Architecture
Technical architecture overview of KAI stack
Overview
KAI uses a hybrid architecture that combines single-tenant and multi-tenant components to ensure both data isolation and operational efficiency:
KAI Instance: Single-tenant architecture—each instance runs in complete isolation with dedicated compute and storage
KAI Document Compagnon: Multi-tenant architecture—manages multiple client instances while maintaining data segregation
This architecture provides enterprise-grade security through instance-level isolation while enabling efficient management and scaling.
Core Components
KAI Instance
The semantic indexing engine that powers KAI's intelligent document understanding:
Architecture: Single-tenant—each instance is completely isolated
Semantic Graph: Proprietary neural network that maps relationships across documents
Indexing Engine: Processes and indexes documents from client repositories
API Layer: REST API for querying semantic nodes and relationships
Auto-scaling: Automatically adjusts compute and storage based on load
Isolation: Dedicated pod (AKS) and Elastic Cloud database per instance
KAI Document Compagnon
The intelligent document maintenance layer built on top of KAI Instance:
Architecture: Multi-tenant—manages multiple client instances
Conflict Detection: Analyzes user queries to detect document inconsistencies
Gap Analysis: Identifies missing topics users are asking about
Content Generation: Creates new documents based on admin responses
Alert System: Provides actionable recommendations for document updates
Instance Management: Orchestrates multiple KAI Instances while maintaining data segregation
API Gateway
The integration layer that connects KAI with client systems:
REST API: Standard API for all integrations
Authentication: Secure API key management
Rate Limiting: Ensures fair resource usage
Integration Architecture
How KAI Connects to Your Systems
Integration Points
Document Repositories → KAI
Connection: Via API connectors (Confluence API, SharePoint API, etc.)
Access: Read-only access to documents for indexing
Frequency: Initial full index, then incremental updates as documents change
AI Search System → KAI Document Compagnon
Connection: REST API integration
Data Flow: Your AI system sends user queries to KAI Document Compagnon
Real-time: Queries are analyzed as they happen
KAI → Client Systems
Alerts: KAI Document Compagnon provides alerts via API
Recommendations: Actionable insights on document conflicts and gaps
Generated Content: New documents ready for your knowledge base
Data Flow
Document Indexing Flow
Key Points:
Documents are temporarily stored during processing only
After processing, documents are removed from temporary storage
KAI maintains pointers to original documents, not document copies
All semantic relationships are stored in the dedicated Elastic Cloud instance
Query Processing Flow
Security & Data Isolation
Hybrid Architecture: Single-Tenant Instances, Multi-Tenant Management
KAI Instance: Single-Tenant Architecture
Each KAI Instance operates in complete isolation:
Dedicated Compute: Each instance runs in its own pod on Azure Kubernetes Service (AKS) in France
Dedicated Storage: Each instance has its own Elastic Cloud database
No Shared Resources: No compute, storage, or network resources are shared between instances
Data Segregation: Complete separation ensures no data leakage between clients
Complete Isolation: Each client's semantic graph and indexed data are completely isolated
KAI Document Compagnon: Multi-Tenant Architecture
KAI Document Compagnon manages multiple client instances while maintaining strict data segregation:
Instance Orchestration: Manages multiple KAI Instances (one per client)
Data Segregation: Each client's data is isolated at the KAI Instance level
No Cross-Instance Access: KAI Document Compagnon never mixes data between instances
API Isolation: Each client's API calls are routed to their dedicated KAI Instance
Operational Efficiency: Multi-tenant management layer enables efficient operations while maintaining security
How It Works Together:
Each client's data remains completely isolated in their dedicated KAI Instance, while KAI Document Compagnon provides the management and analysis layer across all instances.
Data Storage & Privacy
Where Data is Stored:
Semantic Graph: Elastic Cloud (dedicated instance per KAI Instance)
Temporary Processing: Azure Blob Storage (documents deleted after processing)
Pointers Only: KAI maintains API pointers to original documents, not document copies
Data Retention:
Semantic Relationships: Stored permanently in Elastic Cloud (as long as instance is active)
Document Content: Not stored permanently—only semantic relationships and metadata
Temporary Files: Deleted immediately after processing
Security Measures
Encryption: All data encrypted in transit and at rest
Access Control: API key-based authentication
Network Security: Isolated network per instance
Compliance: Designed to meet enterprise security and compliance requirements
Scalability & Performance
Auto-Scaling Architecture
Compute Scaling:
Automatically adjusts based on query load
No manual configuration required
Seamless scaling without service interruption
Storage Scaling:
Grows automatically as documents are indexed
No storage limits or thresholds
Transparent to users
Performance Guarantees
Availability:
99.95% uptime SLA
High availability architecture
Automatic failover and recovery
Query Performance:
Semantic graph queries: Always under 8 seconds
Performance consistent regardless of:
Number of documents indexed
Size of document repository
Complexity of semantic relationships
Indexing Performance:
Initial indexing: Speed depends on repository size
Incremental updates: Only changed documents are re-indexed
Minimal impact on ongoing operations
Deployment Models
SaaS (Software as a Service)
Infrastructure:
Hosted on Azure (France region)
Managed by KAI team
Zero infrastructure management for clients
Benefits:
Fast setup and deployment
Automatic updates and maintenance
Full support and monitoring
On-Premises
Infrastructure:
Deployed in client's own infrastructure
Full control over data and compute
Custom integration options
Benefits:
Data residency compliance
Enterprise security requirements
Custom network configuration
Snowflake Marketplace
Infrastructure:
Native integration with Snowflake
Leverages Snowflake compute and storage
Seamless data pipeline integration
Benefits:
Enterprise-grade deployment
Integrated with existing Snowflake workflows
Optimized for Snowflake ecosystem
Architecture Principles
Separation of Concerns
KAI Instance: Handles semantic indexing and retrieval
KAI Document Compagnon: Handles conflict detection and content generation
Client Systems: Handle user experience and response generation
API-First Design
All integrations via REST API
No proprietary protocols or dependencies
Standard authentication and authorization
Easy integration with existing systems
Stateless Operations
API calls are stateless
No session management required
Horizontal scaling without state synchronization
Resilient to individual component failures
Monitoring & Observability
What KAI Monitors
API Performance: Response times and throughput
Indexing Status: Document processing progress
System Health: Instance availability and resource usage
Query Patterns: Usage analytics (anonymized)
What Clients Can Monitor
API Usage: Track API calls and consumption
Indexing Status: Monitor document indexing progress
Alert History: Review conflict and gap detections
Performance Metrics: Query response times
Integration Best Practices
Document Repository Integration
Use Read-Only Access: KAI only needs read access to documents
API Credentials: Use dedicated service accounts with minimal permissions
Incremental Updates: Enable change detection for efficient updates
Error Handling: Implement retry logic for transient failures
AI System Integration
Query Forwarding: Forward user queries to KAI Document Compagnon
Async Processing: Handle alerts and recommendations asynchronously
Rate Limiting: Respect API rate limits for optimal performance
Error Handling: Gracefully handle API errors without impacting user experience
Next Steps
For Technical Teams
Review integration requirements for your document repositories
Plan API integration with your AI search system
Understand deployment model that fits your needs (SaaS, on-prem, Snowflake)
For Business Teams
Understand the value of semantic understanding for your AI system
See how KAI Document Compagnon can improve your knowledge base
KAI Architecture - Enterprise-grade security, performance, and scalability.
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