CASE STUDY

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

Back-Office Support

Client Overview

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

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

KPIs

achieved – based on team performance, quality and overall partnership

LOBs

Acquired additional LOB, formerly handled by a separate vendor

24/7

support provides superior customer success rates