What Is AEO?

Answer Engine Optimization, or AEO, is the practice of structuring a brand's knowledge so AI answer engines can understand it, trust it, cite it, and recommend it. The work spans on-site answer content and entity markup, off-site corroboration such as reviews and third-party publications, and continuous monitoring of how engines describe the brand. Success is measured by accurate representation inside a generated answer rather than by a ranking position.

Why the category exists

G2's March 2026 survey of 1,076 B2B software buyers found that 51% now start research with an AI chatbot more often than with Google, and 71% use a chatbot somewhere in the buying process (G2's Answer Economy research). That changes who writes your first impression, because the model assembles a description of your company from whatever sources it can retrieve, and if nothing on your site is retrievable in the shape it needs, it borrows a description from somewhere else.

Kevin Barber, our Head of AI Growth, puts the stakes this way: "It literally can be the difference between citation and recommendation versus being totally left out." We see a milder version of it constantly in baseline scans, where a company with real rankings and a healthy site never gets named when a buyer asks an engine for the best options in their category, simply because nothing on the site was retrievable as an answer to that question.

What AEO includes in practice

Our AEO Authority System runs in three parts, and the on-site piece is the part people expect. Most of it comes down to two jobs: publishing answers in a form a model can lift cleanly, and making the brand facts underneath those answers explicit through entity and schema markup so an engine isn't left inferring who you are. Off-site work is where consensus gets demonstrated, through expert articles on publications the engines already read, authority listings, targeted outreach, and third-party corroboration of the same brand facts you state on your own site. It usually takes up more of the calendar than the on-site build, since an editor at a trade publication moves on their own timeline and a customer writes a review when they feel like it.

Monitoring is the part most programs skip. We keep a citation dashboard running across ChatGPT, Claude, Gemini, and Perplexity so that when an engine changes its mind about you, or a competitor becomes the default recommendation on a prompt you used to own, somebody notices within weeks instead of at the next quarterly review. The whole system is documented on our AEO Authority System page if you want the detail.

What the evidence supports, and what it doesn't

The research base under AEO is younger than the marketing around it, and it's worth being precise about that. The 2024 KDD paper that formalized optimization for visibility inside generative responses reported gains of up to 40% on its experimental benchmark (the GEO paper), which establishes that answer visibility responds to deliberate work. It stops well short of validating any vendor who promises you a fixed citation rate on a live platform, since production engines swap source sets and rerank on a schedule nobody outside the lab controls.

The honest summary is that the mechanism is established and the dose response isn't, so anyone quoting you a guaranteed citation rate is quoting a number they have no way to control. What that means in practice is that the only evidence worth much to you is your own. Running a fixed set of buyer prompts across the engines before the work starts, then rerunning the identical set afterward, tells you what moved in your category rather than in somebody's benchmark, and it's the measurement any vendor should be willing to be judged on.

AEO works by influencing what an AI engine retrieves before it writes anything. Modern engines fan a question out into multiple searches, gather candidate sources, select individual passages, and compose an answer from them. Optimizing for that pipeline means making a site accessible to the crawlers that feed each engine, then supplying clear answers to the exact questions buyers ask, backed by corroboration on sources the engine already trusts.

Retrieval comes before generation

Google's documentation describes AI Overviews and AI Mode as able to "fan out" into multiple searches across subtopics and sources before composing a response (Google's AI features documentation). The practical consequence is that when a buyer asks about your category, the model is assembling sources in real time rather than reciting something it memorized during training. Your page has to clear retrieval first, then get selected as a passage worth quoting, and it has to carry enough source credibility for the model to name you ahead of a competitor it already has reason to trust.

Kevin Barber, our Head of AI Growth, is direct about the prerequisite: "It's super important that we provide the website in a structured, machine-readable way that gives AI everything it needs to ingest your copy and understand your brand." In practice that means putting the direct answer in the first sentence under each heading, using headings phrased the way buyers actually type their questions, and then checking whether any fact you want cited still makes sense once it's lifted away from the page around it.

Each engine reaches your site differently

Crawl permissions decide whether any of the content work matters. OpenAI uses OAI-SearchBot for ChatGPT's search discovery, so a blanket bot block quietly removes you from that surface no matter how good the writing is (OpenAI's publisher FAQ). Perplexity separates its indexing crawler from user-requested fetches, which means the rules you set for one don't govern the other (Perplexity's crawler documentation).

Before we touch content, we check robots.txt, WAF and bot-mitigation rules, indexation status, and whether the answer text is present in the served HTML or gets rendered later by JavaScript. That last one is the issue we run into most often on otherwise healthy sites, and it's usually a fast fix once somebody looks.

How the loop runs

We start with a baseline: a fixed panel of buyer prompts run across ChatGPT, Claude, Gemini, and Perplexity, recording which brands get named, which sources get cited, and how your company gets described on the occasions it does appear. The prompts where you're absent or described inaccurately become the gap list, and the content plan is built directly off that list rather than off a keyword export.

From there we structure the on-site answers and brand facts, then go earn corroboration on the sources the engines are already pulling from, since a claim you make about yourself carries less weight with a model than the same claim appearing on a publication it already trusts. Then we rerun the identical prompt panel and close whatever is still missing. That rerun matters more than any single asset we publish, because the engines keep shifting their source preferences and without a second measurement you have no reliable way to tell whether a change helped. We put together a demo site that walks through the mechanics if you want to see the structure applied end to end.

AEO covers the entire evidence pool an AI engine can retrieve, most of which sits outside a brand's own domain. Reviews, third-party publications, comparison articles, community threads, YouTube, directories, partner pages, and consistent brand facts across profiles all shape how an engine describes and recommends a company. In measured studies of branded queries, owned content accounts for a minority of the sources an engine actually cites.

Your own site is a minority of the evidence

Omniscient Digital's branded-query research found that owned content accounted for only 23% of AI citations, while reviews and other social proof accounted for 57% (Omniscient's research index). That ratio is a budgeting instruction as much as a research finding, because a program that spends its whole budget on owned pages is competing for less than a quarter of the material an engine uses when it answers a question about your brand.

This is the most common gap we find when we run a baseline. The site, the blog, and often the schema are all in reasonable shape, while almost nothing outside the domain is saying the same things about the company that the company says about itself, and that third-party corroboration is what a model looks for before it hands anyone a recommendation.

What a complete program looked like at HubSpot

HubSpot's own AEO program ran on three pillars: on-site content, off-site publisher amplification, and forum and community growth. By the end of 2025 it had partnered on close to 1,000 third-party pages, and its Reddit citations grew from 178 in May to roughly 146,000 in December (HubSpot's AEO case study). The Reddit figure is the one that tends to surprise people, since no amount of on-page markup produces it and it only comes from sustained, genuine presence in the places buyers already talk to each other.

The shape of that program transfers to companies working with far smaller budgets, provided the plan accounts for the surfaces an engine actually reads beyond your own CMS and someone owns each of them.

The off-site surfaces we work

Off-site AEO is broader than link building, though links are part of it. Our system covers expert articles placed on high-authority publications, authority listings and directories, targeted outreach, review volume on the platforms your buyers actually check, and consistency of your brand facts everywhere your company gets described. The point of all of it is corroboration, so that an engine encounters the same claim about you from several independent sources and treats it as consensus.

Schema still earns its place inside this, mostly as a clarity layer that keeps brand facts, services, authorship, and FAQs machine-readable as a site grows, which is what Schema Rocket handles on our HubSpot builds. Markup alone won't carry a program, though, since the citation evidence keeps pointing at the off-site pool as the bigger lever.

SEO and AEO share the same technical foundation and diverge mainly in how a win is scored. A ranking is measured in position and clicks on a results page, while a citation is measured by whether an engine pulled a passage into its synthesized answer at all. Google states that AI Overviews and AI Mode carry no eligibility requirements beyond standard Search, so AEO layers onto existing SEO work instead of replacing it.

Where the two overlap

Google's guidance is explicit that there are no additional eligibility requirements for AI Overviews or AI Mode beyond the normal Search requirements and people-first practices (Google's AI features guidance). Your domain authority, your indexed content, and your technical health all carry forward, which is why the companies that see the fastest AEO movement are usually the ones who already invested in search. We treat existing SEO as a head start, and we have never recommended dismantling an SEO program to fund an AEO one.

The scorecard, side by side

What changes in the writing

Kevin Barber, our Head of AI Growth, describes the shift like this: "AEO is substantially different than traditional SEO because instead of long-form pages, you're focusing on semantic chunks that answer questions in bite-sized components." A 3,000-word pillar page can rank beautifully and still lose the citation, because the engine needs a self-contained passage it can lift, and a passage that only makes sense after 800 words of preamble is not liftable.

The rewrite is usually less disruptive than people expect, because most of it is restructuring rather than rewriting. The argument you already made stays; it just gets front-loaded into each section instead of arriving as a conclusion, and the numbers or process details you'd normally save for the end move up into the passage where an engine can actually reach them. Done properly, the page holds its rankings while picking up a second job, which is roughly what our AEO Launchpad does to an existing content library before we touch anything off-site.

Why the click math matters now

Pew found that users clicked a traditional search result on 8% of visits when an AI summary appeared, compared with 15% of visits when no summary was present (Pew Research Center). Rankings still have value and the traffic they produce still converts, but a growing share of what a buyer believes about your company now forms inside an answer they never click out of. Being described accurately in that answer has real commercial value even when the session never appears in your analytics, which is the part of the shift that traditional reporting is worst at showing you.

AEO and GEO describe substantially the same work under different labels. GEO, or Generative Engine Optimization, originates in a 2024 research paper on improving a source's visibility inside generative responses. AEO, or Answer Engine Optimization, is the more common buyer-facing term and usually describes a wider program covering on-site structure, off-site authority, reputation, and monitoring. Most vendors use them interchangeably, so the useful comparison is what a provider actually delivers.

Where each term came from

GEO has the cleaner origin story. The 2024 KDD paper defined it as optimizing the visibility of a source within a generative engine's response, and it introduced specific visibility metrics to measure that (the GEO paper). AEO grew out of agency and marketing vocabulary, carried forward from the featured-snippet era, and it stuck largely because buyers immediately understand what an answer is.

The distinction people try to draw between them is one of emphasis. NoGood's terminology guide describes AEO as producing extractable, citable answers and GEO as producing reliable source material for generative systems, while acknowledging that the underlying principles converge (NoGood's guide). You will also see LLMO, LLM SEO, and AI SEO used for the same activity, usually by whoever coined the acronym.

The distinction people try to draw

How we use both lenses

We treat the two terms as lenses on one job. The AEO lens asks whether an engine can lift a usable answer out of your page, while the GEO lens asks whether the engine retrieved your page in the first place, and since a citation requires both things to be true, we deliver them inside a single system instead of selling them as separate products. The on-site portion of that is what AEO Genie handles, and the off-site half is where most of the authority work happens.

Because the acronym on a vendor's pitch deck carries no information about what they'll actually do, the diligence has to happen at the level of the statement of work. Ask who writes the answers, who implements the schema and technical fixes, who runs the publisher outreach, who collects the reviews, how many engines get tracked, and what the plan is when a competitor overtakes you on a high-intent prompt. Two vendors using identical vocabulary will give you visibly different answers to that list, and the answers are the comparison you wanted when you started googling the acronyms.

Traditional content marketing measures a finished asset by the traffic and leads it produces, while AEO also evaluates the individual passages, facts, tables, and brand mentions an engine can extract without any visit to the site. AEO treats distribution as part of the evidence base, since reviews, publications, and community discussion influence an AI answer alongside owned content. Measurement begins with a panel of buyer prompts rerun across engines.

What counts as a result

AEO grades individual passages, so the unit of value gets smaller than the asset most content teams plan around. A content program still asks the questions it has always asked about whether an article got read and whether it converted, and those questions remain worth asking. An AEO program adds a layer underneath them, at the level of the paragraph: whether the third paragraph of that article got quoted in a ChatGPT answer about vendor selection, and whether the comparison table halfway down it got extracted and attributed to you in a recommendation the buyer never clicked out of.

Those outcomes can occur with zero sessions attached, which is genuinely awkward for the reporting model most content teams inherited. We work around it by scoring a prompt panel on a fixed schedule, so there's a defensible number to bring to a board meeting even in a month when the analytics stay quiet.

Why blog-only programs stall

Omniscient Digital's branded-query research found owned content producing only 23% of citations (Omniscient's research), which means a plan whose only deliverable is published articles has capped what it can influence before the first draft ever gets assigned. We build off-site placement, review generation, and community presence into the content plan itself, on the same calendar and with the same owner, which keeps them from becoming a separate PR line item that gets cut in Q4.

How the plan gets built

HubSpot built its program by mapping buyer prompts across awareness, consideration, evaluation, and decision, then tracking visibility, share of voice, citations, and citation share alongside the usual sessions and rankings (HubSpot's AEO case study). We run the same shape. The prompt panel comes first, we score where you and your competitors currently appear across the engines, and the content plan is the gap list that falls out of that scoring. Search demand still informs sequencing, since a prompt with real volume behind it deserves earlier attention, but volume no longer picks the topics on its own.

Where original evidence pays off

The KDD GEO experiments found that adding expert quotations, statistics, and cited sources improved a page's visibility inside generated answers (the GEO paper). That matches what we see in citation data, where pages contributing a real number, a documented process, a dataset, or a defensible position get pulled into answers considerably more often than pages restating what everyone else already published.

As the volume of generic AI-written content climbs, the pages carrying genuine evidence hold a retrieval advantage that keeps getting more valuable. It's a reasonable argument for publishing fewer pieces with an actual expert behind each one, which is the standard we try to hold our own blog to.

AEO benefits companies whose buyers research a considered purchase before contacting sales, which in practice means B2B software, professional services, and similar categories with high deal values and long evaluation cycles. The strongest candidates have a defined offer, verifiable customer proof, subject-matter experts willing to be quoted, and a website AI crawlers can reach and parse. Low-consideration commodity purchases and referral-only sales models see the least return.

The demand profile that pays back fastest

Considered-purchase categories benefit most, because their buyers spend weeks asking exactly the kind of comparison, fit, integration, cost, and risk questions an AI engine is happy to answer. 6sense's 2025 Buyer Experience Report found that 94% of B2B buyers used LLMs somewhere in the purchase process, and that 95% of them ultimately bought from a vendor that was already on their Day One shortlist (6sense Buyer Experience Report). We spend a lot of client calls on that second number, because it means the shortlist is largely set before anyone speaks to your sales team, and the shortlist is now partly assembled by a model.

Deal size then decides whether the payback math works. If your average contract value is a few hundred dollars and buyers decide in ten minutes, a channel that compounds over quarters has a hard time beating paid or conversion work on speed of return, and we'd generally say so. Once lifetime value is high enough that a single extra deal covers several months of program cost, the calculation stops being close.

What needs to be in place before it works

Four things make a company ready, in our experience: a clear offer you can state in one sentence, customer proof you're allowed to publish, at least one internal expert who will go on record, and a site the crawlers can actually access. The last one blocks more programs than people expect. We've run baselines where the content was genuinely good and the whole thing was stalled by a WAF rule and an unindexed subdomain.

Regulated categories are worth a separate note. Healthcare, fintech, and legal-adjacent companies often have the strongest case for AEO, since their buyers bring a lot of anxious questions to a chatbot before they'll talk to a human. Those same companies also have the longest approval chains, so budget the legal review time honestly rather than discovering it in week three.

When AEO compounds later rather than now

Some companies get more out of AEO after another piece of work lands first. Positioning is the usual one, because AEO will faithfully broadcast whatever message it finds, including a message you're planning to rewrite next quarter. Published proof is the other, since the engines lean heavily on corroboration and there isn't any to find when a company has no reviews and no case studies it's cleared to publish yet. Companies whose revenue currently comes mostly through a founder's network are in a slightly different position again: that network is probably still the fastest path to the next few deals, and AEO becomes the thing that scales past it once the positioning is settled and there's proof worth citing.

This is a question of order rather than a verdict on decisions you've already made. We've told founders to spend a quarter collecting ten real reviews and two named case studies before starting, because that groundwork makes every dollar of the program go further once it's running.

Who we tend to fit

We work best with Seed through Series C B2B SaaS companies running on HubSpot that have a real offer and a measurable goal. That's a deliberately narrow lane. It's where our AEO Authority System has the most to work with, since those companies usually have an indexable site, a product story that holds still, and enough margin for a channel that builds over 60 to 90 days rather than overnight.

Outside that lane the mechanics of the work don't change, though the fit does. A company on a fully custom stack, or one selling into a category where buyers still meet their vendors on a conference floor, is buying something different from what we've packaged, and we'd rather say so on the first call than discover it together in month three.

AEO targets the AI assistants and AI search surfaces where buyers ask questions, primarily ChatGPT, Google's AI Overviews and AI Mode, Gemini, Perplexity, Claude, and Microsoft Copilot. Each engine assembles answers from a different source set and crawls the web under different rules, so visibility on one platform does not transfer automatically to another. Most B2B programs weight ChatGPT and Google's AI surfaces first, then expand.

The engines that matter for B2B buyers

ChatGPT is where most B2B research starts. G2's buyer research found ChatGPT was the preferred LLM for 47% of surveyed B2B software buyers in 2025, nearly three times any other model (G2 AI search research). Google's AI Overviews and AI Mode come next by sheer exposure, since they appear inside the search results your buyers already use.

We optimize for ChatGPT, Claude, Gemini, and Perplexity as the core set, and we track Google AI Overviews and Copilot as discovery surfaces that feed the same buying decision. Which of those you weight most heavily should come from your own data rather than from a general ranking, because a security buyer and a marketing ops buyer do not use the same assistant.

Why you can't treat them as one channel

Semrush compared brand and source overlap between ChatGPT and Google AI Mode and found the two engines agreed 67% of the time on which brands to name, but only 30% of the time on which sources to cite (Semrush AI visibility trend update). Two-thirds agreement on brands with less than a third on sources means the engines are frequently reaching the same conclusion by reading completely different pages, and a dashboard number averaged across platforms will paper over that gap.

The practical consequence is that tracking has to happen per engine. When a client tells us they're "showing up in AI," we ask which engine and on which prompts, because the fix for a Perplexity gap and the fix for a ChatGPT gap often live on different sites entirely.

How the engines actually retrieve

Claude and Copilot publish less about their retrieval mechanics than Google, OpenAI, and Perplexity do, so we treat them as monitoring targets and rely on the fundamentals that hold across all of them: accessible text, consistent brand facts, and third-party corroboration. You can see how the whole ingestion path fits together on our demo site for how AEO works.

How to prioritize without spreading thin

Start with the engines your buyers actually use, then narrow to the prompts that carry commercial intent on those engines. A comparison prompt in ChatGPT that names three competitors and not you is worth more attention than a definitional prompt in an engine your market has never opened.

Technical eligibility is the one thing worth doing for every engine at once, since crawl access, indexable pages, and readable text are prerequisites everywhere. After that, the engine-specific work in our AEO program gets sequenced by where your buyers are and where your gaps are largest.

AEO extends across the entire researched buying journey, beginning with early problem framing and continuing through vendor comparison, evaluation, and final verification before purchase. Buyers use AI assistants to define categories, assemble shortlists, check integrations and pricing, and summarize reviews, all before contacting a vendor. Its influence ends at conversion, where the website and the sales process take over.

Every stage has prompts, and they don't look alike

HubSpot's own AEO program mapped buyer prompts across awareness, consideration, evaluation, and decision, then tracked where it or a competitor appeared at each stage (HubSpot AEO case study). Early prompts sound like "how can I improve our onboarding completion rate." Later ones sound like "is Tool A or Tool B better for a 40-person RevOps team," and eventually "does Tool A integrate with Salesforce and what does it cost at 50 seats."

Those late prompts are the ones that decide deals, and they're also the ones most content programs never answer, because pricing, comparison, and integration questions are uncomfortable to publish. The engine answers them anyway, using whatever sources it can reach, which is frequently a competitor's comparison page or a three-year-old forum thread.

The stages most programs leave uncovered

G2 reports that 51% of B2B software buyers now start research with an AI chatbot more often than Google, 69% end up choosing a different vendor than they originally expected, and 33% chose a brand they had not previously heard of (G2 buyer research). A third of buyers landing on a vendor they'd never heard of before the research started means an AI answer can introduce a company cold and still put it on the shortlist, which cuts in both directions depending on whether the company being introduced is you.

Omniscient Digital's research summary reports that educational content tends to win early discovery while reviews and other social proof carry more weight nearer the buying decision (Omniscient research). A program built entirely from blog posts therefore goes quiet at exactly the point where the buyer is choosing, which is why we treat review generation and third-party corroboration as part of the AEO scope rather than as a separate reputation project.

Where AEO hands off

AI can build the shortlist and frame how your brand gets described, and the buyer will still visit your site, read the pricing page, and forward a link to the two colleagues who have to approve the spend. 6sense found buyers average a large number of vendor interactions across a full cycle (6sense Buyer Experience Report), which means a citation is an entry point into a process that still runs on your website and your sales team.

That means the AEO program has to connect to something downstream. We wire cited answers into conversion paths on the site and tag AI referral sources in HubSpot so contacts and deals carry attribution, which also gives sales the same answer language the buyer just read in ChatGPT. When the conversion architecture isn't ready for the traffic, the extra visibility tends to produce well-informed visitors who never identify themselves.

What this means for measurement

A single blended visibility score hides the thing you need to see, because a program can be winning awareness prompts while losing every comparison prompt in the same market. We score each stage on its own terms. Awareness reads as coverage and share of voice. Consideration and evaluation read as citations on comparison and alternatives prompts, where the competitor set is explicit and the loss is easy to trace. Decision prompts are the strictest test, since what you want there is an accurate recommendation with your reviews attached to it, and that only happens when the off-site corroboration has caught up with the on-site work. Pipeline and revenue sit downstream of all of it, usually 60 to 90 days after the foundation is in place in our engagements, and they compound rather than arriving in one piece.