Original research · 2026-07 edition

AI SEO Statistics: Foundation Repair (2026-07 edition)

40 questions · 120 AI responses · 3 models · measured 2026-07-06

See how AI talks about YOUR businessFull data table

The question bank

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

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

Why are there diagonal cracks appearing above my interior door frames?
Is a horizontal crack in a basement wall more dangerous than a vertical one?
My floors feel bouncy and uneven, does that mean the foundation is failing?
Can I just use a DIY foundation crack repair kit from a hardware store?
How much does a structural engineer charge for a foundation certification?
What are the warning signs that a house is settling too much?
Will homeowners insurance pay for foundation repair caused by soil expansion?
How do I distinguish between normal settling and serious structural damage?
Show all 40 questions
What is the average cost per pier for a residential foundation job?
Should I hire an independent engineer or a foundation repair company for the initial inspection?
What are the long-term risks of ignoring a bowing basement wall?
How many days does it typically take to install helical piers on a standard home?
Is polyurethane foam injection better than mudjacking for leveling a concrete slab?
What specific questions should I ask to vet a foundation repair contractor?
Why is my chimney pulling away from the rest of the house?
Can foundation issues cause my windows to get stuck or not lock properly?
What does a 'transferable lifetime warranty' actually cover in foundation work?
How much should I discount my asking price if I'm selling a house with foundation issues?
How does heavy rain or drought affect the slab foundation in my region?
Is it dangerous to live in a home while the foundation is being leveled?
What is the difference between push piers and helical piers for heavy homes?
Can a plumbing leak under the slab cause the foundation to heave?
How can I tell if a contractor is trying to sell me more piers than I actually need?
If I buy a house with a repaired foundation, will I have trouble getting a mortgage?
What exactly is underpinning and when is it necessary for a crawl space?
Do foundation repair companies usually handle the building permits and inspections?
Will the interior of my home be covered in dust during the piering process?
How do I protect my flower beds and bushes during foundation excavation?
Will lifting my foundation cause new cracks to appear in my drywall or tile?
Are there ways to stabilize a foundation without digging up the entire perimeter?
Why is water coming up through the middle of my concrete basement floor?
How do I compare two foundation quotes that suggest completely different repair methods?
What are the common red flags in a foundation repair sales pitch?
Do I need to move my furniture out of the house before they start the lift?
Is it better to fix foundation problems in the dry summer or the wet winter?
Does my house need a drainage system like a French drain instead of foundation piers?
Can a foundation be successfully repaired on a house built on a steep hill?
What is the cheapest way to fix a cracked foundation if I'm on a tight budget?
Should I get a second opinion if a contractor says my house is at risk of collapse?
How does a pier and beam foundation repair differ from a concrete slab repair?

Model by model

22-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 foundation repair buyers.

Behavior rates across 40 foundation repair buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional93%83%35%33%
Suggests DIY first25%18%10%80%
Names specific providers0%0%0%100%
Gives price or cost info25%23%23%70%
Tells to check reviews15%10%0%83%
Tells to verify credentials33%13%8%68%
Mentions case studies / portfolio5%8%0%93%
Mentions local proximity30%15%5%65%
Gives selection criteria43%28%10%60%
Warns about red flags18%25%13%80%
Asks a clarifying question70%78%0%8%
Recommends multiple quotes38%23%10%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 foundation repair buyer to a professional (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.

The divergence index for this study is 22.1 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a foundation repair buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 77.5% (Claude) — a 78-point spread.
  • Recommends hiring a professional: from 35% (Gemini) to 92.5% (ChatGPT) — a 58-point spread.
  • Gives selection criteria: from 10% (Gemini) to 42.5% (ChatGPT) — a 33-point spread.
  • Recommends multiple quotes: from 10% (Gemini) to 37.5% (ChatGPT) — a 28-point spread.
  • Tells the buyer to verify credentials: from 7.5% (Gemini) to 32.5% (ChatGPT) — a 25-point spread.

The widest single gap — asks a clarifying question, 78 points — means a foundation repair 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 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%) — a 58-point spread.
  • Asks a clarifying question: 49.2% on average (ChatGPT 70%, Claude 77.5%, Gemini 0%) — a 78-point spread.
  • Gives selection criteria: 26.7% on average (ChatGPT 42.5%, Claude 27.5%, Gemini 10%) — a 33-point spread.
  • Gives price or cost information: 23.3% on average (ChatGPT 25%, Claude 22.5%, Gemini 22.5%) — a 3-point spread.
  • Recommends multiple quotes: 23.3% on average (ChatGPT 37.5%, Claude 22.5%, Gemini 10%) — a 28-point spread.
  • Warns about red flags or scams: 18.3% on average (ChatGPT 17.5%, Claude 25%, Gemini 12.5%) — a 13-point spread.
  • Suggests a DIY approach first: 17.5% on average (ChatGPT 25%, Claude 17.5%, Gemini 10%) — a 15-point spread.
  • Tells the buyer to verify credentials: 17.5% on average (ChatGPT 32.5%, Claude 12.5%, Gemini 7.5%) — a 25-point spread.
  • Mentions local proximity: 16.7% on average (ChatGPT 30%, Claude 15%, Gemini 5%) — a 25-point spread.
  • Tells the buyer to check reviews: 8.3% on average (ChatGPT 15%, Claude 10%, Gemini 0%) — a 15-point spread.
  • Mentions case studies or portfolio: 4.2% on average (ChatGPT 5%, Claude 7.5%, Gemini 0%) — a 8-point spread.
  • 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 — 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 — 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 were drawn from real foundation repair (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 foundation repair 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 40 answers in which the behavior appeared at least once — 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 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 2026-07-06, 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 →