Why our relationship-aware AI outperforms traditional vector-based systems
Almost every AI system today runs on a flawed piece of technology: vector databases. Vector databases flatten your data into disconnected "vectors" (see image), losing the critical relationships between concepts. This leads to hallucinations, factual errors, and limited reasoning.
Amphibian's knowledge graph approach preserves these relationships, enabling AI that truly understands your documents and all of its intertwined relationships.
Vector-based systems have fundamental limitations that knowledge graphs overcome.
Vector embeddings compress rich, multi-dimensional information into fixed-length vectors, losing critical context and relationships.
Without structured relationships, AI systems make up connections between facts, leading to confident but incorrect responses.
Vector systems can't follow chains of logic across documents, making complex reasoning nearly impossible.
How we build relationship-aware AI that understands your data.
We explicitly model connections between entities, preserving the context that vectors lose.
Our system follows chains of relationships to answer complex queries that connect multiple facts.
Information is organized in a way that mirrors how humans understand relationships between concepts.
The path through the knowledge graph shows exactly how information was retrieved and why it's relevant.
Connections between entities are explicitly defined and typed
Retrieves not just isolated facts, but related contextual information
Enables multi-step reasoning by traversing the graph structure
Discover how Amphibian's knowledge graph technology can transform your AI development.