AI Agents

Context

Designed for Logistics – Carriers within the Fuel efficiency stack, this agent gives operations a continuous, evidence-based view of where trips burn more fuel than they should. It’s used in day-to-day dispatch and weekly reviews to surface operational waste across routes, drivers, equipment, and lanes—so plans can be corrected before unnecessary fuel spend compounds.

What it does

The agent analyzes recent routes and trips alongside utilization patterns to pinpoint where and why fuel is being wasted. It attributes MPG variance to concrete drivers—idling and dwell, speed versus grade and wind, harsh acceleration/braking, out-of-route miles, equipment/tires/aero mismatch, and appointment buffers that trigger hurry-up/idle cycles—and returns concise findings with links to the exact segments and stops that caused the loss. It recommends practical fixes such as corridor-level speed caps, re-sequenced pickups to cut idle, driver-coaching targets tied to events, equipment swaps for lanes with persistent aero/weight penalties, and backhaul zones that reduce empty repositioning.

Core AI functions

Time-aligned fusion of TMS trips, telematics/ELD, and environmental context; normalization of stops, segments, dwell, and speed profiles; contribution analysis that decomposes MPG shortfall into idle, speed/grade, maneuver events, empty miles, and equipment effects; detection of recurring waste patterns by lane, customer, and driver; and reason-code generation that explains each recommendation in plain language with line-of-sight to the underlying trips and timestamps.

Problem solved

Hidden inefficiencies inflate costs. Fragmented views of route plans, telemetry, and utilization obscure where fuel is being lost, leading to generalized coaching and network fixes that miss the real causes.

Business impact

Lower operating costs via smarter plans. Fuel burn falls as waste is exposed with a specific cause and a practical correction; empty miles and idle time shrink; on-time performance improves without “hurry-up/idle” penalties; and savings become repeatable at the lane and customer level.

Integration and adjacent use cases

Integration complexity: Easy. The agent reads trips from your TMS/dispatch, telematics or ELD events, and available environment data, then writes insights and recommended actions back into the planning view; core systems remain unchanged.

It is commonly paired within the same stack, Fuel efficiency, with:

• Driver performance agent to target coaching against the specific behaviors and lanes that drive MPG loss, and with the

• Sustainability analytics agent to translate fuel-efficiency improvements into emissions metrics and program reporting.