ChatGPT, Claude, Gemini, Copilot: The list of systems suitable for AI monitoring is growing longer. Many companies decide surprisingly subjectively. But does that really help?
More models do not automatically mean a better data basis. Sometimes the exact opposite happens. Costs also play a role in the decision.
In the podcast, we discuss our criteria and show an example monitoring with the AI system Claude. Our goal: Your AI Visibility Monitoring should be meaningful and efficient at the same time.
AI models in monitoring: This is often the starting point
Example: Claude monitoring in the software sector
For an example, we used our own AI Visibility Tool JOE and developed a monitoring case from the software sector (project management software). For the prompt queries, we added Claude alongside ChatGPT, Google AI Mode, and AI Overviews. We then compared the AI responses, brand mentions, and sources. We conducted a one-time GEO check.
The result: Similar brands are ahead in all four models, albeit in different positions. Some companies are stronger in individual AI systems. Awork, for example, only appears in Claude, while other providers like Factro or Wrike appear more in Google and ChatGPT. There is also a certain difference in the sources: Claude partially relies on different domains than ChatGPT or Google; among other things, the search behind it apparently runs via Brave. The AI response layout is very similar. Similar follow-up questions are asked – important for the prompts you choose.
Our conclusion: Brand mentions are more similar across models than one would expect. Those who are ahead in one system usually appear high up in the others as well. In contrast, a closer look at the sources is worthwhile. The differences here may be larger. However, this depends on the individual case.
Generally speaking: If you optimize for ChatGPT and Google’s AI systems, you also cover Claude to a large extent. Whether you want to go into detail with Claude is a case-by-case decision, for which you must then plan the budget for GEO monitoring accordingly. More on this in the episode and in the video.
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Conclusion & further aspects from the episode
In the end, two criteria remain: market share and the question of whether working on a model is really worth it for your own company. Anyone who takes both seriously usually arrives at a lean set of two to three systems.
More models do not solve the problem; they can even exacerbate problems. Every additional system costs budget that is then missing elsewhere, such as for more prompts or a deeper analysis. Less is actually more here.
Nevertheless, it is worth looking at the model level. The sources sometimes differ significantly between systems, even if brand mentions are often similar. If you only look at the overall overview, you will miss this.
Most importantly: Monitoring is not an end in itself. Concrete measures should follow from the data, related to your own company and your own market. Only when a measure has visibly achieved something in the tool has the model selection truly paid off.
Other topics we discuss:
- Why the way Claude is used determines whether monitoring is even worth it
- Why we removed Perplexity from our monitoring months ago
- Why the market share calculation is more complicated than it looks at first glance
- Which search engine is behind Claude’s answers
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