Designing Your Information Architecture for Entity-Based Search

Intro

Traditional SEO focused on page hierarchy — how pages linked together and where keywords appeared. But in the era of Answer Engine Optimization (AEO) and entity-based search, that’s no longer enough.

Search engines and AI platforms like Google’s AI Overview, Bing Copilot, and Perplexity.ai now interpret websites the way humans understand ideas — through entities and their relationships.

If your website isn’t structured around these semantic connections, AI won’t understand what you offer, how topics relate, or why your brand deserves to be cited.

This guide explains how to design information architecture (IA) optimized for entity-based search — turning your website into a machine-readable knowledge graph that improves visibility in both traditional SEO and modern AEO.

What Is Entity-Based Search?

Entity-based search is how modern algorithms understand and retrieve information — not by keywords, but by concepts and relationships.

An entity is any unique, identifiable thing — a person, place, organization, or idea. Examples include:

• Ranktracker (Organization)

• Felix Rose-Collins (Person)

• Answer Engine Optimization (Concept)

• Keyword Finder (Product)

When users search for something, AI engines identify the entities mentioned, map their relationships, and retrieve the most trusted content about them.

Designing your site for entity-based search means structuring content and navigation in a way that helps AI understand your topics, authority, and expertise.

Why Information Architecture Matters for AEO

Your site’s architecture isn’t just for usability anymore — it’s how AI systems interpret meaning.

Here’s how information architecture affects both SEO and AEO:

Factor	SEO Impact	AEO Impact
Page Hierarchy	Improves crawlability and ranking	Helps AI model topic relationships
Internal Linking	Distributes authority	Defines entity connections
URL Structure	Clarifies topical relevance	Reinforces semantic grouping
Navigation Design	Enhances user flow	Aids machine comprehension
Schema Markup	Improves snippet visibility	Anchors entities in AI knowledge graphs

When you design your architecture around entities instead of keywords, you future-proof your website for the AI search era.

Step-by-Step: Designing for Entity-Based Search

Let’s go through how to build an entity-first information architecture that serves both SEO and AEO.

Step 1: Identify Your Core Entities

Start by defining the core entities that represent your business, services, and knowledge areas.

Example for Ranktracker:

• Primary Entities: Ranktracker (Organization), SEO Tools (Category), AEO (Concept)

• Secondary Entities: Keyword Finder, SERP Checker, Backlink Monitor, Web Audit

These entities become the backbone of your site architecture — each one should have a dedicated landing page (pillar page) with structured connections to related entities (supporting pages).

Use Ranktracker’s Keyword Finder to identify question-based searches around each entity to help build your content clusters.

Step 2: Organize Content into Semantic Clusters

Once you’ve identified your entities, group them into clusters based on relationships and hierarchy.

For example:

Cluster: SEO Tools

• Rank Tracker

• Keyword Finder

• SERP Checker

• Backlink Checker

• Web Audit

Cluster: Answer Engine Optimization (AEO)

• What Is AEO?

• How Structured Data Powers AEO

• The E-E-A-T Framework for AEO

• Brand Authority and AI Citations

Each cluster should link internally, forming a semantic network of connected entities.

This allows both users and AI systems to understand how each concept contributes to your domain expertise.

Step 3: Create a Logical URL and Folder Structure

Your URLs should reflect entity hierarchy — short, descriptive, and semantically meaningful.

Example:

This kind of structure tells search engines that all pages under /seo-tools/ are related entities within a shared category.

It also improves internal linking consistency and context recognition.

Step 4: Build a Hub-and-Spoke Internal Linking Model

Internal linking defines how entities connect — it’s the structural glue of AEO.

Follow a hub-and-spoke model:

• Hub pages: cover core entities in depth (e.g., What Is AEO?)

• Spoke pages: support subtopics (e.g., How Structured Data Powers AEO)

• Cross-links: connect related spokes within the same cluster

Example:

_What Is AEO?_ → links to → _Understanding Entities_ → links to → _Schema Markup for AEO_

This creates a semantic map of your knowledge domain that AI engines can easily interpret.

Use Ranktracker’s Web Audit to analyze internal link structures and find gaps in your entity connections.

Step 5: Implement Schema Markup for Every Entity Page

Schema provides explicit meaning to search engines — it tells them what each page represents and how it relates to others.

Recommended schema types for entity-based IA:

• Organization — defines your brand entity

• Person — for author bios and expert profiles

• Product — for tools or services

• Article — for informational content

• FAQPage — for question-based sections

• BreadcrumbList — clarifies navigation hierarchy

Example (for Ranktracker’s Keyword Finder):

This allows AI engines to explicitly connect “Keyword Finder” as a tool entity belonging to “Ranktracker.”

Validate your schema using Ranktracker’s Web Audit to ensure no missing or broken markup.

Step 6: Design Navigation Around Entity Relationships

Your site navigation should reflect entity clusters — not just generic categories.

Example:

Navigation Example

• SEO Tools

  • Rank Tracker

  • Keyword Finder

  • SERP Checker

  • Backlink Monitor

• AEO Resources

  • What Is AEO?

  • Structured Data

  • E-E-A-T Framework

  • Brand Authority

• About

  • Felix Rose-Collins

  • Company

  • Contact

This reinforces your brand’s semantic hierarchy, showing AI that your site is both structured and specialized.

Step 7: Add Contextual Relationships Through Content

Your internal linking strategy should mirror real-world relationships between entities.

Example relationships:

• “Ranktracker” → offers → “SEO Tools”

• “AEO” → relies on → “Structured Data”

• “E-E-A-T” → supports → “Brand Authority”

Express these in natural language inside your content:

“Ranktracker’s Web Audit tool plays a critical role in AEO by ensuring structured data accuracy and schema integrity.”

Each sentence like this reinforces semantic connections — helping AI systems understand the relationships between your entities.

Step 8: Integrate Entity-Driven Metadata

Metadata (titles, descriptions, and image alt text) should reinforce your entities and relationships.

Example (for AEO Hub Page):

• Meta Title: What Is Answer Engine Optimization (AEO)?

• Meta Description: Learn how AEO works and how Ranktracker helps you optimize for entity-based search and AI-generated results.

• Image Alt Text: Diagram showing entity relationships in AEO architecture.

This ensures that even your non-visible data helps AI interpret meaning accurately.

Use Ranktracker’s SERP Simulator to preview how your metadata appears in both traditional and AI-enhanced results.

Step 9: Maintain and Evolve Your Architecture

Your entity-based structure should evolve with your business.

Review quarterly using:

• Ranktracker’s Web Audit: detect broken links, schema issues, and missing entities.

• Rank Tracker: identify underperforming entities or clusters.

• Keyword Finder: discover new entity-related topics to expand your map.

As new technologies or product lines emerge, integrate them as new entities in your existing framework. This keeps your knowledge graph alive — and your AEO visibility growing.

Example: Entity-First Architecture in Practice

Brand: Ranktracker Core Entity: SEO Platform

Entity Clusters:

• SEO Tools: Rank Tracker, Keyword Finder, SERP Checker, Backlink Monitor

• AEO Topics: Structured Data, E-E-A-T, Brand Authority, Content Review Process

• Company Entities: Felix Rose-Collins (Person), Ranktracker (Organization)

Structure Example:

/seo-tools/    /rank-tracker/    /keyword-finder/    /serp-checker/  
/answer-engine-optimization/    /what-is-aeo/    /structured-data/    /brand-authority/  
/about/    /felix-rose-collins/  

Result:

• Each entity clearly defined

• Hierarchical but semantically linked

• Schema supports machine interpretation

• Internal links reinforce topic relationships

This setup is perfect for both SEO crawlers and AI systems building entity graphs.

How Ranktracker Helps You Build Entity-Based Architecture

Ranktracker provides everything you need to design and maintain a search- and AI-friendly site structure:

• Keyword Finder: Identify entity-based keywords and questions.

• SERP Checker: See how entities are displayed in AI Overviews and search results.

• Web Audit: Validate schema, link structure, and crawlability.

• AI Article Writer: Create consistent entity-aware content for every cluster.

• Backlink Checker & Monitor: Track entity-based citations and mentions.

• Rank Tracker: Monitor performance of entity clusters and related queries.

Together, these tools ensure your site architecture supports semantic relationships, AI comprehension, and organic authority.

Final Thoughts

Entity-based search has changed how websites are understood. Your content isn’t just a collection of pages — it’s a network of interconnected entities that define your brand’s expertise.

By designing your information architecture around those entities — with clear clusters, schema, and relationships — you transform your website into a machine-understandable knowledge graph that thrives in both SEO and AEO environments.

With Ranktracker’s all-in-one SEO and AEO toolkit, you can audit, structure, and optimize every page to help AI systems not just find you — but understand you.