Give AI answer engines accurate, citable facts about your business

SEO aims to rank pages in traditional search results, AEO aims to have your content used as the direct answer to a question, and GEO aims to have generative AI systems mention and cite your business in the answers they write. They overlap heavily, since all three depend on crawlable pages, clear structure and trustworthy content. AEO and GEO put extra weight on entity consistency, quotable passages and corroboration from other sources.

No one outside those companies controls what their systems say, and anyone promising placement is overstating what is possible. Answers are generated per query, differ between users and runs, and shift as models and source selection are updated. What we can do is remove the reasons you are excluded, make accurate information easy to retrieve and quote, build corroboration on sources these systems already trust, and then measure how often you appear.

Most answer engines retrieve candidate pages from a search index or a live crawl, then a language model selects passages that answer the question and attributes some of them. The exact rules are not published, but citations tend to go to pages that are indexed, render as text, address the specific question directly and agree with other sources. Google AI Overviews draw on Google's own index and Copilot relies on Bing, so ordinary indexing still matters.

That depends on whether you object to model training or to being found, because several AI companies run separate crawlers for each purpose. OpenAI uses GPTBot for training and OAI-SearchBot for ChatGPT search, so blocking only the first keeps you eligible for search citations. Google-Extended controls whether Google may use your content for Gemini models, but it does not affect Google Search, including AI Overviews. We review your robots.txt, firewall and CDN rules against the policy you actually want.

We build a panel of real buyer questions and run it repeatedly across ChatGPT search, Google AI Overviews, Perplexity and Copilot, recording whether you are mentioned, cited and described accurately, and which sources appear instead. Because answers vary, each question is sampled several times. We combine this with referral traffic from AI platforms in Google Analytics and query data from Search Console, accepting that no tool yet gives complete visibility into AI answers.

Some of it. Clicks from ChatGPT, Perplexity, Copilot and Gemini usually arrive as referrals from their own domains, which we group into a dedicated AI platforms channel. ChatGPT search also tags many outbound links with a chatgpt.com source parameter. Clicks from Google AI Overviews are counted as ordinary Google organic traffic, and Search Console folds AI Overview impressions into its web search totals. Many answers produce no click at all, which is why we also sample the answers themselves.

Schema markup helps machines read facts about your business, services, products and authors without guessing, and Google uses it to understand entities and show rich results. It does not earn citations on its own. FAQ markup, for example, no longer produces rich results for most sites, although it still describes the page clearly. Markup must match what is visible, agree with your other profiles and sit on content worth citing, so we generate it from the same data as the page.

Content that answers one specific question completely in a short passage, then backs it with concrete detail, tends to be quoted most. In practice that means a clear definition or verdict at the top of a section, specifics such as criteria, steps and constraints you can state honestly, named sources for claims, and visible authors and dates. Generic overviews that could describe any company give a model little reason to choose your page.

Technical changes such as unblocking crawlers or fixing rendering can take effect within weeks, once pages are recrawled and reindexed. Changes in how often you are mentioned usually take longer, because they depend on new content being indexed, other sources being updated and the engines refreshing what they retrieve. Some questions may not move at all for a while, and the panel results will show that plainly rather than leaving you to judge by impressions.

For most sites it is a low priority. The llms.txt file is a proposed convention for giving language models a curated summary of a site, but it is not an established standard and the major answer engines have not confirmed relying on it for citations. It is cheap to add, so we may include one, but crawlable pages, accurate structured data and well-organised content come first, because those are what retrieval systems use today.

You correct the sources rather than the model. We trace where the wrong detail comes from, often an old directory listing, press article, review profile or outdated page on your own site, then update or remove it and publish a clear, current statement of the correct facts. Some platforms also accept feedback on individual answers. Corrections appear as engines recrawl and refresh their sources, so we keep checking until the answers catch up.

No, although the groundwork overlaps. AEO and GEO influence how public engines such as ChatGPT search and Google AI Overviews describe you, which you cannot control. An assistant on your own site or WhatsApp answers only from content you choose, under rules you set. Both work better when pages are split into self-contained answers with consistent facts, so content restructured for answer engines can later feed a retrieval-augmented assistant if you decide to build one.

Cost depends on how many services, products and markets need coverage, how much technical work the site needs, and how much new content and third-party corroboration is required. We usually start with a fixed-price baseline and audit that shows where you stand across answer engines, then quote a monthly scope with named deliverables. If your gaps turn out to be ordinary SEO problems, we will recommend fixing those first.