AI SEO Statistics: Fire Protection (2026-07 edition)

Reading the 120 answers model by model shows how differently the three assistants treat the same fire protection questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 82.5% (ChatGPT) down to 47.5% (Gemini), a 35-point gap on an identical question set.

Across the 40 fire protection answers it produced, ChatGPT recommended hiring a professional in 82.5% of them and suggested a DIY approach first 25% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 25% of the time. ChatGPT asked a clarifying question before answering in 75% of cases, warned about red flags or scams in 15%, and told the buyer to verify credentials in 55%, averaging 521 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 12.5%, and framed the choice around local proximity in 50%; a selection-criteria checklist appeared in 65% of its answers and a recommendation to gather multiple quotes in 30%.

Across the 40 fire protection answers it produced, Claude recommended hiring a professional in 67.5% of them and suggested a DIY approach first 20% 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 22.5% of the time. Claude asked a clarifying question before answering in 67.5% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 27.5%, averaging 291 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 0%, and framed the choice around local proximity in 45%; a selection-criteria checklist appeared in 40% of its answers and a recommendation to gather multiple quotes in 22.5%.

Across the 40 fire protection answers it produced, Gemini recommended hiring a professional in 47.5% of them and suggested a DIY approach first 10% of the time. It named a specific provider in 12.5% of answers (about 0.6 distinct providers per answer) and included price or cost information 17.5% of the time. Gemini asked a clarifying question before answering in 7.5% of cases, warned about red flags or scams in 10%, and told the buyer to verify credentials in 17.5%, averaging 276 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 25%; a selection-criteria checklist appeared in 25% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a fire protection buyer to a professional (82.5%) and Gemini the least (47.5%). ChatGPT produced the longest answers, at 521 words on average. Specific providers were named most often by Gemini (12.5%) — even there, roughly one answer in 8 carried a name.

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

• Gives price or cost information: 17.5%–25% across all three (a 8-point spread).
• Tells the buyer to check reviews: 5%–12.5% across all three (a 8-point spread).
• Warns about red flags or scams: 10%–20% across all three (a 10-point spread).
• Names a specific provider: 0%–12.5% across all three (a 13-point spread).

Measured question by question, the three assistants coded a response the same way most consistently on "tells the buyer to check reviews" (identical coding in 90% of questions) and least consistently on "asks a clarifying question" (17.5%).

Beyond whether to hire, the rubric codes how carefully each assistant protects the fire protection buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 8.3% of answers on average. Verifying credentials or certifications appeared in 33.3%. Warning about red flags or scams appeared in 15%.

On structuring the decision, a selection-criteria checklist showed up in 43.3% of answers on average and a recommendation to gather multiple quotes in 17.5%. The single least-reproduced protective signal for fire protection is "tells the buyer to check reviews" at 8.3% on average — the clearest opening for content that supplies it, since the models are not yet reliably surfacing that guidance on their own.

The 40 questions behind every percentage on this page were drawn from real fire protection (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 protection 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.