AI Agents

Context

Designed for Logistics – Carriers within the Fuel efficiency stack, this agent helps ops and safety teams improve fuel burn and on-road consistency by turning raw telematics into targeted, lane-aware coaching. It’s used in daily operations and weekly reviews so driver behavior aligns with the plan rather than forcing the network to absorb avoidable fuel and maintenance costs.

What it does

The agent analyzes recent trips against route profiles and equipment to identify behaviors that drive excess fuel use and wear—extended idle, over-speed relative to grade and wind, harsh acceleration/braking, high-RPM driving, sub-optimal cruise/governor usage, out-of-route detours, and stop-start patterns caused by appointment buffers. Findings are returned with clear, driver-specific rationales (“idle > X min at DC-17 with door delay,” “over-speed on I-XX grade after milepost 142”), plus practical corrections matched to corridors and equipment (speed caps on defined segments, earlier cruise engagement, longer coasts ahead of known downgrades, tighter pre-cool windows, or re-sequencing pickups to avoid yard idle). Each recommendation links to the exact segments and timestamps used, so supervisors and drivers can review the same evidence.

Core AI functions

Time-aligned fusion of TMS trips, ELD/telematics, and environment context; normalization of segments, dwell, speed/RPM bands, and maneuver events; contribution analysis that attributes MPG variance to behavior vs. terrain/traffic; detection of recurring patterns by driver, lane, and customer; and reason-code generation that produces concise, plain-language coaching items with line-of-sight to the underlying events.

Problem solved

Generic coaching and after-the-fact scorecards miss lane and equipment context, so behaviors don’t change and fuel waste persists. Ops teams spend time debating instead of correcting specific, repeatable drivers of loss.

Business impact

Lower fuel and maintenance costs with safer, steadier driving. MPG improves where it matters (corridor and customer level), idle and out-of-route miles shrink, brake/tire wear declines, and coaching becomes faster and more credible because every ask is tied to named segments and evidence.

Integration and adjacent use cases

Integration complexity: Easy. The agent reads planned and actual trips from your TMS/dispatch, ELD/telematics events, and available environment data, then writes driver-specific coaching items and follow-ups back into the planning/safety view; core systems remain unchanged.

It is commonly paired in the same stack with:

• Efficiency insights agent (to show where behavior impacts MPG and prioritize coaching) and

• Sustainability analytics agent (to convert fuel improvements into emissions metrics and program reporting).