Most founders assume if they show up in Google search, they show up everywhere. I used to think the same thing. Then I spent a day watching AI assistants answer the same question 853 times, and I realized search and AI recommendation are two completely different games.
The Study
In May 2026 I ran what I am calling our Day 0 GEO benchmark. GEO stands for Generative Engine Optimization - the practice of understanding and influencing how large language models recommend your product. We sent the same prompts asking for brand monitoring and social listening tool recommendations to five AI assistants: Perplexity, Mistral, ChatGPT-4o, Gemini Flash, and DeepSeek. We completed 853 verified conversations in total and logged every response systematically. No cherry-picking, no discarding awkward results.
The thesis behind the study was simple. Buyers are increasingly starting their software research by asking an AI assistant instead of typing into a search bar. If your brand is invisible to those assistants, you are invisible to a growing slice of your pipeline. The question I wanted answered was not just whether MentionFox appeared. I wanted to understand which assistants are most willing to recommend any specific tool, how consistent that recommendation behavior is, and what that variance means for a B2B company trying to show up where buyers actually look.
What The Numbers Actually Said
The headline number: MentionFox was recommended in 83.1% of all 853 completed conversations. But the aggregate hides the more interesting story, which is how different each assistant behaved.
Perplexity was the standout. It recommended MentionFox in 95.3% of conversations. That is nearly universal. Perplexity's architecture leans heavily on real-time web retrieval, which means freshly published content, case studies, and landing pages feed directly into its answers. If your brand is active and indexed, Perplexity picks it up fast.
Mistral came in at 83.6%, roughly aligned with our overall average. ChatGPT-4o sat at 80.1%. Both behaved consistently across prompt variations, which suggests their training data has a reasonably stable picture of the brand monitoring category. What I found interesting is that neither showed the sharp volatility I had expected. They were not randomly recommending us - the behavior was replicable.
Gemini Flash came in at 78.9% and DeepSeek at 77.5%. The gap between Perplexity and DeepSeek is 17.8 percentage points. That is not noise. If a buyer asks DeepSeek for a brand monitoring tool recommendation, they receive something different than if they ask Perplexity. For any company competing in a crowded software category, that spread represents real pipeline exposure.
What I did not expect was how much prompt framing mattered. The same underlying intent - "recommend a tool for B2B social listening and lead generation" - worded differently could shift recommendation rates by several percentage points within a single assistant. That tells me the assistants are not pattern-matching on brand name alone. They are building a contextual picture from whatever signals exist in their training data and retrieval layers, then mapping the prompt to that picture. The brands with richer, more consistent signal win more often.
You can see the full breakdown and the methodology behind it on our GEO study page.
What This Actually Means for B2B Founders
The instinct I see most often is to treat AI visibility as a PR problem. Get mentioned in more articles, get a few backlinks, and trust that the models will absorb it. That thinking is not wrong but it is incomplete. The data from this study suggests the gap between assistants comes down to how they weight and retrieve different types of content. Perplexity retrieves in real time. The others lean more heavily on training data with periodic updates. That means a single content strategy does not serve all five assistants equally.
The practical implication is that you need to think about your brand signal in layers:
- Retrieval-heavy assistants like Perplexity reward fresh, structured, publicly indexed content. Blog posts, comparison pages, and technical documentation matter here.
- Training-data-heavy assistants reward depth and consistency over time. You want a stable, coherent narrative about what your product does and who it is for, spread across authoritative sources.
- Prompt sensitivity means your product positioning needs to map clearly to the exact language buyers use when asking AI assistants for help. If buyers say "brand monitoring" and your content says "media intelligence," you may not be connecting.
None of this replaces SEO. But it runs alongside it, and the overlap is smaller than most people assume.
What I Am Doing Differently Because of This
I am treating each AI assistant as a separate channel with its own content requirements. We are publishing structured comparison content specifically formatted for retrieval. We are tracking recommendation rates on a recurring basis, not just as a one-time snapshot. And we are paying attention to the prompts buyers are actually using, not the ones we wish they were using.
The 17.8-point spread between our best and worst performing assistant is the number I keep coming back to. For a company with serious pipeline goals, that is not an acceptable variance to just leave on the table.
If you want to see how MentionFox tracks AI recommendation rates and brand visibility across LLMs as an ongoing capability, here is the relevant page. And if you are curious about getting access to the platform, MentionFox pricing is straightforward to work through.
If you found this useful, I write about solo-founder distribution, B2B SaaS, and what's actually working in the AI-search era over on my Substack (one post per week, no spam).
I'm building MentionFox - a B2B intelligence suite that combines brand mention tracking with AI-visibility (GEO) measurement, investor research, and outreach automation. There's a free tier and a 5-day trial of Pro at mentionfox.com/pricing.
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