Neural Fabric · Knowledge Graph

How It Works

Save a note, drop in a file, capture a decision. It's instantly available to every agent you use. One Neural Fabric. Everything reads from it.

Write once. Recall anywhere.

Write from any MCP-connected LLM. Ask from any other. It's there, with the date, the confidence, and the source. That's the Neural Fabric.

Agent A · IDE

// Write a decision fabric_add({ name: "Teller vs Plaid decision", type: "Decision", properties: { summary: "Chose Teller over Plaid — simpler API, better webhooks.", date: "2026-02-14" } }) // → ✓ Persisted to Neural Fabric · governed write

14 days later · different agent

// Recall from any connected agent fabric_search({ query: "Why did we pick Teller?" }) // → Decision: Chose Teller over Plaid — simpler API, better webhook reliability. // source: Decision · Feb 14, 2026

Neural Fabric

LLM Agent

✓ saved to Neural Fabric

AI Agent

✓ saved to Neural Fabric

Document

✓ saved to Neural Fabric

Any Agent · Later

"What do we know about this project?"

Synthesised answer · 3 sources

confidence 1.0 · 812ms

LLM
Agent

Client wants the dashboard delivered by end of March.

written 6 days ago

AI
Agent

Budget approved. Team aligned on delivery scope.

written 3 days ago

Document

Spec doc, 5 requirements connected to this project

ingested 8 days ago

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.

Multi-hop

Graph traversal

Answers emerge from connections across your entire knowledge graph, not from a single matching chunk. Follows chains of related entities standard retrieval can't see.

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.

Bring your own LLM

Your keys, your provider

Document extraction runs on your Anthropic, OpenAI, or compatible API key — encrypted at rest with AES-256. You control the model, the cost, and the data flow. Qorbit never stores or proxies your LLM credentials.

Six tools. Every agent.

Connect any MCP-compatible AI tool and access your entire knowledge graph through six governed tools. No SDK. No integration code.

fabric_add

Add entities and documents to the graph

fabric_search

Search by name, type, or natural language

fabric_list

List and filter entities across your graph

fabric_graph

Entity neighborhoods and resonance subgraphs

fabric_delete

Remove entities with tombstone semantics

fabric_provenance

Cryptographic audit trail with tamper detection

Same six tools in Claude, ChatGPT, Cursor, Claude Code, or any MCP-compatible agent.

Validated at scale.

10-hop graph traversal passing at every scale tested, from 211 entities to 5 million. The Neural Fabric scales linearly at ~120 bytes per entity. Spectral radius held below 1.0 at every point.

Scale ValidationAll milestones passed

Entities	Retrieval time	Memory	Max hops	Status
1,000	6.5ms	121KB	10	PASS
10,000	48ms	1.2MB	10	PASS
100,000	363ms	11.8MB	10	PASS
1,000,000	5.3s	118MB	10	PASS
5,000,000	1.9min	591MB	10	PASS

5,188

entities / sec

Encoding throughput. Zero embedding API calls. Frequency-native encoding, not dense vector computation.

120B

per entity

In-memory propagation footprint. Linear scaling validated to 5M entities.

1,097×

vs dense at 10K

Sparse vs dense speedup. Dense matrix at 10K requires 800MB. Sparse requires 1.2MB.

0.998

spectral radius

Measured on real-world corpus. Constrained below 1.0 at every write. The Neural Fabric cannot drift.

Validated to 5 million entities. 99.8% candidate reduction at every scale. Multi-hop traversal verified at 10+ hops. Linear O(n) scaling confirmed.