> For the complete documentation index, see [llms.txt](https://pwt-ai.gitbook.io/pwt.ai-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://pwt-ai.gitbook.io/pwt.ai-docs/3.-technical-architecture-and-business-process.md).

# 3. Technical Architecture & Business Process

#### 21｜What components make up the PWT Intelligent Storage Stack?

The PWT Intelligent Storage Stack consists of four layers:

Asset Layer;\
Abstraction Layer;\
Intelligence Layer;\
Demand Layer.

#### 22｜What does the Asset Layer do?

The Asset Layer is responsible for NFT minting, permanent storage, and on-chain ownership verification, giving content a recognizable, recordable, and continuously manageable digital asset identity.

#### 23｜What does the Abstraction Layer do?

The Abstraction Layer performs AI summarization, tag generation, content transcription, multilingual processing, data cleaning, and semantic structuring.

#### 24｜What does the Intelligence Layer do?

The Intelligence Layer connects distributed computing power, AI model training, model scheduling, evaluation, and inference services, further transforming data into intelligent capabilities.

#### 25｜What does the Demand Layer do?

The Demand Layer delivers content, data, and AI capabilities to content distribution, API services, enterprise applications, and intelligent products.

#### 26｜What happens after content enters PWT.AI?

A piece of content will sequentially complete:

Content Assetization → Data Abstraction → Model Intelligence → Precise Distribution → Automated Settlement & Allocation

#### 27｜What is content assetization?

Content assetization refers to putting content on-chain and completing ownership verification, allowing content to have the foundation for long-term preservation, continuous management, and future utilization.

#### 28｜What is data abstraction?

Data abstraction refers to cleaning, tagging, transcribing, translating, vectorizing, and performing semantic processing on content, transforming it into data that machines can understand and utilize.

#### 29｜What is model intelligence?

Model intelligence refers to connecting processed data with distributed training, model optimization, scheduling, and inference services, enabling content to enter the AI capability production process.

#### 30｜What is precise distribution?

Precise distribution refers to matching content or data with suitable human users, enterprise applications, and AI models based on content semantics, user requirements, and model tasks.

#### 31｜What is automated settlement and allocation?

Automated settlement and allocation refers to completing value settlement through on-chain rules based on records of content, storage, computing power, algorithms, models, and ecosystem contributions.

#### 32｜What types of content does PWT.AI support?

PWT.AI supports various types of content, including text, images, audio, video, research materials, enterprise knowledge, and industry data.

#### 33｜Why does content need to be vectorized?

Vectorization transforms content into machine-computable semantic representations, making it more suitable for intelligent search, similar content matching, RAG knowledge bases, and model invocation.

#### 34｜Does permanent storage only mean preserving original files?

No. PWT.AI not only preserves original content but also stores ownership information, semantic tags, processing results, and subsequent utilization relationships.

#### 35｜How can PWT.AI’s business process be summarized?

A piece of content continuously generates AI value starting from storage.


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