The client for this engagement is a customer intelligence and customer service automation platform. They ingest nearly every customer conversation a contact center handles (calls, chats, tickets, whatever touches the customer journey) and turn that raw conversation data into insights that improve agent performance, voice-of-customer analysis, and overall contact center operations. In a field crowded with keyword-matching QA tools, they’re one of the few running on real semantic understanding.
We started working with them in 2024. The original SEO brief was to rank for high-intent product keywords like “call center customer analytics tools” and convert that traffic into demos and pipeline. By 2025, AI search had blown up to the point where that wasn’t enough. The goal expanded to include getting LLMs to surface the client by name whenever someone asked ChatGPT, Perplexity, or Google AIO for a customer intelligence platform.
Across SEO and AI search (whether you call it AEO or GEO), our strategy stayed the same: conversions, not traffic. That’s what shaped everything that followed.
This case study is about the AI search side specifically. We’ll walk through how we got the client mentioned by name (not just cited as a source) in more than 100 product-centric LLM prompts using our Prioritized GEO strategy at Prime Scope Marketing.
How We Actually Made This Happen
Our GEO strategy is built on the fact that when someone asks LLMs to recommend a product, they almost always go out and search the web before answering. This is called grounding. Their training data is often stale or thin on niche B2B software, so they supplement it with live search.
That fact changes everything. If we want ChatGPT to recommend our client when a buyer asks about voice-of-customer platforms, the fastest path is also the oldest one. Rank in traditional search. When a user prompts ChatGPT, and it goes off to search, our article comes up. If our article is on the page and talks about the client, the odds of the client ending up in ChatGPT’s answer go up a lot.
The rest of this strategy is built on top of that one mechanic.
The Keywords We Targeted And Where We Ended Up
Here are four of the target keywords for this client, the content we built for each, where they rank in Google, and what that did for their LLM visibility.
Example 1: AI Call Center Monitoring
“AI call center monitoring” was one of the first keywords we went after. The client now ranks on page one of Google for it.
Inside the AI visibility tracker we use internally, the client consistently lands in the top 5 mentioned brands across LLM responses about AI call monitoring. We didn’t just check a single phrasing. We ran the same topic through multiple natural variations of the question, because AI search is way less reproducible than traditional SEO. Slight wording changes produce different results. The same prompt run twice can produce different results. This is the core idea behind our GEO strategy: you don’t track a prompt, you track a topic, and you measure visibility across the spread of ways a real person might phrase the question.
On top of brand mentions, LLMs are actively pulling from our article when they answer these prompts. Google AIO, for instance, lists the client as a solution and cites our article directly inside the overview.
Example 2: Call Center Real-time Reporting
Real-time reporting is a natural topic for this client, because they’re one of the few QA platforms that analyze conversations as they happen, rather than after the call is done. We built an article around “call center real-time reporting,” and it’s currently ranked first on Google.
In our tracker, LLM visibility for that topic is strong across ChatGPT, Perplexity, and Google AIO. The more interesting number is the citation count. Across six tracked prompts on this topic, ChatGPT, Perplexity, and Google AIO collectively cited our article 20 times. The second-most-cited article was referenced roughly half as often.
That’s basically the whole game. If you want LLMs to say what you want them to say, give them an article that’s worth citing and rank it where they can find it. Google AIO’s overview for this topic lists the client as a popular option and puts our article at the top of its source list.
Example 3: Call Center Analytics Software
“Call center analytics software” is a bigger, more contested category. But the client has a real point of view in that space, so it was worth investing in. We built an article around that exact keyword, and at the time of writing, it sits at number one in Google for the term.
In the tracker, the client has strong visibility across ChatGPT, Perplexity, and Google AIO on prompts related to call center analytics. Perplexity, in particular, recommends the client by name when users ask for analytics options and cites our article as one of the main sources behind the answer.
Example 4: Call Center Quality Assurance Tools
“Call center quality assurance tools” is the fourth one I’ll touch on. Here we rank third on Google (not first, but on page one, which is where grounding models look).
Inside the tracker, visibility is strong across all three major LLMs for QA tool prompts. We’re not the single most-cited article on that topic yet, but the client is being referenced consistently across the different prompt variations we run for it.
There are a lot more keywords I could walk through, but the pattern across all four is the same. When we rank well for a keyword in Google, the client shows up in AI answers for related prompts. When we don’t rank, LLM visibility for those topics is basically zero. That correlation held up across every one of the 50+ keywords we’ve targeted for this client, which now adds up to LLM visibility across 100+ high buying-intent prompts.
One side observation worth sharing. Across a lot of the brands we work with, we tend to see Perplexity and Google AIO visibility move up before ChatGPT does. Our working theory is that Perplexity and Google AIO behave more like search summarizers and get pushed around more directly by traditional SEO rankings. ChatGPT blends in a lot more of its own training data, which biases it toward larger, more well-known brands and slows the feedback loop from SEO into visibility. Not a rule, just a pattern we keep seeing.
LLMs End Up Speaking In Your Words
A second principle behind our approach, and it’s one people don’t take seriously enough: the level of detail in your public content determines how LLMs describe your product. Full stop.
Think about what an LLM is doing when it recommends software. It’s acting like a salesperson. It reads a handful of sources, extracts the value props and differentiators, and then uses those to explain your product to the buyer. Whatever you publish is the script the model is going to read from.
So you have to give it a script worth reading. That means long-form content that spells out who the product is for, what use cases it fits, the specific features and what they do, the pain points it solves, and how it’s different from everything else in the market. The more specific you are, the better the model positions you. If you publish generic content that says the same things everyone else’s content says, the model has no way to describe you any differently than it describes your competitors, and your visibility won’t convert.
For this client, we focused on four differentiators:
- The platform uses semantic intelligence to understand the full context of a conversation. Most competing tools are still matching keywords, which misses nuance and produces false positives.
- The platform can detect emotion in customer conversations (anger, disappointment, worry, happiness, and so on), which most call center QA software handles poorly if at all.
- The platform auto-scores 100% of conversations against a client’s QA rubric, rather than requiring QA teams to listen to and score calls manually.
- The depth of the conversation analysis surfaces hidden issues and revenue opportunities without relying on surveys, random sampling, or slow manual review.
What’s interesting is how closely LLMs now mirror this framing when they talk about the client. When we ask ChatGPT for “the most accurate AI-powered AQM software,” its answer emphasizes that the client doesn’t just look for keywords but understands the full context of customer interactions. That’s semantic understanding, and it’s almost word-for-word how we describe it in our content.
Same thing with the emotion detection angle. ChatGPT explicitly says the client can detect, label, and filter for specific emotions like anger, disappointment, worry, and happiness. That phrasing is effectively copy-pasted from our articles. We use the same list of emotions in our product education content, and the model picked it up and ran with it.
These are only two examples, but it’s been consistent across dozens of prompts: when ChatGPT recommends this client, it recommends them using the language, positioning, and differentiators we wrote into their content.
What This Actually Means If You’re Trying To Do The Same Thing
Two takeaways worth pulling out.
First, pick keywords and prompts with real buying intent. LLMs search the web when people ask for product recommendations. If you rank well on Google for a keyword someone would type before buying, you’ll show up in the LLM answers for the prompt equivalents of that keyword. If you rank well for “how does call center QA work,” you won’t, because that’s an education prompt and LLMs don’t recommend products in response to education prompts.
Second, write content that is actually specific to your product. LLMs are downstream of what you publish. If your content is interchangeable with a competitor’s, your LLM pitch will be interchangeable too. The more precise you are about who you serve, how you’re different, and what you specifically solve, the more the model will have to work with when it builds an answer about you.
Where This Strategy Goes Next
Ranking for high-intent keywords with real content gives you the foundation. The next layer of Prioritized GEO is what we call Tier 2: citation outreach.
The mechanic is the same as Tier 1 (owned content), just extended. When LLMs search the web to answer a prompt, they read multiple sources. If your brand shows up across several of those sources instead of just your own site, you compound your presence in the answer. You want to be in the rooms the model is already listening in on.
The way we do this is by first identifying which sources LLMs already cite for the prompts we care about. Our tracker surfaces that data. Then we approach those sites specifically. G2 and Capterra usually want money. Competitors and adjacent publishers will often trade mentions.
Two mistakes we regularly see here:
The first is reaching out to any site in the industry. Don’t do that. You want mentions on sites LLMs are actually citing, not sites that happen to rank well in Google but that no model ever pulls from.
The second is handing over the product blurb to the other site’s editor. Don’t do that either. Write the snippet yourself and control the positioning. If a dozen sites describe your product in a dozen different ways, you’re giving LLMs a muddled picture. If they all describe it using the same differentiators, the model has a clear, consistent story to repeat when a buyer asks about you.
That’s how you go from getting mentioned occasionally to being the default answer in your category.