A large enterprise operating a centralized fleet safety control tower was struggling to manage the growing volume of driver safety alerts generated across its operations. The control tower was responsible for monitoring, validating, and escalating thousands of alerts every day to ensure driver and fleet safety.
A large enterprise operating a centralized fleet safety control tower was struggling to manage the growing volume of driver safety alerts generated across its operations. The control tower was responsible for monitoring, validating, and escalating thousands of alerts every day to ensure driver and fleet safety.
The control tower relied heavily on manual workflows to review and validate safety alerts. As fleet size and alert volume increased, several challenges emerged:
The organization needed a way to reduce manual workload while improving speed, accuracy, and focus on high-risk events.
Thousands of alerts per day created alert fatigue and made prioritization difficult.
Manual validation slowed response times for critical incidents.
Large teams were required just to keep up with routine checks.
Consistency and accuracy varied across operators.
High operational costs with limited ability to scale further.
Core9 deployed its AI-powered computer vision and automation platform to
support the control tower’s core workflows.
Filter large volumes of incoming safety alerts automatically and continuously.
Classify alerts based on severity and risk using trained AI models.
Validate events in real time using computer vision before escalation.
Flag only high-confidence, high-risk incidents for operator action.
This shifted the control tower from manual alert processing to AI-led decision support, with human operators focused only where intervention truly mattered.
Core9 deployed its AI-powered computer vision and automation platform to support the control tower’s core workflows.
Filter large volumes of incoming safety alerts automatically and continuously.
Classify alerts based on severity and risk using trained AI models.
Validate events in real time using computer vision before escalation.
Flag only high-confidence, high-risk incidents for operator action.
This shifted the control tower from manual alert processing to AI-led decision support, with human operators focused only where intervention truly mattered.
The impact was immediate and measurable.
What was once a resource-heavy operation became a lean, high-efficiency safety function.
Control tower staffed reduced from 20 operators to just 2.
Alert validation time reduced from minutes to seconds.
Consistent, objective alert classification across the fleet.
Faster response to critical safety incidents.
Operators freed to focus on preventive actions and high-risk scenarios.
By automating alert validation and prioritization, the organization achieved:
Significant operational cost reduction.
Improved scalability without adding headcount.
Stronger, more proactive fleet safety management.
Higher confidence in safety decisions driven by AI.
This AI transformation turned the control tower into a strategic safety hub rather than a manual monitoring center.
By automating alert validation and prioritization, the organization achieved:
Significant operational cost reduction.
Improved scalability without adding headcount.
Stronger, more proactive fleet safety management.
Higher confidence in safety decisions driven by AI.
This AI transformation turned the control tower into a strategic safety hub rather than a manual monitoring center.
Book a 15-minute consultation
