> ## Documentation Index
> Fetch the complete documentation index at: https://docs.cyborg.co/llms.txt
> Use this file to discover all available pages before exploring further.

# CyborgDB Introduction

<img className="block dark:hidden" src="https://mintcdn.com/cyborg/kXyFWu9saA_TjOzS/images/new-hero-light.png?fit=max&auto=format&n=kXyFWu9saA_TjOzS&q=85&s=467ea03181c2475056d1db89780b3523" alt="Hero Light" width="2048" height="1309" data-path="images/new-hero-light.png" />

<img className="hidden dark:block" src="https://mintcdn.com/cyborg/kXyFWu9saA_TjOzS/images/new-hero-dark.png?fit=max&auto=format&n=kXyFWu9saA_TjOzS&q=85&s=f16ed0f506d1f1a1085ca4e23e2fb1d8" alt="Hero Dark" width="2048" height="1309" data-path="images/new-hero-dark.png" />

**CyborgDB** is the first Confidential Vector DB:

* Introduces a novel architecture to keep confidential inference data secure through **zero-trust design**.
* Keeps vector embeddings **end-to-end encrypted** throughout their lifecycle (including at search time).
* Exposes a familiar API, making it **easy to integrate** with existing AI workflows.
* Provides **high-performance indexing and retrieval** which can be [GPU-accelerated with CUDA](https://developer.nvidia.com/blog/bringing-confidentiality-to-vector-search-with-cyborg-and-nvidia-cuvs/).
* Works with DBs such as Postgres and Redis, **transforming traditional DBs into Confidential Vector DBs**.

### Get started now

<CardGroup cols={3}>
  <Card title="Learn About CyborgDB" href="./about" icon="book-open-cover">
    Learn about the architecture, design and deployment of CyborgDB.
  </Card>

  <Card title="Quickstart" href="./quickstart" icon="rocket-launch">
    Get started with CyborgDB and start building your first Confidential AI application.
  </Card>

  <Card title="API Docs" href="../../api-reference" icon="code">
    Explore the API reference to learn how to use CyborgDB in your applications.
  </Card>
</CardGroup>
