AI Agents

Context

Designed for Logistics – Carriers within the Fuel efficiency stack, this agent translates fuel performance into carbon and sustainability metrics that operations, finance, and ESG teams can use. It’s used in monthly/quarterly reporting and continuous improvement programs so reductions from network and driver changes are measured consistently—not estimated ad hoc.

What it does

The agent ingests fuel and trip data, normalizes it by lane, equipment, and operating conditions, and calculates emissions and intensity KPIs (e.g., CO₂e per mile, per ton-mile, per stop). It attributes changes in emissions to concrete drivers—idle time, empty miles, speed profile, equipment/aero choices, routing and appointment buffers—and produces evidence-linked dashboards and period-over-period summaries. It also prepares customer-ready statements for program reporting (Scope 1 views, corridor and customer breakdowns) and flags where operational actions will yield the largest carbon reductions per dollar.

Core AI functions

Time-aligned fusion of TMS trips, telematics/ELD, and fuel data; application of approved emissions factors and conversions; normalization by payload/ton-mile and operating profile; contribution analysis that decomposes CO₂e variance into behavior, routing/empty miles, and equipment effects; detection of recurring high-emission patterns by lane/customer; and reason-code generation that ties each reported change to the underlying segments, timestamps, and fuel records.

Problem solved

Emissions reporting is often manual and inconsistent, mixing spreadsheets, generic factors, and incomplete payload context—making it hard to prove impact, satisfy customer requests, or prioritize the next best action.

Business impact

Credible, actionable sustainability reporting. Carriers demonstrate progress to customers and stakeholders, target carbon reductions where they matter most, align fuel-savings programs with ESG goals, and support differentiated bids on lanes that value lower emissions.

Integration and adjacent use cases

Integration complexity: Medium to High. The agent reads trips from TMS/dispatch, telematics/ELD events, and fuel records, then writes emissions KPIs, driver analyses, and customer-ready summaries back to your reporting and planning views; core systems remain unchanged.

It is commonly paired within the same stack with:

• Efficiency insights agent (to quantify carbon impact of operational waste and network fixes) and

• Driver performance agent (to convert coaching outcomes into verified CO₂e reductions by corridor and customer).