Offshore Data Annotation Case Study in Cambodia
Engagement Model
Dedicated Team
Team Composition
6 Data Operators & 1 Project Manager
Customer Partnership
Over 4 Years of Ongoing Collaboration
Client Overview
This offshore data annotation case study examines how a global smart-home security provider built a secure, scalable annotation workflow in Cambodia. As Arlo Technologies expanded its AI capabilities, it needed accurate image and video labeling to improve its machine-learning models. Rather than developing an internal annotation department, Arlo partnered with Haystack Solutions to deploy a trained offshore team with structured quality controls and secure access.
Objective
Haystack Solutions formed a dedicated team of data annotators in Cambodia to support object detection and classification for Arlo’s machine-learning models. The objective was to deliver accurate labeling at scale while maintaining strict quality, security and communication standards.
Seamless Onboarding & Integration
Aligned workflows, secured VPN access, and enforced data protocols.
Training & Workforce Development
Vetted and trained annotators, maintaining 98%+ accuracy through quality control.
Scalability & Cost Efficiency
Scaled workforce flexibly, cutting costs by 50-70% and leveraging time zone advantages.
Quality assurance & Optimization
Applied multi-step validation to enhance data accuracy and consistency.
Workflow & Security Tools
V7 Darwin
Bounding Box Annotation
Image & Video Annotation
Global Protect VPN
Workflow Optimization
Quality Control
Secure & Scalable
Data Annotation
Building a Secure Offshore Annotation Pipeline for AI Training
Our offshore team built a secure and efficient data annotation pipeline for Arlo Technologies, ensuring high-quality labeling to enhance their AI-driven security solutions. By integrating directly with Arlo’s platform via GlobalProtect VPN, we maintained strict data security while optimizing annotation workflows. Our annotators specialized in bounding box annotation, adhering to precise labeling standards to improve machine learning accuracy.
To ensure maximum efficiency, we implemented multi-tier quality control, achieving 98%+ annotation accuracy while maintaining a scalable and cost-effective workforce. Our structured workflow enabled high-volume data processing, accelerating AI model training and refinement.
Beyond annotation, we continue to monitor performance, optimize processes, and refine validation steps to maintain consistent data quality. Our offshore solution not only reduces operational costs but also provides Arlo with a reliable and scalable framework for continuous AI development.
Operational Efficiency
Streamlined data annotation processes, enabling faster AI model training.
Significant Cost Savings
Significant Cost Savings
Achieved major reductions in labor and operational costs.
Scalability on Demand
Rapid team expansion for high-volume annotation periods.
Enhanced Data Quality
Maintained high-accuracy annotation with rigorous quality control.
Results
The dedicated offshore team gave Arlo a scalable annotation capability without the cost and complexity of building an equivalent internal operation. Structured training, quality control and secure workflows supported faster data processing, consistent labeling and continued AI-model development.
Conclusion
This offshore data annotation case study demonstrates how a trained Cambodia-based team can provide secure, accurate and scalable support for AI development. Haystack’s dedicated-team model enabled flexible capacity, consistent quality control and long-term operational continuity.
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