Project DataEdge

AI Data Annotation for Retail Analytics Firm 

Project Overview & Image Description:

The images in this section highlight our data annotation workflow and quality assurance process:

• Annotated Dataset Samples: Examples of labelled images, text, and retail data points for AI training.
  
  

• Annotation Tools & Dashboards: Screenshots of annotation platforms and project tracking dashboards.
  
  

• Team Collaboration & QA: Snapshots of our team reviewing annotations and performing quality checks.
  
  

• Workflow Charts: Visual representation of data ingestion, annotation, verification, and delivery process.
  
  

Artefacts Created:

• Labeled datasets for machine learning and analytics
  
  

• Annotation guidelines & SOPs to maintain accuracy and consistency
  
  

• Quality check reports ensuring data precision and compliance
  
  

• Project tracking dashboards for monitoring progress and performance

Corebridge’s Role & Collaboration:

• Role: Managed end-to-end data annotation, from dataset preparation to quality verification and delivery.
  
  

• Collaboration: Worked closely with the client’s data science and analytics teams to align labeling requirements, standards, and output formats. Internally, annotation and QA teams collaborated to ensure accuracy and scalability.
  
  

Outcome:

• Delivered high-quality, accurately labeled datasets that improved model performance and analytics insights.
  
  

• Streamlined annotation workflows, reducing turnaround time and improving project efficiency.
  
  

• Helped the client accelerate AI model training and enhance decision-making capabilities.