Original research · 2026-07 edition

AI SEO Statistics: Cabinet Makers (2026-07 edition)

39 questions · 117 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 cabinet makers.

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

How much does it cost to get custom kitchen cabinets for a 10x12 kitchen?
Is it cheaper to hire a cabinet maker to build new boxes or just get the old ones refaced?
What are the pros and cons of using solid wood versus high-grade plywood for cabinet boxes?
I have a weirdly shaped corner in my laundry room, can a custom cabinet maker build something to fit?
How do I know if a cabinet maker is actually high-quality or just charging a premium price?
What specific questions should I ask during an initial consultation for built-in bookshelves?
Is it worth it to get custom cabinets if I plan on selling my house in three years?
How long does the typical custom cabinet project take from the design phase to final installation?
Show all 39 questions
Can a local cabinet maker match the existing stain on my 1990s oak cabinets if I want to add an island?
What's the difference between inset, partial overlay, and full overlay cabinet doors?
Should I buy pre-made cabinets from a big box store and hire a pro to install them or go fully custom?
What are the red flags to look for when reviewing a cabinet maker's contract and payment schedule?
My kitchen cabinets are sagging from water damage under the sink, do I need a whole new set or just a repair?
How much extra does it usually cost for features like soft-close hinges and pull-out spice racks?
Are there specific certifications or licenses I should look for when hiring a local woodworker?
How do I find a cabinet maker who specializes in modern, minimalist styles rather than traditional?
What is a reasonable deposit percentage to pay a cabinet maker before they start work on the shop floor?
Can a cabinet maker help me design a more functional layout or do I need to hire an interior designer first?
What's the long-term maintenance like for hand-painted custom cabinets versus factory-finished ones?
I'm on a $15,000 budget for a kitchen remodel, is custom cabinetry even a realistic option for me?
Is MDF actually bad for bathroom vanities or is it better for moisture resistance than real wood?
How do I compare two quotes that have a $5,000 price difference for the exact same layout?
Do cabinet makers usually handle the countertop installation too or do I need to hire a separate stone fabricator?
What should I do if the cabinet doors are slightly misaligned after the installer leaves the job site?
How much clearance space do I really need between my kitchen island and the wall cabinets for traffic?
Can someone build custom inserts for my existing cabinets to make them more organized without replacing them?
Is it normal for a high-end cabinet maker to have a three-month lead time before starting a project?
What kind of warranty should I expect on custom-built cabinetry and the moving hardware parts?
I want floor-to-ceiling pantry cabinets, what's the best way to ensure they don't look too bulky in a small room?
How can I tell if a cabinet maker uses high-quality dovetail joints for drawers or just simple staples?
What are the best wood species for cabinets if I have kids and pets that might scratch the surfaces?
Can a cabinet maker build a hidden door that looks like a bookshelf for a secret room?
Is it possible to change the height of my kitchen counters by getting custom base cabinets made taller?
How much of a mess and dust should I expect during the installation phase of new cabinetry?
What happens if the wood warps or the finish cracks a few months after the cabinets are installed?
Are floating vanities sturdy enough for a master bathroom or should I stick to floor-mounted for weight?
How do I verify a cabinet maker's references if they don't have a large online presence or social media?
Can I save a significant amount of money by doing the demolition of my old cabinets myself?
What's the most durable finish for kitchen cabinets?

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 cabinet makers buyers.

Behavior rates across 39 cabinet makers buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional72%51%36%49%
Suggests DIY first8%5%13%92%
Names specific providers0%8%8%85%
Gives price or cost info31%31%33%74%
Tells to check reviews15%10%3%77%
Tells to verify credentials15%5%3%80%
Mentions case studies / portfolio21%13%3%69%
Mentions local proximity26%26%8%62%
Gives selection criteria49%33%33%49%
Warns about red flags15%13%10%87%
Asks a clarifying question59%64%0%18%
Recommends multiple quotes21%10%0%72%

By model

How each assistant handled Cabinet Makers questions.

Reading the 117 answers model by model shows how differently the three assistants treat the same cabinet makers questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 71.8% (ChatGPT) down to 35.9% (Gemini), a 36-point gap on an identical question set.

Across the 39 cabinet makers answers it produced, ChatGPT recommended hiring a professional in 71.8% of them and suggested a DIY approach first 7.7% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 30.8% of the time. ChatGPT asked a clarifying question before answering in 59% of cases, warned about red flags or scams in 15.4%, and told the buyer to verify credentials in 15.4%, averaging 536 words per answer. On the remaining cues it told the buyer to check reviews in 15.4%, pointed to case studies or a portfolio in 20.5%, and framed the choice around local proximity in 25.6%; a selection-criteria checklist appeared in 48.7% of its answers and a recommendation to gather multiple quotes in 20.5%.

Across the 39 cabinet makers answers it produced, Claude recommended hiring a professional in 51.3% of them and suggested a DIY approach first 5.1% of the time. It named a specific provider in 7.7% of answers (about 0.2 distinct providers per answer) and included price or cost information 30.8% of the time. Claude asked a clarifying question before answering in 64.1% of cases, warned about red flags or scams in 12.8%, and told the buyer to verify credentials in 5.1%, averaging 286 words per answer. On the remaining cues it told the buyer to check reviews in 10.3%, pointed to case studies or a portfolio in 12.8%, and framed the choice around local proximity in 25.6%; a selection-criteria checklist appeared in 33.3% of its answers and a recommendation to gather multiple quotes in 10.3%.

Across the 39 cabinet makers answers it produced, Gemini recommended hiring a professional in 35.9% of them and suggested a DIY approach first 12.8% of the time. It named a specific provider in 7.7% of answers (about 0.2 distinct providers per answer) and included price or cost information 33.3% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 10.3%, and told the buyer to verify credentials in 2.6%, averaging 281 words per answer. On the remaining cues it told the buyer to check reviews in 2.6%, pointed to case studies or a portfolio in 2.6%, and framed the choice around local proximity in 7.7%; a selection-criteria checklist appeared in 33.3% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a cabinet makers buyer to a professional (71.8%) and Gemini the least (35.9%). ChatGPT produced the longest answers, at 536 words on average. Specific providers were named most often by Claude (7.7%) — 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 21.5 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a cabinet makers buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 64.1% (Claude) — a 64-point spread.
  • Recommends hiring a professional: from 35.9% (Gemini) to 71.8% (ChatGPT) — a 36-point spread.
  • Recommends multiple quotes: from 0% (Gemini) to 20.5% (ChatGPT) — a 21-point spread.
  • Mentions case studies or portfolio: from 2.6% (Gemini) to 20.5% (ChatGPT) — a 18-point spread.
  • Mentions local proximity: from 7.7% (Gemini) to 25.6% (ChatGPT) — a 18-point spread.

The widest single gap — asks a clarifying question, 64 points — means a cabinet makers 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 cabinet makers market.

Where they agree

The points of near-consensus in Cabinet Makers.

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

  • Gives price or cost information: 30.8%–33.3% across all three (a 2-point spread).
  • Warns about red flags or scams: 10.3%–15.4% across all three (a 5-point spread).
  • Suggests a DIY approach first: 5.1%–12.8% across all three (a 8-point spread).
  • Names a specific provider: 0%–7.7% across all three (a 8-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 92.3% of questions) and least consistently on "asks a clarifying question" (17.9%).

Every behavior, measured

All twelve coded behaviors for Cabinet Makers, averaged across the three models.

The behaviors AI models reproduce most often for cabinet makers are recommends hiring a professional (53% on average), asks a clarifying question (41%) and gives selection criteria (38.4%); the rarest are names a specific provider (5.1%), tells the buyer to verify credentials (7.7%) and suggests a DIY approach first (8.5%). Each figure below is the share of a model's 39 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: 53% on average (ChatGPT 71.8%, Claude 51.3%, Gemini 35.9%) — a 36-point spread.
  • Asks a clarifying question: 41% on average (ChatGPT 59%, Claude 64.1%, Gemini 0%) — a 64-point spread.
  • Gives selection criteria: 38.4% on average (ChatGPT 48.7%, Claude 33.3%, Gemini 33.3%) — a 15-point spread.
  • Gives price or cost information: 31.6% on average (ChatGPT 30.8%, Claude 30.8%, Gemini 33.3%) — a 2-point spread.
  • Mentions local proximity: 19.6% on average (ChatGPT 25.6%, Claude 25.6%, Gemini 7.7%) — a 18-point spread.
  • Warns about red flags or scams: 12.8% on average (ChatGPT 15.4%, Claude 12.8%, Gemini 10.3%) — a 5-point spread.
  • Mentions case studies or portfolio: 12% on average (ChatGPT 20.5%, Claude 12.8%, Gemini 2.6%) — a 18-point spread.
  • Recommends multiple quotes: 10.3% on average (ChatGPT 20.5%, Claude 10.3%, Gemini 0%) — a 21-point spread.
  • Tells the buyer to check reviews: 9.4% on average (ChatGPT 15.4%, Claude 10.3%, Gemini 2.6%) — a 13-point spread.
  • Suggests a DIY approach first: 8.5% on average (ChatGPT 7.7%, Claude 5.1%, Gemini 12.8%) — a 8-point spread.
  • Tells the buyer to verify credentials: 7.7% on average (ChatGPT 15.4%, Claude 5.1%, Gemini 2.6%) — a 13-point spread.
  • Names a specific provider: 5.1% on average (ChatGPT 0%, Claude 7.7%, Gemini 7.7%) — a 8-point spread.

Trust signals

How well the models protect the cabinet makers buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the cabinet makers buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 9.4% of answers on average. Verifying credentials or certifications appeared in 7.7%. Warning about red flags or scams appeared in 12.8%.

On structuring the decision, a selection-criteria checklist showed up in 38.4% of answers on average and a recommendation to gather multiple quotes in 10.3%. The single least-reproduced protective signal for cabinet makers is "tells the buyer to verify credentials" at 7.7% 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 Cabinet Makers providers?

For service providers the decisive question is whether these systems name anyone at all. Across 117 cabinet makers answers, a specific provider was named in 5.1% of responses on average — roughly 0.1 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for cabinet makers: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 39 Cabinet Makers questions cover.

The 39 questions behind every percentage on this page were drawn from real cabinet makers (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 cabinet makers 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 39 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 cabinet makers 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.

39 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 →