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

# Introduction

> Welcome to Jabrod - The Enterprise AI Workspace

# Introduction

Welcome to **Jabrod** — a comprehensive AI workspace designed to accelerate the development of intelligent applications. As we establish our foundational ecosystem, our inaugural live offering focuses exclusively on enterprise-grade Retrieval-Augmented Generation (RAG) capabilities, with a broader suite of AI tools on the horizon.

## What is Jabrod?

Jabrod serves as a robust infrastructure platform for engineering teams. At present, our platform provides a sophisticated backend tailored specifically for Retrieval-Augmented Generation (RAG). It seamlessly handles the complexities of document chunking, embeddings, vector database management, and semantic retrieval, enabling you to focus on building high-impact applications.

<CardGroup cols={2}>
  <Card title="RAG Pipelines" icon="pipeline">
    Configure chunking strategies, embedding models, and vector stores for your use case.
  </Card>

  <Card title="Data Sources" icon="database">
    Upload files, provide text, or link URLs to build your knowledge base.
  </Card>

  <Card title="API Keys" icon="key">
    Generate secure, scoped API keys for each pipeline to use in your applications.
  </Card>

  <Card title="Semantic Search" icon="magnifying-glass">
    Query your pipelines with natural language to retrieve highly relevant context.
  </Card>
</CardGroup>

## Getting Started

<CardGroup cols={2}>
  <Card title="Quick Start" icon="rocket" href="/quickstart">
    Learn how to create your first pipeline and run a query.
  </Card>

  <Card title="API Reference" icon="code" href="/api/overview">
    Explore the REST API documentation for programmatic access.
  </Card>
</CardGroup>