Intelligent Memory
Discover Hidden Patterns and Relationships in Your Data
Kaman's Intelligent Memory goes far beyond simple data storage. Built on a sophisticated 5-layer architecture, it automatically discovers relationships between information, identifies patterns, builds knowledge graphs, and surfaces insights that would otherwise remain hidden.
What Makes Memory "Intelligent"?
Traditional databases store data exactly as you put it in. Kaman's 5-Layer Memory System actively analyzes your information across multiple storage tiers:
The 5-Layer Architecture
Layer 0: Working Memory (Redis)
Ultra-fast access for active conversations and immediate context.
| Characteristic | Value |
|---|---|
| Speed | Microsecond access |
| Duration | Minutes to hours |
| Purpose | Current session context |
| Technology | Redis |
Layer 1: Short-Term Knowledge Graph (Neo4j)
Recent entities and relationships that are actively being used.
| Characteristic | Value |
|---|---|
| Speed | Millisecond access |
| Duration | Hours to days |
| Purpose | Recent interactions, hot entities |
| Technology | Neo4j |
Layer 2: Long-Term Knowledge Graph (Neo4j)
Permanent storage of discovered entities and their relationships.
| Characteristic | Value |
|---|---|
| Speed | Millisecond access |
| Duration | Permanent |
| Purpose | Entity relationships, ontology |
| Technology | Neo4j |
Layer 3: Episodic Memory (PostgreSQL)
Historical interactions and conversation summaries.
| Characteristic | Value |
|---|---|
| Speed | Sub-second access |
| Duration | Permanent |
| Purpose | Conversation history, decisions |
| Technology | PostgreSQL |
Layer 4: Semantic Memory (pgvector)
Deep knowledge with vector embeddings for semantic search.
| Characteristic | Value |
|---|---|
| Speed | Sub-second access |
| Duration | Permanent |
| Purpose | Semantic search, RAG |
| Technology | PostgreSQL + pgvector |
Core Capabilities
Automatic Ontology Discovery
The system automatically identifies the types of entities in your data and how they relate to each other - without manual configuration.
What it discovers:
- Entity types (people, companies, products, projects, etc.)
- Relationships between entities
- Attributes and properties
- Hierarchies and groupings
Example: When you add documents, emails, and records, the system automatically recognizes:
- "John Smith" is a person
- "Acme Corp" is a company
- John Smith works at Acme Corp
- Acme Corp is a customer
- John's emails discuss Project Alpha
Pattern Recognition
Identify recurring patterns across your information that humans might miss:
Knowledge Graph
All discovered relationships are organized into a navigable knowledge graph stored in Neo4j:
Semantic Search
Find information based on meaning, not just keywords:
| Traditional Search | Intelligent Memory Search |
|---|---|
| Must use exact keywords | Understands synonyms and related concepts |
| Returns matching documents | Returns relevant information and connections |
| No context awareness | Understands your query intent |
| Results ranked by keyword frequency | Results ranked by relevance to your need |
Multi-Scope Memory
Memory is organized at multiple levels for appropriate sharing:
| Scope | Access | Examples |
|---|---|---|
| Global | All organizations | Platform knowledge, common patterns |
| Organization | All org members | Company policies, shared knowledge |
| Team | Team members | Project context, team decisions |
| User | Individual only | Personal preferences, private notes |
Memory Operations
Retrieval Strategies
Different strategies for different needs:
| Strategy | Use Case | Speed |
|---|---|---|
| L0 Direct | Current context | Fastest |
| Graph Traversal | Related entities | Fast |
| Semantic Search | Meaning-based | Moderate |
| Hybrid | Best of both | Balanced |
Consolidation
Automatic summarization and consolidation of memories:
Consolidation Activities:
- Summarize conversation histories
- Extract key decisions and outcomes
- Update entity relationships
- Refresh vector embeddings
Business Applications
Security & Fraud Detection
The pattern recognition capabilities make Intelligent Memory invaluable for security:
Detectable Patterns:
- Unusual access patterns that may indicate unauthorized activity
- Transaction anomalies suggesting fraudulent behavior
- Communication patterns that deviate from norms
- Relationship networks that reveal hidden connections
Code Analysis & Documentation
For technical teams, Intelligent Memory can:
- Map relationships between code components
- Identify dependencies and impact of changes
- Auto-generate documentation from code patterns
- Suggest test cases based on code structure
- Detect similar code patterns for reuse opportunities
Customer Intelligence
Build a comprehensive understanding of your customers:
- 360-degree customer views across all touchpoints
- Relationship mapping between contacts and organizations
- Interaction history and sentiment tracking
- Predictive insights based on behavior patterns
Compliance & Audit
Support compliance requirements with:
- Automatic classification of sensitive information
- Relationship tracking for data lineage
- Pattern detection for policy violations
- Complete audit trails of information access
How It Works
1. Information Ingestion
Data enters the system from multiple sources:
- Documents and files
- Database records
- Communication logs
- External system data
2. Multi-Layer Processing
3. Continuous Learning
The system improves over time:
- Refines entity recognition
- Strengthens relationship confidence
- Identifies new pattern types
- Adapts to organizational vocabulary
4. Insight Delivery
Discovered knowledge is made available through:
- AI Assistant queries
- Search and exploration interfaces
- Automated alerts and notifications
- API access for applications
Transparency & Control
Understanding AI Decisions
Every insight comes with an explanation:
- What pattern was detected
- What data led to the conclusion
- Confidence level of the finding
- Related information for context
Data Governance
Maintain control over your information:
- Define what data can be analyzed
- Set retention policies by layer
- Control who can access insights
- Audit all access and discoveries
Human Oversight
The system supports, doesn't replace, human judgment:
- Insights are suggestions, not actions
- Patterns require human validation
- Sensitive discoveries route to appropriate reviewers
- Easy to correct or refine conclusions
Use Case Examples
Example 1: Identifying Fraud Risk
Scenario: An insurance company processes thousands of claims daily.
How Intelligent Memory Helps:
- Builds relationship graph of claimants, providers, and events
- Analyzes claim patterns across all submissions
- Identifies unusual relationships using graph traversal
- Detects patterns matching known fraud indicators
- Surfaces suspicious claims for human review
Result: Higher fraud detection rate with same team size
Example 2: Customer Retention
Scenario: A SaaS company wants to reduce customer churn.
How Intelligent Memory Helps:
- Tracks all customer interactions across layers
- Identifies patterns that preceded previous churns
- Recognizes early warning signs in current customers
- Triggers proactive outreach workflows
Result: Earlier intervention, improved retention rates
Example 3: Knowledge Preservation
Scenario: A consulting firm loses institutional knowledge when employees leave.
How Intelligent Memory Helps:
- Captures relationships between people, projects, and expertise in L2 graph
- Stores decision summaries in L3 episodic memory
- Builds semantic search index in L4 for knowledge retrieval
- Makes expertise discoverable regardless of who holds it
- Identifies knowledge gaps when employees depart
Result: Preserved organizational knowledge, faster onboarding
Getting Started
Step 1: Connect Data Sources
Identify the key information repositories to include in Intelligent Memory.
Step 2: Initial Analysis
Allow the system to analyze existing data and populate all five layers.
Step 3: Review Discoveries
Examine the relationships and patterns the system identifies. Provide feedback to improve accuracy.
Step 4: Integrate into Workflows
Use discovered insights to enhance business processes and decision-making.
Intelligent Memory - Turning data into understanding through five layers of intelligence