Diagnosis.
We define the buying questions for your category and run them across four engines. We measure whether you appear, which sources cite you, what they say about you and who gets recommended instead. That becomes the baseline everything is compared against.
Foundations
We fix technical SEO, indexing and rendering, then implement complete, validated schema. Without this stage your content cannot be extracted, however good it is.
Measurement and iteration
We rerun the question set, compare against the baseline and your competitors, connect AI traffic to conversions in GA4 and prioritise the next group of pages. The cycle repeats monthly.
Content and authority
We rewrite and build the priority pages around the questions that decide a purchase, and in parallel we activate mentions, citations and links in the sources engines already consult in your category.
AEO, or answer engine optimization, is the work of organising and strengthening a brand's information so that AI answer engines can find it, understand it and use it correctly. SEO aims to rank a page in a list of links; AEO aims to make your brand part of the answer the assistant writes. The practical difference sits in the query: SEO works on keywords of three or four words, AEO on complete questions averaging 23 words. AEO extends the goal of SEO, from earning a click to becoming a trusted source, and it rests on the same technical foundations.
GEO stands for generative engine optimization and in practice it is used as a synonym for AEO. Both name the same work: appearing inside the answers generated by ChatGPT, Gemini, Perplexity and Google AI Overviews. One common confusion is worth clearing up: GEO is also used as shorthand for local or geographic SEO, which is a different discipline. When this page says GEO it means generative engines. If what you need is to appear in Google Maps and in searches with local intent, that sits inside our technical SEO service and is a separate conversation.
Start by measuring where you stand. Define the ten questions a buyer in your category would ask before deciding, run them in ChatGPT, and note whether you are mentioned, which sources are cited and who gets recommended instead. From there, three things move in parallel: the site has to be crawlable with valid schema, priority pages have to answer those questions in the first paragraph while naming the brand, and consistent brand mentions have to exist in the external sources the model already consults. A model recommends what it recognises, and recognition is built on and off your own site.
Yes, and it is the prerequisite. AEO rests on the same fundamentals: unblocked crawling, clean indexing, correct canonicals, an updated sitemap, internal linking and speed. If a bot cannot read the page, the best content in the world stays invisible. There is one new wrinkle: several LLM crawlers do not execute JavaScript, so your main content has to arrive in the initial server HTML. What changes is not the technical base but what gets built on top of it: answer-first structure, structured data wired as entities, and verifiable external authority.
Schema markup is structured data that tells a machine what a page contains: a product, an article, an organisation, a frequently asked question or a person. It matters because answer engines need to recognise your brand as an entity rather than as loose text. Complete schema uses @id to connect entities to each other and same as to link them to your external profiles, so the system understands that the company on the site, the LinkedIn profile and the directory listing are the same brand. Incomplete or broken schema delivers less than a small, well-built implementation, which is why we always validate it.
AI Overviews are the generated summary Google shows above organic results, with links to the sources it used. Getting in takes the same work as any answer engine, with particular emphasis on structure: a direct answer in the opening paragraphs, headings that mirror the user's question, extractable lists and tables, valid structured data, and clear author and freshness signals. The first step is always to map which queries in your category already trigger an AI Overview and which sources Google cites in them, because that is the real bar you have to clear.
Both matter, with weights the industry was not used to. Across an analysis of 75,000 brands, branded web mentions correlate between 0.66 and 0.71 with appearing in AI answers, and YouTube mentions reach 0.74, while backlinks sit between 0.20 and 0.23. The practical reading: a link from a relevant publication still counts, and a mention of your brand by name in a publication, a podcast or a community counts for more than classic SEO assumed. That is why we run link building and the mention programme as a single piece of work.
With a fixed set of commercial questions run at a constant cadence across several assistants, and three metrics on top. Presence and share of voice: whether you appear and how often against direct competitors. Accuracy: whether the description of your offer, differentiators and pricing is correct, because a repeated error costs more than an absence. Business: how many sessions, leads and sales arrive from assistants, with LLM referrals segmented in GA4. We use Ahrefs Brand Radar, Semrush AI Toolkit and Profound, and connect everything to Search Console and GA4 so the report talks about business rather than mentions alone.
Technical and schema fixes show up within weeks, because they depend on the engine recrawling. Rewritten content usually moves visibility between month two and month three. Authority is the slowest front: mentions and citations take three to six months to change how a model recognises your brand, since these systems reindex gradually. We work in monthly cycles with a first measurement in week one, so every month you compare against the baseline instead of waiting for one global result at the end.
