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JABSTECHNICAL CONSULTING
Logistics · Freight operationsCase study

Cutting manual invoice processing by 80% for a nationwide logistics marketplace

A production automation pipeline for freight invoices — built so accuracy went up while the manual workload went down.

80%
Less manual entry
99%
Extraction accuracy
3
Stage human review
Scales without headcount
The situation

Volume was growing. The process wasn't.

Challenge

Freight invoices arrived from hundreds of carriers in inconsistent formats — PDFs, scans, and email bodies — with rates, accessorials, and reference numbers buried in unstructured text.

Constraint

Payments could not be automated blindly. Incorrect pay dates or duplicate invoices carried real financial consequences, so a person had to remain accountable for every approval.

Cost of inaction

Headcount was the only lever left. Every new carrier and every volume increase translated directly into more hours of keying data by hand.

What we built

An extraction pipeline with people still in the loop.

Automation handled the mechanical work. Humans kept the judgment calls.

01

Document AI + extraction agent

Document AI handled layout and OCR; a purpose-built extraction agent resolved the fields that varied by carrier, with confidence scores attached to every value.

02

Validation & duplicate detection

Cross-references against load records, duplicate invoice detection, and factoring or vendor assignment checks ran before anything reached a reviewer.

03

Three-stage human review

Reviewer, validator, and approver checkpoints with a clear audit trail. Low-confidence extractions were routed to a person rather than pushed through.

04

Pay-date logic & exception alerts

Payment dates were calculated automatically from terms, and anything the pipeline couldn't confidently handle triggered a real-time alert instead of failing silently.

Outcome

80% of the manual work removed — and accuracy went up.

The team stopped keying invoices and started handling exceptions. Volume growth no longer requires proportional hiring, and the audit trail is stronger than it was under the fully manual process.

Operationally

Staff time shifted from data entry to reviewing the small fraction of invoices that genuinely need judgment.

Financially

Pay dates are calculated consistently, duplicates are caught before payment, and processing cost no longer scales with volume.

Structurally

The pipeline is documented and infrastructure-as-code, so adding carriers or document types is a configuration change, not a project.

Similar problem?

If your team is still keying documents by hand, we can tell you what's automatable.

A 30-minute call is enough to know whether the process is worth automating — and we'll say so if it isn't.

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