With the rise of Artificial Intelligence (AI) in the world of digital marketing and content creation in general, generative engine optimisation is now a key element that businesses must pay attention to if they want to increase their visibility, credibility, and online traffic.
Unlike traditional SEO, which focuses on ranking pages at the top of search engines, Generative Engine Optimisation (GEO) aims for AI-based answer systems to cite your content when generating responses on platforms such as ChatGPT, Gemini, Claude or Perplexity. This new approach represents a paradigm shift: in an oversaturated information landscape, being cited as a source by AI may be far more valuable than ranking first on Google.
What is generative engine optimisation?
Let’s break it down. The first thing you need to understand is that GEO involves designing and managing your content, metadata, and brand signals in such a way that AI systems are more likely to display and cite your website when responding to user queries. Keep in mind that, in a context where Large Language Models (LLMs) are increasingly integrated into virtual assistants, chatbots, and smart devices, becoming a trusted and cited source in responses can generate massive visibility and traffic—potentially even more than traditional Google ranking.
However, this new landscape requires the application of a multidisciplinary strategy that combines quality content, technical implementation, and data analysis. The key lies in understanding how LLMs analyse, index, and reference information, and adapting your content to become the go-to authority in the AI response layer.
Key components of a successful generative engine optimisation strategy
To stand out in the new GEO ecosystem, it is crucial to follow certain principles to ensure that AI models can effectively access, understand, and cite your content. Below are the main components your strategy should include:
Content structure
Well-structured content is the raw material LLMs use to extract and reformat information accurately. Best practices include:
- Descriptive headers: Use clear questions and answers such as “What is generative engine optimisation?” to help AI identify key sections.
- Bullet lists and tables: Present information in easy-to-digest chunks, e.g., a table linking “Metric” to “Meaning” helps models capture and cite specific data.
- Schema markup and JSON-LD FAQ pages: Implement machine-readable FAQs and tutorial schema to signal to systems that your content is a direct answer resource.
Semantic signalling
Beyond structure, LLMs work with semantic networks of concepts and entities. Therefore, for your content to be recognised as authoritative:
- Entity references: Mention key entities related to your topic, such as “large language models” or “chain of thought”.
- Synonyms and related terms: Use variations of keywords like “AI answer engine” or “conversational AI platform” to enrich context.
- Topical pillars: Group content into specific thematic silos to demonstrate authority in defined areas, strengthening the system’s trust in your domain.
Brand reinforcement
Mentions of your brand and clear URLs within content help AI systems learn to cite you correctly.
- Consistent internal linking: Use descriptive anchor text that repeats your brand name and keywords.
- Metadata and social tags: Properly set OpenGraph tags and Twitter cards so that metadata reflects your brand and content.
Citation readiness
To enable AI models to cite you effectively, provide clear, machine-readable signals:
- Sitemaps and link metadata: Update and maintain your XML sitemap and use rel=“canonical” tags to avoid duplicate URLs.
- Social metadata: Set tags to show title, description, and preferred URL format across social platforms and rich results.
Feedback Loops
The GEO process is iterative and requires ongoing monitoring:
- AI citation analysis: Use specialised platforms and logs to detect when and how your content is being cited.
- Continuous optimisation: Adjust pages and content based on gathered data, improving authority signals and clarity.
Essential metrics for measuring GEO success
To evaluate whether your generative engine optimisation strategy is working, you need to monitor key metrics:
| Metric | Importance | Description |
| AI Citation Rate | High | Percentage of AI responses explicitly mentioning your URL or brand. A direct indicator of your presence in AI-generated answers. |
| Domain Share | High | Proportion of citations belonging to your domain compared to the total in AI. Shows your authority level in the AI ecosystem. |
| Competitor Visibility Gap | Medium | The difference between your citation rate and that of your main competitors. Helps identify how much improvement is needed to gain more citations. |
Note that an increase in your AI citation rate means your content is becoming a recognised and trusted source. Meanwhile, your domain share reflects how much you dominate your niche, and the visibility gap shows how far you are from surpassing the competition.
The best GEO platforms for 2025
It’s important to understand one thing: GEO success doesn’t depend solely on having great content, it also requires using the right tools to monitor, analyse, and optimise your presence in AI-generated answers. So here are the best generative engine optimisation tools making waves in 2025.
Sellm: The All-in-One Platform
Main Features
- Citation tracking in AI models like ChatGPT, Gemini, Perplexity, and Claude.
- Dashboard with step-by-step guides and action prioritisation.
- PDF audit reports and shareable formats.
Use Case: A marketing agency manages several brands and uses Sellm to compare visibility, detect irrelevant citations or errors. This enables the team to generate client reports with a single click.
Advantages
- Real-time metrics.
- Alerts for lost or dropped citations.
- Workspace setup tailored for agencies.
Disadvantage
- Google Analytics 4 integration is still in beta.
Price: From $29/month for one brand; advanced plans at $119/month.
Goodie AI: Enterprise-Level Engine Visibility
Main Features
- Competitive voice share analysis.
- Global library of ideas for fast A/B testing.
Use Case: A marketing team runs free evaluations to identify content gaps and know which areas to improve for more AI citations.
Advantages
- Deep, enterprise-level insights.
- Free, rapid assessments.
Disadvantages
- Custom pricing.
- No self-service plan.
SEMrush GEO (AI Toolkit): Google-First optimisation
Main Features
- Integration with Google SGE and Gemini in its keyword database.
- Detection of hidden brand mentions and unanswered questions.
Use Case: An SEO manager expands their research to find queries where their brand is not yet cited in AI responses.
Advantages
- Familiar interface for SEMrush users.
- Large keyword database.
Disadvantage
- GEO features limited to Google.
Price: From $120/month.
Scrunch AI: visibility with Journey Insights
Main Features
- Cumulative voice share tracking in AI engines.
- Mapping of user journeys in threaded dialogues.
Use Case: A SaaS company analyses how prospects discover their brand through AI dialogues and adjusts content to capture interest at each stage.
Advantages
- Unique user journey visualisations.
Disadvantage
- Demo version only; no self-service plan.
Profound: Deep Sentiment & Multilingual Analysis
Main Features
- Sentiment scoring in over 20 languages.
- Correlation between emotional tone and citations.
Use Case: A global brand refines its localised content to improve citation rates in non-English-speaking markets, based on sentiment analysis.
Advantages
- Industry-leading sentiment analysis.
- Multilingual and accurate.
Disadvantage
- Enterprise-only; no built-in content creation tools.
Why invest in the best generative engine optimization tools?
The future of digital marketing lies in understanding and leveraging generative engine optimisation. Companies that succeed in getting their content cited by AI models will gain a major competitive edge, even reaching audiences that never click traditional links.
In this sense, investing in the best generative engine optimisation tools will help you enhance your presence in AI responses while gaining insights into how users interact with your brand in this new digital ecosystem. That said, your strategy must be ongoing, data-driven, and multidisciplinary if you want to become a reference in your industry and fully harness the opportunities that AI technology brings.
Ready to take your content strategy to the next level? Generative engine optimization is the new fast lane to greater visibility, traffic, and credibility in the age of artificial intelligence.