How Reliable Are Real-Time Brand-Monitoring Features in Geo-Platforms?

…and why Sellm fires 10 simultaneous queries before showing you a ranking on generative engines

With the rise of AI search, Google Search Console alone is no longer enough to understand search brand positioning. Clicks are dropping to zero and citations are no longer sufficient. We need a way to understand what is being recommended by LLMs such as Gemini or ChatGPT. For this reason, many brand-monitoring geo-platforms have risen.

But how trustworthy are these systems, really? Generative engines sample from probability distributions; temperature, system state and context inject randomness, so the same query can yield different brand lists. As a result, they do not provide deterministic answers as in the SEO era. So, can we trust generative engine monitoring systems at all?

In this post, we conduct an experiment analysing thousands of responses coming from ChatGPT, for the exact same query, and apply statistical methods to understand the variance in the response. By doing so, we pinpoint exactly how many repeats are needed before a GEO‑platform’s brand‑monitoring numbers are reliable.

Executive Snapshot

This is the summary of our study:

• 4,000 identical live searches for "top-rated generative engine optimisation tool" were run from multiple devices and IPs.
• The searches produced 12,000 tool mentions across 17 distinct products; the market leader appeared 24 % of the time, the next two tools 15 % each, and the long-tail of 14 tools shared the remaining 46 %.
• Using a binomial-geometric probabilistic sampling model, we show that platforms that only trigger a query once can mis-state the leader-recommended brand by ± 43 percentage points (ppt), meaning you can barely trust those results. Ten-query platforms are 3x more accurate, and one hundred queries are 10x more accurate.

1. Testing the Reliability of Brand-Monitoring Features

To mimic a real user journey we fired the same question "What is the top-rated generative engine-optimisation tool?" 4,000 times. Each response listed three tools. Counting every appearance gave us 12 000 data points and a clear popularity curve: one clear leader (~24 %), two solid contenders (~15 % each) and a long-tail of 14 brands that each landed below 8 %.

That empirical curve from our data is the benchmark. If the dashboard is working, each new reading should stay close to those percentages instead of jumping all over the place.

To quantify exactly how much any single snapshot can fluctuate around that curve, we apply the Binomial–Geometric probability model. This framework turns raw counts into confidence intervals, telling us the grade of precision we can expect from any query budget.

2. Reliability of Brand-Monitoring Features by Query Budget

Real-time brand-monitoring reliability varies dramatically with how many queries a geo-platform fires under the hood. Here's what that means in everyday business language:

• Single-query platforms (1 query): error ± 43 ppt. A brand leader's percentage of mentions that should read 24% might show up at 0% or spike to 100% on the very next refresh. Nothing to be trusted there.
• Basic platforms (3 queries): error ± 25 ppt. Good enough to get a feeling of the market share but still risky: your "winner" could really be anywhere between 0% and 49%.
• Enterprise-grade platforms (10 queries): 3x more accurate than single-query. Sharp enough to separate winners from runners-up in real time, yet still lightning-fast. Perfect for tactical moves during live campaigns.
• Scientific-level platforms (100 queries): 10x more accurate than single-query. Single-digit fluctuation suitable for quarterly reporting, budget splits, and board-level KPIs - but at the cost of extra compute.

Remember, these numbers are for a 24 % leader. Rarer tools fluctuate more, dominant tools fluctuate less, but the pattern holds: more queries → less noise.

3. Platform Reliability Comparison

We tested the major GEO and brand-monitoring platforms to see how many prompts they actually fire per query. Here's what we found:

Key takeaway: Before trusting any platform's data, ask how many prompts they fire per query. If they can't tell you, or if it's fewer than 10, factor that uncertainty into every decision you make based on their data.

4. Sellm: Focusing on Accuracy of Results

Sellm focuses on tracking brand mentions with accuracy across the major AI models, ChatGPT, Gemini, Claude, Perplexity, and others, then translates that raw data into clear, percentage-based visibility scores. With multi-language support and topic-level tracking, it helps teams see not just if they're mentioned, but how often and in what context. Plus, a suite of free tools lets anyone get started measuring their brand's AI positioning before they commit to a paid plan.

5. Bottom Line

Real-time brand-monitoring is only as good as its sample size. Enterprise-grade platforms using ten queries turn eye-candy into decision-grade data - 3x more accurate than single-query platforms. Sellm uses this approach by default, giving you numbers you can actually trust.