ArcFlow
Company
Managed Services
Markets
  • News
  • LOG IN
  • GET STARTED

OZ brings Visual Intelligence to physical venues, a managed edge layer that lets real-world environments see, understand, and act in real time.

Talk to us

ArcFlow

  • World Models
  • Sensors

Managed Services

  • OZ VI Venue 1
  • Case Studies

Markets

  • Sports
  • Broadcasting
  • Robotics

Company

  • About
  • Technology
  • Careers
  • Contact

Ready to see it live?

Talk to the OZ team about deploying at your venues, from a single pilot match to a full regional rollout.

Schedule a deployment review

© 2026 OZ. All rights reserved.

LinkedIn
ArcFlow Docs
Get Started
  • Get Started
  • Quickstart
  • Installation
  • Project Setup
  • Platforms
  • Bindings
  • Licensing
  • Pricing
Capabilities
  • Vector Search
  • Graph Algorithms
  • Sync
  • MCP Server (AI Agents)
  • Live Queries
  • Programs
  • Temporal
  • Spatial
  • Trusted RAG
  • Behavior Graph
  • Agent-Native
  • Event Sourcing
  • GPU Acceleration
  • Intent Relay
Concepts
  • World Model
  • Graph Model
  • Query Language (GQL)
  • Graph Patterns
  • SQL vs GQL
  • Parameters
  • Query Results
  • Persistence & WAL
  • Error Handling
  • Observations & Evidence
  • Confidence & Provenance
  • Proof Artifacts & Gates
  • Skills
GQL / WorldCypher
  • Overview
  • MATCH
  • WHERE
  • RETURN
  • OPTIONAL MATCH
  • CREATE
  • SET
  • MERGE
  • DELETE
  • REMOVE
  • WITH
  • UNION
  • UNWIND
  • CASE
  • Spatial Queries
  • Temporal Queries
  • Algorithms Reference
  • Triggers
Schema
  • Overview
  • Indexes
  • Constraints
  • Data Types
Functions
  • Built-in Functions
  • Aggregations
  • Procedures
  • Shortest Path
  • EXPLAIN
  • PROFILE
Skills
  • Overview
  • CREATE SKILL
  • PROCESS NODE
  • REPROCESS EDGES
Operations
  • CLI
  • REPL Commands
  • Snapshot & Restore
  • Server Modes & PG Wire
  • Persistence
  • Import & Export
  • Docker
  • Architecture
  • Cloud Architecture
  • Sync Protocol (Deep Dive)
Guides
  • Agent Integration
  • World Model
  • Graph Model Fundamentals
  • Trusted RAG
  • Using Skills
  • Behavior Graphs
  • Swarm & Multi-Agent
  • Migration Guide
  • Filesystem Workspace
  • From SQL to GQL
  • ArcFlow for Coding Agents
  • Data Quality & Pipeline Integrity
  • Code Intelligence
Tutorials
  • Knowledge Graph
  • Entity Linking
  • Vector Search
  • Graph Algorithms
Recipes
  • CRUD
  • Multi-MATCH
  • MERGE (Upsert)
  • Full-Text Search
  • Temporal Queries
  • Batch Projection
  • GraphRAG
Use Cases
  • Agent Tooling
  • Knowledge Management
  • RAG Pipeline
  • Fraud Detection
  • Sports Analytics
  • Grounded Neural Objects
  • Behavior Graphs
  • Autonomous Systems
  • Digital Twins
  • Robotics & Perception
Reference
  • TypeScript API
  • GQL Conformance
  • Compatibility Matrix
  • Glossary
  • Data Types
  • Operators
  • Error Codes
  • Known Issues

Proof Artifacts & Gates

ArcFlow generates cryptographic proof artifacts that verify graph state — what data existed, when, and through what sequence of mutations. This enables audit trails, compliance verification, and trust gates in automated pipelines.

Proof artifacts#

A proof artifact is a signed record of graph state at a point in time:

-- Generate proof artifacts for the current graph state
CALL db.proofArtifacts
 
-- Check proof gates (verification points in a pipeline)
CALL db.proofGates

Graph fingerprint#

Compute a cryptographic hash of the entire graph state:

CALL db.fingerprint
-- Returns: { fingerprint: "sha256:a1b2c3..." }

Two graphs with the same fingerprint have identical content. Use this to verify replication, detect tampering, or validate batch/delta equivalence.

SDK usage#

import { open } from 'arcflow'
 
const db = open('./audited-graph')
 
// Take a fingerprint before processing
const before = db.query("CALL db.fingerprint")
console.log(`Before: ${before.rows[0].get('fingerprint')}`)
 
// Run a pipeline
db.batchMutate([
  "MERGE (e:Entity {id: 'e1', name: 'Entity_01', _observation_class: 'observed', _confidence: 0.95})",
  "MERGE (f:Fact {uuid: 'f1', predicate: 'employment', confidence: 0.95})",
])
 
// Take a fingerprint after
const after = db.query("CALL db.fingerprint")
console.log(`After: ${after.rows[0].get('fingerprint')}`)
 
// Generate proof artifacts
const proof = db.query("CALL db.proofArtifacts")
for (const row of proof.rows) {
  console.log(row.toObject())
}
 
// Verify proof gates
const gates = db.query("CALL db.proofGates")

The run → score → compare → gate flywheel#

Proof artifacts power a quality flywheel for data pipelines:

  1. Run — execute a pipeline (batch or incremental)
  2. Score — generate proof artifacts for the output
  3. Compare — verify batch and delta results produce the same fingerprint
  4. Gate — only promote results that pass proof verification
// Run batch
db.batchMutate(batchMutations)
const batchFingerprint = db.query("CALL db.fingerprint")
 
// Run delta (same input, incremental path)
db.batchMutate(deltaMutations)
const deltaFingerprint = db.query("CALL db.fingerprint")
 
// Gate: batch/delta must agree
if (batchFingerprint.rows[0].get('fingerprint') === deltaFingerprint.rows[0].get('fingerprint')) {
  console.log('PASS — batch/delta equivalence verified')
} else {
  console.log('FAIL — results diverged')
}

Use cases#

  • Financial compliance — prove what data existed at reporting time
  • Pipeline quality — gate promotions on proof verification
  • Replication verification — confirm replicas match the source
  • Audit trails — immutable record of graph state transitions
  • AI safety — verify that agent actions produced expected graph mutations

See Also#

  • Observations & Evidence — the observation classes that proof artifacts summarize
  • Confidence & Provenance — provenance trails from documents to facts
  • Temporal Queries — AS OF seq N for querying the state a proof artifact captured
  • Data Quality & Pipeline Integrity — using proof gates in CI/CD pipelines
Try it
Open ↗⌘↵ to run
Loading engine…
← PreviousConfidence & ProvenanceNext →Skills