With the rise of generative search engines such as ChatGPT, Gemini, and Perplexity, a new era of online visibility is emerging.

Users no longer click through a list of links; they receive a synthesized answer directly, often without ever visiting the source website. In this new landscape, a crucial question arises: how can you ensure that your content is used, cited, or integrated into these answers?

This is precisely where GEO — Generative Engine Optimization — comes into play.

What is GEO?

The term GEO (Generative Engine Optimization) refers to the set of practices aimed at making content visible and useful to generative engines.

Unlike SEO, which focuses on ranking at the top of search results, GEO aims to be understood, selected, and cited in AI-generated responses.

In practical terms, this means:

• structuring content so it can be easily interpreted by language models;
  
• writing in a way that naturally answers questions;
  
• and providing AI systems with signals of reliability, authority, and clarity.

In other words, GEO is search optimization for the era of engines that write.

Why GEO is becoming essential

1. The disappearance of the click

Users increasingly rely on conversational AI to get information. As a result, traditional “organic” traffic is shifting toward platforms where citation replaces the click.

If your content is not considered by these systems, it effectively becomes invisible.

2. Automated knowledge filtering

Models do not read the web like humans do. They analyze, rank, and rephrase. Content that is poorly structured, unclear, or overly promotional has little chance of being selected.

GEO therefore focuses on making your content readable for AI, not just for Google.

3. Authority as a key signal

In a world where AI synthesizes thousands of sources, it prioritizes those that inspire trust: sources cited elsewhere, neutral language, verifiable data, and clearly identified authors.

GEO reintroduces the notion of measurable credibility.

Poor GEO practices

As with SEO in its early days, certain misuses are already emerging:

• stuffing content with “AI-friendly” phrasing that adds no real value;
  
• producing blog posts artificially optimized to be cited;
  
• mechanically repeating technical terms in the hope of being selected.

The result: hollow content that loses reader trust and is ultimately ignored by the models themselves.

GEO is not a new way to game the algorithm, but a discipline rooted in clarity and rigour.

GEO in practice: the discipline of meaning

To be visible in the era of generative engines, the key is not to do more, but to do better.

Here are the foundations of an effective GEO approach:

• Structure information: Use clear headings, short paragraphs, and FAQ-style formats. AI systems thrive on logical hierarchy.
• Address intent, not keywords: Think in terms of questions and answers rather than keywords and rankings. For example, write “How does GEO redefine business visibility?” instead of “GEO SEO marketing.”
• Cite sources: Generative engines value content that grounds its claims in verifiable facts.
• Maintain a conversational tone: Content that sounds natural and human is more likely to be reused than text that feels cold or robotic.
• Ensure content freshness: Models favour recent or updated content as a relevance signal.

Our approach to GEO: guidance and innovation

Practices are evolving rapidly in this early phase of generative digital experiences, and proper guidance is essential when developing a new website or crafting its content.

Current techniques are not definitive and must evolve alongside AI-powered search engines. Staying attuned to the industry, and innovating, has become critical.

At Beet, we are already exploring concrete solutions to make websites more readable and usable by generative engines.

The idea is to go beyond traditional search optimization by offering AI systems content that is clearly structured, contextualized, and easy to summarize.

Concretely, we are working on the development of GEO plugins and SDKs capable of automatically adding “AI-friendly” tags and metadata to web pages. These tags help guide language models by identifying key sections, summaries, frequently asked questions, and author information, making it easier for AI-generated responses to cite or rely on the original source.

In other words, this prototype aims to translate GEO logic into a practical tool by structuring information at the moment it is published. This is not about manipulating algorithms, but about making the web more intelligible to AI systems: one step closer to an ecosystem where websites are not only found, but truly understood.

Our role is to help organizations make this transition: creating content that humans enjoy reading and that AI systems choose to use.

An inevitable and necessary evolution

GEO relates to SEO in the same way generative engines relate to traditional search engines: it is an inevitable evolution.

Where SEO aimed for visibility, GEO aims for usefulness.

Because in the age of instant answers, being found is no longer enough; content must be understood, integrated, and cited.