AI Provenance Execution Engine

Prove the training data.
Verify the deployed model.

Deploy a hybrid AI provenance registry with a public model lineage and a private enterprise audit layer. The Provenance Notary, On-chain Data Models, and DID and VC Ledger modules bind each model version to its training data and its developer identity, so a regulatory inquiry is answered in minutes with cryptographic proof.

The Foundation

The Execution Mechanics

Turn opaque model releases into a verifiable lineage of what data trained a model, who trained it, and which version is live.

01.

Model Version Anchor

Fix every release cryptographically. The Provenance Notary anchors each model version by its weights hash, so the version deployed in production is provably the one that was documented.

02.

Training Data Lineage

Record what went in. On-chain Data Models capture the datasets and their digests used to train each version, making the training corpus verifiable rather than asserted.

03.

Developer Identity

Attribute the work. The DID and VC Ledger binds each version to the verified identity of the team that trained it, so provenance names a real, credentialed developer.

04.

Hybrid Public and Private

Expose lineage, protect secrets. A public layer publishes model lineage for external verification while a private enterprise layer holds sensitive audit detail.

05.

Deployment Attestation

Bind the live model to its record. A deployment attestation ties the running model's hash to its registry entry, so what serves traffic matches what was audited.

06.

Instant Regulatory Answer

Collapse inquiry timelines. Any deployed version resolves to its training data and developer in minutes, replacing weeks of document assembly with a single query.

The Model Provenance Lifecycle

Follow a model version from training-data anchoring through deployment to a regulatory inquiry answered from proof.

Operational log system
cerulea_ai_provenance.log

07:30:11

[SYS] Initializing Training Corpus Manifest...

07:30:11

[CMD] anchorDatasets { model: "RISK_V3", sets: 4, rows: 2100000 }

07:30:12

[AUTH] Hashing datasets and sealing lineage...

07:30:12

[OK] 4 datasets anchored at block 8810233.

Smart Contract Anatomy

Cerulea decomposes AI provenance into modular contracts. Each layer anchors data, registers versions, attests deployment, and exposes lineage across a hybrid public and private boundary.

Applicability Across the Spectrum

Verifiable model provenance is a horizontal capability. Here is how different AI actors put the hybrid registry to work.

AI Platform Providers

Publish verifiable lineage for every hosted model version while keeping sensitive audit detail private, giving enterprise customers proof of what trained the model serving their workloads.

Key Asset Types

  1. 1Model Versions
  2. 2Lineage Records
  3. 3Deployment Attestations

Regulated Enterprises

Answer supervisory inquiries into any deployed model in minutes with cryptographic proof of training data and developer identity, instead of assembling documentation under deadline pressure.

Key Asset Types

  1. 1Audit Layers
  2. 2Compliance Proofs
  3. 3Version Histories

AI Auditors & Regulators

Verify that the model in production matches the audited version and trace its corpus directly from the registry, replacing self-reported paperwork with an authoritative on-chain record.

Key Asset Types

  1. 1Verification Trails
  2. 2Corpus Proofs
  3. 3Attestation Logs

Network & Execution Architecture

Whether you are bridging enterprise MLOps pipelines or publishing model lineage to a public verification layer, Cerulea routes both into one provenance record.

Track A: MLOps Pipeline Bridging

For enterprises with established training pipelines. Model builds and dataset digests are translated into on-chain version records through the API gateway automatically.

MLOps Pipeline

Training Infrastructure

HTTPS / REST

Cerulea API Gateway

Hash Anchoring & Signing

WASM COMPILATION

Cerulea Private Chain

Enterprise Audit Layer

Track B: Public Lineage Publishing

For open model lineage and external verification. Model provenance is signed and routed directly to the public execution layer for anyone to verify.

Developer Wallet

Model Signing

WALLET SIGNATURE

Provenance Validators

Lineage Consensus

STATE EXECUTION

Cerulea Public L1

Public Model Lineage

Accelerated Time-to-Market Simulator

Building a hybrid provenance registry with data lineage anchoring, version notarization, and deployment attestation from scratch requires specialised engineers and careful MLOps integration. Calculate your exact deployment speed using Cerulea.

Required Lineage & Attestation Rules

44Rules
Simple (10)Enterprise (200)

Traditional Deployment

Solidity Coding & Audits

~ 13 Months

Cerulea Edge

Visual Compilation

WASM Logical Artifacts

~ 4 Weeks

>_

Technical Methodology

The legacy timeline reflects enterprise MLOps and audit integration benchmarks. Wiring lineage capture into training pipelines, building version notarization, and constructing a hybrid public and private audit layer for an average platform takes a baseline of 8 months. Building the same architecture on Cerulea takes a baseline of 2 weeks, because Cerulea Studio visually translates your lineage and attestation rules into pre-audited WebAssembly binaries and provisions the public and private provenance layers instantly.