Most keyword research focuses on finding audiences when they're curious, not when they're deciding. That gap matters more than ever because AI assistants are increasingly the ones creating the first cut of vendor shortlists, and they're doing it based on signals most content strategies just aren't built to produce.

At Edgar Allan, AEO keyword research is one piece of a larger diagnostic. We start upstream: auditing how AI systems currently describe a brand, identifying where the category placement is wrong or too narrow, and resolving the brand clarity problems that make keyword research ineffective before it begins. 

Our work with Welldoc is a good example of what that looks like in practice.

For years, their main product (what they were known for) was a diabetes app. Accurate enough, but it placed them in a narrow category with the wrong competitive set. When EA rebranded them as a healthcare AI platform for cardiometabolic care, the category shifted entirely. Enterprise health systems started showing up in buyer conversations. The prompt "healthcare AI for cardiometabolic management" and “healthcare-ready AI” produce entirely different lists than "diabetes apps."  We're currently building the content and signal architecture that gets Welldoc on the right lists, because category placement isn't a content problem. It's a brand problem that content can't solve until the brand is right.

Why volume-based keyword research misses the moment that matters

High-volume keywords tend to reflect early-stage curiosity: "What is [concept]?" or "How does [technology] work?" These queries are fine for building awareness, but they don't move the pipeline.

A buyer asking ChatGPT how something works gets a clean summary and moves on. Your content may have shaped that summary, but your brand almost certainly didn't appear in it.

The prompts that drive evaluation look a little different. Buyers ask "What are the best platforms for [problem]?" or "Who are the leading companies in [category]?" or "What are the alternatives to [competitor]?" Those are the queries where AI assistants generate structured recommendations, and where being named or not determines whether a buyer considers you at all.

When a buyer uses evaluation language, AI assistants don't explain; they recommend

High-intent prompts contain evaluation language: "best," "top," "compare," "alternatives," "pricing," "enterprise," "for [industry]." When those modifiers appear, the buyer isn't there to learn, they’re there to decide, and that changes everything about how AI assistants respond.

Example: Ask a model, "best healthcare analytics platforms for compliance teams," and it doesn't explain what analytics is. It produces a list of vendors. That's the moment most content strategies are unprepared for.

When a high-intent prompt comes in, the AI identifies the category, selects companies it associates with that space, and assembles a response based on available signals: category-level content, structured explanations, third-party references, and consistent brand positioning. It synthesizes an understanding of the category and makes a judgment call about which companies belong. That judgment call is the thing worth building toward.

Category association is the degree to which AI systems reliably connect a brand to the category buyers are searching when they're ready to choose. It's separate from brand awareness and separate from search rankings. A company can have strong SEO and clear messaging and still have a category association problem because the signals AI systems use to build vendor lists are different from the signals that drive clicks.

How to find the prompts that put your brand in the room

Finding high-intent prompts means shifting from keyword lists to prompt patterns. The mechanics are pretty straightforward.

Start with your core category, defined the way a buyer would define it, not the way your internal team describes it. "Growth equity firm." "Enterprise browser." "Healthcare AI for cardiometabolic care." That's your foundation. Then layer in evaluation language: "best [category]," "top [category] providers," "[category] for enterprise," "[category] for [industry]." 

This gives you prompt clusters that reflect how buyers search when they're comparing options, and that’s key.

Map competitor-driven queries next. Prompts like "alternatives to [competitor]," "[brand] vs. [competitor]" are high-intent by definition. If you're not showing up in those responses, you're losing ground in a comparison you're not part of.

The most specific prompts are often the least competitive. "[Category] for distributed teams," "[category] for compliance," "[category] for healthcare providers" connect your product to real buyer problems in ways that broad category queries don't.

Thin coverage in one corner of a topic is an opening for a competitor to fill

Once you've identified your prompt clusters, you need enough content that AI systems have the signal to consistently associate your brand with those prompts. One strong page isn't enough.

You need core service pages that define the category, comparison pages that position your offering against alternatives, use-case pages that demonstrate application in specific contexts, and industry pages that connect your solution to the buyers who need it. Objection-handling content matters too, because buyers evaluating options have concerns, and AI assistants surface those concerns whether or not you've addressed them.

The goal is topical authority across the cluster. AI assistants synthesize across sources, so gaps in coverage are openings for competitors.

What to look for when you run the prompts

Once you've identified your clusters, run them. Test in ChatGPT, Perplexity, Claude, and Gemini, and pay attention to which companies appear, how they're described, which sources get cited, and how consistent the results are across platforms.

This is primary research. It tells you how AI systems currently understand your category and exactly where your brand is missing from the picture.

If your content gets cited for definitions but not for decisions, the coverage exists but isn't aimed at the right moment. If competitors appear consistently and you don't, you have a category association problem, which requires a different fix than a content quality problem.

Signs you're optimizing for the wrong moment:

• Your content ranks in traditional search but doesn't appear in AI-generated answers.
• Competitors show up in recommendation lists while your brand doesn't.
• AI assistants describe your category accurately but never name your company.
• Your content gets cited when someone wants a definition, not when they want a vendor.

Each of these points to the same underlying issue: a strategy built for awareness that's being asked to drive evaluation. Revisit the prompt research with commercial intent as the filter.

AEO keyword research as a pipeline strategy

High-intent prompts sit at the moment where discovery turns into decision. That moment is increasingly happening inside an AI assistant, and the output is a short list of vendors worth talking to.

Building that presence requires identifying the right prompt clusters, producing content across the full cluster, and testing regularly to see where you're being excluded and why. But the prerequisite is brand clarity. A brand that hasn't defined its category clearly enough for AI systems to place it confidently will keep missing from lists, no matter how much content it produces. That's what category association looks like when it hasn't been solved.

The brands that solve it first won't just show up more often in AI search. They'll be the ones defining the category for the AI systems other companies are trying to get into.