Why the same brand gets different scores
Five things move the number, and a vendor rarely tells you which of them it chose.
- Run-to-run variance. An AI answer is generated fresh each time. Ask the same prompt ten times and the brands named will not be identical. One run is an observation, not a result.
- Different prompt sets. A tool that tracks "what is Brand X?" will look better than one that tracks "best tools for Y". Branded prompts flatter you. Where the prompts come from decides the score.
- Different engines. One engine may name you often and another never. A score that covers three engines and a score that covers six cannot be compared.
- API versus app. The surface measured changes the answer. A number taken from an API is not what a buyer sees in the app.
- Blended scores. A single number mixes engines, markets and answer types. When it moves, you cannot tell why.
Buyers say the same in public. A practitioner on LinkedIn, Maria Dykstra, described running nine tools on one brand and getting results from 0% to 88.7%. Austin Richard described four dashboards showing four different numbers. In r/AISearchLab, a poster described a client who saw nothing on their phone while a tool reported 55%. In r/GEO_optimization, posters discussed a brand appearing in 6 of 10 runs. In r/DigitalMarketing, posters called some scores unauditable. These are opinions from forums and posts, not measurements, but the pattern is consistent.
Original element, to be supplied (Adviora data, dated): one prompt run 10 times in each engine for one brand, with how often the brand appears per engine.
What a trustworthy report shows
- Results per engine, not a single average.
- Runs per prompt, stated as a number.
- The raw answers behind each figure, so you can read them.
- Zeros as well as wins. A prompt where nobody names you is a result.
- Mention, citation, recommendation and click kept apart. They are different events; see how to measure AI visibility.
A five-step test before you pay
- Run a known-answer test. Give the tool a brand whose answers you already know, such as your own, and compare it with what you see by hand in each engine. If you cannot reproduce a number, do not trust it.
- Check the engines on the plan you would actually buy. A demo may show more engines than your plan includes. Ask for the list in writing.
- Ask how many days of history you keep. Short retention makes trends impossible to read. Ask for the figure for your plan.
- Ask to see the raw answers behind each number. If the tool cannot show them, the number cannot be audited.
- Ask where the prompts come from, and whether it finds blind spots. A tool that only tracks the prompts you type will only confirm what you already thought. See which prompts to track.
On step 3, Will Colachis noted on LinkedIn in October 2026 that he could not find a plan-level retention figure on the pages of eight vendors. That is one person's check on one day, so verify it yourself before you rely on it.
How Adviora reports it
The free audit checks ChatGPT, Gemini, DeepSeek and Perplexity as a one-off snapshot. A free trial or paid plan covers six AI engines (ChatGPT, Claude, Gemini, Perplexity, DeepSeek and Mistral Vibe) plus Google AI Overviews. Adviora builds its prompts by crawling the site to read its intent, ideal customer profile and messaging, then using search data: what Google shows and People Also Ask questions turned into prompts. Results are tracked per product and per market, with share of voice and the sources each answer cites. The full method is on the methodology page.
Use the same five-step test on us. If you are comparing named tools, see Adviora vs Profound and Adviora vs Peec AI, and start with the free AI visibility checker to get a baseline you can check by hand.
What buyers say: sources
These are opinions from public posts, paraphrased. Links are held by the SEO lead.
- Reddit: r/AISearchLab (a client sees zero on a phone against 55% in a tool), r/GEO_optimization (a brand present in 6 of 10 runs), r/DigitalMarketing (scores that cannot be audited).
- LinkedIn: Maria Dykstra (nine tools, results from 0% to 88.7%); Will Colachis (no plan-level retention figure on eight vendors' pages, October 2026); Austin Richard (four dashboards, four numbers).