• Client: Global Automotive OEM

• Industry: Automotive / Autonomous Driving

• Solution by: GetAnnotator – Remote Annotation Platform

The Challenge

As the automotive industry accelerates towards fully autonomous driving, Advanced Driver-Assistance Systems (ADAS) demand massive volumes of precisely annotated data. A leading Korean car manufacturer faced the dual challenge of scaling annotation output rapidly while maintaining strict quality benchmarks critical for pretraining ADAS models.

They needed:

• A large-scale annotation workforce

• High accuracy levels (above 98%)

• Scalable operations with minimal delays

• Distributed team management without compromising on consistency

The stakes were high—poor data quality could delay model deployment or introduce safety risks in real-world driving.

Solution: GetAnnotator’s Remote Data Annotation Platform

GetAnnotator stepped in with its flexible, scalable solution purpose-built for high-volume, high-accuracy annotation projects. The platform empowered the OEM to onboard 90 remote annotators and 10 QA reviewers within a single month—a feat enabled by AI-powered e-learning modules, streamlined onboarding workflows, and built-in QA automation tools.

Key aspects of the GetAnnotator deployment included:

AI-Powered Onboarding & Training:

Remote annotators were trained via interactive, AI-driven modules tailored for ADAS tasks such as lane detection, pedestrian tagging, traffic sign recognition, and semantic segmentation. This approach reduced ramp-up time and ensured that annotation guidelines were understood universally.

Multi-Tier QA Workflow:

A robust three-layer quality assurance process was introduced:

• Self-verification by annotators

• Peer review by trained QA staff

• Automated validation checks by the platform

This helped maintain accuracy levels above 98–99%, even during aggressive scaling.

Scalable Workforce Management:

GetAnnotator’s platform handled real-time workforce allocation, progress tracking, and performance analytics across the entire remote team. As dataset demands increased exponentially, the platform dynamically adjusted workload distribution without compromising SLAs.

Results

The remote deployment mirrored the carmaker’s in-house efficiency, without the overhead of infrastructure or full-time hires.

Key Outcomes of Remote Annotation Deployment

Outcome	Impact
98–99% Annotation Accuracy	Maintained across millions of ADAS training frames processed.
100+ Annotators in <30 Days	Rapid recruitment and onboarding minimized project downtime.
Seamless Scalability	Effortlessly matched growing data volumes via a distributed workforce model.
40% QA Overhead Reduction	Automated validation loops significantly reduce manual QA efforts.

Why GetAnnotator Worked

This case validates the strength of GetAnnotator’s platform-first, remote-first approach. By enabling large-scale annotation teams with remote onboarding, integrated training, and quality-first workflows, the platform helped one of the world’s top carmakers accelerate ADAS pretraining without compromising quality or timeline.

For any OEM or mobility company navigating the challenges of autonomous vehicle development, GetAnnotator offers a plug-and-play workforce solution backed by enterprise-grade accuracy, flexibility, and scale.

Looking to train your ADAS or computer vision models with production-quality data at scale? Let GetAnnotator help you build the remote annotation pipeline that powers your next breakthrough.