Logistics & Supply Chain

An operations copilot for a freight network running on tribal knowledge

A regional freight provider depended on a handful of veteran dispatchers to keep shipments moving. EIC built an AI copilot that put that expertise in every dispatcher's hands.

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The results

Consistent decisions, faster resolutions, fewer late shipments

22 to 6 min

Average time to resolve a disrupted shipment

34%

Reduction in late deliveries

Faster ramp-up for new dispatchers

92%

Of copilot recommendations accepted or lightly edited

The challenge

Critical operations decisions trapped in a few people's heads

When a shipment was delayed, rerouted, or disrupted, resolution quality depended entirely on which dispatcher happened to be on shift. The best dispatchers were fast and accurate; everyone else escalated, waited, or guessed.

Exception handling varied wildly by dispatcher, leading to inconsistent customer outcomes.

Resolving a disrupted shipment took an average of 22 minutes of manual lookup across five systems.

New dispatchers needed months to become productive, and turnover reset that clock.

Key operating knowledge lived in spreadsheets, chat threads, and individual memory.

Leadership had no reliable view of why shipments were late or how exceptions were resolved.

Challenge diagram

The approach

Codify the expertise, then put it in the workflow

Rather than replace dispatchers, we built a copilot that gathered context across systems, proposed the next best action, and let the dispatcher decide. The goal was to make every dispatcher operate like the best one.

Step 01

Shadow the experts

We sat with senior dispatchers to capture how they actually diagnosed and resolved disruptions, including the signals they trusted most.

Step 02

Unify the context

We connected the fragmented systems into a single retrieval layer so the copilot could assemble a full picture of any shipment in seconds.

Step 03

Recommend next best actions

The copilot proposed concrete resolutions with rationale and confidence, so dispatchers could act quickly or adjust with full context.

Step 04

Embed in the daily workflow

We delivered the copilot inside the tools dispatchers already used, avoiding yet another screen to monitor.

Step 05

Close the feedback loop

Every accepted, edited, or rejected recommendation fed back in, steadily sharpening the copilot's suggestions over time.

Is your operation running on a few people's expertise?

We help logistics and operations teams turn hard-won knowledge into systems everyone can use.

Explore our approach

Expert knowledge captured and made reusable

A copilot embedded in real workflows, not a side tool

Measurable gains in speed and consistency