Neural Fabric

The knowledge
graph that
builds itself.

Add business data once. Every MCP agent sees the same entities, the same connections, and an explanation for every edge.

See how it works →

Validated recall and precision across 124K+ pairs. $0 graph construction. No embedding pipeline.

Works with Claude, Cursor, ChatGPT, OpenClaw, and any MCP client.

Claude Cursor ChatGPT OpenClaw
LIVE DEMO 600 entities 2,738 connections stability 0.922
Direct connections

01

Watch knowledge organize itself.

Qorbit creates the connections no one had to define.

⊞
QORBIT™ Neural Fabric Sign out
Search entities◀ Hide
Find by name, ID, or type...
⊕ Add Document
Entities: 72
Entity Types (16)All None

02

How the Fabric works

Data enters the Fabric, Qorbit structures it into governed entities and relationships, and every connected agent reads and writes against the same graph.

PDF TXT MD CSV JSON XML YAML DOCX Qorbit Entity A Entity B Entity C Agents

03

Three layers. One agent.

AI continuity is not one thing. Each layer carries a different kind of context, for a different amount of time, with a different job.

Layer 1
Session memory
Model context
What the model holds in context. Disappears when the window fills.
Layer 2
Conversational memory
Chat-level preferences
What your agent remembers from past conversations. "User prefers dark mode."
Layer 3
Business knowledge
Neural Fabric
Purchase orders, customers, shipments, invoices, and the mathematical relationships between them.
We complete the stack. We don't compete with chat memory or model context. Use any tool for conversation continuity. Run Qorbit for durable business knowledge every connected agent can query.

04

Three failures. Three fixes.

Problem
Your agents guess.
Resolution
Every answer traces to a source document.
Problem
Your tools are isolated.
Resolution
One graph, every agent, write from one, recall from all.
Problem
Your answers can't be audited.
Resolution
Hash-linked provenance with tamper detection on every edge.

05

What makes it different.

Zero embedding cost
At ingestion
No external embedding provider. No per-ingestion API call to encode your data. Frequency-native encoding runs 250× faster than dense embeddings.
Self-healing graph
Zero administration
Add an entity. Every connection appears. Delete it. Every connection vanishes. Re-add it. They return correctly. No stored relationships to maintain. No stale edges. No orphaned links.
Every edge explains itself
Per-feature forensics
Click any connection and see exactly which properties caused it. Shared customer ID. Matching PO number. Overlapping reference. We haven't found another system that decomposes why two entities are connected.
Governed refusal
Anti-hallucination
When the evidence isn't there, the Neural Fabric returns "No information available", not a guess. Gets quieter, not wronger. Every answer traces to its source.
Cryptographic audit trail
Hash-linked provenance
Every entity carries a SHA-256 provenance chain: actor, timestamp, operation, and graph state at write time. Tampered entries are detected on read. Built for regulated industries where "trust me" isn't compliance.
Bring your own LLM
Your keys, your provider
Document extraction runs on your Anthropic, OpenAI, or compatible API key. Your API key is encrypted at rest with AES-256-GCM. Used only for extraction and classification, never exposed through any API.

06

Eight tools. Every agent.

Connect any MCP-compatible AI tool: Claude, Cursor, ChatGPT, OpenClaw, and any MCP-compatible client. Access your entire knowledge graph through eight governed tools. No SDK. No integration code. Minutes, not months.

fabric_add
Add structured entities directly to your graph. Your agent builds the entity, the Fabric governs the write.
fabric_edit
Make partial, targeted modifications to existing entities. Change one field or many — the graph re-derives connections and governs every write.
fabric_search
Search by name, type, or natural language. Band-code narrowing reduces thousands of entities to a ranked shortlist in milliseconds.
fabric_list
List and filter entities across your graph. Get counts, types, and metadata without loading the full graph.
fabric_graph
Explore entity neighborhoods and resonance subgraphs. See how entities connect through shared frequency signatures.
fabric_read_source
Read source document content from stored files. Pull the original text behind any entity without leaving your agent.
fabric_delete
Remove entities cleanly. Tombstone semantics: connections derived from the deleted entity vanish automatically.
fabric_provenance
Inspect the cryptographic audit trail. Hash-linked entries with actor, timestamp, and tamper detection on every read.

One URL. One token. Two minutes to eight governed tools. Write from one agent, recall from all. Works with Claude, Cursor, ChatGPT, OpenClaw, and any MCP-compatible client.

07

Validated at scale.

Embedding-based retrieval degrades as data grows. Vector spaces lose discriminative power as they get crowded: more entities mean more noise, more false positives, more re-ranking layers to compensate. The Neural Fabric doesn't have this failure mode. Relationships are mathematically derived, not statistically approximated. The spectral radius is constrained below 1.0 at every write.

Validated at 60,000+ entities

Validated on reproducible synthetic corpora with zero steady-state errors.
123 sustained mixed-workload operations at 60K entities, zero errors.

1.0
identity recall
Across 124,000+ verified pairs.
If a connection exists in your documents, your agent finds it.
1.0
Recall@10
Target identity appears in the top 10 every time.
Ask for a customer by name. The right record is in the top results every time.
0.925
MRR
Correct identity ranks near the top.
The correct entity is usually first. Less hunting, less wrong context.
0.993
Precision
False positives stay constrained.
Under 1% false connections. Every edge shows its evidence.
$0
embedding cost
No vector API calls.
Ingestion costs you nothing in tokens. Competitors meter ingestion because their construction runs on LLMs.
None
re-ranker
No compensation layer.
5,188
ENTITIES/SEC
2,149 under default config, 5,188 with aggressive preset. Frequency-native encoding, not dense vector computation.
120B
per entity
In-memory propagation footprint. Linear scaling validated to 60,000+ entities.
1,097×
vs dense at 10K
Sparse CSR vs dense matrix propagation time at 10K entities. Dense: 47.5s. Sparse: 43.3ms.
0.998
SPECTRAL RADIUS
Constrained below 1.0 at every write. Every write is validated for mathematical stability before it lands.
99.8% candidate reduction at every scale tested. Multi-hop traversal verified at 10 hops. Zero embedding cost at any scale.
Your agent reads a shortlist, not a corpus. Smaller contexts, fewer tokens per answer.

Free 14-day trial. Plans from $99/mo.