Your brand can dominate AI answers by optimizing your content for Generative Engine Optimisation (GEO). This means ensuring your information is precisely structured and signalled so that AI models like ChatGPT, Google Gemini, Perplexity, … can easily understand, analyse, and directly cite your brand in their responses. It's about becoming a trusted source for queries, moving beyond traditional SEO to be part of the answer itself.
For B2B SaaS companies, staying at the forefront of digital innovation is crucial for securing high-quality leads and converting them into loyal customers. GEO represents this new frontier—a strategic evolution that reorients your content and digital presence for the age of artificial intelligence.
Generative Engine Optimisation (GEO) is the practice of optimising your content, its structure, and underlying signals to ensure your brand is fully understood and reliably cited by Large Language Models (LLMs).
Unlike traditional SEO, which aims to rank on Search Engine Results Pages (SERPs) and drive clicks to your website, GEO optimises for inclusion directly within AI responses. In an era increasingly defined by "zero-click search", where around 60% of queries conclude without a user ever visiting another webpage, true visibility now means becoming a part of the ever-evolving AI revolution.
Generative Engine Optimisation doesn't replace strong SEO; rather, it builds upon it, reorienting your digital strategy towards the sophisticated needs of intelligent models that read, infer, and generate content, not just index it.
|
Feature |
Traditional SEO |
Generative Engine Optimisation (GEO) |
|
Goal |
Rank in search results to drive traffic |
Be cited or paraphrased in AI-generated answers |
|
Target Engine |
Search crawlers (Google, Bing) |
LLMs (ChatGPT, Gemini, Perplexity, Claude Sonnet, etc.) |
|
Tactics |
Keywords, backlinks, meta tags, and technical SEO |
Semantic HTML, structured data, E-E-A-T, conversational clarity |
|
Key Metrics |
Clicks, traffic, conversions |
Citation frequency, sentiment, and share of voice in AI answers |
To ensure your brand is understood, synthesised, and cited by LLMs, focus on these five foundational pillars:
Semantic HTML and Structured Article Schema are no longer merely about web accessibility; they are the fundamental language through which LLMs interpret your content.
Semantic HTML: Use tags like <main>, <article>, <section>, and <h1>–<h6> to clearly define the hierarchy and meaning of your content. This helps LLMs identify citable, standalone content blocks and separate primary content from less relevant elements like headers, navbars, and footers.
Think of semantic HTML as passive prompt engineering—it pre-structures the data before the model even reads it, making it inherently easier for AI to process.
Structured Article Schema (JSON-LD): While semantic HTML provides a foundational structure, implementing Structured Article Schema explicitly tells AI systems what your content is (e.g., an article, who wrote it, when, and about what). LLMs and AI search engines rely on this structured data to determine trustworthiness, context, and eligibility for summarisation. Without it, your content risks being ignored, misclassified, or excluded from AI-powered answer boxes and summaries.
Structured Article Schema is like a passport for your content—it makes it machine-readable, trustworthy, and eligible for inclusion in LLM-generated results.
Generative engines are increasingly capable of reading and interpreting images, videos, and diagrams. Leveraging multimedia effectively can significantly boost your brand's trustworthiness and clarity for AI.
Image Optimisation: Ensure every image has descriptive alt text and a <figcaption> to provide context.
Video and Audio Transcripts: Provide full transcripts for all videos and podcasts. This makes your rich media content fully accessible and parsable by LLMs.
Schema for Multimedia: Use ImageObject, VideoObject, and AudioObject schemas to provide explicit metadata about your multimedia assets.
Multimedia doesn’t just engage humans—it verifies claims, disambiguates meaning, and boosts trust for AI.
FAQs are inherently AI-ready content, designed for direct question answering.
Match Conversational Queries: Craft FAQs that directly address real-world, long-tail, conversational queries your target audience might ask.
Clean Q&A Pairs: Provide clear, concise, and intent-mapped question-and-answer pairs.
FAQPage Schema: Implement the FAQPage schema to help LLMs accurately scrape and cite your answers.
FAQs act as a brand's best defence against misrepresentation in AI answers. By proactively answering common questions, you guide AI to provide your preferred information.
LLMs learn about your brand not just from your content, but also from what users say about you. Your brand's reputation, as reflected on review platforms and through backlinks, significantly shapes AI opinions and recommendations.
Online Reviews: User reviews become powerful E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) signals at scale. Sentiment analysis derived from these reviews directly influences AI opinions and recommendations, and review summaries are often cited directly in generative outputs.
Qualitative over Quantitative Backlinks: While backlinks remain crucial, the focus shifts. LLMs prioritise the trustworthiness and authority of the source linking to you, rather than just the sheer volume of links.
Strategy: Adopt strategies like guest-posting on highly authoritative sources to give your brand more credibility with LLMs.
Your reputation is their training data. Manage it accordingly.
To combat the risk of hallucination, LLMs increasingly rely on Retrieval-Augmented Generation (RAG). This means feeding them modular, self-contained content chunks that can be retrieved and used to ground their responses.
Modular Content: Ensure each section or content block can answer questions independently, without requiring outside references for core understanding.
Internal Use Preparation: Prepare your content for internal use cases, such as powering your support bots or internal knowledge systems. This naturally creates the modularity LLMs prefer.
RAG is how brands feed real-time context into LLMs. For your content to be effective in this system, it must be easily retrievable, clear, and modular.
Different LLMs have varying preferences and strengths when it comes to content analysis for GEO. Understanding these nuances can help you tailor your optimisation efforts.
ChatGPT
Gemini
Gemini's analysis often extends to a brand's overall digital footprint and reputation, including:
Perplexity
Perplexity AI prioritises content formats optimised for rapid comprehension and direct question answering:
Claude Sonnet
Claude Sonnet excels at understanding nuanced content structure and extracting meaningful information from well-organised, authoritative sources:
The five pillars—Semantic HTML, Multimedia Assets, FAQs, Review Platforms, and Standalone Context (RAG) are fundamental to a modern Generative Engine Optimisation strategy. They form the core of how a brand can directly influence an LLM by making its content perfectly structured for AI consumption.
Beyond these five, LLMs, particularly those integrated into major search engines, research subjects by drawing on several other critical signals and data sources:
In essence, while the five pillars empower you to make your own content perfectly structured for AI, these additional points illustrate how AI validates your content against the broader world of trusted, specialised, and real-time information. The brands that win in the generative age are those who architect for influence, shaping not just rankings, but reality.
Here are the five most impactful things your B2B SaaS business can do right now to embrace GEO: