Answer engine optimization: how to get your company cited by AI

Your buyers stopped Googling. They ask ChatGPT for a shortlist, and it answers with a handful of names. Answer engine optimization is how you become one of them, and it is a distribution problem wearing an SEO costume.

Ask ChatGPT to recommend a marketing agency for a founder who grew on referrals and stalled, and it will not hand back a page of blue links to sift through. It will name three or four companies, in prose, as if a knowledgeable friend answered. That list is the entire game now. If your name is on it, you are in the room. If it is not, the buyer never knows you exist, and no amount of Google ranking changes that.

This is not a forecast. It is already how a large share of B2B buying starts, and the gap between the companies AI names and the ones it does not is widening every quarter. Getting into those answers has a name: answer engine optimization. We are a young company building mrktrs in exactly this environment, so we have had to answer the question every founder is now asking. Here is what actually works, and what we have built into our own site from day one.

The behaviour change that forces the issue

Start with the number that reframes everything. In G2's March 2026 survey of over a thousand B2B software buyers, 51% now begin their software research with an AI chatbot more often than with Google, up from 29% eleven months earlier. Nearly three in four rely on chatbots for research at all. This is not a slow drift; it is one of the fastest behaviour shifts B2B has seen.

What matters more than the starting point is the outcome. In the same research, 69% of buyers said they chose a different vendor than they had originally planned based on chatbot guidance, and one in three bought from a vendor they had never heard of before. Read that twice. The AI is not confirming shortlists buyers already had. It is writing the shortlist, and it is putting unknown companies on it. For a young B2B company, that is the most level playing field you will ever get: the model does not care how long you have been around, only whether the evidence points to you.

The AI is not confirming shortlists buyers already had. It is writing the shortlist, and putting unknown companies on it.

And the buyer doing the asking is younger than you think. The oldest members of Gen Z are now approaching thirty; they are product managers, RevOps leads, and founders already sitting on buying committees. They did not grow up on ten blue links. They ask a full question and expect a synthesised answer back, and they carry that habit straight into how they research vendors at work. Optimising for that is a different discipline, and it has a name.

Why answer engine optimization is not just SEO

The instinct is to assume that if you already do SEO, you are covered. You are not. When Ahrefs studied web mentions and AI visibility across 75,000 brands, it found that brand web mentions correlated roughly three times more strongly with AI Overview visibility than backlinks did. SEO gets you ranked in a list of links. Answer engine optimization gets you retrieved, selected, and named as the source an AI uses to build its answer. The metric shifts from ranking position to citation frequency: how often the model names you when someone asks a question in your category.

It helps to know how the machine chooses. ChatGPT answers from two places: its training data, a static snapshot of the web, and live retrieval, where it searches the web mid-answer and cites what it pulls. When it retrieves, it favours pages it can read cleanly, a direct answer near the top, clear structure, visible dates, and cites only a few sources per answer. So the bar is not “be good,” it is “be the most liftable, most corroborated source on this exact question.”

The three levers of answer engine optimization

Everything that works falls into three buckets. We run them in this order because that is the order of leverage, not the order of ease.

THE THREE LEVERS, RANKED BY EFFORT AND LEVERAGE

Most teams invest heavily in the lever with the lowest leverage (page structure) and skip the one with the highest (earned mentions), because the first is comfortable and the second requires asking people for things.

1. Be an entity the model recognises

Before an AI can cite you, it has to know what you are. That means being described the same way in enough independent places that a pattern forms. Pick one clear line for what your company is and who it is for, and use it everywhere: your site, your review profiles, your social pages, every directory. Scattered, inconsistent descriptions give the model nothing to hold onto. Consistency is the signal.

2. Earn mentions on the sources AI trusts

This is the lever most companies underuse, and it is where the majority of citations are actually earned. Muck Rack's May 2026 study of more than 25 million links cited by ChatGPT, Claude, and Gemini found that earned media accounts for 84% of all AI citations, while paid and advertorial content is a negligible 0.3%. The pattern has held across three editions since mid-2025. Put plainly: what other people publish about you matters far more than what you publish about yourself. Publishing your twentieth blog post does less than getting named in someone else's.

Two surfaces matter most. Review sites are the trust layer of AI search; G2's buyers said citations from software review sites are the most confidence-inspiring signal in an AI-generated answer. Community discussion is the other: Semrush's analysis of 126 million AI prompts found ChatGPT cites an average of 15 sources per answer and leans heavily on community and reference platforms like Reddit and Wikipedia, because genuine peer conversation is exactly the corroboration a model looks for. Neither can be faked with a burner account. Both are earned by being genuinely present where your buyers already talk.

Earned media is 84% of what AI cites. The game is other people talking about you, not you talking about yourself.

3. Structure the page so it can be lifted

This is the cheapest lever and the one you fully control. Models lift answers from content they can read cleanly, so every page and every major section should lead with a clean, self-contained answer of roughly forty to eighty words, stated with a specific number or named source. Vague context-setting gets skipped. Add a real FAQ section using the exact questions people ask, mark it up with schema so the structure is machine-readable, and keep a visible “last updated” date, because recency acts as a tiebreaker when a model chooses between two comparable pages.

What we built into mrktrs from day one

We are early, but we did not want to teach a discipline we had not applied to ourselves, so this site is built on the method above rather than retrofitted to it. A few concrete choices, so this is demonstration and not theory:

01  Every article, including this one, opens with a front-loaded answer block that states the takeaway in plain terms with hard numbers, so a model can lift it without hunting.

02  We built a founder-language glossary that defines the terms our buyers actually search, each marked up with structured data so answer engines can parse and attribute it.

03  Our posts carry FAQ sections written in the literal phrasing of buyer questions, with schema on top, and visible dates we refresh on a cadence.

04  We describe what mrktrs is with one consistent line everywhere it appears, so the entity signal stays clean rather than scattered.

We are candid about where we are: doing this well is a system you run continuously, not a box you tick, and the earned-mention side, reviews and third-party coverage, is a compounding effort that rewards patience. That is precisely why we treat it as an operating discipline rather than a one-off project, and why we are writing about it while we build it rather than after.

The uncomfortable order of operations

Here is the part most agencies will not tell you, because it undercuts the easy sell. The three levers are not equal in payoff, and most teams choose exactly the wrong order. They write more content, because it is comfortable and feels productive, and they neglect reviews and third-party mentions, because those require asking clients for favours and pitching editors. But content is the low-leverage lever and earned mentions are the high-leverage one. If you had ten hours this month, the honest split is roughly one on your own pages and nine on getting named elsewhere. The page work makes a citation stick once it lands; the mentions are what make it land. It is the same reason founders eventually stop doing everything themselves and bring in someone who owns the whole growth function rather than a stack of disconnected tactics.

Frequently asked questions

How does a company get cited by ChatGPT?

Pages need to be easy for a machine to lift, the brand needs to be described consistently across the web, and mentions need to be earned on the third-party sources AI trusts, chiefly review sites and community threads. Because earned media is the large majority of what AI cites, those mentions do most of the work. Site crawlability across the major indexes should be confirmed first, along with checking that AI crawlers are not blocked.

How long does answer engine optimization take to work?

Structural fixes on a company's own pages, like answer blocks and FAQ schema, can surface in the faster engines within days to a few weeks. Reputational signals such as review-site presence and inclusion in roundups take longer, often a month or more, because models need time to ingest them. It is a compounding effort, not an overnight switch.

Should a company hire an answer engine optimization agency or do it in-house?

The page-structure work is doable in-house once someone owns it. The hard, high-leverage part, earning reviews and third-party mentions at a steady cadence, is where most teams stall, and it is where an answer engine optimization or generative engine optimization agency earns its keep. If nobody internally owns it week to week, outside help is usually the difference between a plan and actual citations.

What does genuine participation on Reddit or a review site actually look like?

Answering questions in a relevant subreddit or thread before anyone asks about a company by name, responding to reviews (including critical ones) with specifics rather than a template, and showing up consistently over months rather than in a single burst timed to a launch. Models can tell the difference between a sustained presence and a coordinated push, because the latter has no history behind it.

What should a young company prioritise first if it has no reviews or mentions yet?

Reviews before roundups before community presence, in that order. Reviews are the fastest to earn (a handful of happy clients asked directly), they compound into roundup eligibility (most "best agency" lists check for review-platform presence before including anyone), and community credibility takes the longest because it depends on a history the other two can help establish. The traffic that eventually arrives through this path also tends to be pre-qualified, which shows up in a healthier acquisition cost and payback than most cold channels. Starting with the slowest lever first is the most common reason this stalls.

The companies that win the next few years of B2B are not the ones with the most blog posts. They are the ones AI keeps naming when a buyer asks for a shortlist. That naming is earned, deliberately, through structure you control and mentions you go and earn. Start with the page in front of you and the reviews you can ask for this week. The citations follow the substance, not the other way around.