Category: Semantic & Trust Architecture

JSON LD is wonderful. It is the W3C standard for structured data on websites. It works with Google, Bing, and most search engines.
But your brand lives far beyond your website.
LinkedIn does not read your JSON LD. Twitter (X) does not. YouTube does not. Podcast apps do not. G2 does not.
Each platform has its own structured data language. Some are markup formats. Some are API feeds. Some are simply well formatted bios and descriptions that platforms parse.
If you only implement JSON LD, you are leaving 80% of your digital footprint unstructured. And unstructured means un retrievable.
This post is a field guide to structured data across every major platform type.

You think you know what your brand means.
You have positioning documents. Messaging frameworks. Brand guidelines. Your team can recite the value proposition. Your website articulates the differentiation.
But what you think your brand means is irrelevant.
What matters is what AI systems extract.
Entity extraction is the process by which machines identify and categorize the entities in your content. The results of that extraction determine whether you are discovered, understood, and trusted.

Most marketing claims are not verifiable.
“Our platform is secure.” Says who? By what standard? Compared to what?
“We deliver exceptional value.” What does exceptional mean? How is value measured? For whom?
“We are industry leaders.” Leading what? By which metric? According to whom?
In the attention economy, unverifiable claims were acceptable. Marketers made bold statements. Customers either believed them or did not. The cost of verification was higher than the cost of skepticism.

Most marketing leaders hear “knowledge graph” and assume it is a technical project for their engineering team.

They are half right. Implementation requires technical skills. But design requires strategic thinking. And if you delegate design entirely to engineers, you will get a technically correct knowledge graph that fails to achieve your marketing objectives.

This post is for the non-technical leader who needs to understand what a knowledge graph is, why it matters, and how to build one that serves your brand.

I first built a narrative ledger for a DeepTech founder in 2024.

He had spent years developing proprietary technology. His intellectual property was valuable (but also invisible). He could not share details publicly before patent. Yet investors needed to understand his value proposition to commit capital.

His existing strategy was hope. He hoped journalists would represent him accurately. He hoped analysts would categorize him correctly. He hoped AI agents would not hallucinate his capabilities.

This was a gamble.

We built something different. We built a narrative ledger.

I need to tell you something that might sound counterintuitive.

You should stop designing your marketing for humans first.

This is not because humans do not matter. They matter enormously. They make the final decisions. They sign the contracts. They commit the capital.

But they are no longer the first readers of your content.

AI agents are.