GEO Services: What a Generative Engine Optimisation Agency Delivers

1. AI Visibility Audit

Every engagement begins with a baseline assessment. The AI visibility audit establishes where your brand currently appears (and where it does not) across the major generative platforms: ChatGPT, Perplexity, Gemini, Copilot, Claude, and any sector-specific AI tools relevant to your industry.

This is not a traditional SEO crawl. It involves systematically querying AI models with the prompts your prospective customers actually use, recording whether your brand is cited, how it is described, what competitors appear instead, and which sources the model draws upon to construct its answers.

The output is a visibility map: a clear picture of your current AI presence across platforms, query categories, and intent types. This map becomes the foundation for every subsequent service. Without it, optimisation is guesswork.

2. Entity and Knowledge Graph Optimisation

Large language models do not think in terms of web pages. They think in terms of entities: people, organisations, products, concepts, and the relationships between them. If your brand is not established as a distinct entity in the knowledge sources these models rely on, you are structurally invisible.

Entity optimisation involves strengthening your brand's presence across the knowledge bases that AI models consume during training and retrieval. This includes Wikipedia, Wikidata, Google's Knowledge Graph, Crunchbase, industry-specific databases, and authoritative third-party sources that models treat as ground truth.

The work is meticulous: ensuring consistent naming, accurate attributes, verified relationships with other entities, and sufficient third-party corroboration to establish notability. The goal is to make your brand an entity the model knows, not merely a string it has encountered.

3. Structured Data and Schema Implementation

Structured data has always mattered for search. For generative engines, it matters more. When an AI model retrieves your page during a response, structured data provides the machine-readable context that determines how confidently the model can extract, attribute, and cite your content.

A GEO-focused schema strategy goes beyond basic Organisation and Breadcrumb markup. It includes detailed Service schemas, FAQPage and HowTo markup where appropriate, author and expertise signals via Person schema, and the precise use of sameAs to link your entity across platforms.

The implementation must be technically correct, semantically accurate, and aligned with what the content actually says. Over-marking or inaccurate schema is worse than none at all, because it sends conflicting signals to models that are increasingly sophisticated at detecting inconsistency.

4. Content Optimisation for LLM Citation

This is the core of generative engine optimisation and where most of the ongoing work concentrates. Content must be written, structured, and formatted so that large language models can easily extract factual claims, attribute them to your brand, and incorporate them into generated responses.

In practice, this means writing with explicit claims rather than implied ones, using clear attribution language, providing specific data points that models can quote, structuring content with logical hierarchies, and ensuring that each page's most important assertions appear in positions where retrieval-augmented generation systems are most likely to capture them.

This is not about keyword density or word count. It is about information architecture at the sentence level: making every paragraph a self-contained, quotable unit of expertise that an AI model would choose to cite over a competitor's vague equivalent.

5. llms.txt Setup and AI Crawler Management

Just as robots.txt governs how traditional search engine crawlers interact with your site, llms.txt is emerging as the standard for communicating with AI crawlers. This file tells AI systems what your site is, what it offers, and how its content should be understood and used.

AI crawler management goes further. It involves configuring your server to handle requests from GPTBot, PerplexityBot, ClaudeBot, Google-Extended, and other AI crawlers appropriately. In some cases, you want to encourage crawling. In others, you may want to restrict access to certain content while permitting it for others.

The strategy here is nuanced: blanket blocking AI crawlers removes you from AI answers entirely, but unrestricted access may not serve your interests either. A GEO agency configures this on a case-by-case basis, balancing visibility against content protection.

6. Platform-Specific Optimisation

ChatGPT, Perplexity, Gemini, and Copilot do not all work the same way. They use different retrieval mechanisms, draw on different source hierarchies, and weight different signals when deciding which brands and sources to cite. A strategy that works for one platform may underperform on another.

Platform-specific optimisation involves understanding these differences and tailoring your content, entity presence, and technical configuration accordingly. Perplexity, for example, relies heavily on real-time web retrieval and tends to cite sources with strong topical authority. ChatGPT with browsing draws from a broader range of sources but weights recency and direct relevance differently. Gemini integrates tightly with Google's own knowledge systems.

A competent GEO agency maintains current intelligence on how each platform's retrieval and citation behaviour works, and adjusts strategy as these systems evolve, which they do frequently.

7. Monitoring and Reporting

You cannot optimise what you do not measure. GEO monitoring tracks your brand's citation frequency, sentiment, accuracy, and competitive position across AI platforms over time. This is fundamentally different from traditional rank tracking, because there are no fixed positions to monitor. Instead, the metrics focus on presence, prominence, and the quality of how your brand is represented.

Reporting typically includes citation tracking (how often your brand appears in AI answers for target queries), source attribution analysis (which of your pages or third-party mentions are being cited), competitive benchmarking (how your AI visibility compares to key competitors), and sentiment monitoring (whether AI models describe your brand accurately and favourably).

This data informs every iteration of the strategy. Without it, you are flying blind in a channel that changes faster than any other in digital marketing.

8. Ongoing Optimisation and Iteration

Generative engine optimisation is not a one-time project. AI models update their training data, refine their retrieval systems, and change their citation behaviour regularly. The competitive landscape shifts as more brands invest in AI visibility. Content that earned citations last quarter may not earn them next quarter.

Ongoing optimisation involves continuous content refinement based on monitoring data, regular entity and schema audits, proactive adaptation to platform changes, and the creation of new content assets specifically designed to capture emerging query patterns. This iterative cycle is what separates a genuine GEO programme from a one-off audit that gathers dust.