Core AEO Basics: Key Concepts & Why They Matter

Search engines, voice assistants, answer engines… they’re no longer just trying to reach a web page, they aim to reach understanding.

This transformation pushes optimization practices beyond traditional terminology.

Now, we need to optimize not the page, but the actual meaning. Because the internet is not just about pages anymore.

At the heart of this new answer engine era lie the cores. In this article, we’ll explore what cores mean in digital marketing, how they function, and how we use them in answer engine optimization.

What is a Core in Digital Marketing?

A core is the concise and meaningful part of a piece of information or media that can be directly perceived, understood, and presented by answer engines. It can be in the form of text, image, audio, or data.

Typically, the core is extracted from a larger whole — like a web page, video, podcast, or database. It’s a piece of information that is structured, contextual enough, and meaningful on its own.

For example, it’s not the entire article, but the part of that article that an AI system can target — such as a sentence that makes a statement, carries information, or answers a question.

Now, the smallest accessible unit of information that directly answers a question has a special name: core.

✅ What Can Be a Core?

• A corporate vision sentence in a meta description

• A structured answer in an FAQ block

• Structured price info on a product page (product.price)

❌ What Is Not a Core?

• An entire HTML page

• An inaccessible piece of info buried inside a PDF

• Scattered, out-of-context sentences

• An entire blog post

⚠️ Attention: Defining a clear, meaningful, and accessible piece of information as a core is currently our own proposition. This term is not yet an industry standard.

 💡 This is what we call a core: Imagine a blog post titled “Why Are Cats Active at Night?” Inside it, there’s a sentence: “Cats are naturally programmed to hunt during twilight and nighttime.” This sentence provides a direct and meaningful answer to the question and can thus be considered a core in this context.

👩‍💻 Pro. tip: Because AEO is about producing and optimizing the minimum, structured, reliable units of information a system needs to generate meaning; brands should integrate their message consistently across all sources, not just isolated elements.

Key Features of Cores

1. Structuredness

One of the key characteristics of a core is that its meaning units can be easily separated and processed by systems.

This typically includes:

• Schema markup / JSON-LD

• Headings, bullet points, tables

• Video transcripts, podcast metadata, API data schemas

👇 Structured Data Example:

JSON-LD for a product using schema.org:

{
  "@context": "https://schema.org/",
  "@type": "Product",
  "name": "iPhone 15",
  "brand": {
    "@type": "Brand",
    "name": "Apple"
  },
  "additionalProperty": {
    "@type": "PropertyValue",
    "name": "releaseDate",
    "value": "2023-09"
  },
  "offers": {
    "@type": "Offer",
    "price": 999,
    "priceCurrency": "USD",
    "availability": "https://schema.org/InStock"
  }
}

This data can be read directly by an answer engine to extract the name, price, and availability instantly.

👇 Unstructured Data Example:

A section of a blog post:

“Apple launched the iPhone 15 last month. The starting price is 999 dollars and it is now available in many countries.”

While understandable to humans, this is not structured data. Machines need to interpret this using NLP processes to determine that “999” is a price, which product it refers to, and the timing.

📑 Summary:

• Structured data = Information prepared for machines; clear and instantly accessible

• Unstructured data = Information written for humans; needs interpretation

So, a core shouldn’t be messy content, it should carry systematic meaning.

💡 Analogy: Unstructured data is like a box of LEGOs; the pieces are there, but all mixed up. Or like a library without a catalog; the books are there, but hard to find. A core is the structured version of that mess; like a finished LEGO house — identifiable, taggable, and accessible by systems.

📝 Clarification: A core doesn’t always start out as structured data. If it’s structure-ready, it can be transformed into a core through core optimization; made findable, taggable, and deliverable for systems.

2. Authoritativeness

For a core to be valuable in the eyes of systems, structure alone isn’t enough. It must also come from a trustworthy and authoritative source.

This is ensured by:

• Recognition of the author or organization

• External references (mentions, attributions, citations)

• Consistency across platforms

• Accuracy and freshness

So, a core is also the kind of content that systems can trust.

🧪 Formula:

Core = Content + Structure + Authority

One of the simplest strategic and terminological definitions of a core.

Further broken down:

• Semantic Content → meaningful, answer-oriented content

• Structured Context → readable by systems (schema, context, markup)

• Authoritative Source → trusted and expert source

The Difference Between Content and Core

The terms content and core are often confused.

But there are critical differences:

👉 Why this matters: Answer engines don’t just “read” data, they must understand it. So, to be a core means not just to present content, but to deliver meaning.

🔑 Key finding: Every piece of content can contain cores, but not every core is content. Some are just structured data or relationship graphs. Some prefer to call these content, but that’s content meant for machines, not traditional human-facing content.

Lens: When Does Content Become a Core? 🔎

A core is essentially a unit of meaning, something systems can understand and use. Not all content qualifies.

Raw content is the raw material of a core; PDFs, Word files, plain text, social media posts…

These become core when they are distilled through the following stages or turned into a clear piece of information presented to the machine:

• Structuring (schema, headings, markup)

• Contextualizing (linking to intent, topic, identity)

• Authority signaling (source trustworthiness)

🔑 Key finding: A core is sometimes extracted from content, sometimes produced directly in structured form.

👇 Example:

On your website, you have this:

“We are a veterinary clinic founded in 2005 in New York, committed to pet health.”

From this, we can extract the following cores:

• Founding year: 2005

• Location: New York

• Industry: Veterinary

• Specialization: Pet health

These extracted data points can answer system-level questions like “When was this clinic founded?”, making them cores.

🔑 Key finding: Unstructured data is not a core by default. But through optimization, it can be transformed into a meaningful, authoritative form. This is one of the core functions of answer engine optimization: turning raw data into meaningful information.

A Contrast Table

This table helps clarify the relationship between core, content, code, and data:

🔑 Key finding: Some cores are meaningful in their natural form and don’t require extra markup. However, most of the time, content and/or code is needed, especially for interfaces consumed by humans.

Types of Core

A core manifests in four formats. All digital information types are essentially variations of or derived from these four main formats:

1. Text

The most directly processable content type for NLP systems:

Blog posts, articles, subtitles, titles, descriptions, etc.

2. Visuals

Images, and if the image contains embedded information (e.g., text readable via OCR), it can be a core:

Infographics, photos, diagrams.

3. Audio

When transcribed or processed by voice command systems, audio can become a core:

Podcast episodes, audio from videos, interviews.

4. Structured Data

This type of data is often the most “core-friendly,” providing direct information to answer engines:

JSON, data in tables, schema markup, databases.

🗒️ Quick note: Other formats are either a combination or derivative of these four; e.g., videos combine visual and audio; interactive content involves text, visuals, and data; virtual reality combines visuals, data, and audio.

Core Types & Content/Code Relationship

The Gap Other Terms Couldn’t Fill

There are already many meaningful and descriptive terms.

But we believe the term core captures these meanings:

• An authoritative, structured, meaningful unit of content

• Isolatable from content, interpretable and accessible by systems

• Can individually match queries and directly answer questions

• Can be adapted to content, data, code, media, or hybrid structures

• Can be evaluated and optimized at both the content and source levels to serve answer engines

• High conceptual strength, can be modeled with a full terminology and metrics: On-core, Off-core, Core discoverability, consistency, source alignment, etc.

• Supports modularity, the shift from holistic content to query-focused blocks

• Has a system- and machine-experience-oriented perspective

So this term is a product of our effort to find a concise and holistic expression that meets broad standards of quality and meaning.

We Chose ‘Core’ Over Complexity

“Structured and authoritative content” immediately conveys what systems can trust and process.

However, it’s not a conceptual term, and the word “content” still has human-centric connotations, which obscures the system-centric nature of cores.

That’s why we needed a stronger umbrella concept.

Term: Core
Definition: Structured and authoritative content

What Is Core AEO?

Core optimization, core AEO, or core-focused AEO is the process of structuring, making accessible, and optimizing the cores in your content and data sources so they become visible in answer engines.

The goal is to transform a unit of meaning into a suitable core by enabling it to:

• Be clear
• Be structurally readable
• Be trusted by the system
• Remain consistent across platforms

This happens on two levels:

1. System-Level Meaning

Making information accurate, simple, consistent, and machine-understandable.

In short:

• Clarify the source of meaning.

• Simplify the structure of meaning.

• Harmonize the distribution of meaning.

This is literally “optimizing meaning” — but for machines and systems, not humans.

🗒️ Quick note: To fully achieve this, additional requirements such as technical AEO are needed.

2. Strategic Level

Addresses questions like:

• Is it clear what the brand or person says and on which topic?

• How clear and widespread is the meaning?

• How much do answer engines trust it?

Then, core marketing, a knowledge positioning strategy, is developed. The brand not only creates content but positions the right information, in the right way, in the right place.

This requires meaning engineering and a system-focused marketing mindset. It’s not just what you say, but how you’re understood. That’s why content marketing alone is never enough.

🔑 Key finding: Core optimization has two pillars: simplifying meaning for machines and clarifying the position of the brand and its information.

Components of Core Optimization

There are two main components of core optimization:

• On-core AEO (internal structure): Presenting, structuring, and optimizing the cores within owned content/data sources (site, app, podcast, video, API, etc.) to be selected by answer engines.

• Off-Core AEO (external authority): Optimizing external sources feeding cores related to the brand (e.g., Wikipedia, dictionaries, podcasts, social media) through connection and trust-building, and online reputation strategies.

🗒️ Quick note: These terms alone are not enough. Every core has a source that determines its context, authority, and accessibility. Hence, answer engine optimization (AEO) is still needed, and core optimization is positioned as a sub-discipline of AEO. It’s not just about polishing a “snippet of text,” but also strengthening where it came from.

Core Optimization vs Answer Engine Optimization

Core optimization is the content related and most critical subdiscipline of AEO.

It is also AEO’s heart because it targets the quality of the answers served to users.

🔑 Key finding: Core optimization is the most critical layer, but not the whole picture. AEO also includes technical infrastructure, trustworthiness, source authority, and user experience. Ultimately, core optimization aims to improve AEO performance.

Final Thoughts: Discoverability Starts Here

The shift from optimizing content to optimizing meaning marks a profound evolution in how we approach visibility in the answer-driven web.

As systems move beyond pages and start interpreting fragments of structured, authoritative information, the concept of a core becomes central.

Understanding what a core is, and how it differs from traditional content, enables us to design more precise, accessible, and machine-trustworthy units of information. It’s not just about writing anymore; it’s about engineering meaning at its source.

Core AEO invites us to rethink how we structure, publish, and position information. Not for human audiences alone, but for the systems that guide them. The future of discoverability starts at the core.

Frequently Asked Questions