Claude updates from the website

Author: cybermaggedon

overview/architecture.md

@@ -8,61 +8,85 @@ grand_parent: TrustGraph Documentation
 
 # Architecture
 
-Learn about TrustGraph's system architecture and design principles.
+Learn about TrustGraph's system architecture and design principles for building intelligent AI agent platforms.
 
 ## System Overview
 
 ### High-Level Architecture
-Coming soon - architectural overview
+TrustGraph follows a modular, microservices-based architecture that enables scalable knowledge graph construction and AI agent deployment. The platform is designed to integrate with existing enterprise infrastructure while providing advanced knowledge processing capabilities.
 
 ### Core Components
-Coming soon - component descriptions
+- **Knowledge Graph Builder**: Extracts entities and relationships from enterprise data
+- **Vector Embedding Engine**: Creates semantic embeddings for knowledge elements
+- **GraphRAG Processor**: Combines graph and vector search for contextual retrieval
+- **AI Agent Runtime**: Executes intelligent agents with access to knowledge graphs
+- **Integration Layer**: Connects with external LLMs, databases, and enterprise systems
 
 ### Data Flow
-Coming soon - data flow diagrams
+1. **Ingestion**: Raw data from various sources (documents, databases, APIs)
+2. **Processing**: Entity extraction, relationship identification, and graph construction
+3. **Embedding**: Vector representation of knowledge elements
+4. **Storage**: Persistent storage in graph and vector databases
+5. **Query**: AI agents query knowledge graphs for contextual information
+6. **Response**: Contextually-aware responses based on relationship understanding
 
 ## Storage Layer
 
-### Graph Database
-Coming soon - graph database architecture
+### Knowledge Graph Storage
+Supports multiple graph database backends including Neo4j, ArangoDB, and others. Stores entities, relationships, and metadata in optimized graph structures.
 
-### Data Storage
-Coming soon - data storage details
+### Vector Database Integration
+Integrates with popular vector databases like Pinecone, Weaviate, and Chroma for semantic similarity search and hybrid retrieval.
 
-### Persistence
-Coming soon - persistence mechanisms
+### Knowledge Packages
+Combines graph and vector storage into unified "Knowledge Packages" that provide both structured relationships and semantic search capabilities.
 
 ## Processing Layer
 
-### Query Engine
-Coming soon - query engine architecture
+### Entity Extraction Engine
+Uses advanced NLP techniques to identify entities, relationships, and concepts from unstructured data sources.
 
-### Analytics Engine
-Coming soon - analytics processing
+### Relationship Mapping
+Builds sophisticated relationship maps that understand how different entities connect and influence each other.
 
-### Event Processing
-Coming soon - event processing system
+### GraphRAG Processing
+Implements Graph Retrieval-Augmented Generation that leverages both graph structure and vector similarity for enhanced context retrieval.
 
-## API Layer
+### AI Agent Orchestration
+Manages the execution of multiple AI agents with access to shared knowledge graphs and contextual information.
 
-### REST APIs
-Coming soon - REST API architecture
+## Integration Layer
 
-### GraphQL APIs
-Coming soon - GraphQL implementation
+### LLM Integration
+Supports multiple Large Language Models through standardized interfaces, enabling organizations to use their preferred models.
 
-### WebSocket APIs
-Coming soon - real-time API architecture
+### Enterprise Data Connectors
+Built-in connectors for common enterprise systems including databases, document management systems, and APIs.
+
+### API Gateway
+Provides unified access to all TrustGraph capabilities through REST APIs, GraphQL, and WebSocket connections.
 
 ## Deployment Architecture
 
-### Containerization
-Coming soon - container architecture
+### Containerized Deployment
+Fully containerized using Docker with Kubernetes orchestration for scalable, cloud-native deployments.
+
+### Microservices Design
+Modular architecture allows independent scaling of different components based on workload requirements.
+
+### Multi-Cloud Support
+Designed to run on any cloud platform or on-premises infrastructure with consistent performance and capabilities.
+
+### Security & Compliance
+Built-in security features including data encryption, access controls, and audit logging to meet enterprise security requirements.
+
+## Scalability & Performance
 
-### Scalability
-Coming soon - scaling strategies
+### Horizontal Scaling
+Components can be scaled independently based on demand, from knowledge graph construction to AI agent execution.
 
-### High Availability
-Coming soon - HA design
+### Distributed Processing
+Supports distributed processing for large-scale knowledge graph construction and complex query processing.
 
-Coming soon - detailed architecture documentation!
+### Caching & Optimization
+Intelligent caching strategies and query optimization ensure fast response times even with large knowledge graphs.

overview/features.md

@@ -8,40 +8,42 @@ grand_parent: TrustGraph Documentation
 
 # Features
 
-Explore TrustGraph's comprehensive feature set designed for trust and reputation systems.
+Explore TrustGraph's comprehensive feature set designed for building intelligent AI agents.
 
 ## Core Features
 
-### Graph Database
-Coming soon - graph database capabilities
+### Knowledge Graph Construction
+Transform enterprise data into interconnected knowledge structures that preserve relationships and context. TrustGraph automatically identifies entities, relationships, and hierarchies within your data.
 
-### Trust Analytics
-Coming soon - trust analytics features
+### GraphRAG Technology
+Advanced Graph Retrieval-Augmented Generation that goes beyond traditional RAG approaches. Instead of retrieving isolated documents, GraphRAG understands the relationships between data points for more contextual responses.
 
-### Reputation Systems
-Coming soon - reputation system features
+### AI Agent Intelligence
+Build sophisticated AI agents that can reason about complex relationships in your data. Agents understand not just what information exists, but how different pieces of information connect and relate to each other.
 
-### Query Engine
-Coming soon - query engine capabilities
+### Knowledge Packages
+Combine Knowledge Graphs with Vector Embeddings to create comprehensive "Knowledge Packages" that provide both structured relationships and semantic similarity search capabilities.
 
-### Data Integration
-Coming soon - data integration features
+### Multi-Technology Integration
+Flexible architecture that supports integration with:
+- Multiple Large Language Models (LLMs)
+- Various vector databases
+- Different graph database systems
+- Enterprise data sources
 
-### Visualization
-Coming soon - visualization capabilities
-
-### Security & Privacy
-Coming soon - security and privacy features
+### Open Source Transparency
+Complete visibility into how your data is processed, transformed, and used by AI agents. No black boxes or vendor lock-in.
 
 ## Advanced Features
 
-### Machine Learning
-Coming soon - ML integration features
+### Contextual Reasoning
+Agents can perform sophisticated reasoning about relationships between data points, reducing hallucinations and improving accuracy.
 
-### API & SDKs
-Coming soon - API and SDK information
+### Enterprise Data Unification
+Break down data silos by connecting fragmented information across your organization into coherent knowledge systems.
 
-### Monitoring & Observability
-Coming soon - monitoring features
+### Scalable Architecture
+Designed to handle enterprise-scale data processing and knowledge graph construction with high performance and reliability.
 
-Coming soon - detailed feature documentation!
+### Developer-Friendly APIs
+Comprehensive APIs and SDKs for integrating TrustGraph into your applications and workflows.

overview/index.md

@@ -19,6 +19,20 @@ Learn about TrustGraph's capabilities, architecture, and how it can solve your t
 
 ## What is TrustGraph?
 
-TrustGraph is a modern graph database and analytics platform specifically designed for trust and reputation systems. It enables organizations to model, analyze, and query complex trust relationships at scale.
+TrustGraph is an **Open Source Agent Intelligence Platform** that helps organizations build, deploy, and manage sophisticated AI agents with deep contextual understanding. Unlike traditional AI systems that work with isolated data points, TrustGraph creates interconnected Knowledge Graphs from enterprise data, enabling agents to understand relationships and context.
 
-Coming soon - detailed overview of TrustGraph capabilities!
+### Key Capabilities
+
+- **Knowledge Graph Construction**: Transform fragmented enterprise data into interconnected knowledge structures
+- **GraphRAG Technology**: Advanced Graph Retrieval-Augmented Generation that goes beyond standard RAG approaches
+- **Contextual AI Agents**: Build agents that understand relationships between data points, not just isolated facts
+- **Open Source Transparency**: Full visibility into data processing with no vendor lock-in
+- **Enterprise Integration**: Unify siloed organizational data into coherent knowledge systems
+
+### Why TrustGraph?
+
+Traditional AI agents often struggle with hallucinations and lack of context. TrustGraph solves this by:
+- Creating "Knowledge Packages" that combine Knowledge Graphs with Vector Embeddings
+- Enabling agents to perform contextual reasoning rather than simple pattern matching
+- Providing radical transparency in how AI systems process and understand data
+- Supporting integration with multiple LLMs, vector databases, and graph databases

overview/philosophy.md

@@ -8,5 +8,44 @@ grand_parent: TrustGraph Documentation
 
 # Philosophy
 
-Learn about TrustGraph's philosophy
+TrustGraph's philosophy centers on building trustworthy, transparent AI systems that enhance human intelligence rather than replace it.
+
+## Core Principles
+
+### Transparency First
+We believe AI systems should be transparent in their operations. TrustGraph provides full visibility into how data is processed, how knowledge graphs are constructed, and how AI agents make decisions. No black boxes, no hidden algorithms.
+
+### Open Source Foundation
+By being open source, TrustGraph ensures that organizations maintain full control over their AI infrastructure. This prevents vendor lock-in and enables continuous innovation through community contributions.
+
+### Context-Aware Intelligence
+Traditional AI systems often work with isolated data points, leading to hallucinations and contextual misunderstandings. TrustGraph's philosophy is that true intelligence requires understanding relationships and context, not just pattern matching.
+
+### Human-Centric Design
+AI agents should augment human capabilities, not replace human judgment. TrustGraph enables the creation of AI systems that provide deep insights while keeping humans in control of important decisions.
+
+## Design Philosophy
+
+### Relationship-First Approach
+Instead of treating data as isolated facts, TrustGraph prioritizes understanding the relationships between different pieces of information. This creates more nuanced and accurate AI responses.
+
+### Composable Architecture
+TrustGraph is designed to work with existing technology stacks. Rather than forcing organizations to replace their entire infrastructure, it integrates with current LLMs, databases, and tools.
+
+### Enterprise-Ready
+Built with enterprise needs in mind, TrustGraph addresses real organizational challenges around data silos, knowledge management, and AI governance.
+
+### Continuous Learning
+The platform is designed to evolve with your organization's needs, continuously building and refining knowledge graphs as new data becomes available.
+
+## Trust & Reliability
+
+### Reducing AI Hallucinations
+By grounding AI agents in well-structured knowledge graphs, TrustGraph significantly reduces the likelihood of hallucinations and increases the reliability of AI-generated insights.
+
+### Auditable Intelligence
+Every decision made by TrustGraph-powered AI agents can be traced back to specific data sources and reasoning paths, enabling full auditability.
+
+### Responsible AI
+TrustGraph promotes responsible AI development by providing tools for understanding, monitoring, and controlling AI behavior in enterprise environments.
 

overview/use-cases.md

@@ -8,65 +8,69 @@ grand_parent: TrustGraph Documentation
 
 # Use Cases
 
-Discover how TrustGraph can be applied to solve real-world trust and reputation challenges.
+Discover how TrustGraph can be applied to solve real-world AI and knowledge management challenges.
 
-## Industry Applications
+## Enterprise Applications
 
-### Financial Services
-Coming soon - financial trust use cases
+### 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.
 
-### E-commerce & Marketplaces
-Coming soon - marketplace reputation systems
+### 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.
 
-### Social Networks
-Coming soon - social trust applications
+### 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.
 
-### Supply Chain
-Coming soon - supply chain trust tracking
+### 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.
 
-### Healthcare
-Coming soon - healthcare trust networks
+### 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.
 
-### Education
-Coming soon - educational credentialing
+### 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
 
-### Fraud Detection
-Coming soon - fraud prevention use cases
+### 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.
 
-### Recommendation Systems
-Coming soon - trust-based recommendations
+### Decision Support Systems
+Build AI agents that can reason about complex business decisions by understanding the relationships between different factors, stakeholders, and outcomes.
 
-### Risk Assessment
-Coming soon - risk evaluation scenarios
+### 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.
 
-### Identity Verification
-Coming soon - identity trust networks
+### 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.
 
-### Peer-to-Peer Networks
-Coming soon - P2P trust systems
+### Data Integration & Unification
+Break down data silos by creating unified knowledge graphs that connect information across different systems and departments.
 
 ## Implementation Patterns
 
-### Trust Scoring
-Coming soon - trust score implementations
+### GraphRAG Implementation
+Implement Graph Retrieval-Augmented Generation to improve AI response quality by leveraging relationship understanding rather than simple document similarity.
 
-### Reputation Aggregation
-Coming soon - reputation aggregation patterns
+### Multi-Source Knowledge Integration
+Combine data from multiple sources (databases, documents, APIs) into coherent knowledge graphs that preserve relationships and context.
 
-### Network Analysis
-Coming soon - network analysis use cases
+### Incremental Knowledge Building
+Start with core knowledge domains and gradually expand knowledge graphs as more data becomes available and new relationships are discovered.
 
-### Anomaly Detection
-Coming soon - anomaly detection patterns
+### 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.
 
-## Case Studies
+## Success Factors
 
-### Real-World Examples
-Coming soon - detailed case studies
+### Data Quality & Relationships
+Success depends on identifying and accurately modeling the relationships between different data entities.
 
-### Success Stories
-Coming soon - implementation success stories
+### Iterative Development
+Knowledge graphs improve over time as more data is processed and relationships are refined.
 
-Coming soon - detailed use case documentation!
+### 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.