Knowledge Graph Engine

Connect ideas.
Discover meaning.

Atomixi is a semantic knowledge graph engine. Build knowledge as connected atoms, explore through contextual lenses, and let intelligence emerge from structure.

The Atomixi app is offline for now. A new version is in the works.
See how it works
Apple
→
is_a
→
Fruit
→
is_a
→
Food

Semantic Triples

Entity A → Relation → Entity B. The fundamental unit of knowledge. Bidirectional traversal lets you explore in any direction.

Context Lenses

View the same graph through different perspectives. "Apple" means fruit in nutrition, company in tech. Contexts filter without destroying.

Temporal Knowledge

Facts change over time. Every relation carries validity windows. Ask: "What did I know about X in March 2024?"

Inference Engine

Transitive reasoning: if Apple is_a Fruit and Fruit is_a Food, Atomixi infers Apple is_a Food. Symmetry, inverse rules built in.

Vector Search

Semantic similarity through embeddings. Find concepts related by meaning, not just explicit links. Powered by SurrealDB's native vector index.

Private by design

Your graph lives on our own server in Germany.

How it works

Build knowledge graphs with simple, intuitive operations

# Create atoms of knowledge
Network.create_entity("Apple", "fruit", "A red fruit")
Network.create_entity("Fruit", "category", "Food category")
Network.create_entity("Food", "category", "Sustenance")

# Connect them with meaningful relations
Network.connect("Apple", "Fruit", "is_a",
  contexts: ["nutrition", "biology"])
Network.connect("Fruit", "Food", "is_a",
  contexts: ["nutrition"])

# Query through a context lens
Network.get_connections("Apple", context: "nutrition")

# Transitive inference
Inference.is_related?("Apple", "Food", "is_a")
# => true (via Apple → Fruit → Food)
Evolved from the Widgetizer knowledge graph engine 2008 — 2026

Get in touch

Questions, ideas, or collaboration?

info@atomixi.com Support this project