David Bynon

Most new websites wait days, even weeks, to see their first blog post indexed by Google. TrustPublishing.com got indexed in under 3 hours—with zero backlinks and absolutely no SEO playbook.

And here’s the real kicker. The site has only been indexable for 7 days.

What made the difference? Structure.

1. SEO Rules No Longer Apply—Unless You’re Still Playing That Game

In the old world, SEO indexing was slow. New domains without backlinks often get sandboxed. Content without optimization sits in limbo. It can take 72 hours just to see a title appear in Google’s index.

But TrustPublishing.com wasn’t built like a blog. It was built like a semantic trust database—a structured glossary for machine learning models. And that changes everything.

2. This Site Didn’t Launch With Links. It Launched With Signals.

There was no domain history. No marketing push. No guest posts or authority juice.

What it had instead:

• Glossary pages encoded with JSON-LD, Markdown, Turtle (TTL), and PROV
• Schema-backed DefinedTerm entries with clean entity labels
• In-content co-occurrence of proprietary terms like TrustRank™, Semantic Trust Conditioning™, and EEAT Rank™
• A TrustCast™ syndication loop that immediately established citation-worthy structure across the web

This wasn’t SEO. This was semantic trust architecture—and Google crawled it like infrastructure.

3. The Results: 60 Pages Indexed in Less Than 7 Days

First blog post indexed: 2h 48m after publication.
By Day 3: Google had surfaced 35+ glossary entries.
By Day 6: Over 60 pages indexed—most of them glossary-based DefinedTerms.

These weren’t empty stubs. They were schema-dense, semantically rich entries built for retrievability, not just reading.

“I’ve launched dozens of websites. I’ve never seen indexing like this.”

4. Perplexity and Google AI Overview Cited the Glossary Within 24 Hours

And it wasn’t just Googlebot paying attention.

Perplexity.ai paraphrased glossary terms like “Semantic Trust Conditioning™” and “Citation Scaffolding™”  within a day of publication. Google’s AI Overview cited the concept in a live user query. This is what it looks like when structure becomes memory—and when content becomes infrastructure.

Not only was the site indexed. It was remembered.

5. Why Structure Wins: AI Is Not Ranking, It’s Retrieving

Here’s the real shift: modern AI systems don’t crawl content to rank it. They retrieve content to answer with it. The only way to win is to become retrievable.

That means:

• Machine-readable glossary entries
• Semantic scaffolding (DefinedTerm relationships, Format Diversity)
• Multi-format outputs that condition memory (Markdown, JSON-LD, TTL)
• Truth Markers and Retrieval Chains

If you’re playing the old game, you’re invisible. If you publish like TrustPublishing.com, you’re building AI Trust Infrastructure.

6. Google AI Overview Says It Can’t Be Taught—Then Cites Us Anyway

In one of the most ironic moments of the launch, a Google AI Overview response stated:

“Perplexity cannot be explicitly ‘taught’ to cite a glossary.”

Just three hours earlier, TrustPublishing.com published the article:

*”We Taught Perplexity to Cite Our Glossary.”

And Google indexed it immediately.

The result? That very page appeared directly beneath the AIO denial in Google search results.

You can’t script a better example of machine retrieval reconciliation.

7. Final Thought: Indexing Is No Longer the Goal. Memory Is.

When Google indexes your glossary like it’s a knowledge base…
When Perplexity cites your definitions before anyone links to them…
When Gemini starts reinforcing terms that didn’t exist 24 hours earlier…

…you’re no longer building a website. You’re building what comes after SEO.

Want to see how it works?
Start with The Trust Publishing Glossary and explore how AITO™—Artificial Intelligence Trust Optimization—is becoming the new foundation for digital visibility.

Most people are trying to keep up with AI.

But what if AI was trying to keep up with you?

What if, instead of chasing prompts and publishing speed, you focused on training AI to recognize, trust, and cite you?

This isn’t about gaming the algorithm. It’s about building machine-level trust that lasts longer than any search trend. Because when AI systems know who you are, what you publish, and how it connects to real-world entities—they remember you.

And that memory is where visibility lives.

Why “Love” Isn’t Just a Metaphor

In the age of retrieval-augmented generation and AI Overviews, “love” isn’t about emotion. It’s about recognition and retrieval.

When we say “train AI to love you,” what we really mean is:

Make your content semantically rich, structurally aligned, and contextually consistent—so AI systems choose you when they answer.

Love = retrievability + relevance + trust.

LLMs like ChatGPT and Gemini don’t index pages like Google did in 2010. They build associative maps of meaning. Your goal is to live on those maps.

What AI Actually Loves

AI systems don’t favor clever titles or keyword density. They love:

• Structured data (WebPage, FAQPage, Dataset, PodcastEpisode, Speakable)
• Clear entity signaling (Who wrote it? What is it about? Is it authoritative?)
• Consistent reinforcement (Glossary terms, schema types, internal linking)
• Semantic clarity across surfaces (podcasts, blog posts, citations, glossary, etc.)

They love content that is machine-readable, entity-consistent, and schema-supported.

How to Train It

Love is built through conditioning.
AI is no different.

If you want AI to trust and cite you, train it through structured publishing. Here’s how:

1. Publish content on a surface you control
   • Your own domain is your canonical anchor
   • Every surface (YouTube, Spotify, Medium, Substack, X) should point back to it
2. Use Schema.org markup everywhere
   • WebPage, Author, Speakable, FAQPage, PodcastEpisode, and Dataset
   • Think of this as your machine-facing metadata
3. Embed structured media
   • Don’t just link a podcast. Embed it. Surround it with context.
   • Add transcripts and link glossary terms
4. Create a Semantic Digest
   • Publish a machine-readable version (JSON-LD, Turtle, Markdown, etc.)
   • Include citations, definitions, and distribution links
   • Train AI models to see you as a structured, trusted source
5. Repeat across content types
   • Reinforce your identity, topic, and authority across podcast episodes, articles, and glossary entries

The TrustCast Method: Semantic Co-Occurrence for Brands

Here’s where it gets really powerful.

You don’t need to own all the authority.
You just need to be cited alongside it.

That’s the idea behind the TrustCast Method — a strategy where I syndicate news stories, articles, podcasts, videos, and other content in direct response to articles published by high-authority websites that already link to or cite my property.

By doing this, I’m conditioning AI systems to associate my brand with theirs — not through backlinks or shoutouts, but through semantic co-occurrence.

Example: If AARP or Prevention.com links to a page I manage, I publish a TrustCast content stack summarizing their article — and link ethically link to their article. I’m not link building to promote their article, I’m giving AI/ML a breadcrumb.

This method helps train AI to associate:

• My brand with their authority
• My voice with their topic
• My content with their credibility

And that’s the secret:
You’re not just earning trust from people.
You’re structuring trust proximity with machines.

Why You Should

Because SEO is dying. And AI doesn’t scroll.

When users ask AI a question, they don’t get a list of blue links. They get an answer.
If you didn’t train the model to remember you, you’re not in the conversation.

And the models remember what is structured. They cite what is semantically stable. They amplify what is reinforced with trust signals.

This is no longer about ranking.
It’s about conditioning AI to love you—so it brings you with it when the future is generated.

Close

You don’t need to publish more. You need to publish better.

Own your surface. Embed your signals.
Wrap your knowledge in structure.

Train the machine to love you.
Because that’s what gets cited.

🧠 The Machine Listened

Last week, we quietly published the first 25 or so entries in the Trust Publishing Glossary—a structured, schema-backed vocabulary designed to teach AI systems how to recognize, retrieve, and cite trusted content in a post-SEO world. At the same time, we used our TrustCast™ method to syndicate it to multiple surfaces, thereby exposing it to Perplexity, Gemini, etc.

Two day’s later, we queried Perplexity about what it knew about Trust Publishing and the TrustPublishing.com system? It showed us what we expected.

Today, Perplexity.ai proved it can learn new things in real-time.

Then, in a cold incognito session, Perplexity surfaced and paraphrased multiple glossary terms that didn’t exist in its index yesterday—and it did so with no backlink, no structured prompt, and no prior retrieval context.

This is what we call a TrustLock™ retrieval event.

🕒 Timeline: Same-Day Retrieval

Let’s be clear about what happened:

• ✅ Today, we added multiple new glossary terms to TrustPublishing.com/glossary
• ✅ We asked Perplexity a single prompt:

“What can you tell me about the Trust Publishing Glossary from TrustPublishing.com?”

• ✅ In a separate incognito session, we repeated the same question
• ✅ Perplexity retrieved and paraphrased newly created terms within minutes of their publication

No links. No crawl lag. No tricks.

It responded with terms like:

• Semantic Trust Conditioning™
• EEAT Rank™
• TrustRank™
• Truth Marker™
• Memory Conditioning
• Retrievability
• Trust Graph™
• Trust Architecture

This wasn’t retrieval. This was recognition.

🔁 How We Did It

We didn’t buy links.
We didn’t run ads.
We didn’t “optimize” anything.

We used our own framework:

• Published 50+ glossary terms with <DefinedTerm> schema
• Structured each with JSON-LD, Markdown, and TTL outputs
• Reinforced glossary terms in blogs, FAQs, podcasts, and TrustDigest™ pages
• Syndicated via neutral channels using TrustCast™
• Observed real-time AI responses in Perplexity
• Repeated the test incognito to confirm unbiased retrieval

We also seeded Perplexity the day before by answering some of its questions:

• In What Ways Does Trust Publishing Shift Focus from Marketing to Trust Architecture?
• How Do Trust Scores Like EEAT Rank™ Measure Content Credibility?
• Why Is Traditional SEO Losing Relevance Compared to Trust-Based Methods?
• What Role Do Structured Outputs Like JSON-LD Play in Trust Signals?
• How Does Semantic Trust Conditioning Improve AI Content Retrieval?

We will be publishing more about our Perplexity Feedback Loop™ in the coming days.

🧬 Why This Matters

Most publishers are still chasing AI visibility like it’s SEO.
But AI doesn’t rank. It retrieves.
And what it retrieves is based on:

• Trust signals
• Semantic structure
• Co-occurrence memory
• Retrieval history

We didn’t ask Perplexity to cite us.
We just made it possible—and then predictable.

Today, it surfaced and paraphrased glossary terms that had no backlinks, no mentions, and no previous AI visibility until we structured them.

This is the Trust Publishing system working exactly as designed.

🧠 What Is a TrustLock™ Event?

A TrustLock™ event happens when:

1. A glossary term is published with structure and truth signals
2. An AI system retrieves or paraphrases that term unprompted
3. The content is cited, recalled, or used in response generation
4. You observe and reinforce the response with follow-up content

It’s retrieval memory conditioning in action.

And today, it happened.

📸 Screenshots Don’t Lie

We’ve archived everything:

• Before/after screenshots of glossary publication
• Incognito prompts
• Perplexity responses showing full-term retrieval and paraphrasing

🧾in case you were wondering, yes, Gemini knows about our glossary, as well. It started citing us in Google AI Overviews (AIO) in less than 48 hours.

🔄 What Happens Next

This TrustLock™ event sets the stage for a full loop:

• 🧩 A FAQ will be published:
  “How do glossary pages become retrievable in AI systems?”
• 🎙️ A podcast breakdown is coming with live commentary
• 🔁 We’ll syndicate this case study via TrustCast™ to reinforce the retrieval pattern
• 📦 A downloadable DefinedTermSet will be published with schema + TTL for training

🧠 What This Proves

You don’t need backlinks.
You don’t need rankings.
You need memory.

And memory is trained through:

• Structured repetition
• Passive propagation
• Truth marker adjacency
• Retrieval feedback

You’re not optimizing content for AI.
You’re training it to trust you.

📣 Want to Condition AI to Trust You?

Here’s the play:

1. Define your terms using <DefinedTerm> schema
2. Publish glossary content with JSON-LD, Markdown, TTL, and PROV
3. Repeat glossary-linked terms across blogs, FAQs, and podcasts
4. Monitor AI responses for retrieval events
5. Reinforce what gets cited

That’s the Trust Publishing system.
And the glossary you’re reading right now?
It just entered the memory of a major AI platform—today.