Memory That Thinks.
The first cognitive database for autonomous intelligence.
Don't just store vectors. Evolve memory.
The Broken Stack
Building AI agents with long-term memory today is painful. You are forced to glue together vector databases for similarity, graph databases for relationships, and Redis for short-term context.
It's fragile, complex, and fundamentally dumb. Vectors don't understand causality. Graphs are hard to maintain. And your agent still hallucinates because it lacks true context.
Our Vision
Imagine a single binary that gives your agent human-like memory.
- —Automatic Fact Extraction: You push raw text. The database extracts facts, entities, and relationships automatically.
- —Consolidation & Reflection: Just like humans, the database sleeps on it. It consolidates memories, forms generalizations, and creates wisdom over time.
- —Temporal Grounding: It understands "yesterday" or "last meeting" natively, without complex query filters.
Unmatched Performance
RECALL LATENCY
< 5ms
For full cognitive retrieval combining vector, graph, and temporal indices.
INFRA COST
-80%
Eliminate Pinecone, Redis, and Neo4j. Run everything on a single binary.
THROUGHPUT
100k
Operations per second on standard hardware.
Why we are building this
| Current State | CognitiveDB Vision |
|---|---|
| Manual vector embeddings & chunking | Semantic understanding & concept extraction |
| Separate Vector + Graph + SQL DBs | Unified Cognitive Engine |
| Static retrieval (Top-K) | Reasoning-based retrieval & Reflection |
| Hosted SaaS with latency | Local-first, high-performance Rust binary |
Join the revolution.
We are currently in closed beta with select partners. If you are building autonomous agents and tired of the memory problem, talk to us.
Request Access →