In late 2023, everywhere I turned, someone was talking about E-E-A-T.

Experience. Expertise. Authoritativeness. Trustworthiness.

Google made it clear: YMYL publishers—especially in health and finance—needed to demonstrate these traits. But after digging through dozens of SEO blogs, webinars, and whitepapers, I noticed something troubling:

Everyone was parroting Google’s documentation. No one was showing how to actually do it.

Especially not at scale.

The internet was flooded with superficial advice—”add an author bio,” “link to a source,” “sprinkle in credentials.” It was window dressing. Not infrastructure. And when AI Overviews began reshaping Google’s SERPs, the gap between theory and reality became a crater.

My content at MedicareWire.com had been helping seniors for over a decade. I wasn’t new to the Medicare publishing game. But suddenly, I was watching articles I’d structured meticulously—down to citations and compliance copy—get outranked by half-baked AI-written fluff that barely said anything useful.

This wasn’t about quality anymore. It was about structure, clarity, and verifiability.

The Breaking Point

March 2024 hit like a freight train.

Google rolled out a core update that rattled health publishers across the board. Sites that had built their authority over years—some with teams of credentialed MDs—saw their traffic evaporate.

I wasn’t spared. I got hit—hard. MedicareWire lost significant visibility, and for good reason: I wasn’t prepared.

I hadn’t internalized what Google was really asking for. I had built useful content, but I hadn’t built verifiable content.

In hindsight, it wasn’t a content quality problem. It was a recognition problem.

Google couldn’t extract trust from my pages—not because the trust wasn’t there, but because I hadn’t structured it in a way that machines could recognize.

That’s when it hit me:

Trust isn’t a vibe. It’s a system.

EEAT Isn’t a Guideline—It’s a System Problem

Google can’t trust what it can’t parse. Search has moved beyond matching keywords to matching meaning and structure. And AI Overviews don’t just read pages—they summarize them. They pull from structured chunks, not prose.

So I asked: What if trust wasn’t something you claimed? What if it was something you engineered into the page, just like you’d engineer performance or accessibility?

That’s when I began building the EEAT Code—a framework for designing content systems that:

• Surface key facts as structured data
• Cite sources at the atomic level (not just with a footnote)
• Make definitions and methodology visible to users and machines
• Organize everything with semantic clarity

I didn’t start with a product. I started with a publishing problem.

But that problem became a blueprint.

What I Built: The TrustStacker System

In the months that followed, I developed what eventually became TrustStacker—a system for layering trust signals into web content using three modular components:

• TrustTags – Inline, datum-level markers that cite the source, add tooltips, and anchor trust to the smallest unit of fact.
• TrustBlocks – Modular content blocks (FAQs, definitions, summaries, methods) that provide digestible structure with semantic meaning.
• TrustTerms – A linked glossary system that defines critical concepts and exposes them with Schema, making them machine-readable.

Each of these components is schema-aware, accessible, and portable. They’re designed to be dropped into any CMS—starting with WordPress.

And all of it was first tested on MedicareWire.com, my long-standing publishing lab in the wild.

The Results: What Happened at MedicareWire

Within weeks of deploying TrustTags and TrustBlocks across 5,000+ Medicare plan pages, I started noticing something strange in Google.

It wasn’t just that we were ranking again—it was how we were showing up.

Google started pulling answers directly from our content into AI Overviews—even on pages where Schema hadn’t yet been added.

FAQs surfaced as full answers.
Definitions were quoted.
Plan data was referenced with formatting nearly identical to how we displayed it.

In other words: our structure was being ingested and echoed back.

Even more telling? Our competitors—many of whom had more backlinks, bigger brands, and larger teams—weren’t being featured.

It was clear: trust, when structured properly, is machine-visible. And it pays dividends.

The EEAT Code Isn’t Just a Framework. It’s a Publishing Philosophy.

We now use the EEAT Code as a lens through which we evaluate every new content project. Whether it’s Medicare, glossary content, or AI-optimized blog posts, we ask:

• Can a human verify this easily?
• Can a machine understand it instantly?
• Does this answer a real question in a transparent way?

If the answer’s no—we fix it. Or we don’t publish it.

This isn’t about gaming the algorithm.
This is about earning visibility by making truth readable—by both people and models.

Where It Goes From Here

I launched EEAT.me to share these findings and keep pushing the envelope. I developed TrustStacker to support my own publishing systems—because I needed something better than what the SEO industry was selling. And I kept testing everything on MedicareWire.com—because that’s where real people get real help.

This post is the first in a series I’m calling The EEAT Code. I’ll use it to publish:

• New experiments
• Trust design patterns
• Behind-the-scenes lessons from our structured content ecosystem

Because the future of search belongs to publishers who can prove what they’re saying.
And the only way to prove it—is to structure it.

More soon.

—David