Knowledge Graph Architecture

At the core of eGain Composer is our hybrid knowledge graph approach: we connect to your existing enterprise graphs while maintaining our own internal semantic knowledge model to ensure accurate, governed, and contextually relevant AI answers.

Enterprise Knowledge Graph Integration

eGain Composer connects to and leverages multiple types of enterprise graphs to deliver contextual, personalized responses:

Journey Maps & Customer Journeys

Consume journey mapping data to understand customer intent, context, and interaction stage. The AI tailors answers based on where the customer is in their journey, ensuring responses are timely and relevant to their current needs.

Content Taxonomy & Ontology

Integrate with your structured categorization systems to:

  • Ground AI responses in your organizational terminology
  • Reduce ambiguity in content interpretation
  • Ensure compliance with established content standards
  • Maintain consistency across all customer touchpoints

Customer Graph

Connect to CRM or Customer Data Platform (CDP) systems to personalize responses based on:

  • Customer profile and preferences
  • Entitlements and service tier
  • Historical interactions and support history
  • Account-specific context

Product Graph & PIM

Integration with product information management enables the AI to reason across:

  • Product specifications and features
  • Associated troubleshooting procedures
  • Related policies and documentation
  • Cross-product relationships and dependencies

Employee Graph

In agent-assist scenarios, employee graph integration enables:

  • Role-based content delivery
  • Skills-based routing and guidance
  • Access-level appropriate information
  • Region or team-specific protocols

Conversation Graph

Built dynamically from historical interactions, this graph:

  • Reveals patterns in customer inquiries
  • Identifies successful resolution paths
  • Highlights knowledge gaps and content improvement opportunities
  • Informs continuous optimization of the knowledge base

eGain Internal Semantic Knowledge Model

In addition to consuming external graphs, eGain maintains an internal semantic knowledge model that serves as a unified, governed layer across all knowledge sources.

Core Capabilities

Content Normalization & Indexing

  • Aggregates and normalizes content from all connected sources
  • Creates a unified semantic index for efficient retrieval
  • Maintains source attribution and lineage

Linguistic Intelligence

  • Manages synonyms, acronyms, and industry-specific terminology
  • Handles multilingual equivalencies
  • Adapts to domain-specific language patterns

Relationship Management

  • Maps semantic relationships (e.g., problem → guidance → resolution)
  • Maintains hierarchical and associative connections
  • Enables reasoning across related concepts

Governance & Compliance

  • Enforces content approval workflows
  • Maintains version control and audit trails
  • Ensures regulatory compliance and policy adherence
  • Controls content lifecycle and retirement

Security & Data Governance

All knowledge graph integrations respect:

  • Existing access controls and permissions
  • Data residency and sovereignty requirements
  • Privacy and confidentiality classifications
  • Enterprise security policies

The internal knowledge model operates within your security perimeter and does not expose data outside established governance boundaries.

Benefits of the Hybrid Approach

Contextual Accuracy

Leveraging your enterprise graphs ensures answers reflect your business logic and data

Semantic Precision

Our internal model reduces ambiguity and improves retrieval quality

Governance Assurance

Built-in controls ensure compliance and quality standards

Scalability

The system grows with your knowledge ecosystem without requiring re-architecture

Continuous Learning

Feedback loops improve both the internal model and highlight opportunities to enhance your enterprise graphs