Most Answer Engine Optimization content on the internet is a sales pitch dressed as a guide. Short on specifics, heavy on the word “revolutionary,” and gated behind an email form that dumps you into a drip sequence. This one isn’t.
The goal of this post is to give you enough information that you could run an AEO program yourself, from scratch, for a mid-sized brand, without hiring anybody. If you decide to hire a firm after reading it, you’ll be able to tell which ones know what they’re doing and which ones are repackaging 2019 SEO tactics with a new acronym.
What AEO actually is
Answer Engine Optimization is the discipline of earning citations, brand mentions, and recommendations inside the answers that AI models generate. When a user asks ChatGPT “what are the best payroll platforms for 100-person companies,” the model produces a paragraph that names three to seven brands. You are either in that paragraph or you are not. There is no position four. There is no “ranking on page two.” The answer is the answer.
The discipline goes by several names in 2026. AEO is the most common. GEO — Generative Engine Optimization — is the Search Engine Land framing. LLM optimization is the technical label preferred by more engineering-minded practitioners. Same workflow, three acronyms. Pick the one your stakeholders respond to and keep moving.
Why AEO is different from SEO
The surface is different. The ranking algorithm is different. The feedback loop is different. And the measurement tools don’t overlap cleanly with the SEO stack you already own.
The surface. Traditional SEO optimizes a page to rank on a list of ten results. AEO optimizes a brand’s presence across the underlying sources a language model consults when generating an answer. Those sources are not the same as Google’s top ten. They include major publications, Wikipedia, Wikidata, Reddit, industry trade press, structured data, company websites, and the model’s own training corpus. Winning on Google doesn’t automatically mean winning on ChatGPT. Sometimes it helps. Sometimes it has nothing to do with it.
The algorithm. Google’s ranking is a scoring function applied to a crawl of the web. A language model’s “ranking” is the probability distribution over tokens that the model has learned during training, combined — in the case of retrieval-augmented models — with a live search of the web at query time. Those two pieces are the training layer and the retrieval layer. Both matter, but they respond to different tactics.
The feedback loop. When you change an SEO tactic, you see the effect in the SERPs within days to weeks. When you change an AEO tactic that targets the retrieval layer, you see the effect within a week or two. When you change a tactic that targets the training layer, you might not see the effect until the model is retrained and re-released. That can be six months, or longer.
The measurement. Google Search Console tells you your rank for a keyword. Nothing tells you your citation rate in ChatGPT by default. You have to build that measurement yourself, or pay a specialized tool to do it. The data is less precise than SEO data and a lot harder to attribute to specific tactics.
The four levers that actually move AEO
Most of the advice online misses the forest for the trees. There are four things that actually drive whether a brand gets mentioned in AI answers. Most of the work in a real AEO program maps to one of them.
Lever one: authoritative citations. A language model learns which brands are worth mentioning by weighting the sources that mention them. A brand mentioned in ten low-quality blog posts is invisible. A brand mentioned once in The Wall Street Journal, Wikipedia, and a Reddit thread with 500 upvotes has a much better shot. The first lever is getting your brand named in the sources the model trusts.
For most B2B brands, those sources are: major business publications (WSJ, Forbes, Bloomberg, Axios, trade journals), Wikipedia, Wikidata, high-trust review sites (G2, Capterra, TrustRadius), industry-specific forums, and the major subreddits in your category. For consumer brands the list shifts to include product review sites, YouTube review channels, and more consumer press.
Lever two: entity clarity. The model needs to understand what your brand is before it will recommend it. That means unambiguous entity signals: a complete Wikipedia page, a Wikidata entity, clean structured data on your own site, consistent NAP (name, address, phone) data across the web, author markup on your blog posts, and schema types that match what you actually do. A surprising number of B2B brands are invisible to AI answers because the model doesn’t know whether they’re a product, a company, a consulting firm, or all three.
Lever three: first-party content structure. Your own website matters, but not in the way SEO people think. Language models don’t care about keyword density. They care about whether a page cleanly answers a specific question. Pages structured with clear H2s that read like questions, direct answer paragraphs that follow each H2, and a summary that names the key takeaway in plain language all get cited more often than the same information buried in a wall of text. The FAQ page is not a throwaway — it’s one of the highest-leverage pages on a modern site.
Lever four: freshness and velocity. Some of the retrieval layer is real-time. Models pulling from live search favor recent sources. A brand that published an authoritative guide on a topic eighteen months ago may be outranked in a live answer by a brand that published a less comprehensive guide last week. Publishing cadence matters for the same reason it matters in SEO, but the signals the model weights are slightly different — recency, comprehensiveness, and citation velocity rather than backlink count.
The measurement stack
You cannot improve what you cannot measure, and AEO measurement is harder than SEO measurement. Here’s the minimum viable stack for a real program.
Prompt inventory. Start with a list of 30 to 100 prompts that reflect how buyers in your category actually ask AI models questions. Not keywords — prompts. “What are the best project management tools for remote teams under 50 people” is a prompt. “project management tools” is a keyword. The prompt is what matters.
Baseline test. Run every prompt through ChatGPT, Claude, Perplexity, and Google Gemini. Log the full answer and which brands get mentioned. Do this manually the first time so you understand what you’re actually looking at. Most people skip this step and it kills the program.
Scheduled retesting. Once you have a baseline, run the same prompts on a schedule — weekly for your top priority prompts, monthly for the long tail. Track which brands show up, how many times, and in what position. A spreadsheet is fine for fewer than 50 prompts. Beyond that, a tool like Otterly or Profound starts to earn its keep.
Change logging. Every time you make a material change to your AEO program — a new Wikipedia page, a major press placement, a structural change to your site — log the date. When you see a shift in the prompt testing data, you want to be able to correlate cause and effect. You will not be able to do this perfectly. You will do it well enough to learn what works.
Share of voice calculation. The summary metric is your share of voice across your prompt inventory — the percentage of prompts where your brand is named, weighted by the size of the answer and the prominence of the mention. You want this number going up over time. If it isn’t, something in your program isn’t working.
The mistakes that waste budget
This is the section people skip, and it’s the section that will save you the most money.
Mistake one: treating AEO as SEO with a new label. If your current SEO agency is pitching you AEO services that look identical to their SEO services with a sticker on top, walk away. The work is genuinely different. Press strategy, entity work, and prompt testing are not optional extras — they are the core of the program.
Mistake two: obsessing over your own site. Most of the citations happen off-site. The highest-leverage work for most brands is getting mentioned in authoritative third-party sources. Your blog matters, but it’s lever three out of four, and it’s usually the lever where incremental effort returns the least.
Mistake three: measuring citation count instead of quality. Being mentioned in one major publication beats being mentioned in twenty low-quality SEO blogs. The model weights sources by trust, not by count. Chasing volume is chasing the wrong metric.
Mistake four: expecting training-layer results in weeks. If your AEO program is targeting the training layer, the feedback loop is long. The work you do in April will show up when the next model version ships, which could be months away. Retrieval-layer work shows up faster. Plan your budget and your expectations around both timelines, not just the faster one.
Mistake five: not having a point of view. Models cite brands that say specific, memorable things. A brand whose public content is a generic mush of “solutions” and “innovation” language will not get cited, because the model has nothing distinctive to quote. The brands that win at AEO say clearly what they believe, in plain language, in enough places that the belief becomes identifiable.
A 90-day starter plan
If you’re starting from zero, here’s what the first ninety days look like.
Days 1–7: Build the prompt inventory. Run the baseline test across four models. Write down the current share of voice. Identify the three to five prompts where you should be winning but aren’t.
Days 8–21: Audit your entity signals. Check your Wikipedia page (or lack of one). Check Wikidata. Check your structured data. Fix the cheap stuff. Start the Wikipedia page process if you don’t have one, knowing it will take weeks to months.
Days 22–45: Content restructuring on your own site. Rewrite the top five pages to answer questions cleanly. Add FAQ sections where they make sense. Fix author markup. Make sure the on-site signals are clean before you start chasing off-site ones.
Days 46–75: Begin the press and mentions work. Target five to ten publications that cover your category. Pitch one specific, usable angle to each. This is where a firm with existing journalist relationships earns their money. If you’re doing it yourself, budget time for it to take longer than you expect.
Days 76–90: Retest the prompts. Look at what moved. Figure out which tactic did what. Adjust the plan for the next quarter based on what the data shows.
At the end of ninety days, you won’t have dominated AI answers. What you will have is a working feedback loop, a baseline, and a clear view of what the next six months should cost. That’s the real deliverable of the first quarter of an AEO program. Everything after that is iteration.
What to do now
Pick ten prompts right now. Run them through ChatGPT and Perplexity. Write down which brands are named. If yours isn’t, that’s your baseline. The work starts there.
If you want a structured version of this analysis for your brand, we run a free AEO Rating for any site. It takes about a week and gives you a prioritized list of the fixes that will move your share of voice fastest. Link below.