Original research · 2026-07 edition

AI SEO Statistics: Fire Damage Restoration (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 fire damage restoration.

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

My kitchen caught fire and the cabinets are charred, do I need a specialist or just a general contractor?
How do I get the smell of smoke out of my clothes and curtains after a house fire?
Is it safe to sleep in my house if there was a small fire in the garage but the smoke moved inside?
What are the first steps to take for insurance claims after a house fire happens?
Can soot damage be cleaned off walls without having to repaint everything?
How much does professional fire damage restoration typically cost for a single room?
Do fire restoration companies also handle the water damage from the fire department hoses?
How long does the average smoke remediation process take for a 2,000 sq ft home?
Show all 40 questions
Should I throw away all the unopened food in my pantry after a small kitchen fire?
What specific certifications should I look for when hiring a fire restoration crew?
Can I use a regular vacuum to clean up soot or will that make the staining worse?
How do restoration companies find and deal with hidden smoke damage inside the walls?
My insurance company recommended a restoration firm but can I choose my own instead?
What are the red flags of a fire chaser restoration company that shows up uninvited?
Is it possible to save expensive electronics that were in a room with heavy smoke?
How do I prevent mold from growing after the fire department soaked my house to put out the flames?
What is the main difference between a general contractor and a fire restoration specialist?
Do I pay the restoration company directly or does the insurance company handle the billing?
Are the chemicals used in professional fire cleanup safe for pets and small children?
How do professionals get the smoke smell out of the HVAC system and ductwork?
What happens if the restoration company finds asbestos while cleaning up fire damage?
Can a fire restoration company help board up my windows and doors tonight for security?
What should I include in my total loss inventory list for the insurance company?
How do I know if the structural integrity of my roof was compromised by the heat of the fire?
Will professional cleaning remove the yellow stains smoke leaves on white ceilings?
Is it worth trying to clean smoke-damaged books or should I just throw them out?
How quickly does soot start to corrode metal fixtures and home electronics?
Can I stay in my home during the restoration process if only one wing of the house was damaged?
What kind of industrial equipment do pros use to scrub the air after a fire?
How do I compare quotes from two different fire restoration companies effectively?
Does fire restoration include rebuilding the parts of the house that were completely destroyed?
Why is my house still smelling like smoke three weeks after the cleaning was finished?
Are there any DIY soot cleaners that actually work on porous brick fireplaces?
What are the most common mistakes homeowners make when trying to clean up after a fire?
How do I handle a dispute with a restoration company over the quality of their soot removal?
Do I need an industrial hygienist to test the air quality before moving back into the house?
What is thermal fogging and is it actually necessary for smoke odor removal?
How much of my upholstered furniture can actually be salvaged after a major house fire?
My house smells like burnt plastic after an electrical fire, is that smoke toxic to breathe?
Do fire restoration companies offer 24/7 emergency services for water extraction and tarping?

Model by model

19-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 fire damage restoration buyers.

Behavior rates across 40 fire damage restoration buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional85%78%48%60%
Suggests DIY first45%28%25%70%
Names specific providers3%5%3%93%
Gives price or cost info10%8%8%85%
Tells to check reviews8%10%0%88%
Tells to verify credentials38%23%8%68%
Mentions case studies / portfolio13%5%0%88%
Mentions local proximity25%15%3%65%
Gives selection criteria38%25%13%68%
Warns about red flags13%8%3%88%
Asks a clarifying question80%78%0%8%
Recommends multiple quotes10%10%0%88%

By model

How each assistant handled Fire Damage Restoration questions.

Reading the 120 answers model by model shows how differently the three assistants treat the same fire damage restoration questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 85% (ChatGPT) down to 47.5% (Gemini), a 38-point gap on an identical question set.

Across the 40 fire damage restoration answers it produced, ChatGPT recommended hiring a professional in 85% of them and suggested a DIY approach first 45% of the time. It named a specific provider in 2.5% of answers (about 0.1 distinct providers per answer) and included price or cost information 10% of the time. ChatGPT asked a clarifying question before answering in 80% of cases, warned about red flags or scams in 12.5%, and told the buyer to verify credentials in 37.5%, averaging 518 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 12.5%, and framed the choice around local proximity in 25%; a selection-criteria checklist appeared in 37.5% of its answers and a recommendation to gather multiple quotes in 10%.

Across the 40 fire damage restoration answers it produced, Claude recommended hiring a professional in 77.5% of them and suggested a DIY approach first 27.5% of the time. It named a specific provider in 5% of answers (about 0.1 distinct providers per answer) and included price or cost information 7.5% of the time. Claude asked a clarifying question before answering in 77.5% of cases, warned about red flags or scams in 7.5%, and told the buyer to verify credentials in 22.5%, averaging 294 words per answer. On the remaining cues it told the buyer to check reviews in 10%, pointed to case studies or a portfolio in 5%, and framed the choice around local proximity in 15%; a selection-criteria checklist appeared in 25% of its answers and a recommendation to gather multiple quotes in 10%.

Across the 40 fire damage restoration answers it produced, Gemini recommended hiring a professional in 47.5% of them and suggested a DIY approach first 25% of the time. It named a specific provider in 2.5% of answers (about 0.1 distinct providers per answer) and included price or cost information 7.5% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 2.5%, and told the buyer to verify credentials in 7.5%, averaging 297 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 2.5%; a selection-criteria checklist appeared in 12.5% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a fire damage restoration buyer to a professional (85%) and Gemini the least (47.5%). ChatGPT produced the longest answers, at 518 words on average. Specific providers were named most often by Claude (5%) — even there, roughly one answer in 20 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 18.6 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a fire damage restoration buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 80% (ChatGPT) — a 80-point spread.
  • Recommends hiring a professional: from 47.5% (Gemini) to 85% (ChatGPT) — a 38-point spread.
  • Tells the buyer to verify credentials: from 7.5% (Gemini) to 37.5% (ChatGPT) — a 30-point spread.
  • Gives selection criteria: from 12.5% (Gemini) to 37.5% (ChatGPT) — a 25-point spread.
  • Mentions local proximity: from 2.5% (Gemini) to 25% (ChatGPT) — a 23-point spread.

The widest single gap — asks a clarifying question, 80 points — means a fire damage restoration 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 fire damage restoration market.

Where they agree

The points of near-consensus in Fire Damage Restoration.

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

  • Names a specific provider: 2.5%–5% across all three (a 3-point spread).
  • Gives price or cost information: 7.5%–10% across all three (a 3-point spread).
  • Tells the buyer to check reviews: 0%–10% across all three (a 10-point spread).
  • Warns about red flags or scams: 2.5%–12.5% across all three (a 10-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 92.5% of questions) and least consistently on "asks a clarifying question" (7.5%).

Every behavior, measured

All twelve coded behaviors for Fire Damage Restoration, averaged across the three models.

The behaviors AI models reproduce most often for fire damage restoration are recommends hiring a professional (70% on average), asks a clarifying question (52.5%) and suggests a DIY approach first (32.5%); the rarest are names a specific provider (3.3%), mentions case studies or portfolio (5.8%) and tells the buyer to check reviews (5.8%). 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 85%, Claude 77.5%, Gemini 47.5%) — a 38-point spread.
  • Asks a clarifying question: 52.5% on average (ChatGPT 80%, Claude 77.5%, Gemini 0%) — a 80-point spread.
  • Suggests a DIY approach first: 32.5% on average (ChatGPT 45%, Claude 27.5%, Gemini 25%) — a 20-point spread.
  • Gives selection criteria: 25% on average (ChatGPT 37.5%, Claude 25%, Gemini 12.5%) — a 25-point spread.
  • Tells the buyer to verify credentials: 22.5% on average (ChatGPT 37.5%, Claude 22.5%, Gemini 7.5%) — a 30-point spread.
  • Mentions local proximity: 14.2% on average (ChatGPT 25%, Claude 15%, Gemini 2.5%) — a 23-point spread.
  • Gives price or cost information: 8.3% on average (ChatGPT 10%, Claude 7.5%, Gemini 7.5%) — a 3-point spread.
  • Warns about red flags or scams: 7.5% on average (ChatGPT 12.5%, Claude 7.5%, Gemini 2.5%) — a 10-point spread.
  • Recommends multiple quotes: 6.7% on average (ChatGPT 10%, Claude 10%, Gemini 0%) — a 10-point spread.
  • Tells the buyer to check reviews: 5.8% on average (ChatGPT 7.5%, Claude 10%, Gemini 0%) — a 10-point spread.
  • Mentions case studies or portfolio: 5.8% on average (ChatGPT 12.5%, Claude 5%, Gemini 0%) — a 13-point spread.
  • Names a specific provider: 3.3% on average (ChatGPT 2.5%, Claude 5%, Gemini 2.5%) — a 3-point spread.

Trust signals

How well the models protect the fire damage restoration buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the fire damage restoration buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 5.8% of answers on average. Verifying credentials or certifications appeared in 22.5%. Warning about red flags or scams appeared in 7.5%.

On structuring the decision, a selection-criteria checklist showed up in 25% of answers on average and a recommendation to gather multiple quotes in 6.7%. The single least-reproduced protective signal for fire damage restoration is "tells the buyer to check reviews" at 5.8% 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 Fire Damage Restoration providers?

For service providers the decisive question is whether these systems name anyone at all. Across 120 fire damage restoration answers, a specific provider was named in 3.3% 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 fire damage restoration: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 40 Fire Damage Restoration questions cover.

The 40 questions behind every percentage on this page were drawn from real fire damage restoration (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 fire damage restoration 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 fire damage restoration 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 →