Dasein.
Knowledge Graph Embeddings for RAG
RETRIEVAL
Reasoning Behavior
SEMANTICS
Vivid Depth via Asymmetry
AGENT-ERA
Ultra Low Latency
100x
Faster Than Graph Based Approaches at Scale
43%
Reduced Hallucination Than Semantic Embedding Models
Vector infrastructure for knowledge graphs.
Our KGE engine uses quaternions for quality embeddings at speed, enabling superior retrieval-augmented generation.
KGE engine.
Runs ultra-fast, powering real-time RAG.
Knowledge Graph Embeddings.
Purpose-built for Information Retrieval
Enterprise ready.
Scalable, secure, and production tested.
Trusted by RAG Enthusiasts Worldwide
Join a growing community of developers who choose Dasein for its scaleable quality and ease of use.
How It Works
Dasien's proprietary extraction model enables unparalleled scale and quality at minimal cost
1
Knowledge Extraction
Entities and relationships are extracted directly from text cutting signal from noise.
2
Embedding
The extracted graph structure is converted into a embedding space using quaternions
3
Query
Our KGE specific query parser turns complex vague multihops questions into real results
Your Questions, Answered
Find everything you need to know about Cryptix, from security to supported assets.
What is KGE (Knowledge Graph Embeddings)?
What does “Dasein” mean?
Is this for me?
Are there complimentary credits?
Does it scale?
Is it secure?
What is multihop retrieval?
Why does asymmetry matter?
Ready to take control of your RAG Stack?
Join thousands of users who trust Dasein-KGE for scaled reasoning-like retrieval at speed.
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