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The problem: AI visibility isn’t keyword tracking with a new name
Tools like Morningscore and Accuranker have done well within classic SEO, where keyword tracking makes a lot of sense – there’s search volume, there’s historical data, and a keyword like “best running shoes” is searched more or less the same way by everyone.
That logic has been carried over directly into AI visibility: the user is asked to type in a set of prompts, and the tool then tracks whether the company gets mentioned in the answer to those exact prompts.
The problem is that prompts don’t behave like keywords. There’s no search volume on prompts, and in all likelihood there never will be. A prompt isn’t a fixed search string – it’s a natural-language expression, and two users who are genuinely looking for the same answer rarely phrase it the same way. One user writes “best CRM for a small B2B sales team,” another writes “which CRM should I choose as a small business,” a third asks “what’s the difference between HubSpot and Pipedrive for sales.” Three completely different prompts that cover essentially the same information need – and none of them has any search volume to measure against.
When AI visibility is tracked based on a narrow set of manually entered prompts, you’re actually measuring very little. You get a snapshot of whether the model mentions the company in that exact phrasing – not an accurate picture of the company’s real visibility within a topic.
The future is topic-based – not prompt-based
Everything points to both the AI models themselves and the tools that measure them moving toward a topic-based approach rather than a prompt-based one. The clearest sign of this is already visible at Bing, which has started exposing so-called grounding queries in its Webmaster Tools.
In its simplest form, a grounding query is the specific information gap a language model needed to fill, and that a given website helped close. Instead of showing a user-phrased prompt, it shows the underlying topic or question the model actually searched for information about. If a user asks the AI for a comprehensive travel guide, the grounding query behind it might be something like “tax rules for digital nomads in Portugal.” If your page gets used as a source here, it’s not because you happened to match one specific prompt – it’s because you own the authority on the underlying topic.
This is exactly what will, in the future, be referred to as Topical Authority in AI search: the ability to prove to a language model that your page is the most reliable source for answering a specific subtopic – not the ability to match a single phrasing.
In other words: the broader and more systematically you cover a topic, the higher the probability that you show up, regardless of how any individual user phrases their question. That’s the principle our entire tool is built around.
We’ve turned tracking upside down
Most AI visibility trackers start with the prompt. The user is asked to type in the prompts they want to track themselves – often because that’s the easiest way to build a product, not because it produces the best data foundation.
We do the opposite. We don’t start with prompts – we start with topics.
Instead of the user having to guess which phrasings are relevant, our model starts from the topics that are relevant to the client’s business, and then pulls data across multiple keyword databases to uncover how those topics are actually being searched and asked about. Based on that data, we systematically generate a broad set of prompts that together cover the topic from many angles – instead of betting everything on a handful of manually selected phrasings.
That’s also why our cheapest package starts at 200 prompts. By structure, this ensures a minimum of four distinct topics—each containing between 25 and 50 carefully crafted prompts. It’s not an arbitrary number – it’s the volume required to say anything statistically meaningful about a company’s visibility on a topic, rather than simply measuring a few random samples.
The broader you track, the higher the statistical probability that you actually capture the cases where a company does – or doesn’t – get mentioned. Narrow tracking on a handful of manually chosen prompts gives a distorted picture, because you’re really only seeing a small, random fraction of the overall conversation happening between users and AI models about a given topic.
From data to action plan
Measuring visibility is only half the job. The other half – and it’s at least as important – is turning that data into action.
That’s why we don’t stop at showing how often a company gets mentioned. We build an action plan grounded in data: how are the client’s competitors being mentioned on the same topics? Which subtopics do competitors own that the client isn’t yet present on? And which specific information gaps – the same kind of gaps that Bing’s grounding queries reveal – does the client need to fill to increase the probability of being cited by AI models going forward?
The result isn’t just a dashboard full of numbers, but a prioritized plan for where effort needs to go in order to actually move the needle on visibility.
What sets us apart
In summary, three things set our approach apart from most other AI visibility trackers on the market:
- We build on data, not guesswork. Many competing tools ask the user to manually type in the prompts to be tracked. We generate prompts from topics and real keyword data instead, so the selection doesn’t depend on what one person happens to think of.
- We track broadly enough to be statistically useful. Where many competitors’ packages start as low as 20 prompts – which doesn’t provide any real statistical basis for assessing topical authority – our cheapest package starts at 200 prompts, precisely because breadth is a prerequisite for reliable results.
- We don’t stop at measurement – we deliver an action plan. Many tools only track and report. We turn data on competitors’ visibility and a client’s own gaps into a concrete plan for how visibility can actually be increased.
AI search is rapidly moving away from individual prompts and toward topics and authority. The companies that understand this early – and measure accordingly – will be in a significantly stronger position as AI models increasingly become the first point of contact between customers and businesses.