A mid-size STL fleet operator was drowning in manual compliance work. Custom AI workflow automation connected their existing systems and gave the time back to their people.
Gateway Logistics runs 47 trucks on the I-270 corridor. They had a working TMS, telematics data coming in from every vehicle, and experienced dispatch managers who knew their operation well. What they didn't have was any of that information talking to each other in a useful way.
Compliance reports got assembled by hand from exports. Fuel variance meant someone opening five spreadsheets and doing math. Maintenance alerts existed inside the telematics platform but nobody had time to review them systematically. The managers were competent. The work just kept piling up in a way that didn't need to.
We didn't replace their TMS or their telematics platform. We built a custom AI automation layer that sat on top of both, connected to their existing data sources, and made the information useful without requiring anyone to manually compile it. The managers' expertise didn't go anywhere. The repetitive data work did.
HOS data pulls automatically from their TMS, formats against FMCSA requirements, flags any anomalies, and delivers a completed report. What used to take 2 hours per manager per week now takes zero minutes of human time.
Telematics data is analyzed continuously for engine codes, mileage thresholds, and historical failure patterns. When a truck shows early indicators, a prioritized alert goes to the maintenance scheduler before the problem becomes a breakdown.
Fuel card transactions are automatically reconciled against route and vehicle data. Routes and drivers showing variance beyond threshold get flagged weekly, with the relevant data already assembled. No more spreadsheet archaeology.
Every Monday morning, leadership gets a clean summary of the previous week: miles run, fuel costs, maintenance events, compliance status, and anything that needs attention. No one had to write it.
The results below are based on 8 months of post-deployment data compared against the prior 8-month period. The maintenance cost reduction surprised even the client. Predictive scheduling catching problems early turned out to be worth significantly more than the time savings alone.