generative engine Optimisation (GEO) is no longer a side project—it sits at the centre of every search strategy we build for clients in 2025. The shift from traditional blue-link rankings to AI-generated answers means brands must understand where AI models pull their information, how to get cited, and how to audit their visibility across these new surfaces. This week, a cluster of practical frameworks landed that map directly onto the work our team does daily. Here is what matters and what we are doing about it.

Key Takeaways

• AI visibility audits are now a structured, repeatable process—not guesswork.
• Understanding where AI sources its information is the first step to earning citations.
• Topic clusters remain the most reliable architecture for both traditional and AI Search visibility.
• AI-powered competitor analysis workflows have matured well beyond single-tool prompts.
• Rigorous editorial frameworks keep content quality high enough to survive AI evaluation.

Run Structured AI Visibility Audits for Every Client

We have added AI visibility audits to our quarterly review cycle. The process is straightforward: measure how often a brand appears in AI-generated answers, identify gaps, and report findings in a format stakeholders can act on. A detailed six-step audit framework published by Semrush breaks this into discovery, benchmarking, gap analysis, recommendation, implementation and reporting phases. We follow a near-identical sequence. The critical point is repeatability—clients need to see movement quarter on quarter, not a one-off snapshot.

Know Exactly Where AI Models Source Their Answers

AI platforms like ChatGPT, Gemini and Perplexity draw from three main channels:

• Training data — large web crawls baked into model weights during pre-training.
• Live retrieval — real-time web searches triggered at query time.
• Licensing partnerships — direct content deals with publishers and data providers.

Our team uses this understanding to prioritise content placement. If a client’s information lives only behind a login wall or in a PDF, it is invisible to most retrieval pipelines. We restructure that content into crawlable, well-marked-up HTML—a practical step reinforced by Semrush’s breakdown of how AI models gather and cite information. Getting cited starts with being findable.

Build Topic Clusters That Serve Both Google and AI Search

Topic clusters are not new, but their value has compounded. A well-linked pillar page surrounded by supporting articles signals topical authority to Google’s algorithms and gives AI retrieval systems a dense, coherent body of content to pull from. We follow a five-step method: choose a core topic, map subtopics through keyword and intent research, create the pillar, produce supporting content, then interlink everything deliberately. This mirrors the topic cluster creation guide recently updated by Semrush, which confirms clusters now drive visibility on AI search platforms as well as traditional SERPs. For our clients, clusters are non-negotiable information architecture.

Use Multi-Tool Workflows for AI Competitor Analysis

Dropping a competitor’s URL into ChatGPT and asking “what are they doing well?” produces surface-level noise. Proper AI competitor analysis in 2025 requires layered workflows—combining large language model queries with structured data from dedicated SEO platforms. A step-by-step competitor analysis workflow using Semrush MCP demonstrates how to pipe real ranking, backlink and traffic data into AI prompts for genuinely useful strategic output. We have adopted a similar pipeline internally, feeding verified data into AI models rather than relying on the model’s training data alone.

Maintain Editorial Rigour at Scale

Volume means nothing if quality slips. AI search platforms favour content that is accurate, well-structured and clearly authored by subject-matter experts. Our editorial team applies multiple review passes—structural edits, factual checks, tone alignment, SEO refinement and final proofing. This layered approach aligns with an editing framework shared by a Semrush editor who reviews over 115 articles per year. The takeaway: match the editing method to the content’s purpose and never skip a pass.

GEO is maturing fast. The brands that treat AI visibility as an ongoing discipline—auditing regularly, structuring content deliberately, analysing competitors with real data, and holding every published page to a high editorial standard—will dominate both traditional and AI-driven search surfaces through 2025 and beyond. Our team is embedding these practices into every client engagement right now.

Frequently Asked Questions

What is an AI visibility audit and why does my business need one?

An AI visibility audit measures how often your brand appears in answers generated by AI platforms like ChatGPT, Gemini and Perplexity. It identifies gaps in your content and technical setup so you can take specific steps to earn more AI citations.

How do web designers and SEO teams use topic clusters for AI search?

They build a central pillar page linked to a set of supporting articles that cover a topic comprehensively. This interconnected structure helps AI retrieval systems recognise topical authority and pull accurate answers from your site.

Where does AI get its information when answering search queries?

AI models draw from pre-trained data, live web retrieval at query time, and licensed content partnerships. Making your content crawlable, well-structured and publicly accessible is the most direct way to appear in those answers.

Why does editorial quality matter for generative engine optimisation?

AI platforms prioritise accurate, clearly structured content authored by credible sources. Poor-quality or thin content is unlikely to be cited, so rigorous multi-pass editing directly impacts your visibility in AI-generated results.

How do agencies use AI tools for competitor analysis in 2025?

Effective competitor analysis combines verified SEO data—rankings, backlinks, traffic—with AI-driven interpretation through structured workflows. Relying on a single chatbot prompt without real data produces unreliable, surface-level insights.