The Memory Infrastructure Behind Learning Agents
engramIQ is the first multi-modal memory platform designed specifically for AI agents. Our infrastructure transforms stateless interactions into intelligent agents that remember, learn, and improve with every conversation.
Built around four complementary memory types and an intelligent orchestration engine that makes agents smarter over time while maintaining enterprise security and compliance.
π§ Multi-Modal Memory Architecture
Four memory types working together to give agents complete context and learning capabilities.
ποΈ Structured Memory
Facts & Metadata
User preferences, account information, transaction records, system state
Relational Data
Normalized records with ACID compliance and transactional updates
π§ Semantic Memory
- β’ Vector Embeddings: Document understanding and similarity search
- β’ Contextual Search: Find related concepts and ideas
- β’ Semantic Clustering: Group related memories automatically
- β’ Multi-Model Support: OpenAI, Cohere, and custom embeddings
πΈοΈ Graph Memory
- β’ Entity Relationships: Model complex connections between data
- β’ Traversal Queries: Follow paths through relationship networks
- β’ Pattern Recognition: Discover hidden relationships automatically
- β’ Knowledge Graphs: Build rich domain models
β° Temporal Memory
- β’ Event Sequences: Track what happened when
- β’ Time-Series Data: Monitor trends and patterns over time
- β’ Causality Tracking: Understand cause and effect relationships
- β’ Temporal Queries: Search within specific time windows
π Agent Learning System
Memory operations designed to capture outcomes, learn from feedback, and continuously improve agent performance.
1. Interaction Capture
- β’ Record all agent-user interactions with context
- β’ Capture user feedback and satisfaction signals
- β’ Track conversation outcomes and success metrics
- β’ Store interaction metadata for analysis
2. Pattern Recognition
- β’ Identify successful interaction patterns
- β’ Detect user preference trends over time
- β’ Recognize context clues that improve responses
- β’ Learn from edge cases and failure modes
3. Memory Refinement
- β’ Update memory based on new information
- β’ Strengthen connections that lead to success
- β’ Archive outdated or incorrect memories
- β’ Optimize memory structure for faster access
4. Intelligent Orchestration
- β’ Memory Router: Select optimal memory types for queries
- β’ Query Planner: Optimize multi-modal queries for performance
- β’ Cache Manager: Predictive pre-fetching based on patterns
- β’ Learning Engine: Continuous optimization of memory operations
5. Performance Optimization
- β’ Sub-100ms P99 latency for memory queries
- β’ Horizontal scaling for millions of agents
- β’ Multi-level caching hierarchy
- β’ Auto-scaling based on demand patterns
6. Developer Experience
- β’ Single API for all memory operations
- β’ Native SDKs with type safety
- β’ Local development with memory persistence
- β’ Comprehensive observability and debugging
π Universal Data Ingestion
Connect any data source to feed your agent's memory. 500+ connectors with automatic semantic enhancement and relationship extraction.
π Batch Processing
Documents, CSVs, databases with full semantic enhancement
β‘ Real-Time Streams
Live data feeds with immediate memory updates
π Scheduled Sync
Automated ingestion from SaaS tools and APIs
π‘ Agent SDK & Integration
Built for developers, designed for agents. Simple integration with any agent framework or custom implementation.
- β’ Language SDKs: Python, TypeScript, Java with full type safety
- β’ Framework Integration: LangChain, LangGraph, AutoGPT compatible
- β’ Custom Adapters: Build your own agent framework integration
- β’ Local Development: Test agents with memory persistence locally
π― Advanced Query Capabilities
Sophisticated memory queries that give agents complete context for intelligent decision-making.
Semantic Queries
Vector similarity search across all content with contextual relevance scoring
Structured Queries
SQL-like queries on structured data with full ACID compliance
Graph Traversal
Follow relationship paths to discover connected information
Temporal Queries
Time-based searches with event sequences and causality tracking
π’ Enterprise-Grade Infrastructure
Built with enterprise security, compliance, and performance from day one.
β‘ Performance & Scale
Designed for production workloads with enterprise performance requirements.
- β’ Sub-100ms P99 latency for memory operations
- β’ Auto-scaling for millions of concurrent agents
- β’ Multi-region deployment with data locality
- β’ 99.9% uptime SLA with automatic failover
π‘οΈ Security & Compliance
Enterprise security and compliance built in, not bolted on.
- β’ Zero-trust architecture with end-to-end encryption
- β’ HIPAA, SOC2, GDPR compliance out of the box
- β’ Fine-grained access controls and audit trails
- β’ Data residency controls and privacy protection
π§ Complete Developer Toolkit
Development Tools
- β’ Local memory persistence for development and testing
- β’ Memory inspection and debugging tools
- β’ Performance profiling and optimization insights
- β’ Comprehensive documentation and examples
- β’ CLI tools for data import and management
- β’ Migration utilities for existing systems
Production Operations
- β’ Real-time monitoring and alerting
- β’ Memory usage analytics and optimization
- β’ Agent performance tracking and insights
- β’ Automated backup and disaster recovery
- β’ Cost optimization and usage reporting
- β’ 24/7 support for enterprise customers