> ## 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.
# Cyborg Vector Search Introduction
Cyborg Vector Search was renamed to CyborgDB in `v0.9.0`. This version of the docs will refer to the project as Cyborg Vector Search.
**Cyborg Vector Search** 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
Learn about the architecture, design and deployment of Cyborg Vector Search.
Get started with Cyborg Vector Search and start building your first Confidential AI application.
Explore the API reference to learn how to use Cyborg Vector Search in your applications.