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

# Introduction to PixlHub

> PixlHub is a high-performance data annotation platform designed to transform raw data into structured, production-ready training sets for AI models. It serves as a unified workspace that bridges the gap between massive datasets and high-precision ground truth.

<img src="https://mintcdn.com/pixldata/ewssWq1snHj1EmD3/524_1x_shots_so.png?fit=max&auto=format&n=ewssWq1snHj1EmD3&q=85&s=34406d132125255676d9816a458e4870" alt="" title="" style={{ width:"100%" }} className="mx-auto" width="1920" height="1080" data-path="524_1x_shots_so.png" />

### The Architecture

PixlHub uses a structured hierarchy to maintain order across large-scale data operations:

* **Organizations:** The highest administrative level. This is where teams, permissions, and multiple workspaces are managed.
* **Projects:** The operational hub for specific labeling goals. Each project contains a unique **Ontology** (classes and attributes) and specialized annotation tools.
* **Datasets & Assets:** The library of raw source files. Assets are uploaded once and can be utilized across multiple projects without data duplication.

**Tasks:** The actual unit of production. PixlHub separates *Assets* from *Tasks*, allowing for diverse labeling objectives to be generated from the same raw source material.\
\
**Optimized Annotation Flow** The interface is built for speed. With a keyboard-first design and a high-performance canvas, the platform remains fluid even when handling complex polygons or hundreds of objects in a single frame.

**Integrated Quality Control** Quality is enforced through a dedicated **Review Workflow**. Managers can track every label in real-time, providing feedback, rejecting errors, or approving completions to ensure the final output meets strict accuracy thresholds.

**Operational Insights** Project progress is monitored through live dashboards. PixlHub provides granular analytics on label distribution, annotator velocity, and time-per-task, offering full visibility into the project lifecycle.

<Columns cols={2}>
  <Card title="Quickstart Guide" icon="lightbulb">
    The fastest way to get up and running. This section covers creating an organization, setting up a first project, and uploading the initial dataset to start labeling in minutes.
  </Card>

  <Card title="Key Essentials" icon="key-skeleton">
    A deep dive into the PixlHub hierarchy. Learn how Organizations, Projects, Assets, and Tasks interact to keep large-scale data operations organized and scalable.
  </Card>

  <Card title="Annotation Tools" icon="map">
    A detailed look at the labeling interface. This guide explores the full suite of tools—from precision bounding boxes and polygons to complex classification attributes and keyboard shortcuts.
  </Card>

  <Card title="Workflows & Exports" icon="network-wired">
    Understand how to set up review cycles for quality control, track team performance through analytics, and export final annotations in various formats.
  </Card>
</Columns>