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

# Introduction

> RAG-as-a-service for developers building AI apps

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  <img src="https://mintcdn.com/agentset/142yXB_-ZjnrQ4s4/images/hero.png?fit=max&auto=format&n=142yXB_-ZjnrQ4s4&q=85&s=e8ed6c24b0e7a56370909ff5498e5904" alt="Agentset platform overview" width="2800" height="2044" data-path="images/hero.png" />
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Building production RAG is deceptively complex. Document parsing, chunking strategies, embedding models, vector storage, retrieval tuning, reranking—each piece affects accuracy, and getting them to work well together takes time.

Agentsets get you state-of-the-art accuracy so you can add RAG to your app in minutes, not months.

## Why Agentset

* **Production accuracy out of the box** — Advanced retrieval, high-res parsing, ranking, and agentic mode that plans and reasons.
* **Developer experience** — Chat and search playgrounds, chunk viewer, TypeScript and Python SDKs, and more.
* **Flexible deployment** — Run on Agentset Cloud, bring your own infrastructure, or deploy on-premise.

## Get started

* Ready to build? Start with the [quickstart](/get-started/quickstart)
* Explore the [API reference](/api-reference/introduction) and [SDKs](/get-started/sdks)
* Browse the source on [GitHub](https://github.com/agentset-ai)
* Have questions? Join our [Discord](https://discord.gg/AqMkKAYZCu)

Not sure whether to build RAG yourself or use a managed solution? Learn about the [benefits of RAG-as-a-service](/get-started/why-rag-as-a-service).