Original research · 2026-07 edition

AI SEO Statistics: Concrete Contractor (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 concrete contractor.

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

My concrete driveway is starting to flake and peel on the surface; is this something that can be resurfaced or do I need a full replacement?
What is the typical price difference between a basic broom finish and a decorative stamped concrete patio for a 20x20 area?
I have a very steep backyard; what kind of experience should I look for in a contractor who needs to pour a retaining wall on a slope?
Can I pour a new concrete slab directly over my old, cracked patio to save on demolition costs?
How do I verify if a concrete contractor is actually using the 4000 PSI mix they quoted me instead of something cheaper?
Is it better to hire a specialized concrete company or can a general landscaper handle a new walkway and steps?
What are the warning signs that a concrete contractor is cutting corners during the site preparation phase?
Do I need to install a gravel subbase before pouring a concrete shed pad, or is compacted dirt okay?
Show all 15 questions
How long should I wait after a new pour before applying a sealer, and is that something I can do myself to save money?
My garage floor is sinking in one corner; should I look for a mudjacking service or just rip it out and start over?
What kind of contract protections should I have in place regarding cracks that appear within the first year of a new pour?
Is it realistic to get a concrete driveway poured in late November, or will the cold weather ruin the curing process?
I'm getting quotes for a pool deck; why is there such a massive price gap between exposed aggregate and brushed finishes?
Does a residential concrete contractor usually handle the permit process with the HOA and the city, or is that on the homeowner?
If my contractor doesn't mention expansion joints in the estimate for a large patio, is that a red flag I should worry about?

Model by model

24-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 concrete contractor buyers.

Behavior rates across 15 concrete contractor buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional73%67%27%33%
Suggests DIY first13%13%13%100%
Names specific providers0%0%7%93%
Gives price or cost info20%33%27%67%
Tells to check reviews7%7%0%87%
Tells to verify credentials33%13%7%67%
Mentions case studies / portfolio33%13%0%67%
Mentions local proximity33%27%7%47%
Gives selection criteria67%47%27%40%
Warns about red flags33%33%27%73%
Asks a clarifying question80%40%0%13%
Recommends multiple quotes27%20%0%73%

By model

How each assistant handled Concrete Contractor questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same concrete contractor questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 73.3% (ChatGPT) down to 26.7% (Gemini), a 47-point gap on an identical question set.

Across the 15 concrete contractor answers it produced, ChatGPT recommended hiring a professional in 73.3% of them and suggested a DIY approach first 13.3% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 20% of the time. ChatGPT asked a clarifying question before answering in 80% of cases, warned about red flags or scams in 33.3%, and told the buyer to verify credentials in 33.3%, averaging 543 words per answer. On the remaining cues it told the buyer to check reviews in 6.7%, pointed to case studies or a portfolio in 33.3%, and framed the choice around local proximity in 33.3%; a selection-criteria checklist appeared in 66.7% of its answers and a recommendation to gather multiple quotes in 26.7%.

Across the 15 concrete contractor answers it produced, Claude recommended hiring a professional in 66.7% of them and suggested a DIY approach first 13.3% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 33.3% of the time. Claude asked a clarifying question before answering in 40% of cases, warned about red flags or scams in 33.3%, and told the buyer to verify credentials in 13.3%, averaging 326 words per answer. On the remaining cues it told the buyer to check reviews in 6.7%, pointed to case studies or a portfolio in 13.3%, and framed the choice around local proximity in 26.7%; a selection-criteria checklist appeared in 46.7% of its answers and a recommendation to gather multiple quotes in 20%.

Across the 15 concrete contractor answers it produced, Gemini recommended hiring a professional in 26.7% of them and suggested a DIY approach first 13.3% of the time. It named a specific provider in 6.7% of answers (about 0.1 distinct providers per answer) and included price or cost information 26.7% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 26.7%, and told the buyer to verify credentials in 6.7%, averaging 271 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 6.7%; a selection-criteria checklist appeared in 26.7% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a concrete contractor buyer to a professional (73.3%) and Gemini the least (26.7%). ChatGPT produced the longest answers, at 543 words on average. Specific providers were named most often by Gemini (6.7%) — even there, roughly one answer in 15 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 24.4 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a concrete contractor buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 80% (ChatGPT) — a 80-point spread.
  • Recommends hiring a professional: from 26.7% (Gemini) to 73.3% (ChatGPT) — a 47-point spread.
  • Gives selection criteria: from 26.7% (Gemini) to 66.7% (ChatGPT) — a 40-point spread.
  • Mentions case studies or portfolio: from 0% (Gemini) to 33.3% (ChatGPT) — a 33-point spread.
  • Recommends multiple quotes: from 0% (Gemini) to 26.7% (ChatGPT) — a 27-point spread.

The widest single gap — asks a clarifying question, 80 points — means a concrete contractor 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 concrete contractor market.

Where they agree

The points of near-consensus in Concrete Contractor.

On other behaviors the three models move almost in lockstep — the points of near-consensus for concrete contractor, where all three landed within a few points of each other:

  • Suggests a DIY approach first: 13.3% across all three models.
  • Warns about red flags or scams: 26.7%–33.3% across all three (a 7-point spread).
  • Names a specific provider: 0%–6.7% across all three (a 7-point spread).
  • Tells the buyer to check reviews: 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 "suggests a DIY approach first" (identical coding in 100% of questions) and least consistently on "asks a clarifying question" (13.3%).

Every behavior, measured

All twelve coded behaviors for Concrete Contractor, averaged across the three models.

The behaviors AI models reproduce most often for concrete contractor are recommends hiring a professional (55.6% on average), gives selection criteria (46.7%) and asks a clarifying question (40%); the rarest are names a specific provider (2.2%), tells the buyer to check reviews (4.5%) and suggests a DIY approach first (13.3%). 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:

  • Recommends hiring a professional: 55.6% on average (ChatGPT 73.3%, Claude 66.7%, Gemini 26.7%) — a 47-point spread.
  • Gives selection criteria: 46.7% on average (ChatGPT 66.7%, Claude 46.7%, Gemini 26.7%) — a 40-point spread.
  • Asks a clarifying question: 40% on average (ChatGPT 80%, Claude 40%, Gemini 0%) — a 80-point spread.
  • Warns about red flags or scams: 31.1% on average (ChatGPT 33.3%, Claude 33.3%, Gemini 26.7%) — a 7-point spread.
  • Gives price or cost information: 26.7% on average (ChatGPT 20%, Claude 33.3%, Gemini 26.7%) — a 13-point spread.
  • Mentions local proximity: 22.2% on average (ChatGPT 33.3%, Claude 26.7%, Gemini 6.7%) — a 27-point spread.
  • Tells the buyer to verify credentials: 17.8% on average (ChatGPT 33.3%, Claude 13.3%, Gemini 6.7%) — a 27-point spread.
  • Recommends multiple quotes: 15.6% on average (ChatGPT 26.7%, Claude 20%, Gemini 0%) — a 27-point spread.
  • Mentions case studies or portfolio: 15.5% on average (ChatGPT 33.3%, Claude 13.3%, Gemini 0%) — a 33-point spread.
  • Suggests a DIY approach first: 13.3% on average (ChatGPT 13.3%, Claude 13.3%, Gemini 13.3%).
  • Tells the buyer to check reviews: 4.5% on average (ChatGPT 6.7%, Claude 6.7%, Gemini 0%) — a 7-point spread.
  • Names a specific provider: 2.2% on average (ChatGPT 0%, Claude 0%, Gemini 6.7%) — a 7-point spread.

Trust signals

How well the models protect the concrete contractor buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the concrete contractor buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 4.5% of answers on average. Verifying credentials or certifications appeared in 17.8%. Warning about red flags or scams appeared in 31.1%.

On structuring the decision, a selection-criteria checklist showed up in 46.7% of answers on average and a recommendation to gather multiple quotes in 15.6%. The single least-reproduced protective signal for concrete contractor is "tells the buyer to check reviews" at 4.5% 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 Concrete Contractor providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 concrete contractor answers, a specific provider was named in 2.2% of responses on average — roughly 0 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for concrete contractor: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Concrete Contractor questions cover.

The 15 questions behind every percentage on this page were drawn from real concrete contractor (home services; 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 concrete contractor 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 concrete contractor 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 →