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Logistics

Series B logistics & fulfillment startup

Document AIAutomation

~70% reduction

in manual re-keying time across the three highest-volume document types

Review queue, not black box

low-confidence extractions are still reviewed by a person before they hit billing

3 weeks to first production document type

shipped incrementally instead of waiting for full coverage

The Challenge

Incoming shipping manifests, customs forms, and carrier invoices arrived in a dozen inconsistent formats, and a team of five was manually re-keying them into the operations database.

Data entry backlog was growing faster than headcount, and re-keying errors were causing downstream billing disputes.

My Approach

Built a structured extraction pipeline that parses incoming documents and assigns a confidence score to every extracted field, not just the document as a whole.

Anything below the confidence threshold routes to a review queue with the source document highlighted next to the extracted value, instead of silently guessing.

Shipped incrementally against the three highest-volume document types first, then expanded coverage once accuracy held up in production.

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