We Asked 3 AI Models the Same 20 Buyer Questions — Here's Which Brands They Recommend

Across 180 AI answers to 20 common buyer questions, three AI models named HubSpot in one out of every three answers — more than any other brand — followed by Semrush, Ahrefs, and Google Analytics. The models agreed far more than they disagreed: 27 brands were named by all three, and just 51 distinct brands soaked up all 557 mentions. If your brand isn't in that short, shared list, AI assistants are effectively recommending your competitors instead of you.

We ran this study to answer a practical question for anyone doing GEO (generative engine optimization): when buyers ask an AI assistant "what's the best tool for X," which brands actually come out of its mouth — and how much do different models agree?

How we ran the study

We took 20 high-intent US buyer questions across marketing and SaaS — "best CRM for startups," "best SEO tools for small business," "best email marketing software," "best project management software," and so on. We asked each question to three AI models — DeepSeek V3.2, Llama 3.3 70B, and Amazon Nova — three times each, for a total of 180 real answers. Then we counted, for every answer, which brands were named.

This measures what the models have internalized about brands from their training — the reputation that shows up when they answer from knowledge. (It does not measure live web citations; that's a separate study.) Every number below comes from real model responses, not estimates.

Which brands do AI models recommend most?

Out of 51 distinct brands named across all answers, the top 10 captured 54.6% of every mention — a small set of brands dominates. The leaders:

• HubSpot — named in 59 of 180 answers (about 1 in 3)
• Semrush — 47
• Ahrefs — 41
• Google Analytics — 40
• Moz, WordPress, Salesforce — 21 each
• Hootsuite, Buffer, Mailchimp — 18 each

The average answer named about 3 brands, so buyers asking an AI assistant aren't getting one recommendation — they're getting a short shortlist, and the same names recur.

How much do different AI models agree?

A lot. 27 of the 51 brands were named by all three models — a shared "consensus shortlist" that every model reaches for. Only 10 brands were named by a single model. Each model named at least one brand in 85–90% of its answers, so AI assistants almost always give a specific brand answer rather than hedging.

That consensus is the important part for GEO. These models were built by different companies on different data, yet they converge on the same brands. That convergence is what you're optimizing against: to be recommended, you need to be part of the shared reputation these models have already absorbed.

What this means for your brand

• AI recommendations are winner-take-most. A handful of brands own the majority of mentions. Being "known" isn't enough — you need to be in the top cluster the models actually name.
• Consensus is the target. Getting named by one model is a start; getting into the set every model names is the goal, and it's a measurable one.
• You can't manage what you don't measure. The only way to know whether AI assistants recommend you — and whether that's improving — is to sample the models repeatedly and track it. That's exactly what Orem does: it measures your mention and citation rate across ChatGPT, Perplexity, Google AI Overviews, Gemini, and Claude, with confidence intervals so you can tell a real gain from noise.

Frequently asked questions

Which AI models were tested?

DeepSeek V3.2, Llama 3.3 70B, and Amazon Nova, each run through AWS Bedrock. We asked 20 US buyer questions three times per model, for 180 total answers.

Which brand did AI models recommend most?

HubSpot, named in about one of every three answers, ahead of Semrush, Ahrefs, and Google Analytics.

Do different AI models recommend the same brands?

Largely yes. 27 of 51 brands were named by all three models, and the top 10 brands accounted for 54.6% of all mentions — strong agreement on a small consensus shortlist.

How can I get my brand recommended by AI models?

Measure where you stand first — how often each model names you versus competitors — then close the gaps in the sources and reputation the models draw on. Orem tracks this across the major AI engines with statistical confidence.

Methodology: 180 answers from DeepSeek V3.2, Llama 3.3 70B, and Amazon Nova (via AWS Bedrock), 20 US marketing/SaaS buyer prompts, 3 runs per model, brand mentions counted by exact name match. Data generated by Orem's sampling engine, July 2026.