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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.

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.

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How It Works

1

Browse

Explore the store for the resources you need - storage buckets, datasets, vector databases, RAG pipelines, and more.
2

Configure

Set your parameters: project, region, naming. Each resource knows what it needs.
3

Auto-sync

Pragmatiks provisions everything and keeps it in sync. When sources change, dependent resources automatically update.

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Why Pragmatiks

Install in minutes, not weeks

Skip the 2-3 weeks of manual setup. Get production-ready infrastructure in 15 minutes.

Changes propagate automatically

Update a source schema and watch dependent resources adapt. No manual intervention.

No platform team required

Stop waiting on infrastructure tickets. Install what you need, when you need it.

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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: 
provider: gcp
resource: storage
name: documents
config:
  location: EU
  lifecycle:
    delete_after_days: 365

provider: gcp
resource: bigquery-dataset
name: embeddings
config:
  location: EU
depends_on:
  - documents

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.

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Get Started

Quickstart

Install your first resource in 5 minutes.

How It Works

Understand the Pragmatiks model.

CLI Reference

Complete command reference.

API Reference

REST API documentation.