Quality and SLAs

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.)

Accuracy targets apply to Avala’s managed labeling service. Self-service annotation accuracy depends on your team’s annotators and QA configuration.

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.

Need a specific SLA for your project? Contact sales@avala.ai to discuss guaranteed turnaround commitments.

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

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

# 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.

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 for setup instructions.

Next Steps

Quality Control Guide

Configure multi-stage review, consensus, and acceptance workflows for your projects.

Traceability

Trace any annotation back to its source data, annotator, and QA review.

Why Avala

See what makes Avala different from Scale AI, Labelbox, and Label Studio.

Talk to Sales

Discuss managed labeling, custom SLAs, and enterprise deployment.

Getting Started

Platform

Use Cases

Quality Process

Layer 1: Automated Checks

Layer 2: Human Review

Layer 3: Expert Audit

Accuracy Targets

Turnaround Times

Workforce Quality

Domain Specialization

Why Retention Matters

Quality Metrics via API

Project-Level Metrics

Task-Level Quality Data

Export with Quality Metadata

Quality Control Configuration

Next Steps

​Quality Process

​Layer 1: Automated Checks

​Layer 2: Human Review

​Layer 3: Expert Audit

​Accuracy Targets

​Turnaround Times

​Workforce Quality

​Domain Specialization

​Why Retention Matters

​Quality Metrics via API

​Project-Level Metrics

​Task-Level Quality Data

​Export with Quality Metadata

​Quality Control Configuration

​Next Steps

Quality Process

Layer 1: Automated Checks

Layer 2: Human Review

Layer 3: Expert Audit

Accuracy Targets

Turnaround Times

Workforce Quality

Domain Specialization

Why Retention Matters

Quality Metrics via API

Project-Level Metrics

Task-Level Quality Data

Export with Quality Metadata

Quality Control Configuration

Next Steps