MIA: Trinet's AI That Handles the Operational Work So People Can Do the Human Work

Turning Carrier Emails Into Automatic System Updates

When a carrier confirms a load, the key details often arrive by email, a pickup reference, a trailer number, a driver name. Traditionally, a team member had to open the email, identify its purpose, extract the information, locate the right shipment in the TMS, and update the record manually.

MIA listens to incoming carrier messages, classifies each one as a pickup confirmation or a tracking-related update, matches it to the correct shipment, and writes the update directly into the internal system.

The result is faster status accuracy and cleaner records, both critical in a real-time tracking environment where downstream decisions depend on reliable data. The NIST AI Risk Management Framework highlights 'reliability' and 'accuracy' as core trustworthiness properties for AI systems in operational contexts, principles that guide how MIA is designed.

Supporting sources: NIST AI Risk Management Framework (AI RMF 1.0)

Faster Dispatch With Built-In Validation

Once pricing is confirmed and a shipment is created, it must pass a series of checks before dispatch can proceed. MIA supports this step by automating the validation sequence, moving shipments forward when all conditions are met and routing exceptions to a human reviewer when they are not.

This keeps the process moving at pace while ensuring that human oversight remains where it matters most, on edge cases and judgment calls.

The approach reduces friction between shipment creation, dispatch, and customer notification, which is a meaningful gain in supply chain efficiency across high-volume Canada–US corridors.

OCR That Processes Invoices and Organizes Supporting Documents

MIA includes an OCR module that reads logistics documents and extracts invoice data for direct integration into the system. It also separates supporting documents, such as proof of delivery, from invoice content and routes each to the appropriate workflow.

Keeping invoice data and supporting evidence organized from the point of capture reduces the manual effort required downstream and limits the risk of mismatched records.

Cleaner document handling strengthens freight data analytics across day-to-day operations, giving operations and accounting teams reliable information without requiring them to sort through unstructured file batches.

Freight Audit Support Through Automated Cost Validation

Charge discrepancies can arise for many reasons, accessorial fees, rate mismatches, or duplicate billing. Catching these variations early, before they progress through approval, is a key part of cost control.

MIA validates charges against expected costs, detects unexpected variations, and flags anything that falls outside tolerance for human review. This supports stronger freight audit practices and keeps cost accuracy high without requiring a dedicated manual audit step on every shipment.

The U.S. Government Accountability Office has documented freight audit and payment processes as an area where automation can meaningfully improve accuracy and reduce overpayment risk across commercial transportation programs.

Supporting sources: GAO, Freight Management: DOD Can Better Leverage Its Data

Automation That Empowers People

MIA is not designed to replace people. It is designed to remove the repetitive operational work that consumes time without requiring judgment, so internal teams can focus on what humans do best: customer service, relationship building, problem solving, and managing the exceptions that require experience.

The impact shows up in speed, consistency, and a reduction in manual touches across the operation. Some improvements can be measured through invoice processing volume, shipment throughput, and automation coverage. Others are felt every day in how the team operates.

That is the intended outcome: an operation where AI handles the structured, repeatable tasks and people handle the moments that matter.

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