> For the complete documentation index, see [llms.txt](https://docs.mulearn.org/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.mulearn.org/partner-engagement/how-companies-connect-with-mlearn.md).

# How Companies Connect with μLearn

Companies engage with μLearn through four tracks. Each track is a different type of relationship pick the one that fits your objective.

### <mark style="color:$primary;">Talent and Hiring</mark>

Access μLearn's skills-first hiring platform in Launchpad. Candidates are shortlisted based on domain Karma and validated Proof of Work not resumes alone.

What this looks like: post an opportunity with eligibility criteria, receive a shortlist of pre-validated candidates, hire through a challenge-based assessment process.

### <mark style="color:$primary;">Community as a Service</mark>

Engage μLearn's community for product testing, UX feedback, early customer validation, research, and ground marketing.

What this looks like: brief the community on what you need, receive structured outputs from active learners, engage contributors directly for ongoing work.

### <mark style="color:$primary;">Innovation and Challenges</mark>

Bring a real problem into the ecosystem as a challenge. The community builds solutions. You get working outputs and identify talent simultaneously.

What this looks like: define a problem statement, μLearn routes it to relevant Interest Groups, teams build and submit, you review outputs and engage the best contributors.

### <mark style="color:$primary;">CSR and Skilling</mark>

Deploy skilling programs through μLearn's campus network without building your own infrastructure.

What this looks like: define your skilling objective and geography, μLearn deploys through campus nodes and Interest Groups, participants build verifiable Proof of Work, you receive documented outcomes.

<h3 align="center"><a href="https://mulearn.org/contact">Click here</a> to Partner with μLearn.</h3>


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

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

Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:

```
GET https://docs.mulearn.org/partner-engagement/how-companies-connect-with-mlearn.md?ask=<question>&goal=<user_goal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.

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.