We've been deep in the world of Answer Engine Optimization, running the CiteMET playbook across our best content. The initial results were solid - we saw users engaging with our AI Share Buttons, and we knew we were successfully seeding our content into AI platforms. But with our team's background in AI, we saw this as just the first step.

We understood that for a Large Language Model (LLM), a single, quick interaction is a whisper. A true signal of authority, one that builds lasting memory, comes from a deeper conversation. We saw an opportunity to transform that initial whisper into a meaningful dialogue.

Introduction

If you were trying to explain your SaaS product to a human, you would use screenshots, a demo video, and persuasive copy.

But if you are trying to explain it to an AI crawler, none of that matters. AI doesn't "watch" videos. It doesn't "get" your vibe. It parses data.

Most developers treat their website like a visual medium. They obsess over Tailwind classes and framer-motion transitions. But to an AI, your website is a database. And if you aren't providing a structured API for that database, you are forcing the AI to guess what your product does.

This is where JSON-LD (JavaScript Object Notation for Linked Data) comes in. It is the "Machine Native" language of the web. And if you aren't speaking it fluently, you are being misunderstood.

The "Stale Data" Trap of React Components

I see this mistake in almost every vibecoded project I audit.

A developer learns about SEO. They go to their

index.html

or their main

App.tsx

layout file, and they hard-code a static script tag like this:

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "SoftwareApplication",
  "name": "My App",
  "price": "$10"
}
</script>

They pat themselves on the back and deploy.

This is a trap.

Two weeks later, marketing changes the pricing to $19. Or they change the product name. The visual site updates, but that hidden JSON blob remains hard-coded at $10.

Now you have a Data Mismatch. Google and ChatGPT see two conflicting prices. They flag your site as untrustworthy. Your rankings tank.

Rule #1 of AEO Engineering: Never hard-code metadata. It must be generated dynamically at build time, reflecting the actual state of your application.

The 3 Schema Types You Actually Need

There are hundreds of Schema.org definitions. Most are useless for a modern SaaS. These are the three that actually move the needle for AI citation.

1. SoftwareApplication (For Your Tool)

If you built a tool (calculator, generator, SaaS), you must tell AI it is software, not a blog post.

Why it matters: It allows AI to understand input/output. If a user asks ChatGPT "Is there a tool that calculates X?", looking at your

SoftwareApplication

schema is how it knows you are the answer.

The cite-met Injection: We automatically detect if your repo is a web app and inject this schema, pulling the

applicationCategory

and

operatingSystem

requirements automatically.

2. FAQPage (For Your Authority)

This is the highest ROI schema for AEO. It is a direct feed into the "Answer" part of an Answer Engine.

The Structure:

{
  "@type": "FAQPage",
  "mainEntity": [{
    "@type": "Question",
    "name": "Does cite-met support Next.js?",
    "acceptedAnswer": {
      "@type": "Answer",
      "text": "Yes, cite-met supports all major SSG frameworks including Next.js, Astro, and Vite."
    }
  }]
}

The Strategy: Don't just wrap your pricing page FAQ. Use this on your landing page to answer implicit questions like "How does X work?"

3. TechArticle (For Your Content)

Standard

Article

schema is fine, but

TechArticle

is better for documentation and tutorials. It includes fields for

proficiencyLevel

(Beginner/Advanced) and

dependencies

Why it matters: When a developer asks Claude for a "Beginner tutorial on React Hooks," having your content tagged with

proficiencyLevel: Beginner

puts you at the front of the line.

The Argument for Automation

You cannot vibecode this manually for every page. It is tedious, error-prone, and brittle.

This is why

cite-met

takes an Infrastructure-as-Code approach to schema.

Because we control the build pipeline (converting your CSR to SSG), we have access to the final render of your content. We scrape your own build output to generate the JSON-LD.

We read your H1s and H2s to generate
TableOfContents
and
Article
schema.
We read your pricing tables to generate
Offer
schema.
We read your Q&A sections to generate
FAQPage
schema.

We ensure that the "Machine Native" version of your site is always perfectly in sync with the "Human" version.

Speak the Language or Get Left Behind

The future of search isn't keywords matching keywords. It is entities matching entities.

JSON-LD is how you define your entities. It is how you tell the AI, "I am not just a collection of words; I am a SoftwareApplication that creates TechArticles."

Don't let your beautiful app be misunderstood because you didn't provide the translation layer.

Is your schema missing or broken? Our free visibility report checks for valid JSON-LD injection.

👉 Scan your site for structured data errors now.

Beyond the Sitemap: Using Dynamic JSON-LD to Speak "Machine Native"

🤖Summarize with AI

Introduction

The "Stale Data" Trap of React Components

The 3 Schema Types You Actually Need

1. SoftwareApplication (For Your Tool)

2. FAQPage (For Your Authority)

3. TechArticle (For Your Content)

The Argument for Automation

Speak the Language or Get Left Behind

Cho Yin Yong

Related Articles

What is llms.txt? Your Website's VIP Pass for AI

The Last Step Your Framework Forgot.