Glean vs. Knowledge Graphs: Understanding the Difference

Glean has emerged as a popular enterprise AI product, promising to connect your company's knowledge and make it searchable. When evaluating Glean, enterprises often ask: how does it compare to knowledge graph approaches?

The answer: they solve different problems, and understanding the difference helps you choose the right tool.

What Glean Does

Glean is an enterprise search and AI assistant platform:

Connectors: Integrates with many enterprise applications (Google Workspace, Microsoft 365, Slack, Confluence, etc.)

Search: Unified search across connected applications

AI assistant: Chat interface that answers questions using connected data

Personalization: Results tailored to the user based on their activity and role

Glean's strength is breadth of integration and user-friendly search experience.

What Knowledge Graphs Do

Knowledge graphs provide structured semantic understanding:

Entity resolution: Understanding that "Acme Corp," "ACME," and "Customer ID 4412" are the same entity

Relationship mapping: Explicit connections between entities (who manages what, what depends on what)

Structured facts: Verified attributes attached to entities

Query capability: Semantic queries that traverse relationships

Knowledge graphs' strength is depth of understanding about your specific organizational entities.

The Core Difference

Glean: "Find documents about Acme" Knowledge Graph: "Understand what Acme is, how it relates to other entities, and provide verified facts"

Glean excels at finding things. Knowledge graphs excel at understanding things.

A technology company used Glean successfully for document search—engineers found relevant docs faster. But when they asked "What's our relationship with Acme?", Glean returned documents mentioning Acme. It couldn't provide: "Acme is a Tier 1 customer, $2.3M annual revenue, managed by Sarah Chen, with 5 active contracts and a QBR next month." That required a knowledge graph.

When Glean Is Right

Glean fits well when:

Primary need is document findability: Users struggle to find content scattered across applications

Questions are document-answerable: Answers exist in single documents that can be retrieved

Entity complexity is low: Entity resolution isn't a major problem

Relationship queries are rare: Most questions don't require understanding how entities connect

Speed to value matters: Glean deploys faster than custom knowledge graphs

For many enterprises, Glean provides meaningful value for general search and document Q&A.

When Knowledge Graphs Are Right

Knowledge graphs are necessary when:

Entity resolution matters: Same entities appear under different names across systems

Relationships are important: Understanding how things connect is central to queries

Cross-system synthesis required: Answers require combining data from multiple systems

AI accuracy on internal entities is critical: You need verified facts, not document approximations

Operational systems are data sources: CRM, ERP, and transactional systems (not just documents)

For enterprises deploying AI that must understand organizational specifics, knowledge graphs are essential.

The Integration Pattern

Many enterprises use both:

Glean for document search: Find policies, procedures, marketing content, general information

Knowledge graph for entity knowledge: Accurate facts about customers, products, projects, people

The architecture:

Specific Capability Comparison

Entity Resolution

Glean: Limited. May surface documents with variant names but doesn't resolve them to canonical entities.

Knowledge Graph: Core capability. All representations map to canonical identities.

Relationship Queries

Glean: Implicit through document content. Can find documents where two entities appear together.

Knowledge Graph: Explicit relationships. Can traverse: Customer → Contracts → Products → Dependencies.

Structured Data Integration

Glean: Primarily document-oriented. Can connect to some structured data but treats it as searchable content.

Knowledge Graph: Native structured data support. Entities and relationships are first-class citizens.

Real-time Currency

Glean: Depends on connector sync frequency. May lag behind source systems.

Knowledge Graph: Can be updated in real-time or near-real-time as source systems change.

Cross-System Synthesis

Glean: Limited. Returns documents from multiple systems but doesn't synthesize.

Knowledge Graph: Native capability. Queries can traverse data from multiple systems unified through entity resolution.

Use Case Mapping

Use Case	Glean	Knowledge Graph
Find the vacation policy document	✓	–
Search for documents about a topic	✓	–
Get aggregate view of customer relationship	–	✓
Understand entity relationships across systems	–	✓
Answer questions requiring verified facts	–	✓
Document Q&A with simple retrieval	✓	–
Cross-system entity analysis	–	✓

The Accuracy Question

According to analysis from enterprise AI deployments, accuracy on internal entity questions differs significantly:

Document search AI (like Glean): 60-75% accuracy on questions about specific organizational entities

Knowledge graph-augmented AI: 85-95% accuracy on the same questions

The gap comes from entity resolution and relationship understanding—capabilities that document search doesn't provide.

Cost and Complexity

Glean:

SaaS pricing (per user, typically)
Fast deployment (weeks to months)
Limited customization
Connector-dependent coverage

Knowledge Graph:

Infrastructure + development cost
Longer deployment (months)
High customization
Coverage determined by you

For document search, Glean's simplicity is valuable. For organizational understanding, the knowledge graph investment is necessary.

The Decision Framework

Ask these questions:

What types of questions do users ask?
- Document questions → Glean may suffice
- Entity/relationship questions → Need knowledge graph
How important is entity resolution?
- Low importance → Glean may suffice
- Critical → Need knowledge graph
Do answers require cross-system synthesis?
- Rarely → Glean may suffice
- Often → Need knowledge graph
What's the accuracy requirement?
- Good enough → Glean may suffice
- Must be reliable → Need knowledge graph
What's the timeline?
- Need value fast → Start with Glean
- Building strategic capability → Invest in knowledge graph

The Bottom Line

Glean and knowledge graphs solve different problems.

Glean is excellent enterprise search with AI assistance—valuable for document findability and general Q&A.

Knowledge graphs provide semantic understanding of your organization—necessary for accurate AI on internal entities and relationships.

For many enterprises, the right answer is both: Glean for documents, knowledge graphs for organizational understanding.

See how Phyvant builds organizational knowledge graphs → Book a call