Invisible Metadata for AI-Generated Content
Embed invisible metadata in AI-generated content to verify authenticity, track provenance, and build trust.
Use Cases
Content Authenticity
Verify the source and authenticity of AI-generated content with embedded metadata that remains invisible to users.
Provenance Tracking
Track the origin and history of content across platforms while maintaining the integrity of the original text.
Compliance & Transparency
Meet regulatory requirements for AI content disclosure without disrupting the user experience.
Key Features
Invisible Embedding
Metadata is embedded using Unicode variation selectors that are invisible to humans but detectable by machines.
Streaming Support
Works seamlessly with streaming responses from LLMs like OpenAI, Anthropic, and others.
Content Verification
Verify the integrity of content with built-in HMAC authentication to detect tampering.
Framework Agnostic
Integrates with any Python framework or application with minimal configuration.
Ready to Get Started?
Check out our documentation to get started or explore our features for more information.