A large enterprise operating a 1,000-vehicle fleet was facing a critical safety crisis. Prior to adopting AI-based monitoring, the fleet experienced multiple severe accidents within a single year, resulting in loss of life and exposing major gaps in driver visibility, safety compliance, and risk prevention.
Fuel consumption was becoming a major cost driver across daily operations.
New environmental benchmarks demanded measurable efficiency gains.
A controlled pilot ensured results could be proven before scaling.
Before deploying Core9’s technology, the fleet faced serious safety issues:
The cost was not just operational. It was human. Leadership required a solution that could fundamentally change safety outcomes, not just report on them.
Six major accidents recorded in one year.
Seven fatalities resulting from fleet incidents.
Limited visibility into real-time driver behavior.
No proactive detection of fatigue, distraction, or risky driving.
Reactive safety processes that intervened only after incidents occurred.
Core9 deployed DriveSense, its AI-powered fleet monitoring system, across all 1,000 vehicles.
Drivers and fleet managers received clear, actionable insights, showing exactly where fuel was being wasted and where behavior changes could improve efficiency. This enabled immediate intervention and ongoing optimization across the fleet.
Core9 deployed DriveSense, its AI-powered fleet monitoring system, to deliver real-time visibility into fleet performance.
Drivers and fleet managers received clear, actionable insights, showing exactly where fuel was being wasted and where behavior changes could improve efficiency. This enabled immediate intervention and ongoing optimization across the fleet.
The impact was immediate and transformative.
The pilot demonstrated that real-time monitoring could quickly translate insight into impact.
Approximately 10,000 liters of fuel saved during the pilot.
3 percent reduction in total fuel consumption across 200 vehicles.
26 tons of CO₂ emissions reduced per month.
Improved driving behavior and operational efficiency across the fleet.
By combining AI-driven insights with continuous monitoring, the fleet achieved:
Lower fuel costs and reduced operational waste.
Tangible progress toward sustainability targets.
Improved accountability and visibility for fleet managers.
A scalable foundation for long-term efficiency gains.
The initiative proved that cost savings and environmental responsibility can be achieved simultaneously with the right technology.
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