> For the complete documentation index, see [llms.txt](https://lucid-ai.gitbook.io/introducing-bluwhale/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://lucid-ai.gitbook.io/introducing-bluwhale/personalization-protocol/architecture/overview.md).

# Overview

The Bluwhale Protocol features modular design, managing the end-to-end data lifecycle across various layers. Components within each layer can be seamlessly integrated:

* Identity Layer: This layer authenticates users via both traditional (web2) and blockchain (web3) mechanisms, aggregated under the Bluwhale ID(ERC-7231). It integrates identities and data through Bluwhale Link, Oracle, and other data verification services.
* Storage Layer: A flexible data storage system, organizing data across different levels based on cost considerations and duration of persistence.
* Computation & Training Layer: Utilizes a Trusted Execution Environment (TEE) cluster to process data and train AI models confidentially, with outcomes and TEE attestation relayed to the execution layer.
* Execution Layer: Established on a consensus foundation, it interprets data consumers’ needs and allocates rewards according to the results of processing and verification activities.

Verification Layer: This layer consists of verifiers from the community, tasked with validating the TEE attestation to confirm its secure and accurate operation within the TEE.