Case Study

Data Labelling for an AI-Enabled Driver and Fleet Safety Platform

9.5/10
CSAT Score

The Results

  • 80% growth in team size since inception
  • KPI’s achieved – based on team performance, quality and overall partnership
  • Acquired additional LOB, formerly handled by a separate vendor
  • 24/7 support provides superior customer success rates

Real-time, AI-Enabled Driver and Fleet Safety Platform Customer Success Story

Back-Office Support
Our client is the only real-time, AI-enabled driver and fleet safety platform to predict, prevent, and reduce high-risk events in the mobility ecosystem. By analysing billions of data points from over 1 billion AI-analysed video miles, our client’s machine learning algorithms continuously improve and impact driver behaviour before events happen, not after.

The Challenge

Each reviewer needed to learn 24 different process steps to ensure smooth operational delivery, including learning traffic rules and regulations for different countries, while handling an infinite number of complex events.
  • Our client needed 24/7 support while maintaining a specified number of reviewers across different countries and time zones.
  • 99% accuracy scores needed to be maintained across operations.

The Solution

Step 1

The creation of a ‘Buffer team’ (20 FTE) – working as an internal QA team to audit and provide feedback and coaching to existing team members.

Step 2

Buffer team covers reviewers on leave and attrition to maintain the required headcount.

Step 3

Additional new teams created – Internal escalation and In-house training teams, to review complex events, facilitate quick onboarding, training and coaching, and share feedback.

The services implemented include:

The Results

  • 80% growth in team size since inception
  • KPI’s achieved – based on team performance, quality and overall partnership
  • Acquired additional LOB, formerly handled by a separate vendor
  • 24/7 support provides superior customer success rates

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