> For the complete documentation index, see [llms.txt](https://faia.liccium.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://faia.liccium.com/technology/implementation.md). # FAIA Reference Implementations FAIA supports different levels of implementation depending on the required level of persistence, verification, and infrastructure integration. ## FAIA Statement Generator The FAIA Statement Generator provides a lightweight mechanism for creating portable AI attribution statements using the FAIA vocabulary. The generated statements can be embedded into websites, attached to publications, or shared alongside digital content. They support both human-readable and machine-readable disclosure of AI involvement. However, these statements are not persistently bound to the content and are not independently verifiable. Without cryptographic signing, registry publication, and persistent content–metadata binding, there is no reliable mechanism to verify authorship, integrity, or long-term association with the underlying asset.
## Liccium Reference Implementation The Liccium platform provides a reference implementation of persistent and verifiable FAIA declarations. The implementation combines: * ISCC-based content fingerprinting * machine-readable JSON-LD declaration metadata * cryptographic signatures and verifiable credentials * declaration APIs * federated registry infrastructure Using the Liccium implementation, FAIA declarations can be: * created through applications or APIs * digitally signed by the declarant * persistently linked to digital content through ISCC identifiers * published to interoperable registry infrastructure * independently resolved and verified across platforms and workflows The implementation includes the Liccium Declaration API, registry infrastructure, and user-facing applications supporting declaration creation, signing, publication, and verification workflows. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://faia.liccium.com/technology/implementation.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.