Memory-as-a-Service for Autonomous AI
Intelligence without memory is noise. Persistent, auditable, decay-aware memory infrastructure for long-running autonomous systems. Hash-chain integrity, causal graphs, and built-in forgetting.
Modern AI systems are stateless. Every inference starts from scratch. RAG is not enough.
Purpose-built memory infrastructure, not prompt hacking
Everything autonomous agents need for durable memory
SHA-256 linking. Tampering invalidates all subsequent hashes. Merkle proofs for partial verification.
Track cause-and-effect between memories. Replay reasoning chains for audit and debugging.
Multiple decay functions: linear, exponential, step, sigmoid. Memories fade unless reinforced.
Isolated memory spaces per agent or project. Independent hash chains per namespace.
Identify poisoning attempts. High-volume bursts, salience manipulation, pattern anomalies.
Agent-signed events for non-repudiation. Replay protection with nonce/timestamp checks.
FTS5 for SQLite, tsvector for PostgreSQL. Advanced filter DSL with AND/OR/NOT.
Semantic similarity search. Knowledge graph extraction. LLM-powered summarization.
MNEMOSYNTH is the cognitive layer connecting our autonomous systems
MNEMOSYNTH provides verifiable knowledge to BEAMRIFT's blockchain layer. Memory events become immutable on-chain records with cryptographic proofs. Audit trails that survive beyond any single system.
Learn about BEAMRIFTMNEMOSYNTH drives decision-making for NAVSCOUT's autonomous navigation. Learned obstacles, successful paths, and environmental patterns persist across missions. Drones that actually learn.
Learn about NAVSCOUTNative integrations with LangChain, LangGraph, AutoGPT, CrewAI, and OpenAI Assistants. Drop-in memory backend for any agent framework.
Connects with FORTELLA for threat intel memory. SOC agents remember incidents, correlate patterns, and build institutional knowledge.
Learn about FORTELLAMemory infrastructure for mission-critical autonomous systems
Security agents remember threats, correlate incidents, and build institutional knowledge. Replay attack chains for forensics.
Quant bots remember market observations, link trades to signals, and learn from outcomes. Auditable decision trails.
Embodied AI learns obstacles, successful paths, and environmental patterns. Navigation decisions linked to prior learning.
Personal AI remembers preferences, adapts behavior, and maintains context across months of interaction.
Production-ready client libraries for every stack
Memory is not a feature. It's infrastructure. Build autonomous systems that actually remember.
