Case study:
SEO and
AI search

First I made sure Google could see and index what matters. Without that, new content would not pay off.

• →Indexing and crawlability analysis.
• →Review of URL structure, redirects and canonicals.
• →Fixing technical errors and indexing problems.
• →Cleaning up information architecture and page hierarchy.
• →Internal linking analysis and rebuild.
• →Site speed review.

Instead of a keyword list I built a topic map where each query group has one page responsible for it.

• →Keyword potential and search intent analysis.
• →Grouping keywords into topic clusters.
• →Assigning topics to existing and new pages.
• →Finding topic gaps against competitors.
• →Expanding the service structure and supporting content.

I aligned content with what clients actually look for before contacting a service company.

• →Optimising existing content for user intent.
• →New pieces answering specific queries.
• →Expanding expert content.
• →Improving headings, titles and meta descriptions.
• →Topical links between pages.
• →Fitting content to search result features.

Once SEO gave a stable base, I extended the strategy to the brand’s presence in AI-generated answers.

• →Clear answers to specific user questions.
• →Expanded expert sections, definitions and explanations.
• →Connections between entities: the company, its services and topics.
• →Showing sources and strengthening content credibility.
• →Content accessibility for search and AI crawlers.
• →Schema.org structured data: organisation, authors and services.

Every next step came from data, not from a publishing calendar.

• →Regular visibility and ranking analysis in Search Console and SEO tools.
• →Evaluating how each topic group performs.
• →Brand monitoring in ChatGPT, Gemini, Claude and Google AI Overviews with my tool, Answerlyzer.
• →Comparing AI visibility with competitors and analysing pages cited in answers.
• →Updating the strategy based on data.