AEO FAQ: The 10 Questions Every Marketer Asks First

Every week I get a version of the same message: "I just realised my brand doesn't show up when I ask ChatGPT about our category. What do I do?"

That moment — of searching for yourself in an AI and finding nothing — is the entry point for most marketers into AEO. What follows is usually a fast sequence of questions. I've answered them hundreds of times. Here are the 10 that come up most, answered directly.

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1. What is AEO (AI Engine Optimization)?

AEO is the practice of improving how your brand is cited by AI-generated responses. When someone asks ChatGPT "what's the best marketing automation platform for mid-market B2B?" the model generates an answer that cites specific products. AEO is the work of making sure your brand appears in that answer — and appears accurately.

The term sits alongside GEO (Generative Engine Optimization), which is sometimes used interchangeably. The distinction is subtle: GEO often refers to the broader practice of optimising for all generative AI outputs, while AEO specifically focuses on citation frequency and accuracy. For practical purposes, they describe the same discipline.

The underlying concept is what we call the Citation Economy: AI models are replacing the first page of Google for informational queries, and the brands that get cited in those AI responses capture the traffic, trust, and pipeline that used to flow through organic search rankings.

2. How is AEO different from SEO?

SEO earns a position. AEO earns a sentence.

In SEO, you're competing for a spot in a ranked list of ten blue links. The user chooses which link to click. In AEO, the AI model synthesises an answer and cites 2–4 sources inside the prose. There's no page two. There's no position three. Either you're named or you're not.

The signal overlap is real — structured data, crawlability, and domain authority matter for both — but the divergences are significant:

| Signal | SEO weight | AEO weight | |--------|-----------|-----------| | G2 review volume | Low | Critical | | Comparison pages (/vs-competitor) | Medium | Critical | | llms.txt file | Not applicable | High | | SoftwareApplication schema | Medium | High | | Pricing page crawlability | Low | High | | Backlink profile | High | Medium | | Page speed | High | Low |

The brands that are winning at AEO right now are often not the SEO leaders in their category. They have better review infrastructure, more crawlable comparison pages, and cleaner entity data — and that translates directly into citations.

3. Which AI platforms should I be optimising for?

Four platforms account for nearly all B2B software buying queries today:

Perplexity is the highest-citation-density platform for B2B software queries. It explicitly surfaces sources and cites them in-line, which means being cited on Perplexity is visible to the buyer in a way that ChatGPT citations often aren't. G2 and TrustRadius appear in Perplexity results more than anywhere else.

ChatGPT (with web browsing enabled) is the highest-volume platform. Its citation behaviour is less transparent — it often doesn't show sources — but the underlying model still weights G2 presence, comparison pages, and review volume when generating recommendations.

Google AI Overviews (Gemini) is the most important platform for brands targeting buyers who start searches in Google. AI Overviews appear above organic results for increasingly many B2B queries, and they favour brands with FAQPage schema, SoftwareApplication markup, and strong G2 presence.

Claude is growing rapidly in enterprise buying contexts, particularly when used via company-deployed tools. It tends to weight analyst recognition (Gartner, Forrester, IDC) more heavily than the other models, making it strategically distinct for enterprise-targeting SaaS.

For most B2B SaaS companies, optimise in this order: Perplexity → ChatGPT → Google AI Overviews → Claude.

4. How do I know if my brand is being cited by AI right now?

Three methods, in increasing reliability:

Manual testing (free, imprecise). Open ChatGPT, Perplexity, and Claude. Ask your most common buyer queries — "best [category] software for [use case]," "alternatives to [top competitor]," "[your brand] reviews." Screenshot the results. Do this every month for a trend signal. The problem: results vary by session due to LLM non-determinism.

Pixel tracking (free, measures downstream traffic). Install the unCited pixel on your site. It detects when a visitor arrives from an AI-generated link — capturing the referrer signal that AI tools embed in outbound clicks. This measures AI-sourced traffic, not citation frequency, but it tells you whether your citations are converting to visits.

Structured audit (most reliable). Run the same set of prompts against multiple models consistently, score the results, and track over time. This is what unCited does — running 8+ buyer-intent prompts per brand across Discovery, Evaluation, Trust, and Conversion stages, then scoring citation rate and tracking it against your category average.

5. What's the single most impactful thing I can do today?

If you have fewer than 50 G2 reviews: start a systematic review programme. Nothing else moves the needle faster. AI models weight G2 review volume as a primary trust signal for B2B software. Under 50 reviews, you're effectively invisible in AI-generated comparisons regardless of everything else you do.

If you have 50+ G2 reviews but no comparison pages: build one first-party comparison page. A page at /vs-[top-competitor] targeting "alternative to X" and "X vs Y" queries is the highest-ROI AEO content investment you can make. It gives AI models a first-party, crawlable, citable source for evaluation-stage queries — the stage where buying decisions get made.

6. Does G2/review platform presence really matter that much?

Yes — more than most marketers expect. In our audit data across 250+ B2B SaaS sites, G2 review volume is the single strongest predictor of AI citation frequency for category and comparison queries. The full analysis is in The G2 Effect, but the short version: AI models use G2 as a verified, structured, high-authority source because it's server-side rendered, frequently updated, and cross-referenced across thousands of buyer queries.

The platforms that matter most, ranked by citation frequency across AI tools:

1. G2 — dominant for all four major platforms
2. TrustRadius — particularly important for Perplexity
3. Capterra — secondary signal, stronger for SMB-targeting products
4. Gartner Peer Insights — high weight for enterprise queries, especially Claude
5. Forrester / Gartner analyst reports — strongest signal for enterprise positioning

7. Is my pricing page really invisible to AI?

Probably, if it's rendered with JavaScript. Most modern SaaS pricing pages are built with React or Next.js and return an empty HTML shell to AI crawlers — which means the crawler sees no content, infers the page is gated or empty, and skips it.

The test: use curl -A "Googlebot" https://yoursite.com/pricing in a terminal. If you get back a mostly-empty HTML page with <div id="app"></div> and no pricing content, your page is invisible to AI.

The fix is server-side rendering for the pricing page specifically. You don't need to rebuild your whole site — just ensure /pricing returns meaningful HTML content (plan names, price points, feature list) in the initial server response. The full fix guide is in The Pricing Page Problem.

8. What is llms.txt and do I need one?

llms.txt is a proposed standard (similar to robots.txt) that gives AI models a structured, concise guide to your site's most important content. It lives at yoursite.com/llms.txt and tells models: here are our key pages, here is our product description, here are our main use cases.

It's not yet universally adopted, and the major AI platforms haven't publicly confirmed they weight it — but Perplexity has indicated it uses the file, and early adopters in our audit data show modestly higher citation rates for trust and conversion queries.

The cost of adding it is low (one static text file), and the upside is real. For B2B SaaS, the llms.txt file is most valuable for conversion-stage queries like "how do I get started with [brand]" and "does [brand] integrate with [tool]." The full implementation guide is in llms.txt for B2B SaaS.

9. How long does it take to see results?

The honest answer: 4–12 weeks for most changes, with significant variance by signal type.

Fast signals (2–4 weeks): Crawlability fixes, adding llms.txt, correcting structured data errors. These are picked up on the next AI crawler pass.

Medium signals (4–8 weeks): New comparison pages, FAQPage schema, pricing page SSR fix. These require indexing, trust-building, and integration into the models' next training or retrieval update.

Slow signals (8–16 weeks): G2 review volume accumulation, analyst recognition, backlink-driven domain authority. You can't shortcut these — they require sustained effort and time. But they also compound: a brand that builds to 500 G2 reviews doesn't lose that signal easily.

The most reliable way to track progress is to run the same set of prompts on a fixed cadence (every 2–4 weeks) and score citation rate consistently. Score drift without controlling for the prompt set is noise, not signal — which is why we track prompt set changes alongside score history in the unCited dashboard.

10. How do I measure AEO success? What KPIs should I track?

Four metrics form a complete AEO measurement framework:

Citation rate — the percentage of tracked prompts where your brand is cited (fully or partially). This is your headline number. Track it per funnel stage: Discovery (category/best-of queries), Evaluation (comparison/alternatives), Trust (reviews/credibility), Conversion (pricing/getting started).

Category rank — where you sit versus competitors in your category on the same prompt set. A score of 42/100 means nothing without context. Being #3 of 18 in HR Tech means something.

AI-sourced traffic — tracked via pixel. The number of sessions arriving at your site from AI-generated links, segmented by platform (Perplexity, ChatGPT, etc.). This connects AEO activity to pipeline reality.

Prompt coverage — are all four funnel stages covered by your tracked prompt set? Many brands discover they have strong Discovery scores but zero Conversion coverage — meaning AI helps buyers find them but not decide to buy.

A practical starting dashboard: track citation rate (overall and by stage), category rank, and AI traffic weekly. Run a full re-audit monthly. Flag any score changes that coincide with prompt set changes — those are methodology shifts, not real improvement.

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The brands that figure this out now are compounding an advantage that will be very difficult to close in 18 months. The ones that wait are betting that the AI-driven buyer journey doesn't reach them. It already has.

Run a free AEO audit for your domain at uncited.ai.