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

# Quality and SLAs

> Avala's quality process, accuracy guarantees, turnaround times, and workforce model

Avala's managed labeling service delivers production-quality annotations backed by a structured QA process, career domain experts, and measurable quality metrics. This page documents what you can expect when you use Avala's workforce for annotation.

## Quality Process

Every annotation passes through a 3-layer quality assurance pipeline before it reaches your export.

### Layer 1: Automated Checks

Before a human reviewer sees the result, automated validation catches structural errors.

| Check                    | What it catches                                                                              |
| ------------------------ | -------------------------------------------------------------------------------------------- |
| **Schema validation**    | Missing required attributes, invalid label values, out-of-range coordinates                  |
| **Geometric validation** | Zero-area bounding boxes, self-intersecting polygons, cuboids outside the point cloud bounds |
| **Consistency checks**   | Duplicate object IDs, broken tracking links across frames, label/attribute mismatches        |
| **Coverage checks**      | Unannotated regions that should have labels based on the project ontology                    |

### Layer 2: Human Review

A dedicated reviewer -- a senior annotator with deep knowledge of your ontology -- inspects each result for accuracy, completeness, and adherence to your labeling guidelines.

Reviewers check for:

* Correct object classification and attribute values
* Tight bounding box / polygon / cuboid fit
* Consistent object tracking across frames
* Edge cases handled per your project-specific instructions

### Layer 3: Expert Audit

A random sample of reviewed results is escalated to domain experts for a final audit. This layer calibrates reviewer accuracy and catches systematic issues before they affect your training data.

Audit findings feed back into annotator training and guideline refinements, creating a continuous improvement loop.

## Accuracy Targets

| Metric                      | Target                                                       |
| --------------------------- | ------------------------------------------------------------ |
| **First-pass yield**        | > 99% of annotations accepted without rework                 |
| **Classification accuracy** | > 99% correct label assignment                               |
| **Localization accuracy**   | Bounding box IoU > 0.90 with ground truth                    |
| **Tracking consistency**    | > 99% correct object ID continuity across frames             |
| **Attribute accuracy**      | > 99% correct attribute values (occlusion, truncation, etc.) |

<Tip>
  Accuracy targets apply to Avala's managed labeling service. Self-service annotation accuracy depends on your team's annotators and QA configuration.
</Tip>

## Turnaround Times

Turnaround depends on annotation complexity and volume. The table below shows typical timelines for common annotation types when using Avala's managed labeling service.

| Annotation Type                      | Typical Turnaround | Notes                                                   |
| ------------------------------------ | ------------------ | ------------------------------------------------------- |
| **2D bounding boxes** (images)       | 1-3 business days  | Standard object detection                               |
| **2D polygons** (images)             | 2-5 business days  | Instance segmentation                                   |
| **Semantic segmentation** (images)   | 3-7 business days  | Pixel-level classification                              |
| **3D cuboids** (LiDAR)               | 3-7 business days  | Point cloud annotation with BEV + perspective views     |
| **Multi-sensor 3D** (LiDAR + camera) | 5-10 business days | Synchronized sensor annotation                          |
| **Video object tracking**            | 3-7 business days  | Per-sequence, depends on frame count and object density |
| **Keypoint annotation**              | 2-5 business days  | Pose estimation and landmark labeling                   |

Turnaround begins when data is uploaded and the project ontology is finalized. Pilot datasets (\< 1,000 items) can often be completed faster.

<Tip>
  Need a specific SLA for your project? Contact **[sales@avala.ai](mailto:sales@avala.ai)** to discuss guaranteed turnaround commitments.
</Tip>

## Workforce Quality

### Domain Specialization

Avala's annotators are career professionals, not gig workers. Each annotator specializes in a specific domain (autonomous driving, robotics, medical imaging) for 12 months or more.

| Attribute                 | Details                                                                                    |
| ------------------------- | ------------------------------------------------------------------------------------------ |
| **Specialization period** | 12+ months on a single customer domain                                                     |
| **Training**              | Project-specific onboarding with your ontology, edge case library, and labeling guidelines |
| **Retention rate**        | > 90% annual retention -- annotators build deep institutional knowledge                    |
| **Team size**             | 15,000+ annotators across all domains                                                      |

### Why Retention Matters

High annotator retention directly impacts data quality:

* **Institutional knowledge** -- Annotators learn your edge cases, naming conventions, and domain-specific nuances over time. A new annotator takes weeks to reach the same level.
* **Fewer rework cycles** -- Experienced annotators produce fewer errors on the first pass, reducing review overhead and turnaround time.
* **Ontology evolution** -- When you update your label taxonomy, experienced annotators adapt faster because they understand the reasoning behind the changes.

## Quality Metrics via API

Quality metrics for your projects are available programmatically through the API and SDKs.

### Project-Level Metrics

```python theme={null}
from avala import Client

client = Client()

# Get project details including quality metrics
projects = client.projects.list()

for project in projects:
    print(f"Project: {project.name}")
    print(f"  Tasks completed: {project.task_count}")
```

### Task-Level Quality Data

```python theme={null}
# List tasks with their review status
tasks = client.tasks.list(project="project_uid")

for task in tasks:
    print(f"Task: {task.uid}")
    print(f"  Status: {task.status}")
```

### Export with Quality Metadata

When you create an export, each annotation result includes its QA review status, allowing you to filter by quality level in your training pipeline.

```python theme={null}
export = client.exports.create(
    name="Training data - QA passed only",
    format="avala-json-external",
    projects=["project_uid"]
)
```

## Quality Control Configuration

For self-service annotation, Avala provides configurable QA workflows.

| Feature                       | Description                                                           |
| ----------------------------- | --------------------------------------------------------------------- |
| **Multi-stage review**        | Route annotations through one or more review stages before acceptance |
| **Consensus workflows**       | Require multiple annotators to agree on the same label                |
| **Acceptance criteria**       | Set minimum quality thresholds for task acceptance                    |
| **Issue tracking**            | Flag and track annotation issues with comments and resolution status  |
| **Inter-annotator agreement** | Measure consistency across annotators on the same data                |

See [Quality Control](/docs/annotation/guides/quality-control) for setup instructions.

## Next Steps

<CardGroup cols={2}>
  <Card title="" icon="shield-check" href="/docs/annotation/guides/quality-control">
    <p style={{fontWeight: 600, fontSize: '18px', marginBottom: '4px', marginTop: '8px', color: 'inherit'}}>Quality Control Guide</p>
    <p style={{fontSize: '14px', marginTop: '0px', opacity: 0.6}}>Configure multi-stage review, consensus, and acceptance workflows for your projects.</p>
  </Card>

  <Card title="" icon="link" href="/docs/traceability">
    <p style={{fontWeight: 600, fontSize: '18px', marginBottom: '4px', marginTop: '8px', color: 'inherit'}}>Traceability</p>
    <p style={{fontSize: '14px', marginTop: '0px', opacity: 0.6}}>Trace any annotation back to its source data, annotator, and QA review.</p>
  </Card>

  <Card title="" icon="star" href="/docs/why-avala">
    <p style={{fontWeight: 600, fontSize: '18px', marginBottom: '4px', marginTop: '8px', color: 'inherit'}}>Why Avala</p>
    <p style={{fontSize: '14px', marginTop: '0px', opacity: 0.6}}>See what makes Avala different from Scale AI, Labelbox, and Label Studio.</p>
  </Card>

  <Card title="" icon="phone" href="mailto:sales@avala.ai">
    <p style={{fontWeight: 600, fontSize: '18px', marginBottom: '4px', marginTop: '8px', color: 'inherit'}}>Talk to Sales</p>
    <p style={{fontSize: '14px', marginTop: '0px', opacity: 0.6}}>Discuss managed labeling, custom SLAs, and enterprise deployment.</p>
  </Card>
</CardGroup>