> For the complete documentation index, see [llms.txt](https://whitepaper.cortexs.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://whitepaper.cortexs.ai/technical-architecture.md).

# Technical Architecture

<figure><img src="/files/zqW1gwAFXHYsrt63Cd4x" alt=""><figcaption></figcaption></figure>

### Overview

The Cortex AI Agent is built on top of the OpenAI Large Language Model (LLM) and utilizes cutting-edge natural language processing (NLP) and machine learning algorithms to deliver its capabilities.

**- Data Ingestion Layer:** Scans and indexes content from supported sources (markdown files, PDFs, YouTube videos, web pages).

**- Knowledge Graph:** Establishes relationships between content, enabling cross-referencing and context-aware responses.

**- Query Processing Engine:** Uses NLP models to understand user questions and retrieve the most relevant information.

**- User Interface:** Intuitive dashboard for accessing indexed content, conducting searches, and managing integrations.

### Security and Privacy

Data privacy is a top priority for Cortex AI. The system uses encrypted storage for your content and ensures that all data processing is done locally or through secure, private cloud services.

**- Data Encryption:** All stored content is encrypted at rest and in transit.

**- User Control:** Users have full control over their data, with options to delete or export indexed content at any time.

**- No Data Sharing:** Cortex AI does not share your data with third parties.

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# 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 dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://whitepaper.cortexs.ai/technical-architecture.md?ask=<question>&goal=<endgoal>
```

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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.