AI SEO Statistics: Brewery (2026-07 edition)
15 questions · 45/45 expected AI responses · 3 models · measured 2026-07-04
Apply these findings to your brewery SEO strategy.
This benchmark explains how AI assistants advise buyers. The related service page turns those findings into the technical, content, authority, and conversion priorities for this market.
The question bank
The questions we tested: a frozen buyer-intent benchmark for brewery.
The question set was curated from a predefined buyer-intent taxonomy and held constant for this edition. Each model received the same wording. These are the prompts behind every percentage on this page.
Show all 15 questions
Model by model
17% question-level model disagreement.
This rate is the average pairwise disagreement between binary behavior codes across questions and behaviors. It is not the gap between the highest and lowest aggregated model percentages.
Behavior prevalence across 15 brewery benchmark questions, 2026-07 edition. Last column: equal-model mean.
| Behavior | ChatGPT | Claude | Gemini | Equal-model mean |
|---|---|---|---|---|
| Recommends hiring a professional | 13.3% | 6.7% | 0% | 6.7% |
| Suggests DIY first | 46.7% | 26.7% | 0% | 24.5% |
| Names specific providers | 6.7% | 0% | 26.7% | 11.1% |
| Gives price or cost info | 20% | 26.7% | 26.7% | 24.5% |
| Tells to check reviews | 26.7% | 33.3% | 13.3% | 24.4% |
| Tells to verify credentials | 6.7% | 0% | 0% | 2.2% |
| Mentions case studies / portfolio | 0% | 0% | 0% | 0% |
| Mentions local proximity | 60% | 60% | 53.3% | 57.8% |
| Gives selection criteria | 80% | 66.7% | 66.7% | 71.1% |
| Warns about red flags | 6.7% | 13.3% | 13.3% | 11.1% |
| Asks a clarifying question | 86.7% | 80% | 46.7% | 71.1% |
| Recommends multiple quotes | 6.7% | 0% | 0% | 2.2% |
Question-level agreement
How often all measured models received the same binary code.
Agreement is calculated question by question for each behavior. A high value can coexist with a low behavior prevalence; it means the models usually agreed on whether the behavior appeared.
All-model binary agreement by behavior across 15 benchmark questions.
| Behavior | All-model agreement |
|---|---|
| Recommends hiring a professional | 86.7% |
| Suggests DIY first | 46.7% |
| Names specific providers | 66.7% |
| Gives price or cost info | 80% |
| Tells to check reviews | 73.3% |
| Tells to verify credentials | 93.3% |
| Mentions case studies / portfolio | 100% |
| Mentions local proximity | 66.7% |
| Gives selection criteria | 53.3% |
| Warns about red flags | 93.3% |
| Asks a clarifying question | 40% |
| Recommends multiple quotes | 93.3% |
By model
How each assistant handled Brewery questions.
Reading the 45 answers model by model shows how differently the three assistants treat the same brewery questions. On the most consequential behavior, whether to send the buyer to a professional at all, the rate ranged from 13.3% (ChatGPT) down to 0% (Gemini), a 13-point gap on an identical question set.
Across the 15 brewery answers it produced, ChatGPT recommended hiring a professional in 13.3% of them and suggested a DIY approach first 46.7% of the time. It named a specific provider in 6.7% of answers (about 0.3 distinct providers per answer) and included price or cost information 20% of the time. ChatGPT asked a clarifying question before answering in 86.7% of cases, warned about red flags or scams in 6.7%, and told the buyer to verify credentials in 6.7%, averaging 411 words per answer. On the remaining cues it told the buyer to check reviews in 26.7%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 60%; a selection-criteria checklist appeared in 80% of its answers and a recommendation to gather multiple quotes in 6.7%.
Across the 15 brewery answers it produced, Claude recommended hiring a professional in 6.7% of them and suggested a DIY approach first 26.7% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 26.7% of the time. Claude asked a clarifying question before answering in 80% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 0%, averaging 275 words per answer. On the remaining cues it told the buyer to check reviews in 33.3%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 60%; a selection-criteria checklist appeared in 66.7% of its answers and a recommendation to gather multiple quotes in 0%.
Across the 15 brewery answers it produced, Gemini recommended hiring a professional in 0% of them and suggested a DIY approach first 0% of the time. It named a specific provider in 26.7% of answers (about 1.8 distinct providers per answer) and included price or cost information 26.7% of the time. Gemini asked a clarifying question before answering in 46.7% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 0%, averaging 261 words per answer. On the remaining cues it told the buyer to check reviews in 13.3%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 53.3%; a selection-criteria checklist appeared in 66.7% of its answers and a recommendation to gather multiple quotes in 0%.
Taken together, ChatGPT is the assistant most likely to route a brewery buyer to a professional (13.3%) and Gemini the least (0%). ChatGPT produced the longest answers, at 411 words on average. Specific providers were named most often by Gemini (26.7%). Even there, roughly one answer in 4 carried a name.
Where they disagree
The behaviors where the choice of model changes the answer.
Question-level model disagreement is 17%. This is the average pairwise rate at which models received different binary codes across questions and behaviors. The observed rate spreads below are a separate measure showing where which assistant a brewery buyer happens to ask matters most:
- Suggests a DIY approach first: from 0% (Gemini) to 46.7% (ChatGPT). The spread is 47 points.
- Asks a clarifying question: from 46.7% (Gemini) to 86.7% (ChatGPT). The spread is 40 points.
- Names a specific provider: from 0% (Claude) to 26.7% (Gemini). The spread is 27 points.
- Tells the buyer to check reviews: from 13.3% (Gemini) to 33.3% (Claude). The spread is 20 points.
- Recommends hiring a professional: from 0% (Gemini) to 13.3% (ChatGPT). The spread is 13 points.
The widest single gap concerns suggests a DIY approach first at 47 points. This means a brewery buyer can receive materially different guidance on the same question depending only on which assistant they happen to open, so any visibility strategy built on a single model's behavior describes only part of the brewery market.
Where they agree
The points of near-consensus in Brewery.
On other behaviors the three models move almost in lockstep. The points of near-consensus for brewery, where all three landed within a few points of each other:
- Mentions case studies or portfolio: 0% across all three models.
- Warns about red flags or scams: 6.7%–13.3% across all three (a 7-point spread).
- Gives price or cost information: 20%–26.7% across all three (a 7-point spread).
- Tells the buyer to verify credentials: 0%–6.7% across all three (a 7-point spread).
Measured question by question, the three assistants coded a response the same way most consistently on "mentions case studies or portfolio" (identical coding in 100% of questions) and least consistently on "asks a clarifying question" (40%).
Every behavior, measured
All twelve coded behaviors for Brewery, averaged across the three models.
The behaviors AI models reproduce most often for brewery are gives selection criteria (71.1% on average), asks a clarifying question (71.1%) and mentions local proximity (57.8%); the rarest are mentions case studies or portfolio (0%), recommends multiple quotes (2.2%) and tells the buyer to verify credentials (2.2%). Each figure below is the share of a model's 15 answers in which the behavior appeared at least once, averaged across the 3 models with the full per-model range in parentheses:
- Gives selection criteria: 71.1% on average (ChatGPT 80%, Claude 66.7%, Gemini 66.7%). The spread is 13 points.
- Asks a clarifying question: 71.1% on average (ChatGPT 86.7%, Claude 80%, Gemini 46.7%). The spread is 40 points.
- Mentions local proximity: 57.8% on average (ChatGPT 60%, Claude 60%, Gemini 53.3%). The spread is 7 points.
- Suggests a DIY approach first: 24.5% on average (ChatGPT 46.7%, Claude 26.7%, Gemini 0%). The spread is 47 points.
- Gives price or cost information: 24.5% on average (ChatGPT 20%, Claude 26.7%, Gemini 26.7%). The spread is 7 points.
- Tells the buyer to check reviews: 24.4% on average (ChatGPT 26.7%, Claude 33.3%, Gemini 13.3%). The spread is 20 points.
- Names a specific provider: 11.1% on average (ChatGPT 6.7%, Claude 0%, Gemini 26.7%). The spread is 27 points.
- Warns about red flags or scams: 11.1% on average (ChatGPT 6.7%, Claude 13.3%, Gemini 13.3%). The spread is 7 points.
- Recommends hiring a professional: 6.7% on average (ChatGPT 13.3%, Claude 6.7%, Gemini 0%). The spread is 13 points.
- Tells the buyer to verify credentials: 2.2% on average (ChatGPT 6.7%, Claude 0%, Gemini 0%). The spread is 7 points.
- Recommends multiple quotes: 2.2% on average (ChatGPT 6.7%, Claude 0%, Gemini 0%). The spread is 7 points.
- Mentions case studies or portfolio: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).
Trust signals
How well the models protect the brewery buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the brewery buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 24.4% of answers on average. Verifying credentials or certifications appeared in 2.2%. Warning about red flags or scams appeared in 11.1%.
On structuring the decision, a selection-criteria checklist showed up in 71.1% of answers on average and a recommendation to gather multiple quotes in 2.2%. The single least-reproduced protective signal for brewery is "tells the buyer to verify credentials" at 2.2% on average. This is the clearest opening for content that supplies it, since the models are not yet reliably surfacing that guidance on their own.
Referral behavior
Do AI models name Brewery providers?
For service providers the decisive question is whether these systems name anyone at all. Across 45 brewery answers, a specific provider was named in 11.1% of responses on average, or roughly 0.7 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for brewery: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 15 Brewery questions cover.
The 15 questions behind every percentage on this page form a frozen brewery (hospitality; buyer hiring decisions for this specific service) buyer-intent benchmark. Each was put to all 3 models once, with identical wording, so the rates above describe how the assistants handled this exact brewery question set rather than a general prior or a hand-picked subset. The full list is shown earlier on this page; the coded percentages are what those specific questions produced.
How to read this
A note on the numbers.
A percentage here is the share of a model's 15 answers in which the behavior appeared at least once. It is not a confidence score. Because each model answered every question exactly once on 2026-07-04, the figures describe this specific brewery question set and snapshot rather than a general prior. The full protocol and coding rubric are documented in the study methodology.
Methodology
A controlled snapshot, documented end to end.
15 frozen benchmark questions, one expected response per model per question (ChatGPT API (gpt-5-mini), Claude API (claude-sonnet-5), Gemini API (gemini-3-flash-preview)), collected 2026-07-04 and coded against a fixed 12-behavior rubric. The pipeline validates the schema, recomputes aggregates and reports consistency issues. AI outputs vary with model version, location and time, so the figures describe this edition's exact sample and measurement window. Read the full methodology →
Citation
Cite this edition.
Authority Specialist. “AI SEO Statistics: Brewery (2026-07 edition).” AuthoritySpecialist.com. https://authorityspecialist.com/research/ai-seo-statistics/services/hospitality/brewery