AI Data Labeling Case Study in Cambodia
Engagement Model
Dedicated Team
Team Composition
10 Data Operators & 1 Project Manager
Customer Partnership
Over 4 Years of Ongoing Collaboration
Client Overview
This AI data labeling case study describes how a global technology provider expanded its machine-learning data operations through a dedicated offshore team in Cambodia. As labeling volumes and task complexity increased, the company needed a secure and scalable solution capable of maintaining consistent quality across image, video and story-based annotation workflows.
Objective
The objective was to build and progressively scale a dedicated data-labeling operation in Cambodia. The team needed to support multiple annotation methods, integrate with the client’s existing workflows and maintain dependable quality as requirements and data volumes evolved.
Seamless Onboarding & Integration
Aligned workflows, implemented secure platform access and followed the client’s data-handling protocols.
Training & Workforce Development
Recruited and trained dedicated annotators for evolving image, video and story-labeling requirements.
Scalable Team Capacity
Expanded the operation from an initial small team to 10 dedicated data annotators.
Quality Assurance and Optimization
Applied structured review, feedback and validation processes to maintain consistent labeling quality.
Workflow & Security Tools
Secure Annotation Platform
Bounding Box Annotation
Label-Based Story Labeling
Video-Group Story Labeling
Image and Video Annotation
Workflow Optimization
Quality Control
Scaling AI with High-Precision Data Labeling
How a Dedicated Offshore Team Expanded Across Multiple Annotation Workflows
Haystack Solutions established a secure data-labeling workflow for a global technology provider, integrating a dedicated Cambodia-based team with the client’s existing platform and operating procedures. The engagement began with a small group of trained annotators and expanded as data volumes and project requirements increased.
The team initially focused on bounding box annotation for image and video data. Its responsibilities later expanded to include label-based story labeling and video-group story labeling, requiring additional training, updated guidelines and new quality-control procedures.
Haystack used structured onboarding, documented labeling standards and multi-step review processes to
maintain consistency across the different annotation types. Regular feedback and workflow adjustments
enabled the team to respond as requirements evolved.
The operation has now scaled to 10 dedicated data annotators supported by project management. This structure gives the client flexible capacity, retained process knowledge and a dependable framework for ongoing machine-learning data preparation.
Operational Efficiency
Structured workflows supported consistent processing across multiple annotation types.
Cost-Efficient Delivery
The offshore model provided dedicated capacity without requiring the client to build an equivalent internal operation.
Scalable Capacity
The team expanded to 10 data annotators as labeling volumes and requirements increased.
Broader Annotation Capability
The engagement grew from bounding boxes to label-based and video-group story labeling.
Results
The client gained a stable and scalable data-labeling operation capable of supporting increasingly varied machine-learning requirements. The dedicated team model provided continuity, flexible capacity and experience across image, video and story-based annotation.
Conclusion
This AI data labeling case study demonstrates how a dedicated offshore team can grow alongside changing machine-learning requirements. By scaling to 10 annotators and expanding across several labeling methods, Haystack Solutions created a flexible and dependable operation for long-term data preparation.
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