Original research · 2026-07 edition

AI SEO Statistics: Brewery (2026-07 edition)

15 questions · 45 AI responses · 3 models · measured 2026-07-04

See how AI talks about YOUR businessFull data table

The question bank

The questions we tested — sampled from real buyer journeys in brewery.

Each model answered every question once, same wording, same day. These are the prompts behind every percentage on this page.

What are the best local breweries that actually have a full kitchen and aren't just relying on rotating food trucks?
Is it cheaper to rent a private room at a brewery for a 30th birthday or just reserve a few tables in the main taproom?
Which breweries in my area are actually dog-friendly inside the building and not just on an outdoor patio?
What is the typical per-person cost for a guided craft beer tasting and production tour for a corporate team-building event?
Should I book a large commercial brewery for a rehearsal dinner or go with a smaller microbrewery for a more intimate vibe?
Are there any breweries near the city center that have dedicated parking lots or is everything street parking only?
What red flags should I look for in a brewery event contract regarding hidden cleaning fees or automatic gratuity?
I need a venue for a last-minute happy hour for 25 people this Friday; which spots usually have enough space for walk-ins?
Show all 15 questions
Is it better to hire a mobile tap truck for a backyard party or just buy a few kegs directly from a local brewery?
How can I tell if a brewery has a balanced tap list for a group of 10 people who mostly don't like bitter IPAs?
Are there any kid-friendly breweries with a dedicated play area or board games so the adults can actually talk?
Do breweries typically charge a flat room rental fee for private parties or is it based on a minimum drink spend?
Where can I find a brewery that offers a good selection of gluten-free options like hard seltzer or cider for a mixed group?
What is the difference between a production brewery tour and a brewpub experience if I want to show guests how beer is made?
Which breweries have a quiet enough side room with a projector and screen for a casual business presentation?

Model by model

17-point average divergence: which AI you ask changes the answer.

The divergence index is the average gap between the most and least likely model per behavior. Higher = the models disagree more about brewery buyers.

Behavior rates across 15 brewery buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional13%7%0%87%
Suggests DIY first47%27%0%47%
Names specific providers7%0%27%67%
Gives price or cost info20%27%27%80%
Tells to check reviews27%33%13%73%
Tells to verify credentials7%0%0%93%
Mentions case studies / portfolio0%0%0%100%
Mentions local proximity60%60%53%67%
Gives selection criteria80%67%67%53%
Warns about red flags7%13%13%93%
Asks a clarifying question87%80%47%40%
Recommends multiple quotes7%0%0%93%

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.

The divergence index for this study is 17 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a brewery buyer happens to ask matters most:

  • Suggests a DIY approach first: from 0% (Gemini) to 46.7% (ChatGPT) — a 47-point spread.
  • Asks a clarifying question: from 46.7% (Gemini) to 86.7% (ChatGPT) — a 40-point spread.
  • Names a specific provider: from 0% (Claude) to 26.7% (Gemini) — a 27-point spread.
  • Tells the buyer to check reviews: from 13.3% (Gemini) to 33.3% (Claude) — a 20-point spread.
  • Recommends hiring a professional: from 0% (Gemini) to 13.3% (ChatGPT) — a 13-point spread.

The widest single gap — suggests a DIY approach first, 47 points — 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%) — a 13-point spread.
  • Asks a clarifying question: 71.1% on average (ChatGPT 86.7%, Claude 80%, Gemini 46.7%) — a 40-point spread.
  • Mentions local proximity: 57.8% on average (ChatGPT 60%, Claude 60%, Gemini 53.3%) — a 7-point spread.
  • Suggests a DIY approach first: 24.5% on average (ChatGPT 46.7%, Claude 26.7%, Gemini 0%) — a 47-point spread.
  • Gives price or cost information: 24.5% on average (ChatGPT 20%, Claude 26.7%, Gemini 26.7%) — a 7-point spread.
  • Tells the buyer to check reviews: 24.4% on average (ChatGPT 26.7%, Claude 33.3%, Gemini 13.3%) — a 20-point spread.
  • Names a specific provider: 11.1% on average (ChatGPT 6.7%, Claude 0%, Gemini 26.7%) — a 27-point spread.
  • Warns about red flags or scams: 11.1% on average (ChatGPT 6.7%, Claude 13.3%, Gemini 13.3%) — a 7-point spread.
  • Recommends hiring a professional: 6.7% on average (ChatGPT 13.3%, Claude 6.7%, Gemini 0%) — a 13-point spread.
  • Tells the buyer to verify credentials: 2.2% on average (ChatGPT 6.7%, Claude 0%, Gemini 0%) — a 7-point spread.
  • Recommends multiple quotes: 2.2% on average (ChatGPT 6.7%, Claude 0%, Gemini 0%) — a 7-point spread.
  • 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 — 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 — 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 were drawn from real brewery (hospitality; buyer hiring decisions for this specific service) buyer journeys. 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 — not 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 — 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 standardized buyer questions per industry, one response per model per question (ChatGPT (gpt-5-mini), Claude (claude-sonnet-5), Gemini (gemini-3-flash-preview)), collected 2026-07-04, coded against a fixed 12-behavior rubric with human QA. AI outputs vary with model version, location and time — figures describe this sample and window, and are refreshed each edition. Read the full methodology →