Excited to share our first newsletter on the zero-to-visible in AI 90-day experiment!
This is a live experiment testing what it actually takes for a new brand to appear inside AI search (like ChatGPT, Perplexity, Gemini, etc). We’re treating No Fluff as a real-time case study and documenting every step to understand what it takes to become the answer in AI results.
Before we went live, we had a rare opportunity that most brands don’t get. We weren’t inheriting a legacy site or undoing years of mixed signals; we were building the foundation for how AI would read a B2B brand before it entered the market.
So let’s talk about the prework:
Learned How LLMs Source and Cite Information
Before launching anything, we spent close to 50 hours (aiming for 100!) digging into how large language models work right now, before writing or building anything. It boils down to a few key concepts:
- Training data: what AI already knows
- Grounding data: how AI verifies accuracy
- RAG (Retrieval-Augmented Generation): how AI stays current
- Understanding these mechanics comes before influence.
Tested AI Visibility Technology
Spoiler alert! Here’s the part most AI search tech providers don’t say out loud: leading AI search engines (ChatGPT, Gemini, more) do not share raw prompt data (the exact text a user types in) with third-party platforms. Not SEMrush. Not Scrunch. Not Writesonic. Not Profound. None.
Privacy, IP protection, and model security make that data inaccessible. So when a platform says it “tracks real prompts,” it’s usually inferred; built from seed keywords, semantic expansion, buyer intent, and LLM-generated variants. Read about it in “Are AI SEO Tools Really Tracking Prompts or Just Keywords?”
Market Research & Entity Mapping
ChatGPT, Gemini, and Perplexity don’t read your site as humans do. They connect facts, names, and relationships into a knowledge network. If those connections are missing or inconsistent, your brand won’t appear; no matter how much content you publish. Those connections are called entities. We defined and structured No Fluff’s core entities (semantic triples, entity relationships, and attributes) so AI can understand who we are. We even published the exact 7-step entity-mapping worksheet we used.
Built the AI Visibility System
Our research led us to three core layers that must work together for AI systems to understand and surface a brand. All of this is driven by upfront analysis and prompt engineering:
- Understanding (Can AI recognize you?) Make your brand recognizable to machines through clear structure, schema, and fully crawlable content.
- Validation (Does AI trust you?) Prove credibility with consistent, trusted third-party mentions and aligned brand details across the web.
- Explanation (Does AI have something to repeat?) Create plain-language, structured content that answers real questions AI is likely to surface and repeat.
And that’s a wrap! We’re documenting everything, so check out the links above. Next week, we’ll dig into the technical side: prompt engineering and website setup, from schema to LLMs.txt.
Catch up on all our insights
Stat of the Week
Since our brand just launched, I can proudly say we rank zero in AI search. Instead, here’s a more interesting AI-visibility stat.
80% of buyers now rely on AI-generated or zero-click results for research (Bain & Co)
Fresh Reads
Each newsletter, we’ll share our favorite articles and breaking news.
Why AI Brand Mentions Are Becoming a Business Metric (Entrepreneur)
Google Launches Gemini 3 (News Direct from Google)
AI Update, December 5, 2025: AI News and Views From the Past Two Weeks (Marketing Profs)
As always, reach out anytime at katie@nofluffmktg.com if you’ve got questions or if you’re building your own visibility story too.
Frequently Asked Questions About Zero-to-Visible and No Fluff
What is the Zero-to-Visible experiment?
Zero-to-Visible is an ongoing experiment that documents how a new brand becomes discoverable, explainable, and citable by AI systems. It tracks which signals influence whether AI systems recognize and recommend a company during early buyer discovery.
What does “AI visibility” mean in Zero-to-Visible?
In Zero-to-Visible, AI visibility means whether AI systems can recognize a brand, accurately explain what it does, and include it in AI-generated answers when buyers ask category-level questions.
Who is running the Zero-to-Visible experiment?
The Zero-to-Visible experiment is run by No Fluff, a B2B growth firm focused on how AI systems recognize, explain, and recommend brands in AI-generated answers.
Why is No Fluff running this experiment publicly?
No Fluff runs Zero-to-Visible publicly to document real-world signals that influence AI visibility. The goal is to replace speculation about AI search with observable patterns and repeatable methods.
Are the results specific to one AI platform?
No. Zero-to-Visible observes patterns across multiple AI systems, including retrieval-based and generative models. While individual outputs vary, consistent signals tend to produce similar visibility outcomes across platforms.
Can other B2B brands replicate these results?
Yes. When the same structural signals—clear brand definitions, consistent category positioning, structured content, and third-party validation—are applied consistently, similar AI visibility patterns can be reproduced.
Does Zero-to-Visible replace SEO?
No. Zero-to-Visible shows how AI visibility builds on SEO foundations. SEO enables access to content, while Zero-to-Visible focuses on whether AI systems trust, explain, and recommend a brand once that access exists.
