AEO Keyword Research: How to Find the Queries That Matter for AI Search
AI search queries look different from Google queries. This guide covers how to find, categorize, and prioritize the prompts your potential customers are actually typing into ChatGPT, Perplexity, and Gemini.
SEO practitioners are comfortable with keyword research. They know how to find search volume, assess competition, and group terms into clusters. That workflow does not translate cleanly to AEO.
AI search queries are longer, more conversational, and more intent-specific than typical Google queries. More importantly, the tools and signals that reveal which queries matter are completely different. This is how to build an AEO query list from scratch.
Why AI queries are different
When someone searches on Google, they often use short fragments: "best crm 2024" or "project management tool." When they ask an AI assistant, they use full sentences: "What's the best CRM for a 10-person sales team that already uses HubSpot?" or "Which project management tool works best for agencies billing by the hour?"
That specificity matters because AI engines answer the full question, not just the keyword. A brand that is well-positioned for "best CRM" in Google may never appear in the AI answer to the longer question, because the longer query requires specific use-case coverage the brand hasn't produced.
The implication is directional: AEO query research should start with how people phrase questions to a human expert, not how they phrase short searches. The natural language pattern reveals intent and context that keyword tools miss.
The four types of queries that drive AI recommendations
Most AI queries that lead to brand recommendations fall into four categories. Your query list should include all four.
Category queries ask for the best option in a space: "best email marketing tool for e-commerce," "top tools for managing freelancers." These pull from comparison articles and review roundups.
Problem queries start from a pain, not a category: "how do I stop losing track of client feedback," "what's the easiest way to collect customer testimonials." These pull from how-to content and forum discussions. Many brands miss this type entirely because they think in product categories, not customer problems.
Comparison queries are high intent: "is [your product] worth it," "[your product] vs [competitor]," "alternatives to [competitor]." A user asking a comparison question has usually narrowed their choice. Appearing in that answer is more valuable than appearing in a broad category query.
Feature queries are specific to what your product does: "which project tools support time tracking," "does [category] software integrate with Slack." These pull from documentation, feature pages, and detailed reviews.
How to discover the right queries for your brand
Start by listing every problem your product solves, not every feature it has. A time tracking tool solves "I don't know where my team's hours are going" and "I keep underestimating project budgets." Those problems, phrased as questions, become your first batch of queries.
Talk to recent customers and ask what question they were trying to answer before they found you. The phrasing they use is far closer to what they would type into ChatGPT than any keyword tool output.
Check Reddit, Quora, and niche communities in your space. Search for your product category and look at the threads where people ask for recommendations. The exact questions posted are AI queries waiting to happen. A thread titled "What are you using to manage client approvals?" is a query type you should be testing.
Look at competitor reviews on G2, Capterra, or Trustpilot. The "pros and cons" sections reveal the feature-level attributes that users care about most, which map directly to feature queries.
How to prioritize your query list
Once you have a raw list of thirty to fifty queries, not all of them are worth the same effort.
Prioritize queries where purchase intent is high. A user asking "what CRM should I use for my agency" is closer to buying than one asking "what is a CRM." The high-intent query is worth more coverage even if fewer people ask it.
Prioritize queries where you have a genuine advantage. If your product is the only one built specifically for solo consultants, queries framed around that use case are winnable quickly. Broad category queries where ten established competitors already appear are harder to move.
Prioritize queries where AI engines currently give incomplete or generic answers. Run the query yourself in ChatGPT and Perplexity. If the answer names only two or three brands and hedges heavily, there's room for a new source to improve the answer. If the answer is detailed and confident, the query is already well-served and harder to break into.
How to validate queries by running them
The best way to understand what AI engines are doing with a query is to run it. Type each query into ChatGPT, Perplexity, and Gemini, and record three things.
First, which brands appear. If you're not in the answer, note who is. Second, what sources are cited. Footnotes, links, and attribution text tell you where the engine is pulling from. Third, how the answer is framed. Does it give a list, a recommendation, a comparison? The format tells you what type of content the engine is drawing on. Content formats AI engines prefer covers how to match your content format to what each query type needs.
Run each query on all three engines. A brand may appear consistently in Perplexity but rarely in ChatGPT because each engine weights different source types. If your goal is visibility across all three, you need queries that reveal those gaps, not just one engine's view.
Turning queries into a content and distribution plan
Your validated query list is not a content calendar. It is a gap analysis.
For each query where you don't appear, identify what would need to exist for you to appear. That answer is usually one of three things: a piece of content you haven't written, a source you're not listed on, or an attribute your brand hasn't been described as having in public.
Group the gaps by type. If ten of your missing queries trace back to the same review platform not listing you, that's one action: get listed and build reviews there. If five queries require a comparison page you haven't published, that's a content task. Why your competitors show up in AI answers and you don't explains the most common source gaps and how to close them.
Keep the query list alive. Add new queries as your product evolves, as competitors change, and as you hear new questions from customers. Run the list monthly and track which queries you're winning.
The queries you're probably missing
Most brands underinvest in two query types: problem queries and feature queries.
Problem queries require thinking like your customer before they know your product exists. That is uncomfortable for marketing teams close to the product, but those queries are often the least competitive and easiest to win. A blog post that answers "how do I know if my agency is undercharging" can drive AI visibility for a project management or invoicing tool without ever mentioning the product category.
Feature queries require detailed, specific content: documentation, feature pages, integration guides. These are often treated as support resources rather than marketing assets. AI engines treat them as evidence of relevance when a user asks whether a product supports a specific capability.
Building coverage across all four query types, category, problem, comparison, and feature, is what topical authority in AI search looks like in practice. Query research is how you discover which parts of that map you've left blank.
QuickAEO lets you run a structured query set across ChatGPT, Perplexity, and Gemini and see exactly where your brand appears, which queries you're missing, and what sources the engines are citing instead.