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

# What is Pragmatiks

> Install pre-built data and AI infrastructure resources in minutes, not weeks

Pragmatiks lets you treat your data and AI infrastructure like an app store: instead of manually wiring together storage, compute, and AI services, you browse a catalog of pre-built resources and install what you need. Dependencies resolve automatically. When upstream data changes, downstream resources update themselves.

## How It Works

<Steps>
  <Step title="Browse">
    Explore the store for the resources you need - storage buckets, datasets, vector databases, RAG pipelines, and more.
  </Step>

  <Step title="Configure">
    Set your parameters: project, region, naming. Each resource knows what it needs.
  </Step>

  <Step title="Auto-sync">
    Pragmatiks provisions everything and keeps it in sync. When sources change, dependent resources automatically update.
  </Step>
</Steps>

## Why Pragmatiks

<CardGroup cols={3}>
  <Card title="Install in minutes, not weeks" icon="clock">
    Skip the 2-3 weeks of manual setup. Get production-ready infrastructure in 15 minutes.
  </Card>

  <Card title="Changes propagate automatically" icon="arrows-rotate">
    Update a source schema and watch dependent resources adapt. No manual intervention.
  </Card>

  <Card title="No platform team required" icon="users">
    Stop waiting on infrastructure tickets. Install what you need, when you need it.
  </Card>
</CardGroup>

## Example: RAG Pipeline

Building a RAG pipeline traditionally means weeks of work - setting up storage, configuring vector databases, wiring embedding services, managing sync jobs. With Pragmatiks, you describe what you want:

```yaml theme={"theme":{"light":"min-light","dark":"min-dark"}}
provider: gcp
resource: storage
name: documents
config:
  location: EU
  lifecycle:
    delete_after_days: 365
```

```yaml theme={"theme":{"light":"min-light","dark":"min-dark"}}
provider: gcp
resource: bigquery-dataset
name: embeddings
config:
  location: EU
depends_on:
  - documents
```

```bash theme={"theme":{"light":"min-light","dark":"min-dark"}}
pragma resources apply .
```

Pragmatiks provisions both resources and establishes the dependency. When new documents land in storage, the embeddings dataset knows about it. The full RAG pipeline example with vector search is coming soon.

## Get Started

<CardGroup cols={2}>
  <Card title="Quickstart" icon="rocket" href="/quickstart">
    Install your first resource in 5 minutes.
  </Card>

  <Card title="How It Works" icon="book" href="/concepts/how-it-works">
    Understand the Pragmatiks model.
  </Card>

  <Card title="CLI Reference" icon="terminal" href="/cli/overview">
    Complete command reference.
  </Card>

  <Card title="API Reference" icon="code" href="/api-reference/introduction">
    REST API documentation.
  </Card>
</CardGroup>