Generative Engine Optimisation (GEO)
Being trusted is just as important as being seen.
Online searching has undergone a significant shift.
Traditional search methods are evolving. Industrial buyers and procurement committees now use AI answer engines such as ChatGPT, Perplexity, SearchGPT and Gemini to source parts, evaluate vendors, and create project shortlists.
If your technical specifications, compliance data, and service capabilities are hidden behind AI-generated text, gated forms or unstructured text, AI crawlers cannot index them. Without machine-readable data, your business will not be cited.
Generative Engine Optimisation (GEO) structures your digital footprint so autonomous AI search agents can easily locate, verify, and recommend your business.
The rise of AI in search
According to a comprehensive “State of Consumer AI” report published by Menlo Ventures in June 2025, the global AI user base has swelled to an estimated 1.7 to 1.8 billion people. Approximately 500 to 600 million people engage with AI tools every day. The global AI market is expected to expand at a Compound Annual Growth Rate (CAGR) of approximately 35.9% between 2025 and 2030.
This rapid expansion underscores why forward-thinking companies are aggressively optimising their digital footprints for generative search. By structuring their business information so engines like ChatGPT, Gemini, Perplexity, and SearchGPT clearly understand their identity, products and services, brands ensure they are accurately highlighted and recommended to the millions of enterprise buyers who navigate these platforms daily, driving continuous market momentum.
GEO Framework
We apply precision data architecture to transform your website into an open, authoritative reference library that drives ongoing market momentum.
Entity Mapping and Salience: Structure your content with advanced schema markup, enabling AI engines to accurately interpret your products, services, capabilities, and industry relationships.
Structured Fact Blocks: Convert technical data sheets into high-density, machine-readable blocks, making it easy for AI assistants to reference your brand as the definitive source.
Citation Share Optimisation: We shift your metrics from traditional keyword rankings to citation share, tracking how often your business is presented as the primary answer to complex buyer queries.
Ungated Architecture Balancing: We optimise data delivery by keeping foundational information open for AI indexing and strategically gating advanced engineering tools to protect high-value lead capture.
Figure as a topical authority
in your area of expertise
Rank higher in the search results
by applying EEAT principled optimisation.
the citation list
EEAT Framework in Generative Engine Optimisation (GEO)
Trust is the key factor in modern search visibility. By integrating Google’s EEAT framework (Experience, Expertise, Authoritativeness, Trustworthiness) with Anthropic’s Constitutional AI principles, we position your website as a safe, honest, and reliable resource. AI models are designed to reduce misinformation by prioritising verified human-written content and excluding generic AI-generated material. Creating an open, transparent repository of accurate engineering data is the most effective way to ensure your business is chosen by both human buyers and machine learning algorithms.
We implement this commitment by optimising your technical infrastructure to ensure full discoverability by LLMs. This involves using advanced Schema.org markup to map corporate relationships, restructuring data sheets to improve crawlability, and configuring open access via your robots.txt file. By combining human expertise with machine-readable accuracy, we enhance your search visibility and support sustained market growth and momentum.
EEAT is a set of guidelines developed by Google to evaluate the quality of a website’s content and determine which content or websites offer helpful, reliable information for users.
- Experience: This relates to the author’s background, or the extent of firsthand knowledge and experience a website demonstrates with its content.
- Expertise: This focuses on the author’s in-depth knowledge and understanding of the written content based on his experience, background and credentials.
- Authoritativeness: This refers to the reputation of being widely recognised as a primary source of information within the industry. It concerns the reputation of the brand and the company as a whole.
- Trustworthiness: This aspect of the website or brand encompasses safety, reliability, accuracy, and user security. Making the site more secure and serving it over HTTPS is beneficial and improves its crawlability for AI.
Frequently Asked Questions
No, while each LLM has developed its unique framework and approach to AI, Google created the EEAT framework, and it is explicitly cited in the Gemini AI rulebook. That being said, all LLMs aim for trustworthiness, and Google’s EEAT framework offers a solid foundation to gain universal trust.
That’s a great question, and it’s a prevalent point of confusion. It would not be appropriate to say that OpenAI, Gemini, Microsoft, and Meta are all LLMs. The key distinction is between the company that creates the technology and the technology itself.
Let’s break down your list with that in mind:
- OpenAI: This is the company (an AI research lab) that creates LLMs. Its most famous LLM series is GPT (e.g., GPT-4), which powers ChatGPT.1
- Microsoft: This is a company that heavily invests in and uses LLMs. It partners with OpenAI and integrates its technology into products like Microsoft Copilot 2
- Meta: This is the company (which owns Facebook, Instagram, and WhatsApp) that creates its own family of LLMs called Llama.
- Gemini: This is the name of the LLM itself, which was created by the company Google.
A more accurate statement would be:
“OpenAI, Google, and Meta are companies that create LLMs like the GPT series, Gemini, and Llama. Microsoft is a company that integrates these powerful AI models into its products.”
Artificial Intelligence(AI) is a vast branch of computer science dedicated to creating machines and systems that can perform tasks that typically require human intelligence.
A Large Language Model (LLM) is a specific application of AI that falls under the subfields of machine learning and natural language processing. LLMs are trained on massive amounts of text data to understand, generate, and interact with human language.
GEO is the process of optimising content to increase a website’s visibility and to appear in responses from AI Learning Language Models such as OpenAI, Gemini, Microsoft and Meta.
Generative Engine Optimisation (GEO) targets generative AI responses such as OpenAI, Gemini, Microsoft and Meta. In contrast, Search Engine Optimisation (SEO) targets traditional search engines such as Google and Bing.
EEAT stands for Experience, Expertise, Authoritativeness, and Trustworthiness. Using this framework increases AI credibility and generates results in queries made in LLM Models such as OpenAI, Gemini, Microsoft and Meta.
Content with an effective EEAT structure is more likely to be cited or surfaced in a generative answer. AI uses this principle as a signal to determine which content to trust and display in the results.
No, they complement each other. They have different targets and serve different purposes. It is better to optimise both to increase visibility in search engine results pages and AI generative results.
AI was built to address misinformation; it aims to filter out misinformation, favouring content with a strong EEAT structure.
With the rise of Large Language Learning Models, GEO enhances the chances of content being included in relevant responses synthesised by generative AI such as OpenAI, Gemini, Microsoft and Meta.
