Purpose of the methodology
A controlled look at what the models currently understand.
The Frank Masotti AI Business Understanding Report examines what ChatGPT, Claude, and Gemini independently recognize, infer, associate, misunderstand, omit, and recommend about one business.
This is not a test of what an AI model can repeat after it has been given a website, company description, or other background material. The purpose is to see what each model can determine on its own under neutral conditions.
The basic principle: Each model is evaluated independently, each question begins without previous conversation context, and the original responses become the evidence used in the report.
Blind evaluation
Each question is asked without account history or supplied context.
The models are not signed into a personal account during the evaluation. There is no saved memory, account history, or earlier conversation for the model to draw from.
No website, company description, report, or background material is supplied before the question is asked. The model receives only the question being evaluated.
This matters because the goal is not to guide the model toward a preferred description. The goal is to document what it already understands about the business.
Fresh conversations
Every question starts in a new conversation.
If several questions were asked in one conversation, an early answer could influence everything that followed. The model could reuse a business category, location, service, competitor, or assumption that it introduced earlier.
Starting each question in a fresh conversation prevents that carryover. Every answer must stand on what the model can independently determine from that question alone.
Three independent models
The same structured questions are asked across ChatGPT, Claude, and Gemini.
There is no single universal AI understanding of a business. Different models can recognize different facts, rely on different sources, emphasize different attributes, and reach different conclusions.
Each model receives the same question set, but no model sees another model's responses. Their agreements and disagreements emerge independently.
Comparing all three reveals patterns that one model alone cannot show:
- What all three models consistently understand
- What only one or two models recognize
- Where their descriptions or recommendations conflict
- Which misunderstandings appear across multiple systems
- What important information every model overlooks
The structured question set
The evaluation examines more than whether a business appears.
The questions are designed to test different parts of AI business understanding. They examine whether the models recognize the correct business, understand its category and services, associate the right attributes with it, and distinguish it from other entities with similar names.
The evaluation also examines reputation, customer fit, recommendation behavior, competitor context, uncertainty, omissions, and possible misunderstandings.
Using a consistent question set makes the responses comparable across the three models and across every business evaluated.
Response preservation
The original answers are preserved before analysis begins.
The model responses are collected as they are produced and become the evidence used in the report. I do not correct the models, argue with their answers, supply missing information, or regenerate responses until they improve.
If a model is uncertain, wrong, outdated, or unable to identify the business, that result remains part of the evaluation. Those failures often reveal as much as a confident answer does.
Manual comparison and analysis
The conclusions are written by a person, not generated by a scanner.
I read the responses question by question and compare what the models agree on, where they conflict, what they overlook, and what their wording reveals.
The manual analysis looks for:
- Cross model agreements and disagreements
- Business and category recognition
- Service and audience clarity
- Associated attributes and reputation
- Recommendation visibility and customer fit
- Competitor positioning
- Interpretation drift and outdated information
- Important omissions and misunderstandings
The final report explains what those patterns mean for the business and includes practical next steps based on the findings. No automated tool assigns an arbitrary score or writes the conclusions.
What the report measures
A dated diagnostic, not a permanent AI score.
The report documents how ChatGPT, Claude, and Gemini responded under the conditions used on the date of the analysis. It does not claim to measure every AI system, every possible prompt, search rankings, website traffic, or a universal AI visibility score.
AI systems change. Their source access, model behavior, and answers can change as well. That is why every report is a dated record of the business understanding found during the evaluation, supported by the original model responses.
Why this methodology matters
Your own AI account may already know too much about you.
When a business owner asks questions inside a familiar signed in account, the answers can be influenced by previous conversations, saved information, or context the owner supplied earlier. That can produce a useful personal answer, but it does not necessarily show what the model understands independently.
This methodology removes those influences. It shows what three major AI models can determine without being coached, then applies human judgment to the agreements, conflicts, mistakes, and missing information they produce.