AI Agents

Context

Designed for Logistics – Carriers within the Fleet optimization stack, this agent gives planners and operations a continuous, evidence-based view of how tractors, trailers, and drivers are being used across lanes and time. It’s used in day-to-day dispatch and weekly network reviews to spot underuse trends and steer capacity plans before problems show up in empty miles and missed targets.

What it does

The agent ingests recent and live movement data—planned vs. actual trips, tractor–trailer assignments, dwell and detention timestamps, HOS usage, appointment windows, and geofenced events—and surfaces asset performance insights: where utilization falls below target, which lanes or customers drive dwell, and where recurring deadhead patterns appear. It explains the drivers behind each gap (dwell, appointment mismatch, HOS constraints, equipment misalignment) and recommends practical corrections such as re-sequencing pickups, swapping equipment, adjusting buffers, or directing next-best backhaul zones. Findings include short rationales and links to the exact segments and stops that triggered each insight, so teams can act without digging through boards and spreadsheets.

Core AI functions

Time-aligned series construction from TMS/telematics; normalization of stops, segments, and status codes; contribution analysis that decomposes underutilization into dwell, empty repositioning, appointment misses, and HOS limits; pattern detection for recurring deadhead and late-window clusters; and reason-code generation that explains each recommendation in plain language with line-of-sight to the underlying trips and timestamps.

Problem solved

Hidden underutilization drives cost. Fragmented views of trips, appointments, and telemetry make it hard to see why assets underperform, leading to repeated issues and uneven fleet use.

Business impact

More capacity without adding fleet. Utilization rises and empty miles shrink because underuse is surfaced with a specific cause and a practical fix. Planners make faster, more consistent decisions, detention/dwell charges fall, on-time performance improves, and revenue per mile becomes steadier across lanes and weeks.

Integration and adjacent use cases

Integration complexity: Medium. The agent reads planned and actual movements, telematics or ELD events, appointment data, and equipment rosters from your TMS/dispatch and tracking sources, then writes insights and suggested actions back into the planning view; core systems remain unchanged.

It is commonly combined with the Load matching agent to translate insights into better next-load pairings and with pricing or network-balancing tools to reinforce lanes that systematically lift utilization over the planning horizon.