Entity-first Information Architecture for AI Search
Executive Summary
How multi-product B2B teams need to redesign IA around entities for AI Discovery
AI search systems don’t read your site the way a person does. They stitch together answers by connecting entities (products, audiences, industries, problems, standards) across pages, then pull the passages they trust enough to quote and summarize. Google’s docs on AI Overviews and AI Mode describe these AI features as drawing on web content and surfacing links as supporting sources. (Source: https://developers.google.com/search/docs/appearance/ai-features)
For a multi-product B2B company, keyword-first IA often turns into a complicated mess with overlapping pages, duplicated intent, no clear “source of truth,” and internal links that reflect org charts instead of buyer questions. Entity-first IA fixes this by designing the site as an entity graph where products connect to audiences, audiences connect to use cases, use cases connect to problems, and everything connects to proof, definitions, and implementation detail.
This post is for the digital marketing lead at a multi-product B2B company who needs a practical redesign approach that supports GEO/AEO outcomes, improves AI assisted product discovery, opens opportunities for revenue growth and stays vendor-neutral throughout 2026.
From keyword trees to an entity graph
Keyword trees were built for a world where search engines mostly matched pages to queries and ranked links. Entity graphs fit a world where AI systems build an answer by connecting concepts and checking facts.
A useful shift:
Keyword tree: “We need a page for every high-volume term.”
Entity graph: “We need a page for every durable thing buyers ask about, then connect those things so people and machines can follow.”
This matches how knowledge graphs are described in information retrieval: a graph model of entities and their relationships. Wikipedia’s definition centers on “interlinked descriptions of entities” plus relationships. (Source: https://en.wikipedia.org/wiki/Knowledge_graph)
What “ENTITIES” mean in SEO and GEO
In practice, an “entity” is a uniquely identifiable real-world thing: your company, your product, an integration partner, a compliance standard, a job role, an industry category, a common problem, a methodology.
Two (2) things matter most for AEO and GEO:
Disambiguation
Systems need to know whether “Mercury” means a planet, a chemical element, or a product name. Entity linking is a formal NLP area where a named entity is connected to a unique identifier (often a Wikipedia page). (Source: https://en.wikipedia.org/wiki/Entity_linking)Extraction
Systems pull answers more cleanly when a page states, in plain terms: what the entity is, what it relates to, and what it is not. Named-entity recognition (NER) is a standard NLP technique for locating and classifying named entities in text. (Source: https://en.wikipedia.org/wiki/Named-entity_recognition)
Entity-first IA makes your site easier to parse, easier to cite, and harder for AI to misread.
Map your core entity set
Entity-firstIA starts with an inventory. The job is to find the smallest set of entities that explains most buyer journeys, then expand once the core graph holds together.
Products, roles, industries, problems
For a multi-product B2B company, start with four (4) buckets:
1) Products and modules
Product line A
Product line B
Modules/features buyers search as “things,” not only benefits
2) Roles and audiences
CMO / VP Marketing
Head of RevOps
Security leader
IT admin
Procurement
3) Industries and contexts
Manufacturing
Healthcare
Financial services
SaaS
Public sector
4) Problems, use cases, outcomes
Reduce onboarding time
Improve compliance reporting
Consolidate tools
Increase pipeline output
Prevent incidents
You’re not building a spreadsheet for its own sake. You’re building an entity map you can publish into IA, internal linking, and schema.
A simple entity map (fictional example)
Company: “Northrise Systems” (multi-product B2B platform)
Entity type
Entity
Connects to
Primary page type
Product
Northrise Platform
Audiences, industries, integrations, security
Product hub
Product module
Workflow Automation
Use cases, features, competitors
Module page
Audience
RevOps Leader
Problems, metrics, implementation
Persona hub
Industry
Healthcare
Standards, use cases, case studies
Industry hub
Problem
Manual handoffs
Product modules, workflows, ROI
Problem page
Standard
SOC 2
Security posture, procurement
Trust page
Integration
Salesforce
Setup, data flows, use cases
Integration page
That’s the point of Entity-firstIA: stable “things,” connected by explicit relationships.
Design an Entity-firstIA
Once you know your entities, design navigation and internal linking so the graph is visible and crawlable.
Google’s link best practices explain that Google uses links to find new pages and understand context, and recommends crawlable links with clear anchor text. (Source: https://developers.google.com/search/docs/crawling-indexing/links-crawlable)
Hub-and-spoke patterns
Entity-firstIA runs on hubs. A hub defines an entity and gives structured routes into adjacent entities.
Useful hubs for multi-product B2B sites:
1) Product hubs (one per product line)
Definition: what it is, who it’s for
Key modules (spokes)
Primary use cases (spokes)
Implementation overview (spoke)
Proof (case studies, benchmarks)
2) Audience hubs (one per top persona)
The job context
Common problems
How your products map to those problems
“How to evaluate” checklist
Proof shaped for that persona
3) Use-case hubs (the buyer’s task)
What the use case is
What good looks like
Requirements and constraints
Product mapping
Implementation pattern
4) Trust hubs
Security overview
Compliance posture
Data handling, subprocessors
Procurement FAQ
In an Entity-firstIA, navigation must be more than “Products → Features → Pricing.” It must become “Products ↔ Audiences ↔ Use cases ↔ Proof.”
Cross-link rules
Hubs create structure. Cross-links create meaning.
Use these rules:
Rule 1: Link entities at decision points
Product page links to the top 3 persona hubs and top 3 use-case hubs
Persona hub links back to the modules that solve its top problems
Use-case hub links to implementation guides and proof
Rule 2: Use anchor text that names the entity
Google calls out anchor text as a way to help people and Google understand linked pages. (Source: https://developers.google.com/search/docs/crawling-indexing/links-crawlable)
Rule 3: Put a canonical definition block on hubs
Make it easy for Answer Engines to lift a clean definition and tie it to the right entity.
Rule 4: Don’t publish orphan “SEO pages”
If a page exists only to rank, with no strong links in or out, it adds little to the graph.
Rule 5: Attach proof to the entities it proves
Case studies should link to:
the product used
the use case solved
the industry context
the persona who championed it
That turns proof into evidence inside the graph.
Use schema and structured data
Entity-firstIA gets stronger when your site also describes entities in structured form.
Google says it uses structured data it finds on the web to understand page content and gather information about the world, including information about entities like people and companies. (Source: https://developers.google.com/search/docs/appearance/structured-data/intro-structured-data)
Schema.org is the shared vocabulary many systems use. Its “getting started” docs describe using schema.org with Microdata, RDFa, or JSON-LD to add information to web content. (Source: https://schema.org/docs/gs.html)
Google also recommends JSON-LD in some contexts because it’s separated from user-facing code and easier to maintain. (Source: https://support.google.com/merchants/answer/7331077?hl=en)
Schema types that matter most
These types carry the most weight on multi-product B2B sites:
1) Organization
Use it to anchor your company as an entity (name, logo, contact points, sameAs profiles). Schema.org defines Organization as a core type. (Source: https://schema.org/Organization)
Google’s Organization structured data page recommends using the most specific subtype when possible. (Source: https://developers.google.com/search/docs/appearance/structured-data/organization)
2) Product and Software Application
Schema.org’s Product covers offered products or services broadly. (Source: https://schema.org/Product)
For software, Schema.org provides Software Application, and Google supports software app structured data for marking up software application information. (Sources: https://schema.org/SoftwareApplication) and (https://developers.google.com/search/docs/appearance/structured-data/software-app)
3) FAQ Page (use it sparingly)
FAQ Page describes a page presenting FAQs and answers. (Source: https://schema.org/FAQPage)
Google’s FAQ structured data docs spell out requirements like using Question/Answer in mainEntity. (Source: https://developers.google.com/search/docs/appearance/structured-data/faqpage)
4) Structured data as relationship glue
Schema pays off when you use it consistently across related pages: the same product entity referenced in a product hub, module pages, case studies, and integration pages.
Here’s a minimal JSON-LD example showing a stable identifier you can reference across pages:
{
"@context": "https://schema.org",
"@type": "Organization",
"@id": "https://example.com/#org",
"name": "Northrise Systems",
"url": "https://example.com/",
"sameAs": [
"https://www.linkedin.com/company/Northrise-systems"
]
}
Keep it stable, then reuse @id wherever you describe products and pages that belong to that organization. The goal is continuity across your graph, not maximal markup.
Important Note: your structured data must match visible content and follow policy requirements, since eligibility depends on compliance and accuracy. (Source: https://developers.google.com/search/docs/appearance/structured-data/intro-structured-data)
Maintain the graph over time
Entity-firstIA isn’t a onetime redesign. It’s a living, adaptive content system.
1) Assign owners
Give each top-level entity set a real owner:
Product line hubs (Product Marketing)
Persona hubs (Demand Gen or Content Strategy)
Use cases (Solutions Marketing)
Trust entities (Security/Legal)
Ownership means:
a quarterly review cadence
maintained canonical definitions
current internal links
updated proof
2) Add new entities only when you can connect them
A common issue is generating and publishing your new web pages faster than the graph can absorb. An entity with weak links is hard for customers to discover and easy for AI systems to ignore.
Before you publish a new entity page, define:
at least 5 internal links from existing hubs to the new page
at least 5 outbound links from the new page to related entities
one definition block and one proof block
one clear next step
3) Watch citations and “source behavior”
GEO research describes generative engines as systems that synthesize responses from multiple sources and proposes methods to improve source visibility, including citations and supporting statistics. (Source: https://arxiv.org/abs/2311.09735)
Entity-firstIA helps because it makes your pages across your site easier to cite correctly and keeps language consistent across your website.
4) Run an “entity drift” audit quarterly
Entity drift shows up when:
product names vary across pages
a new vertical becomes priority but pages don’t reflect it
proof pages cite old capabilities
persona pages describe roles your buyers no longer have
A simple audit for you to run:
pick 10 priority entities
confirm definitions match across hubs
confirm links reflect current packaging
confirm schema matches visible content
update dateModified and internal references where needed
5) Use Wikipedia as a disambiguation anchor (carefully)
Wikipedia pages often serve as stable identifiers for entity linking in NLP contexts. (Source: https://en.wikipedia.org/wiki/Entity_linking)
Use Wikipedia for broad, non-branded concepts (standards, industries, general terms) and as a quick check when terminology is ambiguous. Don’t treat it as a primary source for claims that need primary evidence.
A practical 30–60–90 day plan for you to roll out with your team
Days 1–30: Map and pick one product line
Inventory entities (products, personas, industries, problems)
Pick one product line as the pilot
Draft hubs for the product + top 2 personas + top 2 use cases
Days 31–60: Build hubs and links
Publish hubs with definition blocks and proof blocks
Add internal links using crawlable best practices (Source: https://developers.google.com/search/docs/crawling-indexing/links-crawlable)
Add Organization + Software Application/Product schema where it fits (Sources: https://schema.org/Organization, https://developers.google.com/search/docs/appearance/structured-data/organization, https://schema.org/SoftwareApplication)
Days 61–90: Standardize and govern
Create an “entity definition template” for each of your hubs
Pick a date and set a quarterly entity drift audit
Start a lightweight “citation check” routine on 20 priority questions, using GEO research as a guide
For CMOs
Entity-first IA isn’t an SEO only project. It changes how your entire site explains what you sell, who it’s for, and why buyers should trust it. Here’s what to do with that:
What you can do this quarter
Pick the pilot with revenue in mind. Choose the product line where pipeline value for your brand is highest or where deals stall because buyers can’t quickly understand scope, fit, and proof.
Define the few entities you want the market to repeat. Your top product lines, your top 2–3 use cases per line, and the 2–3 personas that usually sponsor the deal. Keep names consistent across pages, decks, and sales talk tracks.
Make proof easy to route to. Ask for case studies and benchmarks that map cleanly to product → use case → industry → persona. If proof can’t be placed on the graph, it won’t show up when buyers or AI systems look for it.
Fix the “who owns what” problem early. Product Marketing owns product hubs, Solutions Marketing owns use-case hubs, Security owns trust hubs. If nobody owns a specific hub, it won’t make it. Assign owners early.
Measure what changes. Don’t only watch rankings.
Track the following:
demo requests and contact rates from hub pages
deal velocity for the pilot product line
win/loss notes that mention “confusing positioning,” “unclear packaging,” or “couldn’t find proof”
sales enablement pull-through with how often your reps share hub URLs
What to tell the rest of your exec team
This reduces duplicated content and internal debate over “which page is correct.”
It cuts the time it takes a buyer to connect product claims to proof.
It gives AI answer systems fewer chances to confuse your products, use cases, and packaging.
Your next step
Build an entity map for one product line, then keep content, schema, and internal links consistent so the same relationships show up everywhere: product ↔ audience ↔ problem ↔ use case ↔ proof.
As the pilot makes the site clearer for humans, it makes it clearer for machines. That’s the clear bet behind Entity-first IA for B2B AI discovery and growth.
Last updated 01-05-2026
