Original research · 2026-07 edition

AI SEO Statistics: Scaffolding (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 scaffolding.

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

How high can I legally go with a ladder before I'm required to use professional scaffolding for a chimney repair?
Is it cheaper to hire a scaffolding company or just rent the equipment and build it myself?
What is the average weekly hire rate for a basic two-story scaffold on a residential street?
Do I need to apply for a council permit if the scaffolding legs are going to be on a public sidewalk?
How do I verify if a scaffolding contractor has the right public liability insurance for home services?
Can a scaffolding structure be safely built on a steep slope or tiered garden?
What are the mandatory safety features I should look for like handrails and toe boards?
How long does it typically take a crew to set up scaffolding for a standard semi-detached house?
Show all 40 questions
Are there extra fees for 'over-roof' scaffolding structures to reach a ridge tile?
What happens if the scaffolding poles leave permanent marks or damage on my driveway?
Does the scaffolding company usually handle the local authority permits or is that the homeowner's job?
Is it safe to leave scaffolding up for six months during a slow DIY renovation project?
What's the difference between tube and fitting scaffolding versus system scaffolding for a house extension?
How can scaffolding be installed if I have a glass conservatory blocking access to the back wall?
Why do most scaffolding companies have a minimum four-week hire charge even if I only need it for three days?
How do I check if a local scaffolding firm is NASC registered or has equivalent safety credentials?
Do I need to inform my home insurance company that I'm having scaffolding erected on my property?
What are the red flags to watch out for while the crew is assembling the scaffold towers?
Can I just hire a mobile scaffold tower for a weekend painting job instead of a fixed structure?
How much does it cost to add debris netting or weather protection sheeting to a scaffold quote?
Is it standard practice for a scaffolding firm to ask for the full payment before the boards are even down?
What should I do if the scaffold feels like it's swaying or unstable when I'm walking on it?
Can scaffolding be built over a neighbor's fence if they give me verbal permission?
How many boards wide does a residential walkway need to be to meet safety regulations?
Does a metal scaffold need to be grounded or earthed against potential lightning strikes?
What measures can be taken to stop burglars from using the scaffolding to climb into my upstairs windows?
If my roofer says they provide their own scaffolding, should I still ask to see their specific safety certifications?
How much extra does it cost to have the scaffold modified or moved once it's already been built?
What exactly is a 'lift' in a scaffolding quote and how many do I need for a gutter replacement?
Are there specialized scaffolding setups designed specifically for solar panel installations?
Will a scaffolding company work in heavy rain or high winds or will my project be delayed?
Do I need an internal birdcage scaffold for a renovation in a room with vaulted ceilings?
What are the typical lead times for booking a reputable scaffolding crew during the peak summer season?
Should I receive a formal handover certificate once the scaffold is completed and ready for use?
Is a scaffold tower more stable than a traditional pole-and-plank setup for a small gable end repair?
How do I compare two quotes when one is significantly cheaper but doesn't mention safety inspections?
Are there hidden costs like 'dismantle fees' or 'delivery surcharges' that aren't usually in the initial estimate?
Am I legally allowed to use the scaffolding myself for DIY tasks while the main contractors are off-site?
How much clearance do the workers need to carry poles through a side alley to reach my back garden?
Do I need a structural engineer to sign off on scaffolding if my house is a fragile historic building?

Model by model

20-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 scaffolding buyers.

Behavior rates across 40 scaffolding buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional60%50%15%43%
Suggests DIY first8%3%3%90%
Names specific providers0%0%0%100%
Gives price or cost info13%5%15%83%
Tells to check reviews8%3%0%93%
Tells to verify credentials40%25%8%53%
Mentions case studies / portfolio3%0%0%98%
Mentions local proximity15%20%5%68%
Gives selection criteria35%28%18%50%
Warns about red flags8%18%13%83%
Asks a clarifying question70%73%0%8%
Recommends multiple quotes10%15%0%78%

By model

How each assistant handled Scaffolding questions.

Reading the 120 answers model by model shows how differently the three assistants treat the same scaffolding questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 60% (ChatGPT) down to 15% (Gemini), a 45-point gap on an identical question set.

Across the 40 scaffolding answers it produced, ChatGPT recommended hiring a professional in 60% of them and suggested a DIY approach first 7.5% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 12.5% of the time. ChatGPT asked a clarifying question before answering in 70% of cases, warned about red flags or scams in 7.5%, and told the buyer to verify credentials in 40%, averaging 443 words per answer. On the remaining cues it told the buyer to check reviews in 7.5%, pointed to case studies or a portfolio in 2.5%, and framed the choice around local proximity in 15%; a selection-criteria checklist appeared in 35% of its answers and a recommendation to gather multiple quotes in 10%.

Across the 40 scaffolding answers it produced, Claude recommended hiring a professional in 50% of them and suggested a DIY approach first 2.5% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 5% of the time. Claude asked a clarifying question before answering in 72.5% of cases, warned about red flags or scams in 17.5%, and told the buyer to verify credentials in 25%, averaging 283 words per answer. On the remaining cues it told the buyer to check reviews in 2.5%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 20%; a selection-criteria checklist appeared in 27.5% of its answers and a recommendation to gather multiple quotes in 15%.

Across the 40 scaffolding answers it produced, Gemini recommended hiring a professional in 15% of them and suggested a DIY approach first 2.5% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 15% 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 269 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 17.5% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a scaffolding buyer to a professional (60%) and Gemini the least (15%). ChatGPT produced the longest answers, at 443 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 19.9 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a scaffolding buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 72.5% (Claude) — a 73-point spread.
  • Recommends hiring a professional: from 15% (Gemini) to 60% (ChatGPT) — a 45-point spread.
  • Tells the buyer to verify credentials: from 7.5% (Gemini) to 40% (ChatGPT) — a 33-point spread.
  • Gives selection criteria: from 17.5% (Gemini) to 35% (ChatGPT) — a 18-point spread.
  • Mentions local proximity: from 5% (Gemini) to 20% (Claude) — a 15-point spread.

The widest single gap — asks a clarifying question, 73 points — means a scaffolding 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 scaffolding market.

Where they agree

The points of near-consensus in Scaffolding.

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

  • Names a specific provider: 0% across all three models.
  • Mentions case studies or portfolio: 0%–2.5% across all three (a 3-point spread).
  • Suggests a DIY approach first: 2.5%–7.5% across all three (a 5-point spread).
  • Tells the buyer to check reviews: 0%–7.5% across all three (a 8-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 Scaffolding, averaged across the three models.

The behaviors AI models reproduce most often for scaffolding are asks a clarifying question (47.5% on average), recommends hiring a professional (41.7%) and gives selection criteria (26.7%); the rarest are names a specific provider (0%), mentions case studies or portfolio (0.8%) and tells the buyer to check reviews (3.3%). 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:

  • Asks a clarifying question: 47.5% on average (ChatGPT 70%, Claude 72.5%, Gemini 0%) — a 73-point spread.
  • Recommends hiring a professional: 41.7% on average (ChatGPT 60%, Claude 50%, Gemini 15%) — a 45-point spread.
  • Gives selection criteria: 26.7% on average (ChatGPT 35%, Claude 27.5%, Gemini 17.5%) — a 18-point spread.
  • Tells the buyer to verify credentials: 24.2% on average (ChatGPT 40%, Claude 25%, Gemini 7.5%) — a 33-point spread.
  • Mentions local proximity: 13.3% on average (ChatGPT 15%, Claude 20%, Gemini 5%) — a 15-point spread.
  • Warns about red flags or scams: 12.5% on average (ChatGPT 7.5%, Claude 17.5%, Gemini 12.5%) — a 10-point spread.
  • Gives price or cost information: 10.8% on average (ChatGPT 12.5%, Claude 5%, Gemini 15%) — a 10-point spread.
  • Recommends multiple quotes: 8.3% on average (ChatGPT 10%, Claude 15%, Gemini 0%) — a 15-point spread.
  • Suggests a DIY approach first: 4.2% on average (ChatGPT 7.5%, Claude 2.5%, Gemini 2.5%) — a 5-point spread.
  • Tells the buyer to check reviews: 3.3% on average (ChatGPT 7.5%, Claude 2.5%, Gemini 0%) — a 8-point spread.
  • Mentions case studies or portfolio: 0.8% on average (ChatGPT 2.5%, Claude 0%, Gemini 0%) — a 3-point spread.
  • Names a specific provider: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).

Trust signals

How well the models protect the scaffolding buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the scaffolding buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 3.3% of answers on average. Verifying credentials or certifications appeared in 24.2%. Warning about red flags or scams appeared in 12.5%.

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 8.3%. The single least-reproduced protective signal for scaffolding is "tells the buyer to check reviews" at 3.3% 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 Scaffolding providers?

For service providers the decisive question is whether these systems name anyone at all. Across 120 scaffolding 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 scaffolding: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 40 Scaffolding questions cover.

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