Case Study
We turned a 4-hour dispatch process into a 45-second decision.
FleetOps15,000 vehicles, multi-city operations, dispatch managed via phone and spreadsheet.
Business Impact
4 hrs → 45 sec
Dispatch Time
30%
Fuel Cost Reduction
1,000+
Stolen Vehicles Recovered
The Problem
Dispatchers spent 4 hours every morning assigning routes. Fuel theft was undetected for months. Stolen vehicles were rarely recovered because location data was delayed by minutes.
Our Approach
An autonomous dispatch and route-optimization layer integrated with their existing GPS hardware and ERP. AI assigns routes based on real-time traffic, driver workload, and fuel efficiency — with human override and full audit logging.
How we solved it
Adaptive AI routing · Predictive ETA modeling · Intelligent dispatch
Integrated with the client's existing infrastructure without a rip-and-replace.
FleetOps ran one of the largest multi-city fleets in the region with dispatch still living in phones and spreadsheets. We engineered an intelligence layer on top of their existing GPS and ERP — not a rip-and-replace — so route assignment, fuel anomalies, and recovery signals moved from manual guesswork to audited automation.
The Impact
4 hrs → 45 sec
Dispatch Time
Fuel Cost Reduction
Stolen Vehicles Recovered
Stack: React Native · Node.js · AWS Lambda · DynamoDB · Custom route-optimization engine
Read the architecture review →