance
This is newer territory than traditional rank tracking, but a few practical approaches work well: regularly running your own target queries through ChatGPT, Perplexity, and Google AI Overviews and logging whether and how you're cited; monitoring referral traffic from AI platforms in your analytics (this is becoming a distinct traffic source); and tracking brand mention frequency and sentiment when AI tools are asked about your category or competitors.
If you want a deeper technical walkthrough specifically for showing up inside ChatGPT responses, we've written a dedicated guide on how to rank in ChatGPT, and a companion piece on ranking in Google AI Overviews. It's also worth reading how this connects to conventional AI SEO practice and broader AI content marketing, since none of these disciplines work well in isolation anymore.
Common mistakes we see businesses make
The most common mistake is treating AI search optimisation as purely a technical checklist item — adding schema markup and calling it done — without addressing the underlying content depth and cross-web authority signals that actually drive citation. A second common mistake is expecting fast results; unlike a paid advertising campaign, this is a compounding discipline that typically takes a few months of consistent work before visibility shifts become clearly measurable. Businesses that understand both of these points upfront tend to invest more patiently and see considerably better results than those expecting quick, purely technical fixes.
Where to start if you're new to this
If you're beginning from scratch, start by simply running your own priority customer questions through ChatGPT and Google to see where you currently stand — this single step usually clarifies more about your realistic starting point than any amount of theory.
FAQs
What is AI search optimisation? It's the practice of structuring your content and building your online authority so that AI platforms like ChatGPT and Google AI Overviews can find, trust, and cite your business when answering relevant questions.
Is AI search optimisation different from SEO? It overlaps significantly with SEO but adds a layer focused on answer clarity, structural extractability, and cross-web authority signals, since the goal shifts from ranking a link to being cited inside a generated answer.
How do you get mentioned by ChatGPT? By building genuine topical authority (comprehensive, well-structured content), ensuring your content is technically accessible to crawlers, and having your expertise corroborated across multiple credible third-party sources.
Want to know exactly where your business currently stands in AI search results? Book an AI visibility audit with our team.
