# Overview

## Overview

Sunbird Knowlg (*pronounced as the word "knowledge"*) consists of a set of tools and services that enable you to organise, curate, create, and discover millions of assets.

For example, organizations across multiple domains such as e-Commerce, OTT, Education etc can leverage Sunbird Knowlg building block to organise their content and knowledge assets as per their domain, create targeted collections, courses, programs, projects, video repository, etc.

## Key capabilities of Sunbird Knowlg

**Rich and Diverse Assets** - Enable a variety of learning experiences such as simulations, explanation, e-books, games, virtual labs, AR/VR experiences using multiple formats - html, videos, h5p, pdf, audios and ePub.

**Organised Collections** - Organise your assets into various categories such as playlists, courses, textbooks, web-series, episodes etc.

**Asset Lifecycle Management** - Microservices to manage the asset creation, curation and publish lifecycle.

**Powerful Discovery** - Enable meaningful tagging & discovery of assets with comprehensive search capability by defining your own taxonomies. These services are built for scale and enable various kinds of searches irrespective of their data type.

**Phygital Discovery** - Ability to codify and link physical resources such as textbooks with online resources using QR codes.

**Observability** - Out of the box dataproducts to analyse the health of the asset creation workflows. Create custom reports and dashboard to derive usage related insights by leveraging the telemetry generated by the players and editors.

### Adopters:

* **DIKSHA by NCERT***,* **Ministry of Education, India**
* **Lex by Infosys**
* **Contributors**

Ekstep


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# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://knowlg.sunbird.org/learn/readme.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
