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

# QIM SDK Overview

> Step by step guide for building AI pipelines using QIM SDK

<Note>
  **Before you begin:** QIM SDK must be installed — see the [Installation Guide](../installation).
</Note>

# The 5 basic stages of vision AI pipelines

The diagram below shows the five stages of a typical vision AI pipeline. <br />
⚡ Highlighted steps are hardware-accelerated by QIM SDK.

```mermaid theme={null}
flowchart LR
    classDef hw fill:#31017D,color:#ffffff,stroke:#1a3aad,font-weight:bold,min-width:160px,padding:12px
    A["Step 1\nData Source"] --> B["⚡ Step 2\nAI Preprocessing"] --> C["⚡ Step 3\nAI Inference"] --> D["⚡ Step 4\nPost-Processing"] --> E["Step 5\nUse AI Metadata"]
    class A,B,C,D,E hw
```

<Steps>
  <Step title="Data Source">
    Capture frames from the selected media source, ensuring compatibility and efficient data acquisition.
  </Step>

  <Step title="AI Preprocessing">
    Prepare raw frames for the AI model by resizing, reformatting, and normalizing pixel data into the tensor format the model expects. QIM SDK accelerates this step with the [qtimlvconverter](../plugin-reference/qtimlvconverter) plugin, which performs color space conversion and tensor layout transformations directly on the GPU — eliminating CPU bottlenecks before inference.
  </Step>

  <Step title="AI Inference">
    Run the prepared tensors through the AI model to generate predictions. QIM SDK accelerates inference with the [qtimltflite](../plugin-reference/qtimltflite) and [qtimlqnn](../plugin-reference/qtimlqnn) plugins, which delegate computation to the on-device NPU or HTP — delivering low-latency, high-throughput inference without taxing the CPU.
  </Step>

  <Step title="Post-Processing">
    Decode the model's output tensors into actionable results — such as bounding boxes, class labels, confidence scores, or segmentation masks. QIM SDK's [qtimlpostprocess](../plugin-reference/qtimlpostprocess) plugin interprets model outputs and formats them as metadata, which can then be rendered onto video frames using the GPU-accelerated [qtivoverlay](../plugin-reference/qtivoverlay) plugin.
  </Step>

  <Step title="Using the AI Metadata">
    Use the structured AI metadata output to drive downstream workflows. Common applications include:

    * **Overlay on video** — draw bounding boxes, labels, or masks directly on frames for real-time visualization
    * **Stream enrichment** — embed metadata into RTSP or WebRTC streams for remote monitoring
    * **Cloud/edge messaging** — publish to MQTT, Kafka, or other backends for storage, alerting, or further analysis
    * **Automated actions** — trigger events or control external systems based on detection results
  </Step>
</Steps>