8
annotations per image
95–100%
accuracy achieved
90 days
turnaround
90
annotators deployed
1
The Challenge
A computer vision team (Airbus use case) required highly precise image annotation to train models for object detection and image generation. Each image required multiple bounding boxes, increasing annotation complexity and risk of inconsistency.
- Maintaining precision across 8 annotations per image
- Ensuring consistency across a 90-member team
- Avoiding annotation drift over long project timelines
- Meeting strict accuracy expectations for production models
Client
Computer vision team (Airbus use case)
Industry
Aerospace / Computer Vision
Timeline
90 days
2
Our Approach
- Built a specialized 90-member annotation team
- Standardized bounding box and labeling guidelines
- Structured workflows to maintain speed without sacrificing accuracy
- Enforced strict accuracy benchmarks (95–100%)
Quality Assurance
- Every image was annotated and independently validated by a second annotator
- Cross-verification ensured bounding box consistency
- Edge cases were escalated to senior QA reviewers
- Only double-verified outputs were approved
3
The Results
- Achieved 95–100% annotation accuracy at scale
- Maintained consistency across high-density annotations
- Delivered large datasets within strict timelines
- Received strong positive client validation
Quality Assurance
OUTCOME
Delivered production grade datasets that improved model performance and reduced retraining cycles.