Website Search Needs A Reboot

Search Failed. Context Wins. AI Agents Need GraphRAG.

Single-word keyword, traditional search “failure” stems primarily from its fundamental reliance on keyword matching, which struggles to understand the true intent and context behind a user’s query

The solution isn’t better keyword matching. It’s new natural language search with understanding backed by queryable knowledge graphs.

Why Search Structurally Failed

Content vs Context: Why Keywords Don’t Scale

The Failure Point:

Small Dataset (1990s-2000s):

100-1,000 pages
Keyword matching works
“Search for ‘passport’” returns 5-10 relevant pages
User can scan and find what they need

Large Dataset (2010s-2025):

10,000-100,000+ pages
Keyword matching fails
“Search for ‘passport’” returns 500 pages
User drowns in irrelevance

The Real Problem: Content Without Context

User Intent	Keyword Search Finds	What User Actually Needed
“I need to renew my passport”	All pages mentioning “passport” (500 results)	Passport renewal process specifically
“housing benefit eligibility”	Pages with “housing”, “benefit”, “eligibility” anywhere	The specific eligibility criteria page
“industrial cables outdoor use”	Every cable product page	Cables rated for outdoor environmental conditions

Why Traditional Search Can’t Fix This:

❌ More synonyms → More noise, still no context
❌ Better ranking → Reorders noise, doesn’t understand intent
❌ Faceted filters → User must know the right facets
❌ Popularity boosting → Makes popular pages appear everywhere, regardless of relevance

The Core Issue

Search engines match content (what words appear). Users need context (what those words mean in their situation, and in their language, or dialect).

Why Chatbots Without GraphRAG Fail

Traditional chatbots seemed like the solution—conversational interfaces that could handle natural language. But most implementations fail because they lack structured knowledge to query.

What chatbots CAN do (with LLMs):

Understand natural language intent
Maintain conversation context
Bridge vocabulary gaps

What chatbots CANNOT do (without knowledge graphs):

Reason about relationships between entities
Provide accurate, grounded answers at scale
Query structured properties and constraints
Guarantee consistency across responses
Cite authoritative sources

The problem: A chatbot without a knowledge graph is just an LLM hallucinating answers based on unstructured content. It might sound helpful, but it’s unreliable.

The solution: GraphRAG-backed chatbots query structured knowledge, ensuring accurate, verifiable, relationship-aware responses.

The Natural Language Solution

From Keywords to Intent: How GraphRAG Changes Everything

The Architecture That Actually Works:

1. User Interface Layer (Natural Language Input)

User types: “Which cables are suitable for outdoor industrial use?”

2. NLP Intent Layer (Translates human intent to structured queries)

Understands:

Intent: Product recommendation
Context: Industrial application
Constraint: Outdoor environment
Entity type: Product (Cable)
Property needed: Environmental rating

Translates to structured query:

type=Product
category=Cable
application=Industrial
property=outdoorRated:true

3. GraphRAG Backend (Queries knowledge graph)

Queries using:

Entity relationships
Property constraints
Semantic connections
Contextual relevance

4. Knowledge Graph (Schema.org Structured)

Product entities with:

Type definitions (Cable)
Properties (outdoorRated: true)
Relationships (isUsedFor: IndustrialApplication)
Specifications (IP68, UV resistant)

5. Response: “These cables are rated for outdoor industrial use: [Cable A – IP68], [Cable B – UV resistant] based on environmental specifications.”

Why This Works (And Keyword Search Doesn’t)

The User Says: “outdoor industrial cables”

Keyword Search Thinks:

Find: pages with words “outdoor” AND “industrial” AND “cables”
Return: 200 results, ranked by word frequency
User: Must read through to find environmental specs

GraphRAG Thinks:

Intent: Product recommendation
Constraint: Environmental condition = outdoor
Entity: Product type = Cable
Context: Application = industrial
Query graph: Product → hasProperty → outdoorRated:true
Query graph: Product → isUsedFor → IndustrialApplication
Return: 3 specific products with reasoning

The Critical Difference

Aspect	Keyword Search	GraphRAG with NLP
User Input	Keywords only	Natural language intent
Backend Query	Text match	Semantic graph traversal
Understanding	None (lexical)	Context + relationships
Results	Pages containing words	Entities matching intent
Reasoning	“These pages have your words”	“These entities meet your criteria because…”
Scalability	Fails at 100k+ pages	Excels at millions of entities

Why GraphRAG Requires Schema.org

The Backend Must Be Structured (Even If Users Aren’t)

The User Doesn’t Type Schema.org:

User types: “cables for outdoor use”

NOT: {“@type”: “Product”, “category”: “Cable”, “outdoorRated”: true}

But the Backend Must Store It That Way:

{
  "@context": "https://schema.org",
  "@type": "Product",
  "name": "Industrial Cable Type A",
  "category": "Cable",
  "additionalProperty": [
    {
      "@type": "PropertyValue",
      "name": "outdoorRated",
      "value": true
    },
    {
      "@type": "PropertyValue", 
      "name": "environmentalRating",
      "value": "IP68"
    }
  ],
  "isRelatedTo": {
    "@type": "IndustrialApplication",
    "name": "Outdoor Industrial Use"
  }
}

Why This Matters

Gartner emphasises knowledge graphs as key enablers of enterprise-wide AI success, noting that semantic metadata creates a “living map of enterprise knowledge, powering discovery, lineage, and trustworthy automation.”

The NLP interface can translate:

“outdoor cables” → query for outdoorRated:true
“industrial use” → query for relationship to IndustrialApplication
“suitable for” → traverse isRelatedTo relationships

But only if the backend knowledge graph has:

Typed entities (@type: Product)
Defined properties (outdoorRated, environmentalRating)
Explicit relationships (isRelatedTo, isUsedFor)
Semantic consistency (same vocabulary across all products)

Why This Architecture Works

❌ Bad approach: Force users to learn Schema.org
❌ Bad approach: Hope keyword search magically understands context
✅ The solution: NLP layer translates human → structured queries → Schema.org graph

The User Experience:

Simple: Natural language input
Powerful: Graph-backed reasoning
Scalable: Works with millions of entities

The Technical Reality:

Complex: GraphRAG backend
Structured: Schema.org knowledge graph
Intelligent: NLP intent translation

GraphRAG vs RAG vs Search

Why GraphRAG Is Different

Traditional Search (Elasticsearch):

Query: “outdoor cables”

Process:

Tokenize: [“outdoor”, “cables”]
Match: Find documents with these words
Rank: Sort by relevance score
Return: List of documents

Problem: No understanding of “outdoor” meaning “environmental rating”

Vector RAG (Embeddings):

Query: “outdoor cables”

Process:

Embed query as vector
Find similar vectors in corpus
Retrieve similar documents
LLM synthesises answer

Problem: May find contextually similar content, but can’t reason about properties and relationships (e.g., “is this cable outdoor-rated?”)

GraphRAG (Knowledge Graph):

Query: “outdoor cables”

Process:

NLP interprets intent: [Product recommendation, constraint: outdoor]
Translate to graph query: MATCH (p:Product)-[:HAS_PROPERTY]->(prop) WHERE p.category = “Cable” AND prop.name = “outdoorRated” AND prop.value = true
Traverse relationships to get full context
Return entities with reasoning paths

Result: Structured answers based on explicit properties and relationships

GraphRAG Enhances Vector RAG

Important: GraphRAG doesn’t replace vector RAG—it enhances it. The most powerful systems combine:

Vector embeddings for semantic similarity
Knowledge graphs for relationship reasoning
RAG for grounding in authoritative sources

Leading implementations integrate knowledge graphs or vector databases for contextual reasoning and personalized insight, using retrieval-augmented generation to access relevant information.

Why This Matters for Scale

Dataset Size	Keyword Search	Vector RAG	GraphRAG
100 pages	Works fine	Overkill	Overkill
10,000 pages	Starts failing	Works	Works better
100,000 pages	Catastrophically fails	Struggles	Excels
1,000,000 entities	Unusable	Expensive/slow	Designed for this

At scale:

Keywords drown in noise
Vectors become imprecise (too many similar things)
Graphs maintain relationships (precise traversal)

The Agent Economy Needs GraphRAG

Why AI Agents Require This Architecture

What is Agentic AI?

According to Forrester and Gartner, agentic AI refers to systems that can:

Plan: Break down goals into actionable steps
Decide: Make autonomous choices within defined parameters
Act: Execute tasks and interact with systems
Adapt: Learn from outcomes and adjust approach

This goes beyond traditional AI that merely assists—agentic systems operate autonomously.

Agents Don’t Want Your Search Box—They Want Your Knowledge Graph

Agentic Commerce Scenario: Customer Agent Shops For You

❌ If you only have keyword search:

Agent: “Find industrial cables suitable for outdoor marine environment”

Your site: Returns 200 product pages

Agent: Must scrape each page, parse HTML, extract specs, compare

Result: Slow, error-prone, probably wrong

✅ If you have GraphRAG with MCP:

Agent: Queries your MCP endpoint

Query: {“entity”: “Product”, “filters”: {“category”: “Cable”, “application”: “Industrial”, “environment”: [“Outdoor”, “Marine”]}}

Response: Structured JSON with 3 products matching exact criteria

Agent: Makes informed recommendation in seconds

Two Types of Agent-Accessible Knowledge

Forrester distinguishes between “owned experiences” (your website/app) and “non-owned experiences” (answer engines like ChatGPT, Perplexity).

Owned Experiences:

You control the agent interface
Brand-controlled recommendations
Direct customer relationship
Example: VISEON’s “Ask” interface powered by its own knowledge graph

Non-Owned Experiences:

Agents query your knowledge externally
You’re one of many options presented
No direct customer relationship
Example: ChatGPT queries your MCP endpoint

Strategy: Build for both. Your knowledge graph serves owned experiences (immediate control) AND enables discovery in non-owned environments (broader reach).

The Backend Powers Everything

Same GraphRAG backend serves:

Your revised “Search/Ask” interface (humans via NLP)
Customer agents (via MCP/API)
Partner agents (B2B procurement)
Internal tools (employee knowledge access)

The Architecture:

1 Multiple Interface Layers

Search/Ask Interface (Humans/NLP)
MCP Endpoint (AI Agents)
GraphQL API (Custom Apps)

2 GraphRAG Backend

Knowledge Graph (Schema.org)

Build once. Query everywhere.

The GraphRAG Advantage Compounds

Network Effects of Structured Knowledge:

Adopt Early (Now):

Build comprehensive knowledge graph
Expose via MCP/APIs to agents
Agents learn your entity structure
Your vocabulary becomes standard
Competitors must map to your terms

Late Adoption (18 months):

Try to build knowledge graph
Find agents already trained on competitor schemas
Must conform to established patterns
Play catch-up on entity coverage
Lower trust scores from agents (newer = less established)

Industry Adoption Timeline

Gartner predicts 40% of enterprise applications will be integrated with task-specific AI agents by end of 2026, up from less than 5% in 2025.

By 2028, at least 15% of day-to-day work decisions will be made autonomously through agentic AI, up from 0% in 2024.

The window for adoption is now-24 months—not years.

The Compounding Value

Month 1: You expose 1,000 products via GraphRAG
Month 6: Agents query 10,000 times, learn your patterns
Month 12: Agents prefer your structured data (reliable source)
Month 18: Competitors finally implement, but you’re the reference
Month 24: Your knowledge graph is the industry standard

Block Search, Expose Knowledge

You Blocked Search to Crawlers. Now Expose Knowledge APIs.

For 20 years:

robots.txt:
Disallow: /search
Disallow: /*?s=*

“Don’t let Google crawl our search result pages”

Reason: Duplicate content, wasted crawl budget, low-quality pages

The New Reality

For 2025+:

Schema.txt/mcp:
Knowledge-Graph: /api/knowledge
Query-Protocols: MCP, GraphQL, REST

“Do let AI agents query our knowledge graph”

Reason: Authoritative source, structured data, high-quality entities

What Changed

Then: Blocking access to interface (HTML search result pages)

Now: Exposing access to knowledge (structured graph data)

These Aren’t Contradictory

Still block: Disallow: /search (search result pages)
Now expose: Allow: /api/knowledge (knowledge graph queries)

Different layers:

UI layer (block crawlers)
Data layer (open to agents)

Sunset Search In Favour of Knowledge

You blocked search because it failed at scale (noise, duplicates, irrelevance).

Now you’re exposing knowledge graphs, via API, because they succeed at scale (structure, relationships, precision).

The : Interfaces age. Knowledge compounds.

How VISEON Builds This

From Content to GraphRAG-Ready Knowledge

The VISEON Process:

Phase 1: Knowledge Extraction

Audit existing content (CMS, documents, databases)
Identify entities (Products, Services, Organisations, People)
Extract properties and relationships
Map to Schema.org vocabularies

Phase 2: Graph Construction

Build knowledge graph with category theory coherence
Define entity relationships (isPartOf, offers, manufacturer)
Validate semantic consistency
Create canonical identifiers

Phase 3: GraphRAG Deployment

Deploy backend query infrastructure
Build NLP interface layer
Expose MCP endpoints for agents
Implement APIs for programmatic access

Phase 4: Interface Deployment

“Ask” interface for humans
MCP server for AI agents
GraphQL/REST APIs for custom integrations
Monitor and optimise based on queries

What You Get

For Humans:

Natural language “Ask” interface on your site
Context-aware answers (not keyword matching)
Conversational follow-ups

For AI Agents:

MCP endpoints for Claude, ChatGPT, etc.
Structured query APIs
Authoritative knowledge source

For Your Business:

Agent-discoverable (appear in AI recommendations)
Query analytics (what agents ask about)
First-mover advantage in agent economy

The Opportunity

GraphRAG Is Infrastructure for the Agent Era

The Parallel:

2007: “Do we need a mobile-responsive site?” Answer: Soon it won’t be optional

2025: “Do we need GraphRAG-backed knowledge?” Answer: Soon it won’t be optional

What’s Different This Time

Mobile was about interface (CSS media queries). GraphRAG is about semantics (knowledge graphs).

Mobile late-adopters lost traffic. GraphRAG late-adopters will be invisible to agents.

The Market Opportunity

BCG projects that agentic AI will influence over $1 trillion in e-commerce spending, representing about 50% of total e-commerce expenditure today. Early adoption is focused on routine purchases (groceries, restaurant orders, personal care), but will rapidly expand to complex B2B procurement.

Traffic to US retail sites from GenAI browsers and chat services increased 4,700% year-over-year in July 2025, according to Adobe.

Industry Consensus – November 2025

Leading analyst firms agree on the transformational nature of agentic AI:

BCG: Projects $1 trillion in agent-influenced commerce
Gartner: Predicts 40% of enterprise apps will include AI agents by 2026
Forrester: Confirms agentic AI is “the next competitive frontier“
PwC: Reports 79% of executives already using AI agents

The Risk is Digital Obscurity

Agents are the new interface. If agents can’t query your knowledge, you don’t exist-digital obscurity.
Building a GraphRAG infrastructure now provides AI a robust interface to your organisation, its products and services.

Ready to Build Agent-Accessible Knowledge?

Start With an Assessment

Can AI agents discover and query your organisation today?

Contact the VISEON team to assess your current Search state and to help build your AI Search GraphRAG infrastructure.

Email: [email protected]

Agentic Search Solves Search Intent Problem

Website Search Needs A Reboot

Search Failed. Context Wins. AI Agents Need GraphRAG.

Why Search Structurally Failed

Content vs Context: Why Keywords Don’t Scale

The Real Problem: Content Without Context

The Core Issue

Why Chatbots Without GraphRAG Fail

The Natural Language Solution

From Keywords to Intent: How GraphRAG Changes Everything

Why This Works (And Keyword Search Doesn’t)

The Critical Difference

Why GraphRAG Requires Schema.org

The Backend Must Be Structured (Even If Users Aren’t)

Why This Matters

Why This Architecture Works

GraphRAG vs RAG vs Search

Why GraphRAG Is Different

GraphRAG Enhances Vector RAG

Why This Matters for Scale

The Agent Economy Needs GraphRAG

Why AI Agents Require This Architecture

Agentic Commerce Scenario: Customer Agent Shops For You

Two Types of Agent-Accessible Knowledge

The Backend Powers Everything

The GraphRAG Advantage Compounds

Industry Adoption Timeline

The Compounding Value

Block Search, Expose Knowledge

You Blocked Search to Crawlers. Now Expose Knowledge APIs.

The New Reality

What Changed

These Aren’t Contradictory

Sunset Search In Favour of Knowledge

How VISEON Builds This

From Content to GraphRAG-Ready Knowledge

Phase 1: Knowledge Extraction

Phase 2: Graph Construction

Phase 3: GraphRAG Deployment

Phase 4: Interface Deployment

What You Get

The Opportunity

GraphRAG Is Infrastructure for the Agent Era

What’s Different This Time

The Market Opportunity

Industry Consensus – November 2025

The Risk is Digital Obscurity

Ready to Build Agent-Accessible Knowledge?

Start With an Assessment

Learn More