12K+
annotations delivered
95%+
accuracy sustained
90 days
turnaround
120
annotators deployed
1
The Challenge
A robotics AI company needed high-volume, high-precision video annotation to train physical AI systems. The dataset was continuously evolving, with new video data arriving daily. Annotation requirements were not fully defined upfront, and guidance from the client evolved during execution.
- Handling dynamic, daily dataset inflow
- Maintaining consistency across long-form videos (5–7 mins each)
- Adapting quickly to changing annotation instructions
- Scaling a large team without compromising accuracy
Client
Robotics AI company
Industry
Physical AI / Robotics
Timeline
90 days
2
Our Approach
- Deployed a dedicated team of 120 trained annotators specialized in video segmentation
- Created detailed annotation guidelines covering segmentation, bounding boxes, and edge cases
- Designed a rolling workflow to handle continuous data inflow without delays
- Established rapid feedback loops to integrate client updates in near real-time
Quality Assurance
- Every annotation was independently reviewed by two different annotators
- Disagreements were flagged and escalated to senior QA reviewers
- Weekly quality reports were shared with the client
- Only consensus-approved annotations were delivered
3
The Results
- Sustained 95%+ accuracy across large-scale video annotation
- Successfully handled continuous high-volume data streams
- Eliminated inconsistency despite evolving requirements
- Received consistent positive feedback from client teams
Quality Assurance
OUTCOME
Enabled a reliable, production-ready data pipeline for physical AI training, significantly reducing downstream model failures.