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Knowledge Graph Architecture
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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.
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Enterprise Knowledge Graph Integration
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eGain Composer connects to and leverages multiple types of enterprise graphs to deliver contextual, personalized responses:
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Journey Maps & Customer Journeys
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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.
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Content Taxonomy & Ontology
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Integrate with your structured categorization systems to:
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Ground AI responses in your organizational terminology
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Reduce ambiguity in content interpretation
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Ensure compliance with established content standards
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Maintain consistency across all customer touchpoints
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Customer Graph
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Connect to CRM or Customer Data Platform (CDP) systems to personalize responses based on:
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Customer profile and preferences
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Entitlements and service tier
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Historical interactions and support history
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Account-specific context
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Product Graph & PIM
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Integration with product information management enables the AI to reason across:
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Product specifications and features
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Associated troubleshooting procedures
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Related policies and documentation
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Cross-product relationships and dependencies
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Employee Graph
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In agent-assist scenarios, employee graph integration enables:
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Role-based content delivery
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Skills-based routing and guidance
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Access-level appropriate information
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Region or team-specific protocols
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Conversation Graph
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Built dynamically from historical interactions, this graph:
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Reveals patterns in customer inquiries
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Identifies successful resolution paths
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Highlights knowledge gaps and content improvement opportunities
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Informs continuous optimization of the knowledge base
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eGain Internal Semantic Knowledge Model
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In addition to consuming external graphs, eGain maintains an internal semantic knowledge model that serves as a unified, governed layer across all knowledge sources.
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Core Capabilities
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Content Normalization & Indexing
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Aggregates and normalizes content from all connected sources
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Creates a unified semantic index for efficient retrieval
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Maintains source attribution and lineage
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Linguistic Intelligence
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Manages synonyms, acronyms, and industry-specific terminology
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Handles multilingual equivalencies
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Adapts to domain-specific language patterns
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Relationship Management
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Maps semantic relationships (e.g., problem → guidance → resolution)
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Maintains hierarchical and associative connections
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Enables reasoning across related concepts
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Governance & Compliance
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Enforces content approval workflows
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Maintains version control and audit trails
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Ensures regulatory compliance and policy adherence
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Controls content lifecycle and retirement
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Security & Data Governance
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All knowledge graph integrations respect:
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Existing access controls and permissions
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Data residency and sovereignty requirements
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Privacy and confidentiality classifications
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Enterprise security policies
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The internal knowledge model operates within your security perimeter and does not expose data outside established governance boundaries.
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Benefits of the Hybrid Approach
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Contextual Accuracy
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Leveraging your enterprise graphs ensures answers reflect your business logic and data
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Semantic Precision
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Our internal model reduces ambiguity and improves retrieval quality
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Governance Assurance
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Built-in controls ensure compliance and quality standards
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Scalability
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The system grows with your knowledge ecosystem without requiring re-architecture
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Continuous Learning
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Feedback loops improve both the internal model and highlight opportunities to enhance your enterprise graphs