EmergentGraph
REQUEST ACCESS
KNOWLEDGE GRAPH INFRASTRUCTURE · EARLY ACCESS
The knowledge layer your
agents have been missing
Structured facts. Explicit relationships. Full provenance.
Works with any LLM, any agent framework.
INTELLIGENCE
Most knowledge bases store vectors.
EmergentGraph stores intelligence.
Documents are input. Intelligence is the output.
VECTOR RETRIEVAL
— Unstructured text, no relationships
— Contradictions go undetected
— No record of what changed or why
— Expensive reasoning on every query
— No source traceability
— Low ingestion cost, high query cost
EMERGENTGRAPH
→ Connected facts and relationships
→ Conflicts flagged for review
→ Full audit trail, nothing deleted
→ Precise answers, fewer tokens
→ Every fact traced to its source
→ Higher ingestion cost, cheap queries
HOW IT WORKS
From documents to
trusted knowledge
Upload. Connect. Query. That's it.
STEP 1
Upload & Extract
Documents go in. Facts come out.
STEP 2
Connect & Structure
Facts become relationships.
STEP 3
Query & Trust
Relationships become answers.
The economics of building once
Ingest once. Every query after costs a fraction
of traditional retrieval. Your agents get precise
answers without heavy reasoning on every call.
1×
Ingestion cost — once per document
↓ tokens
Precise facts, less reasoning at query time
✓ trust
Every query traceable. Every answer auditable.
⬡
Full Provenance
Trace any fact back to its source.
⚖
Conflict Resolution
Conflicts flagged and shown for review.
Nothing silently overwritten.
◎
Immutable Audit Trail
Every change recorded. Every fact
superseded, never deleted.
⟳
Framework Agnostic
Works with any LLM, any agent framework.
Your orchestration, your choice.
◈
Flexible Query Modes
Query by semantics, by entity, or both.
⬕
Data Sovereignty
For regulated industries — bring your own database.
Your data never leaves your infrastructure.
Building AI agents that need reliable
knowledge?
EmergentGraph is in early access. If
this resonates, we'd love to talk.