A growing share of searches now end in an AI-generated answer rather than a click. The winners are the brands those systems quote. That is a different optimisation problem — and it is solvable.
No mystery line items. Here is exactly what we do and why each part earns its place.
We measure how often you currently appear in AI Overviews, ChatGPT, Perplexity and Gemini answers for your commercial prompts.
Consistent, machine-readable facts about your brand across your site, Wikidata, Crunchbase and your knowledge panel. Language models rely on entity clarity.
Content restructured so a model can extract a clean, attributable answer — direct definitions, clear headings, tables, and sourced claims.
Presence on the review sites, comparison pages and forums that AI systems pull from disproportionately. Reddit and industry roundups matter more than they used to.
Schema, llms.txt, clean HTML and crawlability for AI user agents — plus deciding which crawlers you actually want to allow.
Monthly reporting on share of voice across a fixed prompt set, so you can see whether the work is landing.
Fixed prompt set run across engines to establish where you stand today. Without a baseline this is all guesswork.
Make it unambiguous to a machine who you are, what you do and what you are credible about.
Rewrite key pages so answers can be lifted cleanly and attributed to you.
Get into the sources these systems actually draw from, then track share of voice monthly.
There is overlap — good technical SEO and clear content help both. But the ranking signals differ: AI systems weigh entity clarity, citation breadth and extractability far more heavily than link volume.
Very much. Most answer engines still ground their responses in search results, so organic visibility remains the foundation. This is an additional layer, not a replacement.
No. Nobody can. What we can do is measure your current share of voice, improve the inputs those systems demonstrably use, and report honestly on the movement.