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From SEO to GEO: the changes

From SEO to GEO: the changes

Generative Engine Optimization explained properly

For a couple of years now, I have heard the same word everywhere, GEO, as if it were the new magic formula after SEO. Behind the acronym lies an authentic shift in how people look for information, and it is worth understanding calmly before rushing to buy yet another tool. This piece was sparked by a specific event: the launch of Palmata by Contentful, a platform built to help brands understand how they get described by ChatGPT, Gemini, Perplexity, and the rest. Before getting to Palmata, though, I want to step back and talk about what it actually means to optimize content for generative engines today.

What Generative Engine Optimization is

Generative Engine Optimization (GEO) refers to the set of practices meant to make a piece of content relevant, citable, and credible in the eyes of a model that generates answers, rather than a list of links. The term travels alongside AEO (answer engine optimization), and in daily practice, the two overlap quite a bit: both aim to be chosen as a source when someone asks an AI assistant to explain, compare, or recommend something.

The difference from traditional SEO lies in the end goal. With SEO, we worked to rank as high as possible on a results page, hoping the user would click our link. With GEO, the goal shifts: what matters is no longer just the ranking, it is whether the model includes our brand, our product, or our point of view inside the answer it generates. Sometimes that answer arrives without a clickable link at all.

What changes compared to the SEO we knew

The deepest change concerns the moment of discovery. Previously, a potential customer visited a site and formed an opinion by reading the company’s own content. Today, more and more often, that person first asks an AI assistant to summarize a company, compare it with competitors, or suggest a solution, and forms an impression before ever visiting the page. Content, then, now serves two audiences at once: people, and the generative engines that read, summarize,e and recombine that material to build their answers.

Another practical difference concerns how generative engines process content. Traditional search relied heavily on keywords and textual matches. Generative systems work with entities, relationships, and verifiable claims: they understand synonyms, intent, and context even when there is no exact match between the query and the text. This means chasing every possible variation of a question, multiplying near-identical pages, does not get you far and risks being flagged as scaled, artificial content.

Strategies for getting cited by generative engines

Here comes the practical part, the one that really matters for anyone who writes or manages content every day. The first rule sounds obvious only on the surface: offer a viewpoint that cannot already be found elsewhere. A language model draws on hundreds of sources, so a piece that merely restates what everyone already says has little chance of standing out. Firsthand experience, a data point gathered in person, a real case told with precise detail, carries far more weight.

The second strategy concerns structure. Clear paragraphs, well-separated sections, headings that guide the reading: these elements help both human readers and the systems that need to extract precise information from a page. There is no need to chop text into unnatural fragments to please some hypothetical algorithm; writing in an orderly way is enough, and you should be doing that anyway.

The third strategy concerns consistency across everything a company says across different channels: the website, social media, reviews, interviews, and press releases. Generative engines stitch together fragments from different sources, and if those fragments contradict each other, the model risks picking the wrong version or, worse, inventing one of its own. I wrote something sharper on this after the Munich court ruling that held Google liable for the false claims its AI Overviews generate: if you want to see how high the stakes get when a model tells untrue things about your brand, you can find that piece here.

Finally, technical soundness remains a baseline requirement: a page has to be indexable, fast, and readable by any crawler, long before it can hope for a citation from a generative model.

What the big players are doing

The most widely used generative engines are building dedicated infrastructure for exactly this purpose. Google has folded AI Overviews and AI Mode directly into search, leveraging its own index via retrieval-augmented generation and query fan-out, generating related sub-questions internally to gather fuller answers. OpenAI has pushed ChatGPT toward search and conversational shopping features, while Perplexity has built its entire experience around source citations, making transparency a defining trait of the product. Microsoft, for its part, keeps weaving Copilot into its productivity tools, moving content discovery inside workflows people already use.

Meanwhile, the world of SEO tools has moved just as fast. Semrush launched its own AI Visibility Toolkit, Ahrefs introduced Brand Radar, and HubSpot built a grader dedicated to AEO. And now Contentful joins in too, with Palmata trying to bring AI reputation analysis directly into the editorial flow of people producing content every day, a fairly clear sign that the topic has moved from an insider niche to a product priority for companies of very different sizes.

What Google actually recommends

It is worth clearing up a common misunderstanding: Google, in its official guide on optimizing sites for generative search features, essentially says SEO remains the foundation of everything. There is no need to create special files like llms.txt, no need to chop content into unnatural chunks, no need to chase artificial mentions scattered around the web. Schema markup alone does not guarantee a citation in an AI answer, even though structured data remains valuable for traditional rich results.

What Google genuinely recommends is fairly modest: helpful, reliable content built for people, an original point of view rather than a rehash of what everyone else says, and a clean technical structure that is easy to index. In practice, whoever has always done the SEO fundamentals well already starts ahead in GEO too, without needing to overhaul strategy or process.

Tools for measuring and improving AI visibility

On the practical side, the last few months have given rise to an entire category of tools designed to track how a brand is represented in generative answers. Profound has established itself as the reference point for large enterprises, with features spanning monitoring, auditing, and optimized content generation. Peec AI tracks mentions, ranking, and sentiment at the individual prompt level, with multi-language support designed for international brands. Otterly.AI offers a more affordable entry point for teams looking to start monitoring ChatGPT, AI Overviews, Perplexity, and Copilot without a lengthy sales process. Alongside these sit solutions more tied to the existing SEO ecosystem, like Semrush’s AI Visibility Toolkit or Ahrefs’ Brand Radar, built for anyone who wants to fold AI monitoring into tools they already know.

If you work on content and are wondering where to start without getting lost in acronyms and sales pitches, I’ve tried to bring some clarity to this piece on SEO myths to debunk in 2026: many of the wrong ideas circulating back then are still around, just wearing a new label.

Palmata, Contentful’s bet

On June 23, 2026, Contentful unveiled Palmata, a system built to help organizations understand, measure, and improve their presence in generative engines. The underlying idea is that knowing whether a brand appears in an AI answer is no longer enough: you need to understand why it is described in a certain way, which sources the model draws on to build that narrative, and which levers could change it. Palmata works through four capabilities: Steering Control to point research toward specific business priorities; Adaptive Deep Research to build context around a brand, its competitors, and category; Recommended Actions to turn analysis into concrete, prioritized moves; and Simulated Impact to estimate the effect of those moves before putting them into practice. The whole system runs on a proprietary research engine called Sounder and is also available through an MCP server, so writers and marketing teams can bring the gathered insight straight into their existing tools, without rebuilding context from scratch every time.

A personal closing thought

What strikes me most about this phase is how quickly the center of gravity has shifted: not long ago we talked about ranking, today we talk about a reputation narrated by a machine that can get things wrong, generalize, or even make things up. I do not think SEO has become pointless, quite the opposite, it remains the foundation everything else is built on, but I do think the time has come to treat content also as raw material a model will use to talk about us when we are not in the room to correct it. It is worth writing as if someone were truly listening, because in a sense, that is exactly what is happening.

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Chiara Bonifazi