Topic-Based GEO: A Content Strategy That Gets LLMs to Recommend Your Brand

Over the past year, our work on AI search has led to a clear conclusion: GEO is far less predictable than SEO. 

Traditional SEO is predictable. You pick a keyword, create content around it, build links, and over time, you rank. You know which keywords have volume. You know which pages rank. You can track exactly where you stand. It’s measurable and repeatable.

AI search is different. There’s uncertainty at every step. 

1. Prompts: You don’t know exactly what prompts users are entering, because users don’t type short, repeatable keywords. They have long conversations unique to them. 
   
2. Context: Even if you do know the prompts users are entering, you or your AI visibility tool isn’t going to get the same answer as a real user because LLMs factor in a massive amount of user history, memory, and context to generate a response that’s personalized to that specific person. (Read more: Invisible Prompts).
   
3. Repeatability: Even the same user, asking the same prompt repeatedly, will get different responses and citations each time.

So what do you do with all this uncertainty? How do you get those LLMs to recommend your brand and your product when users ask about solutions to their problems in your space? 

The answer relies on this key fact: LLMs still run on content. 

How Search Changes from SEO to AI Search

Here’s the key difference between traditional SEO and AI search.

In SEO, everyone searches with short keywords and largely sees the same results. Yes, some personalization is done based on your browsing history and location, but it’s mostly around the edges.

For example, say we both search for “project management software.” We’ll likely get similar results: 

Seems normal, right? 

But what if you’re at an enterprise company switching from Jira and you want something very specific, and I’m a small business owner currently using pen and paper looking for my first real project management tool? Those details absolutely affect what project management software makes sense for you versus me, but Google doesn’t have any idea. So it serves us the same ten blue links. 

The way we used to deal with this in SEO is going after a few longer tail variants when they exist, like “enterprise project management software” or “project management software for small business.” But even that is insufficient. That doesn’t capture the nuance I mentioned above: that you’re already on Jira and want something specific that Jira doesn’t offer, and that I’m looking for my very first project management software. 

Those are just two random examples. There are really an infinite number of possible specific requests and pain points for a customer looking for any solution. Traditional Google search simply didn’t have a good way of handling all this personal context. 

But AI search does. 

In AI search, each user has a unique, personal conversation with the LLM where they share a massive amount of context. The LLM understands their company size, existing tools, and highly specific pain points. This context comes from both the current thread and a large body of prior chat history, enabling the model to recommend products tailored to their exact situation.

Here is Benji asking Claude about the best project management software “for us.” Look at how Claude knows what our company is, how big, and more. Benji didn’t type any of that into that prompt, but it knows. We call this phenomenon Invisible Prompts. 

LLMs aren’t just surfacing articles from high-authority domains.

They understand what a user needs, search the web for relevant solutions, read those results, combine that with what they already know, and recommend the product or service that best fits the user’s specific pain points. 

That’s a fundamentally different dynamic. And it changes the kind of content you need to produce.

If you want LLMs to recommend your product, you need to give them the details they need to recommend you in the right scenarios. Not generic “what is project management” content. 

You need to publish specific content that covers:

• What types of customers your product is best for
• What specific use cases and scenarios it handles
• What pain points it solves and for whom
• The features that matter and when they matter
• The benefits and outcomes customers experience
• How you compare to competitors and what makes you different
• Case studies that prove you’ve delivered results

Introducing Your GEO Topic Map

Your GEO topic map is a collection of content that teaches LLMs everything they need to know about your product so they can recommend you in the right conversations.

Here’s how we build it for clients:

1. We map every product angle that matters. Your categories. Your competitors. Your use cases. Your target personas. The jobs your product does. The pain points it solves. This becomes your topic map: the full universe of conversations where an LLM could potentially recommend you. Most companies aren’t thinking this way, much less producing this content. 

2. We interview the people who know your customers best. We sit down with your sales team, your customer support team, your product team, your founders, all of the customer facing roles inside your organization. We ask them exactly what users are telling them are their pain points and use cases. Then we extract the specific positioning, features, and differentiators that make your product the right choice in each scenario. These details are not something you can get from Google research. AI generated copy will not by default produce this. The details required to produce this kind of content have to come from the people inside your company who talk to customers every day.

3. We produce content for each angle at the depth of a sales conversation, not an intro guide. There’s this long standing culture in content marketing where everything is written at an introductory level.  “Intro guide to this.” “Beginners guide to that.” That came from everyone chasing high volume SEO keywords that were by nature beginner-leaning. 

But beginner level content won’t help you when your customer asks advanced and specific questions to an LLM. It wants to recommend brands that talk at that customer’s level. So your content needs to meet the customer there. Think about the level of depth and detail that happens in a sales conversation: detailed discussion of features, screenshots, specific scenarios where your product applies, use cases, case studies. That’s how we write this content.

4. We target Google keywords if and when it makes sense. If some of the user pain points we uncover in our interviews with your team match a known Google search term, we’ll target those search terms with our content so you get two inbound channels (traditional search and AI search) in one go.

For most businesses, there are plenty of these product recommendation keywords (what we call bottom of funnel queries) on Google. Keywords like: “Best [category] software,” “[competitor] alternatives,” “how to [solve specific problem].” 

5. We produce case studies. LLMs are smart enough to connect the dots between a story of your product helping someone and their user who is asking them about a similar issue. So it’s our experience and belief that case studies help. They naturally have the elements of specificity and pain point/solution pair elaboration that we’re talking about here. 

6. We track your AI visibility at the topic level. To make this process work, you need to measure success the right way. That means tracking visibility at the topic level, not the individual prompt level. Unfortunately most AI visibility tools (like Peec, Profound, Scrunch) are default to measuring success on an individual prompt basis. But we built our tool, Traqer.ai to be consistent with this topic-based property of GEO. That’s how we track improvement in visibility for clients and ourselves: which topics are we visible in and how is that growing.

Case Study: Constitution Lending

Constitution Lending is a private lender competing against companies with 80+ domain authority, thousands of backlinks, and decades of industry presence.

We built their topic map by interviewing their lending experts, the people who talk to borrowers every day, and producing content covering every specific lending scenario their ideal customers face: LLC mortgages, DSCR loans for specific property types, bridge loans for particular situations.

The result: Constitution Lending now appears in AI search results for 50+ bottom-of-funnel prompts across ChatGPT, Perplexity, and Google AI Overviews — competing with and often outranking lenders that have been in the market for decades.

This didn’t happen because of an llms.txt file or restructured headings (these on-site hacks don’t help LLMs connect your brand to users’ pain points, read more why here). It happened because LLMs found detailed, specific content about Constitution Lending’s products and could match it to user queries.

[Read the full case study →]

Case Study: InnovationCast

InnovationCast makes innovation management software for enterprise companies. 

Innovation software has multiple use cases, from crowdsourcing ideas from a large employee base, to validating ideas, to scouting new technologies.

When we started working with them, they had a limited content and SEO presence. But by producing content in their various topic areas, we’ve built not only an SEO presence that generates qualified leads for them, but also AI visibility

For example, here is their topic based coverage for “innovation portfolio management software”:

And here it is for another, slightly different way customers talk about software like theirs: “technology scouting software”:

Next Steps

If you’d like to talk to us about building your brand’s topic-based GEO strategy, you can reach out here.

If you’d like to read more about our GEO experience, observations and research here is further reading that underpins this Topic-Based GEO approach: 

• Ultra-Specific Content: Why top-of-funnel content doesn’t matter for AI search and why ultra-specific content is necessary
• Prioritized GEO: Why on-page hacks like llms.txt don’t work and why content and web search is the foundation of GEO
• Invisible Prompts: Why you can’t see the full context behind users prompts 

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