When we launched No Fluff, we decided to test a question every founder is quietly asking:
Can a new brand with no backlinks, history, or authority earn visibility inside ChatGPT, Perplexity, and Google’s AI results in 90 days?
The short answer: we’re about to find out.
We’ve discovered that AI doesn’t search like Google. It recalls entities it already trusts. And since 90% of B2B buyers now use AI tools like ChatGPT for early-stage research, your first impression is no longer your website. It’s whether a model knows who you are, what you do, and whether it can verify your credibility.
To break into that circle of trust, we made No Fluff our test case.
This isn’t a website launch. It’s a visibility experiment based on a 90-day sprint plan to turn an unknown name into one AI can cite confidently.
The Real Problem Isn’t SEO, It’s AI Trust
A few years ago, visibility meant ranking. Today, it means being remembered by generative engines.
If you’re still stuck on traditional SEO, it means you’re handling your marketing to search engines. The real problem is that search index pages are based on keywords and backlinks. If you don’t have that, you remain invisible. On the other hand, Generative Engine Optimization (GEO) optimizes for context, authority, and structured credibility. Yet, only few B2B companies are ready for this change, with about just 11% having most of their content ready for AI discovery.
GEO pulls from different entities like companies, experts, and data sources that they already trust enough to repeat. If your brand isn’t structured or cited in those ecosystems, it doesn’t exist in generative results.
In practice, your brand’s discoverability now depends on three things:
- Whether AI understands what you do.
- Whether it can verify you through external signals.
- Whether your content answers real buyer questions in ways that models can summarize
That’s the opportunity we’re chasing: visibility that compounds because machines can understand and recall you.
Our 90-Day Sprint in Three Phases
After months of testing and research, we distilled AI visibility down to three questions that define success for any brand:
- Can AI read us? (Structure)
- Can AI relate to us? (Content)
- Can AI trust us? (Authority)
Together, those questions define our entire sprint. It’s a three-phase framework that is built to turn theory into measurable visibility.
1. Structure for Readability
For our first ten days, we’ll focus on mapping out our visibility plan, instead of writing content. We will map the landscape based on:
- What B2B buyers actually ask inside AI tools
- Which brands do those systems already cite
- Where the gaps and low-competition prompts are
From there, we build the foundation: schema, metadata, and consistent entity references across every surface we own. By Day 10, we should have a measurable baseline, such as visibility, the signals AI associates with our market, and the next steps to take.
2. Publish What AI Can Parse
Once we have a clear structure in place, we will begin publishing consistently across every surface AI systems can scan: focused blog posts, weekly Substack updates, short-form videos, and real engagement on platforms like Reddit and LinkedIn
Every piece of content has one job: to reinforce who we are, what we know, and why that matters. The team will also spend time listening and monitoring mentions, joining conversations, and commenting where relevant.
Most importantly, we will anchor every piece on who we are, the category we serve, and a proof point of expertise. By Day 60, we expect that No Fluff should begin to show up in partial citations and related-query appearances across AI summaries.
3. Build Authority That Compounds
As we build stability with the content ecosystem, we’ll switch to building authority signals to earn trust through mentions. We know that brands with steady third-party mentions rank higher in AI-generated results, so we’re seeding that from Day 15. We will target earning citations, directories, backlinks, and expert contributions to earn validation signals. Each credible mention in publication, podcast, or partner sites becomes a trust multiplier that makes the AI system see us as a legitimate reference rather than a new entrant.
How We Measure the Lift
You don’t “rank” in AI search; you teach machines to remember you. For us, that means teaching machines not just to find us, but remember us for the right reasons. AI visibility also rewards precision, not noise. So we’ll focus on building a clear structure and credible references rather than on our publishing speed. As a result, we’ll track only what proves visibility. Our milestones would look like this:
- How often our brand shows up in prompts across ChatGPT and Perplexity
- The number of direct or indirect citations we earn
- How consistently do AI systems retrieve us when certain topics come up
- Whether our name begins to surface as a relevant entity
We’ll use tools like Perplexity Pro, Ahrefs, Prompt Layer, Writesonic, and SEMRush to see where we stand and how that changes over time. But our real yardstick is not in the dashboard, but that our ideas are making their way into AI answers. When AI systems start recalling us naturally, that’s when we know the foundation is working. That’s the lift we’re after.
Proof Before Playbooks
The biggest mistake we see is brands paying for “AI optimization” before proving what actually works.
We have a principle: We’ll never teach a framework we haven’t tested on ourselves. As much as this sprint is a build, it is also a classroom for us, as we were clocking over 100 hours to see what works. We will focus on building, testing, and validating our own visibility system first. Because frameworks built on speculation don’t hold up when algorithms shift. So here’s the simple truth we’ve learned:
- Visibility isn’t bought.
- Trust isn’t automated.
- But both can be engineered through structure and credibility.
We’re building a visibility system that can survive every algorithm shift, not because we predicted the future, but because we tested our way into it.
