AI SEO Statistics: Notary (2026-07 edition)

Reading the 120 answers model by model shows how differently the three assistants treat the same notary questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 62.5% (ChatGPT) down to 30% (Gemini), a 33-point gap on an identical question set.

Across the 40 notary answers it produced, ChatGPT recommended hiring a professional in 62.5% of them and suggested a DIY approach first 12.5% of the time. It named a specific provider in 17.5% of answers (about 0.6 distinct providers per answer) and included price or cost information 12.5% of the time. ChatGPT asked a clarifying question before answering in 77.5% of cases, warned about red flags or scams in 15%, and told the buyer to verify credentials in 22.5%, averaging 348 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 47.5%; a selection-criteria checklist appeared in 25% of its answers and a recommendation to gather multiple quotes in 2.5%.

Across the 40 notary answers it produced, Claude recommended hiring a professional in 50% of them and suggested a DIY approach first 12.5% of the time. It named a specific provider in 20% of answers (about 0.4 distinct providers per answer) and included price or cost information 15% of the time. Claude asked a clarifying question before answering in 62.5% of cases, warned about red flags or scams in 5%, and told the buyer to verify credentials in 7.5%, averaging 291 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 30%; a selection-criteria checklist appeared in 15% of its answers and a recommendation to gather multiple quotes in 2.5%.

Across the 40 notary answers it produced, Gemini recommended hiring a professional in 30% of them and suggested a DIY approach first 2.5% of the time. It named a specific provider in 15% of answers (about 0.5 distinct providers per answer) and included price or cost information 15% of the time. Gemini asked a clarifying question before answering in 2.5% of cases, warned about red flags or scams in 0%, and told the buyer to verify credentials in 2.5%, averaging 281 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 12.5%; a selection-criteria checklist appeared in 5% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a notary buyer to a professional (62.5%) and Gemini the least (30%). ChatGPT produced the longest answers, at 348 words on average. Specific providers were named most often by Claude (20%) — even there, roughly one answer in 5 carried a name.

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

• Mentions case studies or portfolio: 0% across all three models.
• Gives price or cost information: 12.5%–15% across all three (a 3-point spread).
• Recommends multiple quotes: 0%–2.5% across all three (a 3-point spread).
• Names a specific provider: 15%–20% across all three (a 5-point spread).

Measured question by question, the three assistants coded a response the same way most consistently on "mentions case studies or portfolio" (identical coding in 100% of questions) and least consistently on "asks a clarifying question" (10%).

Beyond whether to hire, the rubric codes how carefully each assistant protects the notary 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 10.8%. Warning about red flags or scams appeared in 6.7%.

On structuring the decision, a selection-criteria checklist showed up in 15% of answers on average and a recommendation to gather multiple quotes in 1.7%. The single least-reproduced protective signal for notary is "recommends multiple quotes" at 1.7% 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 notary (legal 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 notary 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.