Original research · 2026-07 edition

AI SEO Statistics: Remodeling Company (2026-07 edition)

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

The question bank

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

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

Is it more cost-effective to renovate my current kitchen or should I just sell the house as-is?
How can I tell if a wall in my living room is load-bearing before I start planning an open floor plan?
What are the biggest red flags to look for when reviewing a contractor's portfolio or past project photos?
I have a 30k budget for a master bath remodel; what's the best way to spend that to get a luxury feel?
What is the typical payment schedule for a major home renovation so I don't get scammed?
Should I hire a design-build firm or find an independent architect and a separate builder for a home addition?
How long does a typical 500 square foot basement finishing project take from demo to final inspection?
What questions should I ask a remodeling company's references to see if they're actually reliable?
Show all 15 questions
Does a full kitchen gut renovation usually require a permit in most suburbs, and who is responsible for filing it?
What are the pros and cons of using a general contractor versus acting as my own project manager for a remodel?
How do I handle it if my contractor finds unexpected water damage or mold behind the walls during a project?
Is it realistic to live in my house while the only full bathroom is being remodeled for three weeks?
Which home renovations currently offer the highest return on investment if I plan to sell in two years?
What are the specific differences in quality I should look for when comparing two very different quotes for the same project?
Are there any eco-friendly or energy-efficient remodeling upgrades that qualify for federal tax credits this year?

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 remodeling company buyers.

Behavior rates across 15 remodeling company buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional73%67%27%33%
Suggests DIY first13%13%7%87%
Names specific providers0%0%7%93%
Gives price or cost info27%40%27%67%
Tells to check reviews13%7%0%87%
Tells to verify credentials27%20%0%73%
Mentions case studies / portfolio27%13%7%73%
Mentions local proximity27%27%7%60%
Gives selection criteria33%40%20%47%
Warns about red flags7%27%20%73%
Asks a clarifying question53%60%0%27%
Recommends multiple quotes33%27%0%53%

By model

How each assistant handled Remodeling Company questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same remodeling company 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 remodeling company 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 26.7% of the time. ChatGPT asked a clarifying question before answering in 53.3% of cases, warned about red flags or scams in 6.7%, and told the buyer to verify credentials in 26.7%, averaging 642 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 26.7%, and framed the choice around local proximity in 26.7%; a selection-criteria checklist appeared in 33.3% of its answers and a recommendation to gather multiple quotes in 33.3%.

Across the 15 remodeling company 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 40% of the time. Claude asked a clarifying question before answering in 60% of cases, warned about red flags or scams in 26.7%, and told the buyer to verify credentials in 20%, averaging 323 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 40% of its answers and a recommendation to gather multiple quotes in 26.7%.

Across the 15 remodeling company answers it produced, Gemini recommended hiring a professional in 26.7% of them and suggested a DIY approach first 6.7% 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 20%, and told the buyer to verify credentials in 0%, averaging 251 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 6.7%, and framed the choice around local proximity in 6.7%; a selection-criteria checklist appeared in 20% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a remodeling company buyer to a professional (73.3%) and Gemini the least (26.7%). ChatGPT produced the longest answers, at 642 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 23.7 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a remodeling company buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 60% (Claude) — a 60-point spread.
  • Recommends hiring a professional: from 26.7% (Gemini) to 73.3% (ChatGPT) — a 47-point spread.
  • Recommends multiple quotes: from 0% (Gemini) to 33.3% (ChatGPT) — a 33-point spread.
  • Tells the buyer to verify credentials: from 0% (Gemini) to 26.7% (ChatGPT) — a 27-point spread.
  • Mentions case studies or portfolio: from 6.7% (Gemini) to 26.7% (ChatGPT) — a 20-point spread.

The widest single gap — asks a clarifying question, 60 points — means a remodeling company 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 remodeling company market.

Where they agree

The points of near-consensus in Remodeling Company.

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

  • Suggests a DIY approach first: 6.7%–13.3% across all three (a 7-point spread).
  • Names a specific provider: 0%–6.7% across all three (a 7-point spread).
  • Gives price or cost information: 26.7%–40% across all three (a 13-point spread).
  • Tells the buyer to check reviews: 0%–13.3% 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 93.3% of questions) and least consistently on "asks a clarifying question" (26.7%).

Every behavior, measured

All twelve coded behaviors for Remodeling Company, averaged across the three models.

The behaviors AI models reproduce most often for remodeling company are recommends hiring a professional (55.6% on average), asks a clarifying question (37.8%) and gives price or cost information (31.1%); the rarest are names a specific provider (2.2%), tells the buyer to check reviews (6.7%) and suggests a DIY approach first (11.1%). 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.
  • Asks a clarifying question: 37.8% on average (ChatGPT 53.3%, Claude 60%, Gemini 0%) — a 60-point spread.
  • Gives price or cost information: 31.1% on average (ChatGPT 26.7%, Claude 40%, Gemini 26.7%) — a 13-point spread.
  • Gives selection criteria: 31.1% on average (ChatGPT 33.3%, Claude 40%, Gemini 20%) — a 20-point spread.
  • Mentions local proximity: 20% on average (ChatGPT 26.7%, Claude 26.7%, Gemini 6.7%) — a 20-point spread.
  • Recommends multiple quotes: 20% on average (ChatGPT 33.3%, Claude 26.7%, Gemini 0%) — a 33-point spread.
  • Warns about red flags or scams: 17.8% on average (ChatGPT 6.7%, Claude 26.7%, Gemini 20%) — a 20-point spread.
  • Tells the buyer to verify credentials: 15.6% on average (ChatGPT 26.7%, Claude 20%, Gemini 0%) — a 27-point spread.
  • Mentions case studies or portfolio: 15.6% on average (ChatGPT 26.7%, Claude 13.3%, Gemini 6.7%) — a 20-point spread.
  • Suggests a DIY approach first: 11.1% on average (ChatGPT 13.3%, Claude 13.3%, Gemini 6.7%) — a 7-point spread.
  • Tells the buyer to check reviews: 6.7% on average (ChatGPT 13.3%, Claude 6.7%, Gemini 0%) — a 13-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 remodeling company buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the remodeling company buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 6.7% of answers on average. Verifying credentials or certifications appeared in 15.6%. Warning about red flags or scams appeared in 17.8%.

On structuring the decision, a selection-criteria checklist showed up in 31.1% of answers on average and a recommendation to gather multiple quotes in 20%. The single least-reproduced protective signal for remodeling company is "tells the buyer to check reviews" at 6.7% 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 Remodeling Company providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 remodeling company 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 remodeling company: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Remodeling Company questions cover.

The 15 questions behind every percentage on this page were drawn from real remodeling company (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 remodeling company 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 remodeling company 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 →