When I first started reviewing content for visibility in response engines, I assumed that the keyword research process was pretty much the same as traditional SEO – just with a few tweaks. I was wrong.
Answer Engine Optimization (AEO) keyword research isn’t just about finding what people are looking for. It’s about understanding what response engines are being asked, how they interpret those prompts, and what questions your content needs to answer directly and authoritatively. The entire mental model shifts rank To be quoted.
In this guide, you’ll learn exactly how to approach this shift, which tools actually help, and how to build a workflow that combines question discovery with published, AI-optimized content.
Table of contents
How keyword research differs between AEO and SEO
Traditional SEO keyword research is based on real user data: monthly search volume, keyword difficulty, and potential click-through rate. Tools like Ahrefs and SEMrush reveal what people are typing into Google and you optimize the content to rank for those terms.
AEO reverses several of these assumptions.
SEO keyword research prioritizes:
- Monthly search volume
- Shorter, navigational or transactional queries
- Ranking placement in the 10 blue links
- Traffic is the most important success metric
AEO keyword research prioritizes:
- Question-based and conversational queries
- Fanout queries – the group of sub-questions that trigger a single prompt
- Align with user intent on a semantic level, not just lexical match
- Visibility in Gemini, ChatGPT, Perplexity and other response engines
- Citation probability, not just ranking position
The practical difference is that when someone asks ChatGPT “What is the best CRM for a small marketing team?”, the model does not return a ranking of pages. Instead, it synthesizes an answer from content it has indexed and deemed authoritative.
Your job is to be the source the model trusts.
AEO keyword research tools help discover conversation and question-based queries that match the way users prompt response engines. AEO tools differ from SEO tools in that they focus on response engine visibility, prompt patterns, and opportunities for “reply first” content – not just search volume and backlinks.
professional TIP: Start your AEO keyword research by reading your own brand’s AI overview appearances on Google. Search for your category (e.g. “Best Email Marketing Software”) and note which questions trigger AI-generated summaries.
These are the AEO targets worth owning first.
Keyword research tools for AEO by objective
There is no single “AEO keyword tool”. The best stack combines traditional question discovery tools with newer answer engine visibility trackers and synthetic query generators. Here’s how I categorize them and which ones I would actually use.
Traditional keyword research tools
Traditional SEO tools are still essential for AEO, but you need to know how to use them differently. Instead of chasing high-volume header terms, I use these tools to isolate question-based queries, extract “people also ask” clusters, and identify long-tail prompts that match conversational search behavior.
AEO keyword research is built on this foundation: These tools give you a basic understanding of what people are asking, which you can then expand on with fanout analysis and AI prompt modeling.
Semrush
Semrush’s Keyword Magic Tool allows you to filter by “Who, What, How, Why, When” questions. This is exactly the format that AEO content needs to answer. I’ve found the “Questions” filter in SEMrush particularly useful for identifying how a topic branches into multiple user intents – a precursor to fanout query mapping.
What we like: The Topic Research feature presents semantically related questions and subtopics in a visual card format, making it easier to identify content gaps around a core AEO topic.
professional TIP: Export SEMrush “Questions” results for your top 5-10 seed keywords. This is your initial set of questions. From there you can use fanout tools (see below) to expand it into an AI-native set of prompts.
Best for: Enterprise teams that need comprehensive question discovery, competitive gap analysis and content optimization in one platform.
Ahrefs
Ahrefs’ Content Explorer and Site Explorer lets you see which pages on competitor sites are generating the most links and traffic – useful for figuring out which AEO-style content (FAQs, guides, comparison pages) signals authority.
The Questions filter in Keywords Explorer is another solid source of conversational queries.
What we like: Ahrefs’ “Also Rank For” report shows what else a page is ranking for – ideal for uncovering the semantic neighborhood around your AEO target topics. For more options in this category, check out our roundup of the best tools for finding long-tail keywords.
Best for: Teams that need comprehensive keyword data, in-depth competitor content analysis, and reliable search volume estimates.
Also asked
AlsoAsked scrapes Google’s “People Also Asked” data and presents it as a visual tree – showing how a question branches into related sub-questions. This is one of the most direct inputs to the AEO content structure: the branches represent the natural follow-up prompts that users make after an initial query, which is close to how LLM fanout works.
What we like: The visual hierarchy makes it easy to see which questions are “parent” questions (probably your H2 questions) and which are sub-questions (your H3 questions and direct answers). It’s one of the tools I use almost every time I create an AEO content brief.
Best for: Map question hierarchies and understand how people move from general questions to specific follow-up questions.
AnswerThePublic
AnswerThePublic visualizes question-based and preposition-based queries around a seed keyword. This is a quick way to generate a large pool of AEO candidates, organized by question type (What, How, Why, Can, Will, etc.).
What we like: The export feature makes it easy to transfer hundreds of question variations to a spreadsheet for prioritization. Combine it with SEMrush or Ahrefs volume data to determine which questions actually have search demand.
Best for: Comprehensive question discovery on a topic, especially for teams new to AEO who need a comprehensive overview of people’s questions.
Tools for finding fanout queries
LLM query fanouts reveal related prompts, comparisons, and follow-up questions triggered by a single input. When someone types “how do I choose a CRM” in ChatGPT, the model may internally generate and answer multiple subqueries before displaying an answer.
Understanding that fanout is one of the most underused levers in AEO keyword research.
Question discovery tools produce human-first questions and long-tail prompts. Fanout tools go one step further by modeling how AI systems expand and interpret these questions.
Otterly.ai
Otterly.ai monitors visibility in ChatGPT, Perplexity, and other response engines. By tracking which prompts trigger consumption of your content, you can reverse engineer the top fanout clusters.
What we like: Otterly shows the visibility of prompts by platform – so you can see that they appear in Perplexity for “best CRM for small teams” but not in ChatGPT for the same query. This gap analysis can be implemented directly.
Best for: Teams who want to see how their brand and content appear across multiple AI platforms and use that data to drive rapid audience targeting.
Dejan.ai
Dejan.ai provides tools for semantic analysis, entity mapping, and understanding how AI systems interpret content. Entity mapping improves content clarity and citation likelihood—and Dejan’s tools help you model these relationships before writing.
What we like: Entity-level analysis is more sophisticated than most tools in this category. If you are serious about structured AEO content that AI systems can confidently analyze and cite, Dejan.ai is worth exploring.
Best for: Advanced SEO and AEO practitioners who want to model semantic query expansion and understand how entities relate to each other in AI-generated answers.
Screaming frog + Gemini
This pairing is one of my favorite DIY approaches to fanout query modeling. Use Screaming Frog to crawl your website and extract existing H2s, H3s and meta descriptions.
Enter this into Gemini using the API (or Google AI Studio) with a prompt like this: “What follow-up questions would users ask after reading about (topic)? List 10 specific conversation questions.” The output gives you a synthetic fanout – an approximation of how AI models expand the thematic footprint of your current content.
professional TIP: Run this process on your best-performing pages first. If a site is already generating traffic or visibility for a topic, expanding its AEO coverage by incorporating fanout questions requires less effort than building it from the ground up.
Best for: Technical SEO teams looking to leverage existing crawling infrastructure to enrich content with AI-generated question expansion.
AEO visibility tracker
AEO trackers measure mentions, citations and visibility in response engines, filling the gap that traditional rank trackers leave completely empty. Competitive insights from these tools help you identify gaps in coverage – where competitors are emerging and what incentives brands are completely missing.
HubSpot AEO Grader
HubSpot AEO Grader supports a basic response engine visibility assessment – and I would recommend this tool to any team just starting to measure their AEO performance. It shows you how your brand appears in AI-powered search results, where you have authority and where your content falls short.
What we like: It’s free and provides instant clarity on response engine visibility. Use it to gain executive buy-in before making a broader investment in an AEO tool.
Best for: Teams that want a free and quick baseline assessment of their response engine visibility before investing in a full AEO tool stack.
HubSpot AEO – Instant tracking and AI-powered suggestions
HubSpot’s AEO product includes prompt tracking that lets you monitor which questions show your brand in answer engines – and AI-powered suggestions that actively recommend new prompts and questions to track based on your existing visibility and content gaps.
This is the feature I find most valuable: the tool not only shows you where you are, but also tells you where to go next. Additional questions are displayed to monitor based on semantic similarity and competitor coverage, effectively automating a significant portion of the fanout detection process.
What we like: HubSpot AEO creates a single response engine visibility score for ChatGPT, Perplexity, and Gemini, then translates the underlying data into plain-language recommendations that any marketing team can act on without an AEO specialist. The competitive comparison view makes citation gaps immediately visible.
Best for: Marketing teams who want a quick overview of their response engine visibility and a prioritized roadmap to close the gaps without stitching together multiple monitoring tools.
Marketing Hub Pro and Enterprise
AEO is integrated Marketing Hub Pro and EnterpriseThis means the same visibility score, prompt tracking, and recommendations are directly linked to the CRM, content, and reporting tools marketing teams already use. Because it relies on CRM data, quick suggestions are automatically tailored to specific industries, competitors and customer segments – and the recommendations become sharper as the platform learns the business.
What we like: Teams can identify their AEO gaps and seamlessly create content in the Content Hub. Native integration means that the different tools work together.
Pro tip: First, set up instant tracking for your 10 to 15 primary AEO targets. After 30 days, use the AI-powered suggestions to advance to the next level of prompts.
Best for: Marketing teams looking to unify their AEO research, tracking, and execution within the platform are already running their content and pipeline reports. This phased approach ensures your tracking is focused and actionable, rather than overwhelming your team with hundreds of data points at once.
AI prompt ideation tools with synthetic query generation
By generating synthetic queries, you can approximate the range of inputs that users might enter into answer engines – without having to wait for organic search data to accumulate. This is particularly valuable for newer products, new categories, or topics that don’t yet have an established search volume.
Claude
Claude is one of my favorite tools for generating synthetic queries.
A request like: “You are an expert on (topic). Generate 20 different questions a user could ask an AI assistant about (topic), from beginner to advanced, including comparison questions and follow-up questions.” produces a high quality starting inventory.
The higoodie.com query fanout methodology describes a structured approach: start with query analysis to understand intent, then expand to related prompts, and finally map content gaps. Claude handles all three phases well.
What we like: Claude is particularly good at generating comparison and consideration queries—“Claude vs. ChatGPT for customer support,” “Which CRM integrates best with HubSpot”—that reflect how real users approach response engines when making purchasing decisions.
professional TIP: After you generate synthetic queries, test them directly in ChatGPT and Perplexity. Note which ones return AI-generated answers (as opposed to a traditional results page) – these are your highest priority AEO goals.
Best for: Generate rich synthetic prompt sets, model fanout queries, and verify that your content directly answers the questions answer machines are likely to ask.
For more information on optimizing for AI-generated answers, check out our guide to AI SEO.
Step-by-step workflow for finding AEO keywords
The tools mentioned above are only as useful as the workflow that connects them. Here is the process I would recommend for a team starting AEO keyword research from scratch – or auditing an existing program.
This is how you use autocomplete and people also ask about AEO
Step 1: Identify the seed query.
Start with five to ten core topics that belong or want to belong to your brand. These are typically product categories, use cases or customer problems – not brand terms.
Step 2: Autocomplete the extension.
Enter each seed topic into Google and collect autocomplete suggestions. These are real, high-frequency queries that often match the response engine’s prompt patterns. Pay particular attention to auto-completion of the question format (“How do I do this”, “What is best”, “Why?”).
Step 3: Assignment of the people who were also asked.
For each starting topic, search Google and take a screenshot of the “People Also Asked” box. Use AlsoAsked to expand this into a full question hierarchy. This gives you a two-tiered map: primary questions (what people ask first) and follow-up questions (what they ask next). Both are important for AEO.
Step 4: Prioritization.
Compare your PAA question list with SEMrush or Ahrefs to find out which questions have meaningful search volume. FAQs with AI overview appearances in the SERP are your top AEO targets – they already have AI-generated answers, meaning it’s possible to appear in them with the right content.
How to Use LLM Query Fan-Outs to Expand Interrogative Sets
Step 1: Query analysis.
Take your prioritized list of questions and group them by intent cluster. “What is X”, “How does X work?” and “X vs. Y” are different intent clusters that require different content treatments.
Step 2: Synthetic Expansion.
Pass each cluster to Claude or ChatGPT with a fanout prompt: “A user asks: ‘(main question).’ What eight follow-up questions might they ask after receiving an answer?” Document the output.
Step 3: Cross-engine validation.
Test your best synthetic prompts in ChatGPT, Perplexity and Gemini. Record which prompts generate AI-synthesized answers and which return standard links. AI-generated response triggers are your AEO keywords.
Step 4: Gap analysis.
For each confirmed AEO destination, check whether your website currently appears in the AI-generated response. Use HubSpot or Otterly.ai’s AEO prompt tracking to systematize this. Gaps become your content roadmap.
Step 5: Creating the content letter.
For each confirmed gap, create a content brief that includes:
- The core question (your H1/title)
- A direct answer in the first 50-100 words
- Supporting entities (related concepts, products, brands that AI should associate with your answer)
- FAQ section for fanout questions
- Schema markup (FAQ or HowTo, if applicable)
- Internal links to related content clusters
Content descriptions for AEO should include the core question, direct answer, supporting entities, schema, and internal links. This is where research workflow merges with execution – and most teams drop the ball by storing their AEO findings in a spreadsheet that never reaches the author.
Frequently asked questions about keyword research tools for AEO
Does AEO replace SEO?
No, but AEO expands the scope of what SEO teams are responsible for. Traditional organic search isn’t going away – Google still serves billions of searches that traditional results pages return – but the share of searches solved by AI-generated answers is growing and this trend is accelerating.
Teams that view AEO as a complement to SEO rather than a replacement will be better positioned than those who wait to see who wins. The underlying capabilities overlap significantly—technical soundness, strong content, and authority signals are important in both worlds—but the alignment, structure, and measurement differ. For more information on this change, see our guide Response engine optimization.
Can I use ChatGPT alone for AEO keyword research?
ChatGPT is a useful tool for synthetic query generation and fanout expansion, but it is not enough on its own. It doesn’t provide search volume data, can’t track your answer engine visibility over time, and doesn’t show you where competitors are showing up.
Use it as a question generation and validation layer in addition to tools that provide real search data (Semrush, Ahrefs) and answer engine visibility tracking (HubSpot AEO, Otterly.ai). ChatGPT is an important contribution to the research process; It is not the research platform.
Which engine should I prioritize first for AEO?
Start with Google AI Overviews. Google still commands the largest share of global search traffic and displays AI overviews for a growing range of commercial and informational searches. To appear in a Google AI overview, you often need to meet the same EEAT standards as traditional Google rankings – allowing existing SEO investments to transfer more directly. Check out our guide to Google EEAT to learn what it takes to earn that trust.
Once the team has a basic Google AEO program, expand to Perplexity (strong with researchers and tech-savvy users) and ChatGPT (relevant to purchase considerations and comparison queries). Covering multiple engines within 6 to 12 months is a reasonable goal – but most teams shouldn’t start there.
How often should I update AEO keyword research?
More common than traditional SEO research. Response engines regularly update their indexing and response generation, and new fanout patterns emerge as user behavior evolves. My recommendation: Conduct a full AEO keyword audit quarterly and review prompt tracking data monthly.
If you use a tool like HubSpot’s AEO product with AI-powered suggestions, let the tool flag emerging prompt opportunities between formal review cycles. The worst outcome with AEO is creating content for questions that answer machines no longer answer. Therefore, staying current with your timely reporting is an ongoing operational requirement and not a one-time project.
What budget should I plan for AEO tools?
It depends on the team size and maturity level. An exploratory stack under $500 per month can combine free tools like HubSpot AEO Grader, Google Search Console, and AnswerThePublic’s free tier with AlsoAsked ($15-$49 per month) and Claude Pro ($20 per month) – enough to cover question discovery, fanout generation, and basic visibility testing.
A growth package of $500-2,000 per month typically includes SEMrush or Ahrefs ($120-500 per month, depending on tier), Otterly.ai for response engine tracking, and HubSpot AEO for integrated prompt tracking and suggestions. The biggest mistake teams make is investing in a six-figure stack before building the workflow to process the data – start with the minimum viable toolset, prove the process works, and then scale up. For more information on AI-integrated rank monitoring, check out our roundup of the best rank trackers.
How to choose your AEO keyword research stack
AEO keyword research is not one task, but three. Discover the questions shoppers ask, model how AI answer engines turn those questions into fanout prompts, and track which prompts the brand actually performs for. No single tool covers all three categories well, which is why the right stack is more important than any individual platform.
For teams that want a unified starting point, HubSpot AEO consolidates visibility, tracking, and recommendation layers in one place. It creates a single response engine score for ChatGPT, Perplexity, and Gemini, shows which prompts lead to quoting competitors instead of the brand, and delivers prioritized, plain-language recommendations starting at $50 per month. Marketing Hub Pro and Enterprise extend this with CRM-powered instant suggestions that help teams fill gaps.
The quickest way to see where the brand stands today is free HubSpot AEO GraDum. It’s a baseline exam, not a requirement – and it’s the cleanest first step into a structured AEO program.