By the time a buyer asks an AI tool about your company, the answer is already written. We work on the sources it was written from.
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100% ConfidentialAsk an AI tool about a company and you get an answer, not a page of links. That answer is assembled from sources the model already treats as reliable, and the company being described does not choose those sources. Most of the time it is not among them.
BrightEdge tracking put AI Overviews on 48% of Google searches in 2026.1 Add ChatGPT, Gemini, Perplexity and Copilot to that, and a meaningful share of your buyers now form a first impression of your company without ever loading a page you control.
For a B2B seller, the cost lands earlier in the funnel than most teams look. The AI answer is where the shortlist gets built. A company that is described thinly, described inaccurately, or not named at all loses the opportunity at a stage that never shows up in the CRM, because there was no inquiry to lose. Pipeline does not dip in a way anyone can point to. It just fails to form.
There are two ways this goes wrong, and the rest of this page maps to both.
The first is absence. Buyers ask which firms handle a problem you solve, and your competitors get named while you do not. The second is misrepresentation. You do get named, but the description leans on a review thread from four years ago, a lawsuit that settled, or a news article that was corrected after publication. AI systems do not weigh recency the way a person reading the same page would, and they rarely surface the correction alongside the claim. Fixing that means correcting or removing inaccurate content at the source, not arguing with the model.
Generative engine optimization is the practice of shaping the sources AI systems draw on, so the answers those systems produce about a brand are accurate, current and favorable.
That definition turns on one distinction most explanations skip. GEO does not change the model. Nobody edits ChatGPT. What changes is the body of material the model reaches for when your company comes up, and how clearly that material identifies you, agrees with itself and holds up against competing claims. The work happens on the sources, which is why it belongs closer to reputation management than to technical SEO.
Generative engine optimization services, delivered as an engagement, cover four things: finding out what AI tools currently say about you, identifying which sources they are drawing that from, correcting or displacing the weak ones, and building material strong enough to be cited in their place. The section below breaks that into the specific work.
The reason this overlaps with reputation management is structural. AI systems lean heavily on third-party material, which is the same territory that earned coverage, digital PR, and Wikipedia and knowledge-panel presence have always occupied. A brand with a thin or contested third-party footprint has an AI problem for exactly the same reason it has a search problem.
The behavioral shift is measurable and it runs against publishers. Pew Research Center found that when an AI summary appears on a Google result, users click through to a website about 8% of the time, against roughly 15% when no summary is present.2 The answer is increasingly the destination.
That raises the stakes on what the answer is built from, and the answer is mostly not built from you. Muck Rack's analysis of AI citations found that 84% come from earned media and third-party sources rather than from brand-owned pages.3 Your website is not the primary input. Coverage about you is.
Here is the arithmetic worth running before you take this to a CFO. Take your average closed deal value. Multiply it by the rate at which shortlist appearances convert to closed business for you. That product is the expected value of a single shortlist you appear on. Then estimate how many buying conversations in your category now start with an AI query rather than a search. The gap between those two numbers is what absence costs you per quarter, and it is a figure you can defend because every input came from your own CRM rather than from a vendor's case study.
This is a managed program, not a tool subscription. You get a named point of contact, a defined scope, and a team that does the work rather than a dashboard that reports on it.
That distinction matters on this page, because most firms selling AI search visibility are selling software, and software will tell you that an AI tool described you badly without doing anything about it.
We run the questions your buyers actually ask through every major AI tool and record what comes back, including how you get described, which competitors get named beside you, and which sources the answers lean on.
We write and place the material AI systems reach for when answering questions in your category. That covers your own pages and the third-party properties models already treat as credible sources.
Clear headings, direct answers and clean question-and-answer structure let a model lift a passage intact. Pages lose citations to formatting far more often than they lose them to weak arguments.
Schema markup tells AI systems what your content covers and which entity it belongs to. It resolves the ambiguity that leads a model to confuse you with a similarly named company in another market.
Most engagements start here, with a baseline of how AI tools describe your brand today. See our AI visibility monitoring service for what ongoing tracking covers and how it works.
These are not alternatives and you need both. They buy different things.
Search engine optimization moves your position in a ranked list of links. It works on your own site, mostly: what you publish, how it is structured, how quickly it loads, and which sites link to it. Success is a position, and a position is something you hold or lose against named competitors on a page a user can see and scroll.
Generative engine optimization determines whether you are named in an answer at all, and which sources the system leans on when it names you. It works mostly off your site, on the third-party material a model already trusts. Success is a citation, which you either have or do not, on a surface with no page two to fall back to.
A note on terminology, because the market has not settled on one. A large share of the industry calls this work AI SEO, and some of it calls the same practice answer engine optimization. They describe the same discipline: making a brand legible and citable to systems that answer rather than list. If you have been asked to budget for AI SEO, this is the page for it.
The practical difference is what you get measured on. SEO gives you a number that moves up and down. GEO gives you a presence that is there or is not, backed by sources you can name. A team that only tracks position will not notice the second one changing until a sales call goes badly.
Appearing in an AI Overview is not something you request or configure. The system builds its answer from sources it has already judged reliable on that topic, so the work is making your material one of those sources and making sure the rest of what it finds agrees.
Four things determine whether that happens.
The first is source strength off your own site. AI Overviews lean on third-party material, so a brand with thin or dated coverage has little for a model to reach for, regardless of how good its website is.
The second is entity clarity. A model has to be confident which company you are before it can say anything about you. Inconsistent business names, a missing or contradictory knowledge panel, and two companies sharing a name across different markets all produce hedged answers or answers about somebody else.
The third is extractability. A model lifts passages, not pages. Copy that answers a question directly in its first sentence, under a heading that matches the question, gets used. Copy that takes three paragraphs to arrive at the point does not.
The fourth is consistency. When sources disagree about a fact, a model tends to hedge, omit, or pick the version that appears most often, which is not always the current one. Aligning the facts across the places a model is likely to look is unglamorous work and it changes outcomes more reliably than anything else on this list.
Google is not the only surface here. The same source-level work carries across ChatGPT, Perplexity, Copilot and Gemini, because they are drawing on overlapping material and applying similar judgments about what to trust.
No, and you should be wary of anyone who says otherwise. There is no removal mechanism for AI-generated answers, no form to file, and no relationship that gets a citation withdrawn on request.
What does change an AI answer is the material behind it. If a damaging claim traces back to a source that is inaccurate, outdated, or published in violation of a platform's own policies, that source can often be corrected or removed, and the answer changes when the input does. Where a claim is accurate but dominant, the work is building enough credible, current material that the model has something better to draw on. That is reputation work, and it is usually paired with crisis management when the claim is actively circulating.
Expect this to be a position you maintain rather than a fix you complete. AI answers regenerate, sources change, and a result that improves can drift back if nobody is watching the inputs.
Because a growing share of buyers now get an answer instead of a list, and that answer is where the shortlist gets built. If AI tools describe your company inaccurately or leave you out of the response entirely, you lose opportunities before anyone visits your site or contacts your sales team. Traditional SEO does not reach this surface, because the problem is not where you rank but what the model has to work with.
GEO works on the sources AI systems draw from, not on the systems themselves. We identify what AI tools currently say about your brand, trace those descriptions back to the specific sources feeding them, then correct, displace or strengthen that material. The work spans your own site, third-party publications, structured data and entity records, so a model finds accurate and consistent information wherever it looks.
Pricing depends on scope rather than on a package tier. The main drivers are how many AI surfaces are in scope, how much correction the existing source base needs, and how much new third-party coverage has to be built from scratch. A brand with strong existing coverage and one inaccurate claim is a different engagement from one with almost no third-party footprint. The free GEO audit is how we scope it and give you a number.
Early signals arrive well before headline changes do. The first things to move are usually which sources an AI tool cites when describing you and how consistently it identifies your company correctly, both of which are visible in a baseline comparison. Broader changes, such as being named in response to category-level questions, take longer because they depend on third-party material being published and then picked up. We do not quote guaranteed timelines.
Not directly, and no vendor can. There is no removal request for AI-generated answers. What can change is the material the answer is built from. Where a damaging claim traces to a source that is inaccurate, outdated or violates a platform's policies, that source can often be addressed, and the answer changes when the input does. Where the claim is accurate, the work shifts to building stronger and more current material.
The major answer surfaces: Google AI Overviews and AI Mode, ChatGPT, Gemini, Perplexity and Copilot. These systems draw on overlapping sources and apply similar judgments about credibility, so source-level work tends to carry across them rather than needing to be repeated platform by platform. Where a platform weights something differently, we account for it, but the foundation is shared.
We start with a documented baseline: a fixed set of buyer questions run across every platform in scope, recording how you are described, whether you are named, and which sources each answer cites. Reporting re-runs that same set on a defined cadence and compares against the baseline, so changes are attributable rather than anecdotal. You get the queries, the responses and the source-level detail, not a single composite score.
The sources an AI model cites are the same sources reputation management has always worked on: news coverage, review platforms, third-party profiles, knowledge panels, and the material that outranks or outlasts them. That is why this work sits with a reputation firm rather than with a search agency. The technical piece matters, but the hard part is what to do when a model is citing something you cannot simply outrank.
A free GEO audit is the starting point. We take the questions your buyers ask, run them across the major AI platforms, and document what comes back: how your company gets described, which competitors appear alongside you, which sources each answer draws on, and where the inaccurate or outdated material is coming from. You get that documentation whether or not you work with us.
Our reputation management case studies show how this work has played out across industries.
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