Original research · 2026-07 edition

AI SEO Statistics: Basement Waterproofing (2026-07 edition)

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

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The question bank

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

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

Why does my basement smell like mildew every time it rains even if I don't see water?
Is that white crusty stuff on my foundation walls a sign of a major leak?
Can I just use waterproof paint to stop water from coming through the floor or is that a waste?
How much does it usually cost to install an interior French drain in a 1,000 sq ft basement?
What's the difference between a basic sump pump and a full basement waterproofing system?
My basement flooded last night, do I need to call a plumber or a specialized waterproofing company?
Does homeowners insurance typically cover foundation seepage or do I have to pay out of pocket?
How can I tell if a crack in my basement wall is structural or just a minor leak?
Show all 40 questions
What are the red flags I should look for when hiring a basement repair contractor?
Is it better to waterproof a foundation from the outside or the inside for a long-term fix?
How long does a typical basement waterproofing job take to complete from start to finish?
Will waterproofing my basement actually increase my home's resale value when I list it?
I'm seeing puddles near the floor joists, where could that water be coming from?
What kind of warranty should I expect for a professional foundation drainage system?
Are there any DIY kits for sealing basement cracks that actually hold up over time?
How do I know if my sump pump is about to fail before the next big storm hits?
What's the average price range for a battery backup sump pump installation in my area?
Does running a dehumidifier solve basement moisture problems or is it just masking a bigger issue?
Why is water coming up through the middle of my basement floor instead of the walls?
What specific questions should I ask during a basement inspection to make sure I'm not being oversold?
Can I finish my basement with drywall after it's been waterproofed or will the system get in the way?
My basement walls are bowing slightly and feel damp, how urgent of a repair is this?
Is it normal for a waterproofing contractor to ask for a 50% deposit before they start the work?
How do I stop water from pooling around my foundation without digging up my whole yard?
What is the cheapest way to keep a crawl space dry without doing a full encapsulation?
Does fixing my landscape grading really help prevent basement leaks or is that a myth?
Should I get a second opinion if a company tells me I need a $20,000 system for a small leak?
How do I find a local waterproofing company that has experience with old stone foundations?
What happens to my house foundation if I just ignore the small amount of water in the utility room?
Is an exterior excavation waterproofing method worth the extra cost compared to interior drains?
Can I install a basement vapor barrier myself or is it too technical for a homeowner?
Why do I still have water in my basement even though my sump pump is running constantly?
What are the signs that my window wells are the main cause of my basement flooding?
How do I vet a waterproofing company's reviews to make sure they aren't fake or paid for?
Is it possible to waterproof a basement from the inside without jackhammering the concrete floor?
What is the best time of year to schedule basement waterproofing for the lowest price?
Do most basement waterproofing companies offer monthly financing plans for large repairs?
How does a sub-floor drainage system actually work to keep the basement dry?
My basement is constantly damp but there's no standing water, do I still need professional help?
What's the technical difference between damp-proofing and true basement waterproofing?

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 basement waterproofing buyers.

Behavior rates across 40 basement waterproofing buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional93%50%23%25%
Suggests DIY first53%40%23%68%
Names specific providers3%5%8%88%
Gives price or cost info20%28%28%65%
Tells to check reviews18%13%5%83%
Tells to verify credentials25%10%3%78%
Mentions case studies / portfolio23%5%0%78%
Mentions local proximity25%13%8%80%
Gives selection criteria35%20%15%63%
Warns about red flags13%18%15%83%
Asks a clarifying question73%75%0%3%
Recommends multiple quotes30%18%3%63%

By model

How each assistant handled Basement Waterproofing questions.

Reading the 120 answers model by model shows how differently the three assistants treat the same basement waterproofing 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 22.5% (Gemini), a 70-point gap on an identical question set.

Across the 40 basement waterproofing answers it produced, ChatGPT recommended hiring a professional in 92.5% of them and suggested a DIY approach first 52.5% of the time. It named a specific provider in 2.5% 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 72.5% of cases, warned about red flags or scams in 12.5%, and told the buyer to verify credentials in 25%, averaging 580 words per answer. On the remaining cues it told the buyer to check reviews in 17.5%, pointed to case studies or a portfolio in 22.5%, and framed the choice around local proximity in 25%; a selection-criteria checklist appeared in 35% of its answers and a recommendation to gather multiple quotes in 30%.

Across the 40 basement waterproofing answers it produced, Claude recommended hiring a professional in 50% of them and suggested a DIY approach first 40% of the time. It named a specific provider in 5% of answers (about 0.2 distinct providers per answer) and included price or cost information 27.5% of the time. Claude asked a clarifying question before answering in 75% of cases, warned about red flags or scams in 17.5%, and told the buyer to verify credentials in 10%, averaging 313 words per answer. On the remaining cues it told the buyer to check reviews in 12.5%, pointed to case studies or a portfolio in 5%, and framed the choice around local proximity in 12.5%; a selection-criteria checklist appeared in 20% of its answers and a recommendation to gather multiple quotes in 17.5%.

Across the 40 basement waterproofing answers it produced, Gemini recommended hiring a professional in 22.5% of them and suggested a DIY approach first 22.5% of the time. It named a specific provider in 7.5% of answers (about 0.3 distinct providers per answer) and included price or cost information 27.5% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 15%, and told the buyer to verify credentials in 2.5%, averaging 287 words per answer. On the remaining cues it told the buyer to check reviews in 5%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 7.5%; a selection-criteria checklist appeared in 15% of its answers and a recommendation to gather multiple quotes in 2.5%.

Taken together, ChatGPT is the assistant most likely to route a basement waterproofing buyer to a professional (92.5%) and Gemini the least (22.5%). ChatGPT produced the longest answers, at 580 words on average. Specific providers were named most often by Gemini (7.5%) — even there, roughly one answer in 13 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 23.8 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a basement waterproofing buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 75% (Claude) — a 75-point spread.
  • Recommends hiring a professional: from 22.5% (Gemini) to 92.5% (ChatGPT) — a 70-point spread.
  • Suggests a DIY approach first: from 22.5% (Gemini) to 52.5% (ChatGPT) — a 30-point spread.
  • Recommends multiple quotes: from 2.5% (Gemini) to 30% (ChatGPT) — a 28-point spread.
  • Tells the buyer to verify credentials: from 2.5% (Gemini) to 25% (ChatGPT) — a 23-point spread.

The widest single gap — asks a clarifying question, 75 points — means a basement waterproofing 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 basement waterproofing market.

Where they agree

The points of near-consensus in Basement Waterproofing.

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

  • Names a specific provider: 2.5%–7.5% across all three (a 5-point spread).
  • Warns about red flags or scams: 12.5%–17.5% across all three (a 5-point spread).
  • Gives price or cost information: 20%–27.5% across all three (a 8-point spread).
  • Tells the buyer to check reviews: 5%–17.5% 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 87.5% of questions) and least consistently on "asks a clarifying question" (2.5%).

Every behavior, measured

All twelve coded behaviors for Basement Waterproofing, averaged across the three models.

The behaviors AI models reproduce most often for basement waterproofing are recommends hiring a professional (55% on average), asks a clarifying question (49.2%) and suggests a DIY approach first (38.3%); the rarest are names a specific provider (5%), mentions case studies or portfolio (9.2%) and tells the buyer to check reviews (11.7%). 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: 55% on average (ChatGPT 92.5%, Claude 50%, Gemini 22.5%) — a 70-point spread.
  • Asks a clarifying question: 49.2% on average (ChatGPT 72.5%, Claude 75%, Gemini 0%) — a 75-point spread.
  • Suggests a DIY approach first: 38.3% on average (ChatGPT 52.5%, Claude 40%, Gemini 22.5%) — a 30-point spread.
  • Gives price or cost information: 25% on average (ChatGPT 20%, Claude 27.5%, Gemini 27.5%) — a 8-point spread.
  • Gives selection criteria: 23.3% on average (ChatGPT 35%, Claude 20%, Gemini 15%) — a 20-point spread.
  • Recommends multiple quotes: 16.7% on average (ChatGPT 30%, Claude 17.5%, Gemini 2.5%) — a 28-point spread.
  • Mentions local proximity: 15% on average (ChatGPT 25%, Claude 12.5%, Gemini 7.5%) — a 18-point spread.
  • Warns about red flags or scams: 15% on average (ChatGPT 12.5%, Claude 17.5%, Gemini 15%) — a 5-point spread.
  • Tells the buyer to verify credentials: 12.5% on average (ChatGPT 25%, Claude 10%, Gemini 2.5%) — a 23-point spread.
  • Tells the buyer to check reviews: 11.7% on average (ChatGPT 17.5%, Claude 12.5%, Gemini 5%) — a 13-point spread.
  • Mentions case studies or portfolio: 9.2% on average (ChatGPT 22.5%, Claude 5%, Gemini 0%) — a 23-point spread.
  • Names a specific provider: 5% on average (ChatGPT 2.5%, Claude 5%, Gemini 7.5%) — a 5-point spread.

Trust signals

How well the models protect the basement waterproofing buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the basement waterproofing buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 11.7% of answers on average. Verifying credentials or certifications appeared in 12.5%. Warning about red flags or scams appeared in 15%.

On structuring the decision, a selection-criteria checklist showed up in 23.3% of answers on average and a recommendation to gather multiple quotes in 16.7%. The single least-reproduced protective signal for basement waterproofing is "tells the buyer to check reviews" at 11.7% 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 Basement Waterproofing providers?

For service providers the decisive question is whether these systems name anyone at all. Across 120 basement waterproofing answers, a specific provider was named in 5% of responses on average — roughly 0.2 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for basement waterproofing: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 40 Basement Waterproofing questions cover.

The 40 questions behind every percentage on this page were drawn from real basement waterproofing (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 basement waterproofing 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 basement waterproofing 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 →