Improving Your Fleet Safety?
Automated Driver Coaching Cuts High-Risk Events 50%, Halves Safety-Team Admin, and Delivers 100% Automated Coaching Workflows
Case Study Summary
Executive summary
Whizzbridge built an automated driver coaching and micro-training platform for a fleet management client whose telematics data was underused. The system, built with C#, .NET, and REST APIs on top of the client's existing telematics ecosystem, converts risky driving events into targeted micro-learning modules delivered to drivers automatically. High-risk driving behaviours dropped by 50% and safety-team administrative overhead was cut in half, with 100% of the coaching workflow automated from event detection to learning delivery: shifting the client to a proactive, continuous coaching model across the entire fleet.
50%
Fewer high-risk driving events
Targeted telematics coaching
50%
Less safety-team admin
Automated coaching delivery
100%
Automated coaching workflow
Event to micro-learning
Challenges
- The client was collecting large volumes of telematics and video-based driver data, but had no effective way to turn raw events into structured coaching and behaviour-changing training.
- High-risk driving patterns such as speeding, harsh braking, and aggressive cornering continued to repeat because there was no consistent corrective workflow in place.
- Safety teams were working reactively, manually identifying incidents, assigning training, following up on completions, and managing the process through daily operational effort.
- Existing training delivery methods were not scalable, especially as fleet size increased across multiple locations and devices.
- Leadership lacked a unified view of driver risk, training completion, and long-term behaviour trends, making it difficult to measure programme effectiveness or make informed safety decisions.
We had the data, but not the system to convert risk signals into timely coaching and measurable safety improvement.
WhizzBridge’s Solution
- Built a rules-driven analytics engine that ingests telematics and video streams, normalizes driver events, and converts incidents such as speeding, hard braking, and cornering into structured behaviour profiles.
- Configured the platform to compare driver behaviour against real-time safety thresholds, allowing risky patterns to be identified automatically.
- Developed a closed-loop coaching workflow that triggers targeted micro-courses as soon as predefined thresholds are exceeded.
- Enabled progressive intervention so that if unsafe behaviours continue, additional training is assigned automatically without manual oversight.
- Designed short, behaviour-focused micro-learning modules that drivers can complete on laptops, tablets, and mobile devices without disrupting day-to-day operations.
- Created a manager dashboard where fleet leaders can configure policies, monitor risk trends, review driver history, and track training progress through a single interface.
- Integrated the platform with the client’s existing telematics ecosystem using custom REST APIs built in C# and .NET, ensuring reliable data flow without requiring changes to core fleet workflows.
Results We Achieved
- Reduced high-risk driving behaviours by 50% through timely, targeted coaching tied directly to telematics events.
- Cut administrative overhead for safety teams in half by automating course assignment, completion tracking, and follow-up actions.
- 100% automated coaching workflow: telematics event to micro-learning delivery with no manual intervention.
- Improved training engagement and completion rates through short, accessible micro-learning experiences instead of traditional long-form courses.
- Gave managers real-time visibility into risk patterns, training history, and behavioural trends to support faster, data-driven decisions.
- Delivered a scalable safety infrastructure that allows new rules, learning content, and platform capacity to be added without major engineering rework.
- Helped shift the client's safety programme from reactive incident management to a proactive, continuous coaching model.
The platform now acts as an automated safety layer across the fleet, turning raw driving data into personalized learning and scalable behavioral improvement.
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