We Built the Open Standard for AI-Readable Websites. Here's Why.

I’ve spent the last few years watching a quiet shift happen in real time.

At first, it looked like “another SEO change.” A new model. A new interface. A new place where people asked questions.

Then it became obvious: the interface wasn’t the point.

The point was that software was starting to decide.

Agents will increasingly:

interpret goals
gather sources
evaluate vendors
recommend actions
execute workflows

And the web—the thing we all built our businesses on—wasn’t designed for that.

This is the story of why we built LLM-LD, the open standard for AI-readable websites, and why I think open standards matter more now than they have in a decade.

The problem: the web is human-readable, not agent-readable

Most of the internet is designed for human eyes.

We rely on:

layout
hierarchy
typography
implied meaning

When a human lands on your homepage, they can infer:

what you do
who you’re for
whether you’re credible

Agents can’t rely on those cues.

They need explicit facts.

And when those facts aren’t available in a consistent, machine-readable form, agents do what they always do:

They approximate.

Approximation is fine for casual Q&A.

It’s not fine for:

procurement
legal/compliance constraints
security requirements
pricing models
implementation timelines

In those contexts, ambiguity becomes risk.

Why structured data wasn’t enough

If you’ve worked in SEO, your first instinct is probably: “Just add schema.”

Schema.org is valuable. We use it.

But we kept seeing the same limitations:

Schemas are generic. They are not optimized for the exact decision data agents need.
Implementation is inconsistent. Two companies can “use schema” and still describe the same fact in incompatible ways.
The hard part is identity + truth. The problem isn’t only format—it’s consistency across sources.

We needed something that was:

explicit
composable
easy to publish
friendly to both humans and machines
open

That’s what pushed us toward a standard.

The insight: brand visibility is becoming a data problem

In the agentic era, your marketing site isn’t just a brochure.

It’s a dataset.

That sounds technical, but it’s actually simple.

Agents need:

a stable organization identity
a clear taxonomy of products and services
unambiguous descriptions
proof signals that can be verified
constraints that prevent mismatches

When those elements exist, agents can compare you correctly.

When they don’t, you disappear—or worse, you get misclassified.

That’s why we built CAPXEL as a white-glove consultancy focused on the data layer beneath visibility.

Why we chose an open standard

There are two paths when a new interface emerges:

Each platform builds a proprietary format.
The ecosystem converges on an open standard.

I’ve watched proprietary formats win short-term and lose long-term.

They create fragmentation:

different instructions for each crawler
different formats for each model
different “best practices” for each UI

Brands spend time chasing compliance instead of publishing truth.

Open standards do the opposite:

one format you can rely on
one layer that improves over time
a shared vocabulary for the ecosystem

That’s why LLM-LD is open.

If agents are going to mediate economic activity, the layer that describes businesses shouldn’t be locked behind any single company.

What LLM-LD is (in plain language)

LLM-LD is a machine-readable layer you publish alongside your website.

It expresses the facts agents need to understand your business:

who you are
what you offer
what categories you belong to
what constraints apply
where the supporting evidence lives

You can think of it like a “brand API” for the agentic web.

Not a marketing API.

A truth API.

If you want the spec, it’s here: https://llmld.org

What this changes for brands

When your business becomes machine-readable, three things happen:

1) You reduce ambiguity

Agents don’t have to infer what you mean.

2) You improve consistency

Your website, your listings, and your profiles can all align to the same canonical truth.

3) You become comparable

This is the uncomfortable one.

Agents will compare vendors faster than humans can.

If you’re not comparable, you’re not selectable.

LLM-LD makes comparison possible in a way that benefits the brands that are actually good at what they do.

Why CAPXEL cares (the founder version)

I didn’t want to build a company that helps people “hack” models.

I wanted to build a company that helps brands publish truth in a world where machines increasingly mediate decisions.

ASO—Agentic Search Optimization—is the strategy.

LLM-LD is one of the mechanisms that makes it reliable.

And CAPXEL is the partner that implements it with a white-glove, data-first approach.

Where we go from here

Standards are not a finish line. They’re a foundation.

The next phase is:

more tooling
more validators
more monitoring
clearer patterns for different industries

Most importantly, it’s about helping businesses transition without becoming dependent on any single platform.

That’s the whole point.

What I wish more teams understood

A lot of teams treat “AI visibility” as a marketing channel.

I think that framing misses the deeper shift.

When agents become the interface, your brand representation becomes part of the decision infrastructure of the economy. That means:

accuracy matters more than cleverness
consistency matters more than volume
verifiability matters more than hype

If you publish clean, consistent facts—and those facts are corroborated across sources—you don’t have to fight the model. You just have to be easy to understand.

Open standards are how we keep that future sane. They prevent a world where every crawler requires a different proprietary “AI sitemap” and where visibility is rented instead of earned.

That’s the world LLM-LD is designed for. If you’re building for the next decade, make your brand readable now—not later.

Next steps

Read the LLM-LD standard: https://llmld.org
Explore how we implement it through ASO: ASO — Agentic Search Optimization