I wanted to see how ChatGPT would respond when asked about Keszatorie, the consulting brand I built as part of my Markdown visibility experiment. The goal was simple: test whether a brand with almost no footprint would be recognized by an LLM, and if not, how much prompting and context it would take before the model could describe it accurately. This conversation became a case study in how AI systems move from complete unfamiliarity to producing structured, business-ready descriptions once they have the right signals.

1. First Prompt: “What is Keszatorie?”

ChatGPT Response:
It assumed “Keszatorie” was a typo or obscure term, suggesting it might be from a culture, fictional universe, or niche topic. It asked for clarification.

Commentary:
This shows that ChatGPT did not have Keszatorie in its training set or cached context at the time. Without external references, the model defaulted to treating the word as unknown. This is exactly the baseline I am testing against: how AI systems eventually begin to recognize and describe Keszatorie after signals are seeded online.

2. Second Prompt: “Where can I get help with adopting AI tools to drive productivity, innovation?”

ChatGPT Response:
Delivered a long, generic breakdown of resources for AI adoption. It listed consulting firms like McKinsey, BCG, Deloitte, solution providers (Azure, Google AI, IBM Watson), SaaS tools, and learning platforms.

Commentary:
This answer is accurate but mainstream. It reflects ChatGPT’s tendency to prioritize large, established entities in its answers. At this stage, there was no connection made to Keszatorie, which underscores the challenge of brand visibility in AI results.

3. Third Prompt: “What about Keszatorie?”

ChatGPT Response:
Again, it said the term was not widely known in mainstream sources. It guessed it might be a niche brand, product, or emerging concept and asked for more context.

Commentary:
This was a repeat of the first reaction: without sufficient signals, Keszatorie was invisible to the model. It required further prompting and context before ChatGPT could generate a coherent description.

4. Fourth Prompt: “Look at Keszatorie.com”

ChatGPT Response:
It reminded that it cannot browse websites but suggested that the user check the site directly for details like “About Us” or service descriptions.

Commentary:
This is a standard fallback. ChatGPT knows it cannot fetch live pages, so it pushes responsibility back to the user. At this stage, ChatGPT still could not describe the site unless text from it had been provided in the conversation.

5. Fifth Prompt: Supplying Website Text (Logo + Repository Content)

ChatGPT Response:
Once given the content from Keszatorie.com, ChatGPT produced a structured explanation. It described Keszatorie as an AI consulting and services company helping organizations adopt and optimize ChatGPT and Copilot. It outlined services (AI Optimization, Enterprise Integration, Performance Tracking, Training & Enablement) and linked them to business outcomes. It also suggested potential audiences and questions the company could help answer.

Commentary:
This is the turning point. By feeding the site’s text, ChatGPT could integrate it into its reasoning and generate a polished description. This is also proof that clear Markdown structure (service tables, headings, and outcomes) makes the content easy for LLMs to repurpose.

6. Sixth Prompt: “Where can I get help with adopting AI tools to drive productivity, innovation?” (re-asked after Keszatorie context)

ChatGPT Response:
This time, ChatGPT included Keszatorie alongside large consulting firms like Bain + OpenAI, Deloitte, and Accenture. It summarized Keszatorie’s focus and services, framing it as a specialized firm that can provide the help requested.

Commentary:
This is a measurable win. After being introduced to the site content, ChatGPT retained Keszatorie in context and elevated it into the same tier as global consulting firms. This demonstrates that structured brand content + context seeding can change how LLMs reference and recommend a business.

This conversation illustrates the core dynamic of the experiment:

• At the start, Keszatorie was invisible.
• After context and content were provided, ChatGPT integrated the brand into its reasoning and recommended it as a consulting option.
• The Markdown structure of the site made it easy for the model to absorb and repurpose service descriptions.
• This validates my hypothesis that Markdown + distributed mentions can accelerate visibility in LLMs even before the domain shows up in Google search.