How UVeye Increases Annotation Speed by 30% and Eliminates Manual Workflow Overhead

Introducing Customer: UVeye

UVeye builds automated, AI-powered vehicle inspection systems that scan a vehicle's undercarriage, tires, exterior, and interior in seconds - detecting damage, defects, and maintenance needs across the entire automotive lifecycle. With deployments at hundreds of locations serving GM, Volvo, Toyota, and Amazon, UVeye's computer vision systems operate at production scale. Their deep learning models, trained on millions of vehicle scans, require precise annotation of complex visual data including video sequences, multi-frame image groups, and detailed object tracking to maintain the accuracy that safety-critical automotive applications demand.

Before Encord

As UVeye's global footprint expanded, scaling computer vision operations required a more integrated data platform. While their previous platform offered basic drawing tools, it fell short in the areas that matter most for production AI teams:

Analytics and visibility gaps: Legacy analytics lacked the granular attribute tracking needed for multi-frame vehicle inspection datasets. The team needed more visibility into individual annotator productivity and a more structured way to monitor team performance.

Workflow limitations: They couldn't effectively manage sampling strategies, assign collaborators flexibly, or adjust workflows to fit their evolving needs. The absence of feedback tools built into the workflow made it difficult to provide timely guidance to annotators and reviewers, impacting both quality control and onboarding efficiency.

Platform limitations: The existing annotation platform offered limited API integration and extensibility, making it difficult to tailor workflows to the project's needs. Capabilities such as grouping related frames and maintaining object associations across video sequences were not natively supported. 

Manual coordination overhead: Manual coordination across large teams created unnecessary administrative overhead that distracted from core AI development. Cross-team workflows needed streamlined, real-time feedback loops to support rapid annotator onboarding and smooth collaboration.

These limitations led UVeye to seek a solution with three clear goals: increase annotation speed and validation throughput, maintain low error rates, and gain clearer insights into team performance.

The Encord Solution

• 20-30% improvement in annotation rate
• Eliminated manual tracking overhead
• Earlier detection of labeling issues
  

Since implementing Encord, UVeye has seen around a 20-30% improvement in their data annotation rate, allowing them to deliver more training data within the same number of working hours while completing projects in shorter cycles.

What Made the Difference

Real-time project intelligence: Encord's real-time project tracking and productivity insights eliminated spreadsheet-based coordination. Task ownership, progress status, and team performance are now visible at a glance through built-in analytics.

Powerful workflow automation: Flexible workflow configuration, sampling strategies for targeted QA, and features like object tracking, image grouping, and improved polygon tools have made both labeling and validation noticeably faster, particularly valuable for annotating vehicle components across video sequences.

Built-in quality control: The review functionality enables efficient validation workflows with structured feedback loops integrated directly into the platform. The team now identifies labeling issues much earlier in the project lifecycle, helping fine-tune error rates and reduce mistakes.

Comprehensive analytics: Class-based, user-based, and issue-based analytics give UVEye clear visibility into team performance and labeling quality, helping them quickly identify areas for improvement and maintain high standards across projects.

Enterprise customization: The active SDK and accessible API enable deep integration with UVeye's existing systems, with per-user configuration making the platform feel adaptable and production-ready.

Unlocking New Capabilities

A breakthrough use case has been the ability to group sequences of images as individual tasks. This has significantly sped up both annotation and validation, provided better context for annotators working with vehicle inspection imagery, and improved workflow management.

"One new use case that has proven to be very beneficial has been the ability to group sequences of images as individual tasks. This has significantly sped up the annotation and validation process, provided better context for annotation and improved workflow management significantly." - Inbal Friedlander, Data Operations Team Lead at UVeye

Partnership and Support

Summary

UVeye's migration to Encord has transformed their annotation operations from a fragmented, manually-intensive process to a streamlined, high-performance workflow, delivering higher quality annotated data in less time while eliminating manual coordination overhead.

With Encord's analytics, workflow automation, and customization capabilities, UVeye has established a foundation for continuous improvement in their computer vision data operations, enabling them to move faster while maintaining the quality standards their safety-critical automotive inspection systems demand.