Use Cases

Discover how TrustGraph can be applied to solve real-world AI and knowledge management challenges.

Enterprise Applications

Knowledge Management

Transform scattered enterprise documentation, databases, and institutional knowledge into interconnected knowledge graphs. Enable AI agents to provide contextual answers that understand how different pieces of information relate to each other.

Customer Support Intelligence

Build AI agents that understand the full context of customer interactions, product relationships, and support history. Provide more accurate and contextually relevant support responses.

Research & Development

Create knowledge graphs from research papers, patent databases, and internal R&D documentation. Enable AI agents to identify connections between different research areas and suggest novel approaches.

Regulatory Compliance

Map complex regulatory requirements and their relationships to business processes. Enable AI agents to provide contextual compliance guidance that understands how different regulations interconnect.

Supply Chain Intelligence

Model complex supply chain relationships and dependencies. Enable AI agents to provide insights about supply chain risks and opportunities based on interconnected data.

Financial Analysis

Connect financial data, market information, and business intelligence into comprehensive knowledge graphs. Enable AI agents to perform more sophisticated financial analysis and risk assessment.

Common Scenarios

Intelligent Document Processing

Go beyond simple document retrieval to understand how documents relate to each other. AI agents can provide answers that span multiple documents and understand contextual relationships.

Decision Support Systems

Build AI agents that can reason about complex business decisions by understanding the relationships between different factors, stakeholders, and outcomes.

Competitive Intelligence

Create knowledge graphs that connect market data, competitor information, and industry trends. Enable AI agents to provide strategic insights based on comprehensive relationship understanding.

Expert Systems

Capture and formalize expert knowledge in knowledge graphs. Enable AI agents to provide expert-level guidance while maintaining transparency about the reasoning process.

Data Integration & Unification

Break down data silos by creating unified knowledge graphs that connect information across different systems and departments.

Implementation Patterns

GraphRAG Implementation

Implement Graph Retrieval-Augmented Generation to improve AI response quality by leveraging relationship understanding rather than simple document similarity.

Multi-Source Knowledge Integration

Combine data from multiple sources (databases, documents, APIs) into coherent knowledge graphs that preserve relationships and context.

Incremental Knowledge Building

Start with core knowledge domains and gradually expand knowledge graphs as more data becomes available and new relationships are discovered.

Contextual AI Agent Development

Build AI agents that understand not just what information exists, but how different pieces of information connect and influence each other.

Success Factors

Data Quality & Relationships

Success depends on identifying and accurately modeling the relationships between different data entities.

Iterative Development

Knowledge graphs improve over time as more data is processed and relationships are refined.

Domain Expertise

Combining domain expertise with TrustGraph’s technology ensures that knowledge graphs accurately represent real-world relationships.

Integration Strategy

Successful implementations integrate TrustGraph with existing tools and workflows rather than requiring complete system replacement.