The Future of AI Memory
EON Memory System
Persistent Memory for intelligent systems – Your AI never forgets again.
Over 1 million data points processed
The Problem with Standard LLMs
Without EON
- Knowledge lost after every session
- No connections between conversations
- Repeated questions at every start
- No learning customer profiles
- Hallucinations without source verification
With EON
- Persistent memory across all sessions
- Causal links in the knowledge graph
- Instant context from the past
- Self-learning customer profiles
- 29 validation tests against hallucinations
Architecture
Three-Layer Architecture
Modular structure for maximum flexibility and performance
Core Components
The Four Pillars of EON
Vector Database
Semantic search across millions of data points using ChromaDB and state-of-the-art embedding models.
- ChromaDB for persistent vector storage
- all-MiniLM-L6-v2 Embedding Model
- Cosine Similarity for semantic search
- Real-time indexing of new memories
- SQLite backup for redundancy
Knowledge Graph
Networking facts into a coherent knowledge network with causal and temporal relationships.
- Bidirectional relations between memories
- Causal links (depends_on, context_for)
- Automatic relation detection
- Graph traversal for context building
- Conflict detection for contradictions
RAG Pipeline
Retrieval-Augmented Generation for precise, context-aware AI responses with source citations.
- Hybrid Search (Semantic + Keyword)
- Dynamic Context Window Optimization
- Project-scoped Memory Retrieval
- Category-based Filtering
- Relevance Scoring with Time Decay
Multi-Agent Coordination
Multiple AI agents work together in coordination with shared, persistent memory.
- Shared Memory Pool between agents
- Session-based isolation
- Automatic memory consolidation
- Cross-Agent Knowledge Transfer
- Meta-Memory for long-term knowledge
Use Cases
Where EON Makes the Difference
Ready for Intelligent Memory?
Let's discuss how EON can take your AI applications to the next level.
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