Why AEO and GEO suddenly matter #

Search stopped being ten blue links a while ago. Now it’s a conversation.

More than 30% of searches today start or end inside an AI tool rather than a traditional results page. Someone asks ChatGPT to compare vendors, asks Gemini to summarize a category, or asks Perplexity for a recommendation — and gets a single, confident answer with no scrolling required. If your brand isn’t the source behind that answer, you don’t just rank lower. You don’t exist in that conversation at all.

That shift is why two new disciplines have moved from “nice to have” to core strategy: Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO). They sound similar, get used interchangeably in most marketing content, and are genuinely different in practice. Getting the distinction right changes how you brief your content team, your dev team, and your SEO strategy as a whole.

What is AEO (Answer Engine Optimization)? #

Answer Engine Optimization is the practice of structuring your content so it can be lifted directly into an “answer slot” — a featured snippet, a voice search response, a People Also Ask box, or a Google AI Overview. The target isn’t a blue link position. It’s the answer position itself, above and outside the normal ranking list.

AEO is largely won or lost on structure:

• A direct, quotable answer in the first 1–2 sentences under each heading
• Questions phrased the way real customers type or speak them
• Clean use of lists, tables and definitions that are easy to extract
• Schema markup (FAQ, HowTo, Article) that tells engines exactly what each block of content is

Think of AEO as writing for a machine that needs to lift one clean paragraph out of your page and present it as the answer, with your brand attached to it.

What is GEO (Generative Engine Optimization)? #

Generative Engine Optimization is broader. It’s about how your brand gets referenced, described, and recommended inside AI-generated answers overall — not just in one answer box, but across ChatGPT conversations, Gemini summaries, and Perplexity research threads that may never touch a traditional search results page at all.

GEO leans less on structure and more on authority and machine-readable identity:

• Being a clearly defined “entity” in Google’s Knowledge Graph and industry databases
• Earning mentions, backlinks and citations from sources AI models already trust
• Consistent facts about your brand across your website, directories and third-party content
• Content that reads as a reliable, citable source rather than promotional copy

Where AEO optimizes a page, GEO optimizes your brand’s reputation across the entire web that AI models are trained on and retrieve from. Our GEO & AEO services are built to run both tracks together rather than treating them as separate projects.

AEO vs GEO: the key differences at a glance #

Why your brand needs both, not one or the other #

Treating AEO and GEO as competing strategies is the most common mistake we see. In practice they feed each other: the same well-structured, clearly-sourced content that wins a featured snippet is exactly the kind of content large language models prefer to cite. A page built only for GEO but ignoring answer-format structure will struggle to get pulled into Google’s AI Overview. A page built only for AEO but with no real authority behind it won’t get cited in a ChatGPT conversation that never touches Google at all.

This is also why GEO/AEO doesn’t replace traditional SEO — it sits on top of it. Technical health, site speed, and backlink authority from our core SEO optimisation work remain the foundation. AEO and GEO are the layer that decides whether that foundation actually gets surfaced when someone asks an AI tool a question instead of typing a query.

How to optimize for AEO #

A practical AEO workflow looks like this:

• Map the exact questions your customers ask, in their words, not internal jargon
• Answer each question in a tight, self-contained 40–60 word block right under its heading
• Add FAQ, HowTo or Article schema so engines can parse the answer format directly
• Use genuine numbered steps only where the content is actually a process
• Keep one clear answer per section — don’t bury it under three paragraphs of preamble

This pairs naturally with the question-mapping we already do inside AI SEO services, and with local intent questions through Local SEO if your business serves specific cities or regions.

How to optimize for GEO #

GEO is slower-moving but compounds over time:

• Establish and correct your brand’s entry in Google’s Knowledge Graph
• Keep your name, description and facts identical across your site, directories and press mentions
• Earn backlinks and mentions from publications AI models already treat as trustworthy sources
• Publish original data, case studies or research — AI models favor primary sources over recycled opinion
• Track and expand your presence across ChatGPT, Gemini, Perplexity and Google’s AI Overviews together, since coverage on one doesn’t guarantee coverage on another

You can see how this plays out for real client sites in our portfolio, including ranking results and revenue and traffic growth case studies.

How to measure AI search success #

Because AI search doesn’t produce a traditional “rank,” measurement has to change too. The metrics that actually matter:

• Brand mention and citation frequency across ChatGPT, Gemini and Perplexity for target questions
• Featured snippet and AI Overview capture rate in Google
• Referral traffic specifically originating from AI platforms
• Share of voice against named competitors within the same AI answers

We report all of this monthly as part of our combined GEO/AEO tracking, so you’re not guessing whether the work is landing.

Common AEO/GEO mistakes to avoid #

• Chasing GEO citations while skipping basic technical SEO — there’s no shortcut past the foundation
• Writing answers that are vague or hedged instead of direct and specific
• Inconsistent brand facts across the web, which confuses entity recognition
• Ignoring schema markup entirely, leaving engines to guess at content structure
• Optimizing for only one AI platform and assuming the others will follow