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AI BUILDERS

Originary for AI Builders

Prove compliance with creator preferences and data policies. Build AI systems that respect rights, maintain consent, and provide verifiable interaction records.

Why Choose Originary

Build AI systems that respect creator rights while providing verifiable compliance.

✓

Provable

Cryptographic receipts provide verifiable proof of policy compliance and creator consent.

⚖️

Neutral

Open protocol standards ensure no vendor lock-in and universal interoperability.

🚀

Practical

Simple integration with existing ML workflows and infrastructure. Production-ready.

How It Works

Integrate policy compliance directly into your AI development workflow.

1

Discover Policies

Check for creator preferences before accessing any content or data.

2

Respect Terms

Honor creator requirements for attribution, usage limits, and consent.

3

Generate Receipts

Create cryptographic proof of every compliant interaction.

4

Audit & Report

Maintain comprehensive records for compliance audits and transparency.

Business Advantages

Build sustainable AI systems that respect creators and reduce legal risks.

✅

Compliance by Design

Built-in respect for creator preferences reduces legal risk and enables sustainable data relationships.

🧼

Model Hygiene

Clean, attributable training data improves model quality and reduces contamination risks.

🏆

Competitive Differentiation

Demonstrate responsible AI practices to customers, partners, and regulators.

Technical Integrations

Seamless integration with modern AI infrastructure and frameworks.

Training Pipeline Integration

Seamless integration with popular ML frameworks including PyTorch, TensorFlow, and JAX. Policy checks run before data ingestion.

PyTorchTensorFlowJAX

Inference-time Compliance

Real-time policy verification for retrieval-augmented generation (RAG) and dynamic data access scenarios.

RAGReal-time

MCP/A2A Compatibility

Native support for Model Context Protocol and Agent-to-Agent communication standards.

MCPA2A

Multi-cloud Deployment

Deploy across AWS, GCP, Azure, and on-premises infrastructure with unified policy management.

AWSGCPAzure

Implementation Patterns

Choose the integration approach that works best for your infrastructure.

SDK Integration

Embed policy checking directly into your training and inference code with our Python, JavaScript, and Go SDKs.

Python SDK Example
import originary

# Check policy before data access
policy = originary.check_policy(url)
if policy.allows_training():
    data = load_data(url)
    receipt = originary.generate_receipt(url, data)
    # Continue with training...

API Gateway

Deploy as middleware in your existing API infrastructure for centralized policy enforcement.

Gateway Configuration
# Kong/Envoy configuration
- name: originary-policy
  config:
    enforce_aipref: true
    generate_receipts: true
    audit_log: true

Start Building Responsibly

Join AI builders who prioritize creator respect and sustainable data practices. Get started with our free development tier.