Core Concepts
CognitiveX is built on four fundamental layers that work together to provide intelligent, explainable, and cost-effective AI.
Architecture Overview
Each layer serves a specific purpose and can be used independently or combined for powerful workflows:
Memory → Store and retrieve knowledge
Cognition → Reason with context
Decision → Optimize routing and orchestration
Collective → Collaborate across systems
Memory Layer
Persistent knowledge storage with automatic embedding generation and semantic search capabilities.
Key Features:
- •Vector embeddings (1536D)
- •Semantic similarity search
- •Tag-based organization
- •Metadata filtering
- •Auto-chunking for large content
Cognition Layer
Reasoning engine that captures thought processes and provides explainable AI responses.
Key Features:
- •Multi-step reasoning
- •Reflection capture
- •Pattern recognition
- •Context management
- •Explainable decisions
Decision Layer
Adaptive routing and orchestration for multi-LLM workflows and agent coordination.
Key Features:
- •Intelligent LLM routing
- •Cost optimization
- •Quality-based selection
- •Multi-agent coordination
- •Fallback strategies
Collective Layer
Coming soon: Distributed knowledge and multi-agent collaboration across organizations.
Key Features:
- •Federated learning
- •Knowledge sharing
- •Collaborative reasoning
- •Consensus mechanisms
- •Privacy-preserving AI
Key Benefits
85% Cost Reduction
Intelligent caching and routing minimize redundant LLM calls
Explainable AI
Capture and audit reasoning processes for compliance
Multi-LLM Support
Switch between OpenAI, Anthropic, Google, Ollama seamlessly
Enterprise Ready
SOC2, GDPR compliant with role-based access control
Next Steps
Now that you understand the core concepts, explore:
- • SDK Reference for detailed API documentation
- • Architecture for deep technical details
- • Examples for real-world use cases