Anonymized B2B logistics workflow

Reducing manual document processing with a reviewable AI workflow.

A logistics operations team needed faster extraction from repeated freight documents without losing human oversight.

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AI document extraction case-study visual — Anonymized example with operational context

Challenge

Operations staff manually reviewed repeated documents, copied fields into systems, and checked exceptions. The process was slow, hard to audit, and dependent on a few experienced people.

Solution

We mapped the document types, defined extraction fields, built a controlled AI workflow, added confidence thresholds, and routed exceptions to human review.

Results and controls

Proof is useful when it explains the context.

These examples are anonymized where client permission is limited. The goal is to show the problem, our role, the delivery shape, and the kind of evidence we use.

80% manual processing time reduced in the target workflow

92% directional extraction accuracy in controlled samples

Human review required for low-confidence or sensitive outputs

System components

AI extraction prototype Review queue CRM/API handoff Audit notes

Controls included

Representative sample testing
Confidence thresholds
Human-in-the-loop approval
Exception logging

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