NODE_01
INGESTION
Structured Intake & User-Provided Context
STATUS: ONLINE
Loading Intelligence...
Loading Intelligence...
How JSS AI Labs solves "Context Amnesia" using advanced RAG orchestration and semantic memory.
Standard LLMs are "stateless" — they reset after every session, forgetting who you are and what you've discussed.
We build a persistent memory layer that mimics human recall, enabling continuity across conversations and time.
Try the Memory Engine yourself. Teach it facts, reset the session, and see it recall.
Long-term memory is empty.
Facts you share in the chat will appear here and persist across sessions.
We don't just use LLMs; we orchestrate them. Our architecture combines multi-modal ingestion, advanced routing, and hybrid memory systems.
Structured Intake & User-Provided Context
Long-Term Memory Persistence via Semantic Embedding
Retrieval-Augmented Generation for Contextual Responses
Bounded Supportive Responses and Organization Prompts
Advanced parsing of notes, documents, and dialogue history using OCR and layout analysis to preserve structure.
Smart chunking strategies (sliding window) to maintain context boundaries and relationships between concepts.
Combining dense vector search for semantic understanding with sparse keyword search (BM25) for more precise context lookup.
Security safeguards are designed to reduce risk, but no transmission or storage method is completely secure.
Data minimization and redaction patterns can reduce unnecessary exposure of sensitive personal information.
Privacy and data-handling choices should stay aligned with actual product behavior, platform settings, and applicable law.
In sensitive product contexts, "it works" is not enough. We engineer for reliability, test coverage, and clear operating boundaries.
Every commit undergoes rigorous Unit and End-to-End testing to ensure stability.
Automated deployment pipelines with staged rollouts and instant rollback capabilities.
Regular security audits and dependency scanning keep the platform resilient.
We are constantly pushing the boundaries of what's possible with personal AI memory.
Running smaller, optimized models locally on devices for zero-latency privacy and offline capability.
Learning from user patterns without centralizing raw data, preserving privacy while improving model performance.