AI SEO Statistics: Foundation Repair (2026-07 edition)
40 questions · 120/120 expected AI responses · 3 models · measured 2026-07-06
Apply these findings to your foundation repair 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 foundation repair.
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 40 questions
Model by model
22.1% 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 40 foundation repair benchmark questions, 2026-07 edition. Last column: equal-model mean.
| Behavior | ChatGPT | Claude | Gemini | Equal-model mean |
|---|---|---|---|---|
| Recommends hiring a professional | 92.5% | 82.5% | 35% | 70% |
| Suggests DIY first | 25% | 17.5% | 10% | 17.5% |
| Names specific providers | 0% | 0% | 0% | 0% |
| Gives price or cost info | 25% | 22.5% | 22.5% | 23.3% |
| Tells to check reviews | 15% | 10% | 0% | 8.3% |
| Tells to verify credentials | 32.5% | 12.5% | 7.5% | 17.5% |
| Mentions case studies / portfolio | 5% | 7.5% | 0% | 4.2% |
| Mentions local proximity | 30% | 15% | 5% | 16.7% |
| Gives selection criteria | 42.5% | 27.5% | 10% | 26.7% |
| Warns about red flags | 17.5% | 25% | 12.5% | 18.3% |
| Asks a clarifying question | 70% | 77.5% | 0% | 49.2% |
| Recommends multiple quotes | 37.5% | 22.5% | 10% | 23.3% |
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 40 benchmark questions.
| Behavior | All-model agreement |
|---|---|
| Recommends hiring a professional | 32.5% |
| Suggests DIY first | 80% |
| Names specific providers | 100% |
| Gives price or cost info | 70% |
| Tells to check reviews | 82.5% |
| Tells to verify credentials | 67.5% |
| Mentions case studies / portfolio | 92.5% |
| Mentions local proximity | 65% |
| Gives selection criteria | 60% |
| Warns about red flags | 80% |
| Asks a clarifying question | 7.5% |
| Recommends multiple quotes | 65% |
By model
How each assistant handled Foundation Repair questions.
Reading the 120 answers model by model shows how differently the three assistants treat the same foundation repair questions. On the most consequential behavior, whether to send the buyer to a professional at all, the rate ranged from 92.5% (ChatGPT) down to 35% (Gemini), a 58-point gap on an identical question set.
Across the 40 foundation repair answers it produced, ChatGPT recommended hiring a professional in 92.5% of them and suggested a DIY approach first 25% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 25% of the time. ChatGPT asked a clarifying question before answering in 70% of cases, warned about red flags or scams in 17.5%, and told the buyer to verify credentials in 32.5%, averaging 552 words per answer. On the remaining cues it told the buyer to check reviews in 15%, pointed to case studies or a portfolio in 5%, and framed the choice around local proximity in 30%; a selection-criteria checklist appeared in 42.5% of its answers and a recommendation to gather multiple quotes in 37.5%.
Across the 40 foundation repair answers it produced, Claude recommended hiring a professional in 82.5% of them and suggested a DIY approach first 17.5% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 22.5% of the time. Claude asked a clarifying question before answering in 77.5% of cases, warned about red flags or scams in 25%, and told the buyer to verify credentials in 12.5%, averaging 311 words per answer. On the remaining cues it told the buyer to check reviews in 10%, pointed to case studies or a portfolio in 7.5%, and framed the choice around local proximity in 15%; a selection-criteria checklist appeared in 27.5% of its answers and a recommendation to gather multiple quotes in 22.5%.
Across the 40 foundation repair answers it produced, Gemini recommended hiring a professional in 35% of them and suggested a DIY approach first 10% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 22.5% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 12.5%, and told the buyer to verify credentials in 7.5%, averaging 305 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 5%; a selection-criteria checklist appeared in 10% of its answers and a recommendation to gather multiple quotes in 10%.
Taken together, ChatGPT is the assistant most likely to route a buyer researching foundation repair toward professional help (92.5%) and Gemini the least (35%). ChatGPT produced the longest answers, at 552 words on average. No model named a specific provider in more than 0% of answers.
Where they disagree
The behaviors where the choice of model changes the answer.
Question-level model disagreement is 22.1%. 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 the choice of assistant matters most for a buyer researching foundation repair:
- Asks a clarifying question: from 0% (Gemini) to 77.5% (Claude). The spread is 78 points.
- Recommends hiring a professional: from 35% (Gemini) to 92.5% (ChatGPT). The spread is 58 points.
- Gives selection criteria: from 10% (Gemini) to 42.5% (ChatGPT). The spread is 33 points.
- Recommends multiple quotes: from 10% (Gemini) to 37.5% (ChatGPT). The spread is 28 points.
- Tells the buyer to verify credentials: from 7.5% (Gemini) to 32.5% (ChatGPT). The spread is 25 points.
The widest single gap concerns asks a clarifying question at 78 points. This means a buyer researching foundation repair 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 foundation repair market.
Where they agree
The points of near-consensus in Foundation Repair.
On other behaviors the three models move almost in lockstep. The points of near-consensus for foundation repair, where all three landed within a few points of each other:
- Names a specific provider: 0% across all three models.
- Gives price or cost information: 22.5%–25% across all three (a 3-point spread).
- Mentions case studies or portfolio: 0%–7.5% across all three (a 8-point spread).
- Warns about red flags or scams: 12.5%–25% across all three (a 13-point spread).
Measured question by question, the three assistants coded a response the same way most consistently on "names a specific provider" (identical coding in 100% of questions) and least consistently on "asks a clarifying question" (7.5%).
Every behavior, measured
All twelve coded behaviors for Foundation Repair, averaged across the three models.
The behaviors AI models reproduce most often for foundation repair are recommends hiring a professional (70% on average), asks a clarifying question (49.2%) and gives selection criteria (26.7%); the rarest are names a specific provider (0%), mentions case studies or portfolio (4.2%) and tells the buyer to check reviews (8.3%). Each figure below is the share of a model's 40 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: 70% on average (ChatGPT 92.5%, Claude 82.5%, Gemini 35%). The spread is 58 points.
- Asks a clarifying question: 49.2% on average (ChatGPT 70%, Claude 77.5%, Gemini 0%). The spread is 78 points.
- Gives selection criteria: 26.7% on average (ChatGPT 42.5%, Claude 27.5%, Gemini 10%). The spread is 33 points.
- Gives price or cost information: 23.3% on average (ChatGPT 25%, Claude 22.5%, Gemini 22.5%). The spread is 3 points.
- Recommends multiple quotes: 23.3% on average (ChatGPT 37.5%, Claude 22.5%, Gemini 10%). The spread is 28 points.
- Warns about red flags or scams: 18.3% on average (ChatGPT 17.5%, Claude 25%, Gemini 12.5%). The spread is 13 points.
- Suggests a DIY approach first: 17.5% on average (ChatGPT 25%, Claude 17.5%, Gemini 10%). The spread is 15 points.
- Tells the buyer to verify credentials: 17.5% on average (ChatGPT 32.5%, Claude 12.5%, Gemini 7.5%). The spread is 25 points.
- Mentions local proximity: 16.7% on average (ChatGPT 30%, Claude 15%, Gemini 5%). The spread is 25 points.
- Tells the buyer to check reviews: 8.3% on average (ChatGPT 15%, Claude 10%, Gemini 0%). The spread is 15 points.
- Mentions case studies or portfolio: 4.2% on average (ChatGPT 5%, Claude 7.5%, Gemini 0%). The spread is 8 points.
- Names a specific provider: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).
Trust signals
How well the models protect the foundation repair buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the foundation repair buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 8.3% of answers on average. Verifying credentials or certifications appeared in 17.5%. Warning about red flags or scams appeared in 18.3%.
On structuring the decision, a selection-criteria checklist showed up in 26.7% of answers on average and a recommendation to gather multiple quotes in 23.3%. The single least-reproduced protective signal for foundation repair is "tells the buyer to check reviews" at 8.3% 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 Foundation Repair providers?
For service providers the decisive question is whether these systems name anyone at all. Across 120 foundation repair answers, a specific provider was named in 0% of responses on average, or roughly 0 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for foundation repair: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 40 Foundation Repair questions cover.
The 40 questions behind every percentage on this page form a frozen foundation repair (home services; 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 foundation repair 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 40 answers in which the behavior appeared at least once. It is not a confidence score. Because each model answered every question exactly once on 2026-07-06, the figures describe this specific foundation repair 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.
40 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 2026-07-06 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: Foundation Repair (2026-07 edition).” AuthoritySpecialist.com. https://authorityspecialist.com/research/ai-seo-statistics/services/home/foundation-repair