To structure your digital infrastructure for ChatGPT and Gemini, organize your content around entities rather than keywords, built on JSON-LD schema markup and semantic HTML with explicit relationships drawn between the entities. AI models do not rank pages. They synthesize answers from the sources they judge authoritative, based on semantic structure and content depth, so the brands that dominate AI search are the ones with deep entity maps, not the ones with the most backlinks.
Beyond Keywords: How AI Actually Understands Your Brand
For twenty years, SEO professionals optimized for keywords: target a phrase, write content around it, point links at it, and watch it rank. That worked when search was a matching game and Google paired your keywords to a query, then ranked you by authority.
AI search plays a comprehension game instead of a matching one. When a user asks ChatGPT "What are the best web design agencies for B2B replatforming?", the model does not hunt for pages containing those exact keywords. It synthesizes an answer from what it understands about:
- What "web design agency" means as an entity
- What "B2B replatforming" involves as a process
- Which brands it has encountered that demonstrate expertise across both entities
- How those brands' content relates to adjacent entities (case studies, methodologies, tools, client outcomes)
Entity-Based SEO is defined as the practice of organizing your digital content around interconnected concepts (entities) rather than isolated keywords, which lets AI models map your brand within their internal knowledge graphs.
If your content does not establish clear entity relationships, you are invisible to this process.
The Anatomy of an Entity Map
An entity map is the structured network of concepts, relationships, and supporting evidence that defines your brand's digital presence. For a web design agency, a basic entity map includes:
- Core Entities:
- Your brand (Space & Story)
- Your services (Web Design, AEO, Replatforming, Content Strategy)
- Your methodologies (AEO Matrix Scanner, Omnichannel Content Engine)
- Your platforms (Cite-met, GiveFeedback.dev)
- Relationship Entities:
- Case studies that connect services to outcomes
- Client testimonials that validate expertise
- Thought leadership that demonstrates depth
- Technical documentation that proves methodology
- Supporting Entities:
- Industry terms you define (AEO, Entity-Based SEO, crawl budget)
- Competitor comparisons that establish positioning
- Data points and statistics that ground claims in evidence
Each entity must be explicitly defined on your site, linked to related entities through internal navigation and content references, and structured with proper schema markup so AI crawlers can parse the relationships programmatically.
Schema Markup: The Machine-Readable Layer
Schema markup (structured data) is the translation layer between your human-readable content and machine-readable data. Without it, AI models must infer meaning from your prose. With it, you are explicitly declaring:
- This page is a Service offered by this Organization
- This article is a BlogPosting written by this Author about these Topics
- This case study is a CreativeWork demonstrating these Skills for this Client
Google supports over 800 schema types. The ones that matter most for AEO:
- ProfessionalService defines your organization and its service catalog
- Article / BlogPosting structures your thought leadership for AI extraction
- FAQPage supplies question-answer pairs that AI models pull nearly verbatim
- BreadcrumbList clarifies your site hierarchy and page relationships
- ItemList structures collections such as portfolios and service listings
Every page on your site should carry at least two schema types: one defining the page content and one defining its position in your site hierarchy.
The Complexity Problem
Most agencies will not tell you this, but implementing Entity-Based SEO properly is genuinely hard.
It requires a full audit of your existing content to find the entity gaps, custom schema markup for every page type rather than generic plugin output, internal linking that builds bidirectional entity relationships, content that defines its terms and draws the connections that establish authority, and ongoing monitoring of AI citation patterns to see what is getting referenced and what is being ignored.
Doing all of that by hand for a site with 50+ pages and an active publishing schedule is a full-time job, and for most B2B brands it is simply not feasible.
The Space & Story Solution
This is where our proprietary tooling eliminates the complexity:
AEO Matrix Scanner: Our diagnostic tool crawls your site and your competitors' sites to map the entity landscape. It identifies exactly which entities your competitors are being cited for, where your entity coverage gaps exist, and which schema implementations are missing or malformed.
Omnichannel Content Engine: Once the gaps are mapped, our content engine produces authoritative, schema-structured content to fill them. Every piece is engineered for AI extraction, with explicit definitions, a proper heading hierarchy, internal entity links, and embedded FAQ schemas.
The theory is free for anyone to read. Executing it at scale takes tooling that most agencies simply do not have.
Getting Started
The first step is understanding where you stand. Our AEO Matrix Scanner can map your current entity coverage in 48 hours and deliver a clear report showing:
- Which AI platforms currently cite your competitors but not you
- Which entity relationships are missing from your digital infrastructure
- Which schema implementations need to be added or corrected
- A prioritized plan for closing the gap
The brands that act now compound their AI authority while competitors are still arguing about whether AEO is real.
If you want to act now rather than debate, start with the free AEO audit. It maps your entity and schema gaps and shows exactly where AI engines lose the thread on your brand.