Generative engine optimisation is the process of making your brand visible and accurately represented in AI-generated answers. When someone asks ChatGPT, Claude or Gemini for a recommendation or category advice, GEO determines whether your brand is included in that response and how it's described. It's a separate discipline from SEO, with different signals and a different outcome to optimise for.

AEO and GEO are closely related terms for the same shift: optimising for AI-generated answers rather than search engine rankings. AEO focuses on how AI tools respond to direct questions; GEO is the broader practice covering visibility, representation and brand signals across all generative AI outputs. Both require understanding how language models interpret and cite your brand.

Search rankings and AI visibility are driven by different signals. Language models don't read search results; they draw on training data, cited third-party sources and their interpretation of how clearly your brand is positioned. You can hold the top spot on Google and still be completely absent from AI-generated shortlists. This gap between SEO performance and GEO visibility is one of the most common things we see.

Usually it comes down to weak or inconsistent brand signals. AI models build their understanding of a brand from structured content, trusted third-party mentions and how clearly your positioning comes through across the web. If those signals are thin, scattered or contradictory, AI systems don't have enough to confidently include you. It's not about size or budget, and it's exactly what AI optimisation addresses.

It means AI models are drawing on inconsistent signals about who you are and what category you belong to. This can be more damaging than not appearing at all, because it shapes how potential customers perceive you at exactly the moment they're deciding. Misrepresentation is one of the core problems generative engine optimisation addresses, and GEO Readiness audit identifies where the inconsistency is coming from: your own content, your external mentions or both.

Yes, and the mechanism is fairly direct. A growing share of purchase decisions now start with a question to an AI tool rather than a search engine. Brands that appear consistently in those answers get discovered at high-intent moments, before the person has even opened a browser tab. Better GEO visibility means more branded discovery and a stronger presence at the point where recommendations turn into decisions.

Any category where people ask AI tools for recommendations before making a decision: SaaS, fintech, e-commerce, healthcare, professional services, travel, B2B and agencies. Answer engine optimization and generative engine optimisation matter most where customers compare options before they buy, because AI visibility directly affects who gets into that consideration set.

Meaningful movement typically starts showing within several weeks. Some improvements are faster; fixing structural content issues or closing gaps in how your brand is described can shift things relatively quickly. Others take longer, particularly building presence in the external sources AI models treat as credible. Ongoing AEO monitoring lets you track progress as it happens.

You run structured queries, the kinds of questions your potential customers actually ask AI tools, and track what comes back. AEO monitoring does exactly this: running real prompts across multiple LLMs on a regular schedule and reporting back on your AI presence, visibility statistics and competitive landscape.

A GEO monitoring report covers Mention Rate, Share of Voice, Average Rank, First Mention Rate, GEO Score and Missed Prompts. Sentiment and brand attribute analysis show not just whether you appear, but how AI describes you and which attributes it consistently associates with your brand.

A GEO Readiness audit evaluates how well your website, content and external presence are structured for AI models to read and cite your brand. It covers technical readiness (crawlability, bot access, indexability), content readiness (citable block density, extractability, content quality) and external visibility (which third-party sources AI cites alongside your brand). The output is a prioritised list of what to fix and in what order.

Monitoring tells you what's happening in AI responses right now: where you appear, who you're competing against and how that changes over time. A GEO Readiness audit tells you why. One tracks the outcome, the other diagnoses the cause.

Yes. Competitive analysis is a standard part of GEO monitoring; you can see which brands consistently appear in AI-generated responses in your category, their Share of Voice and which sources AI associates them with. Understanding who dominates AI shortlists in your space is often the fastest way to identify gaps in your own GEO strategy.

Weekly is the right baseline for most brands, frequent enough to detect changes and measure the impact of your AI optimisation work. Bi-weekly works as a starting point to establish where you stand. In competitive categories, weekly tracking across ChatGPT, Claude and Gemini gives you a materially better picture of what's shifting.

We built our own GEO analytics platform, which means nothing is fixed. The prompts, metrics, segments, languages and reporting structure are all configured around your market and goals. Most tools make you work within their setup; we build the setup around you, and that's what makes our approach to answer engine optimization and GEO monitoring different in practice

The core coverage is ChatGPT, Claude and Gemini, with the option to track one or all three depending on your plan. Each model has different training data, citation behaviour and ways of interpreting brand signals, so running AEO monitoring across all three gives you a complete picture of your AI visibility. If you need to track additional LLMs, we can configure that as part of a custom setup.

Start with a monitoring run and a GEO Readiness audit, ideally together. The monitoring shows your current AI presence across ChatGPT, Claude and Gemini; the audit shows what's structurally holding you back. Together they give you a clear picture of where you stand and a practical starting point for generative engine optimisation.